diff --git a/CLAUDE.md b/CLAUDE.md
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+++ b/CLAUDE.md
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+# Temporal Python Samples
+
+## Serena MCP Server
+
+Always consult Serena memories at the start of a session using `mcp__serena__list_memories` and read relevant ones with `mcp__serena__read_memory`. Save important project-specific learnings to Serena for future sessions.
+
+## Pre-Commit and Pre-Push Checks
+
+**ALWAYS run `poe lint` before committing or pushing** on both repositories:
+
+```bash
+# In samples repo (langgraph_plugin)
+poe lint
+
+# In SDK repo (sdk-python langgraph-plugin branch)
+cd /Users/maxim/temporal/sdk-python-root/langgraph-plugin
+poe lint
+```
+
+This catches import sorting, formatting, type errors, and other style issues. Never push without confirming lint passes.
+
+## Test Failures
+
+**NEVER delete tests just because they fail.** Failing tests indicate real issues with the implementation that need to be fixed. If tests fail:
+
+1. Investigate the root cause of the failure
+2. Fix the implementation, not the tests
+3. Only modify tests if they have incorrect assertions or are testing the wrong behavior
+
+Tests are valuable signals - treat failures as bugs to fix, not inconveniences to remove.
+
+## Client Initialization Pattern
+
+Use the `ClientConfig` pattern for client initialization to support environment-based configuration:
+
+```python
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+config = ClientConfig.load_client_connect_config()
+config.setdefault("target_host", "localhost:7233")
+client = await Client.connect(**config)
+```
+
+This pattern allows configuration via environment variables while providing sensible defaults.
+
+## Design Decisions
+
+**NEVER jump to implementation when presenting multiple design options.** When you identify several possible approaches to solve a problem:
+
+1. Present all options with their pros and cons
+2. Wait for the user to confirm which approach to take
+3. Only implement after receiving explicit confirmation
+
+This prevents wasted effort implementing the wrong solution and ensures alignment with user preferences.
+
+## LangGraph Guidelines
+
+### Agent Creation
+
+- **DO NOT** use `create_react_agent` from `langgraph.prebuilt` - it is deprecated
+- **USE** `create_agent` from `langchain.agents` instead
+
+```python
+# Wrong (deprecated)
+from langgraph.prebuilt import create_react_agent
+agent = create_react_agent(model=model, tools=[...], prompt="...")
+
+# Correct
+from langchain.agents import create_agent
+agent = create_agent(model=model, tools=[...], system_prompt="...")
+```
diff --git a/langgraph_plugin/README.md b/langgraph_plugin/README.md
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+++ b/langgraph_plugin/README.md
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+# Temporal LangGraph Samples
+
+These samples demonstrate the Temporal LangGraph integration - combining LangGraph's agent framework with Temporal's durable execution.
+
+> **Note:** The LangGraph integration is currently available as a preview feature in the `langgraph_plugin` branch of the SDK repository.
+
+## Overview
+
+The integration combines:
+- **Temporal workflows** for orchestrating agent control flow and state management
+- **LangGraph** for defining agent graphs with conditional logic, cycles, and state
+
+This approach ensures that AI agent workflows are durable, observable, and can handle failures gracefully.
+
+## Prerequisites
+
+- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
+- Python 3.9+
+- [uv](https://docs.astral.sh/uv/) package manager (recommended)
+
+## Installation
+
+Since the LangGraph integration is currently in a branch, you need to install from the branch repositories.
+
+### Running the Samples
+
+1. Clone this samples repository:
+   ```bash
+   git clone -b langgraph_plugin https://github.com/mfateev/samples-python.git
+   cd samples-python
+   ```
+
+2. Install dependencies:
+   ```bash
+   uv sync --group langgraph
+   ```
+
+3. Install the SDK from the `langgraph-plugin` branch:
+   ```bash
+   uv pip install "temporalio @ git+https://github.com/mfateev/sdk-python.git@langgraph-plugin"
+   ```
+
+4. Start a local Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+5. Navigate to a sample directory and follow its README for specific instructions
+
+## LangGraph API Styles
+
+LangGraph provides two API styles for defining workflows:
+
+| Aspect | Graph API | Functional API |
+|--------|-----------|----------------|
+| Definition | `StateGraph` + `add_node()` + `add_edge()` | `@task` + `@entrypoint` |
+| Control flow | Explicit graph edges | Python code (loops, conditionals) |
+| State | Shared TypedDict with reducers | Function arguments/returns |
+| Parallelism | Send API, conditional edges | Concurrent task calls |
+| Compile | `compile("id")` | `compile("id")` |
+
+## Examples
+
+Examples are organized by API style:
+
+### Graph API (`graph_api/`)
+
+StateGraph-based examples using nodes and edges:
+
+| Sample | Description |
+|--------|-------------|
+| [hello_world](./graph_api/hello_world/) | Simple starter example demonstrating basic plugin setup and graph registration |
+| [activity_from_node](./graph_api/activity_from_node/) | Calling Temporal activities from a graph node using run_in_workflow |
+| [react_agent](./graph_api/react_agent/) | ReAct agent pattern with tool calling and multi-step reasoning |
+| [human_in_the_loop](./graph_api/human_in_the_loop/) | Human-in-the-loop approval workflows using two approaches |
+| ↳ [approval_graph_interrupt](./graph_api/human_in_the_loop/approval_graph_interrupt/) | Uses LangGraph's `interrupt()` function |
+| ↳ [approval_wait_condition](./graph_api/human_in_the_loop/approval_wait_condition/) | Uses `run_in_workflow=True` with `workflow.wait_condition()` |
+| [supervisor](./graph_api/supervisor/) | Multi-agent supervisor pattern coordinating specialized agents |
+| [agentic_rag](./graph_api/agentic_rag/) | Retrieval-augmented generation with document grading and query rewriting |
+| [deep_research](./graph_api/deep_research/) | Multi-step research with web search and iterative refinement |
+| [plan_and_execute](./graph_api/plan_and_execute/) | Plan-and-execute pattern with structured step execution |
+| [reflection](./graph_api/reflection/) | Self-reflection pattern for iterative improvement |
+
+### Functional API (`functional_api/`)
+
+`@task` and `@entrypoint` decorator-based examples:
+
+| Sample | Description |
+|--------|-------------|
+| [hello_world](./functional_api/hello_world/) | Simple starter example demonstrating basic plugin setup with `@task` and `@entrypoint` |
+| [react_agent](./functional_api/react_agent/) | ReAct agent pattern with tool calling using tasks for model and tool execution |
+| [human_in_the_loop](./functional_api/human_in_the_loop/) | Human-in-the-loop approval workflow using `interrupt()` for pause/resume |
+| [supervisor](./functional_api/supervisor/) | Multi-agent supervisor pattern with tasks for each agent role |
+| [agentic_rag](./functional_api/agentic_rag/) | RAG with document grading and query rewriting using task-based retrieval |
+| [deep_research](./functional_api/deep_research/) | Multi-step research with parallel search execution via concurrent tasks |
+| [plan_and_execute](./functional_api/plan_and_execute/) | Plan-and-execute pattern with step-by-step task execution |
+| [reflection](./functional_api/reflection/) | Self-reflection pattern for iterative content improvement |
+| [continue_as_new](./functional_api/continue_as_new/) | Task result caching across continue-as-new boundaries |
+
+## Usage
+
+### Graph API Usage
+
+The Graph API uses `StateGraph` to define nodes and edges, with each node running as a Temporal activity:
+
+```python
+from langgraph.graph import StateGraph, START, END
+from temporalio import workflow
+from temporalio.contrib.langgraph import LangGraphPlugin, compile
+
+# 1. Define your graph
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+def chatbot(state: State) -> State:
+    response = model.invoke(state["messages"])
+    return {"messages": [response]}
+
+graph = StateGraph(State)
+graph.add_node("chatbot", chatbot)
+graph.add_edge(START, "chatbot")
+graph.add_edge("chatbot", END)
+
+# 2. Register graph with plugin
+plugin = LangGraphPlugin(graphs={"my_graph": graph.compile()})
+
+# 3. Use in workflow
+@workflow.defn
+class MyWorkflow:
+    @workflow.run
+    async def run(self, query: str) -> dict:
+        app = compile("my_graph")  # Get runner for registered graph
+        return await app.ainvoke({"messages": [("user", query)]})
+
+# 4. Start worker with plugin
+async with Worker(client, task_queue="q", workflows=[MyWorkflow], plugins=[plugin]):
+    result = await client.execute_workflow(MyWorkflow.run, "Hello", ...)
+```
+
+### Functional API Usage
+
+The Functional API uses `@task` and `@entrypoint` decorators. Tasks run as Temporal activities:
+
+```python
+from langgraph.func import task, entrypoint
+from temporalio import workflow
+from temporalio.contrib.langgraph import LangGraphPlugin, compile
+
+# 1. Define tasks (run as Temporal activities)
+@task
+def research(topic: str) -> str:
+    """Each @task call becomes a Temporal activity."""
+    return search_web(topic)
+
+@task
+def summarize(content: str) -> str:
+    return model.invoke(f"Summarize: {content}")
+
+# 2. Define entrypoint (orchestrates tasks)
+@entrypoint()
+async def research_workflow(topic: str) -> dict:
+    """The entrypoint runs in the workflow, orchestrating tasks."""
+    # Tasks can run in parallel
+    results = [research(q) for q in generate_queries(topic)]
+    content = [await r for r in results]  # Wait for all
+
+    summary = await summarize("\n".join(content))
+    return {"summary": summary}
+
+# 3. Register entrypoint with plugin
+plugin = LangGraphPlugin(
+    graphs={"research": research_workflow}
+)
+
+# 4. Use in workflow
+@workflow.defn
+class ResearchWorkflow:
+    @workflow.run
+    async def run(self, topic: str) -> dict:
+        app = compile("research")
+        return await app.ainvoke(topic)
+
+# 5. Start worker with plugin
+async with Worker(client, task_queue="q", workflows=[ResearchWorkflow], plugins=[plugin]):
+    result = await client.execute_workflow(ResearchWorkflow.run, "AI trends", ...)
+```
+
+### Key Differences
+
+| Feature | Graph API | Functional API |
+|---------|-----------|----------------|
+| Task definition | Graph nodes | `@task` decorator |
+| Orchestration | Graph edges | Python control flow |
+| Parallel execution | `Send` API | Concurrent `await` |
+| State management | Shared `TypedDict` | Function arguments |
+| Compile function | `compile("graph_id")` | `compile("entrypoint_id")` |
+| Plugin class | `LangGraphPlugin` | `LangGraphPlugin` |
+
+### Configuration Options
+
+Both APIs support activity configuration using `activity_options()` helper:
+
+```python
+from datetime import timedelta
+from temporalio.common import RetryPolicy
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+
+# Graph API - use activity_options() in node metadata
+def build_graph():
+    graph = StateGraph(MyState)
+    graph.add_node(
+        "expensive_node",
+        expensive_func,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=30),
+            retry_policy=RetryPolicy(maximum_attempts=5),
+        ),
+    )
+    # ... add edges ...
+    return graph.compile()
+
+plugin = LangGraphPlugin(
+    graphs={"my_graph": build_graph},
+    default_activity_options=activity_options(
+        start_to_close_timeout=timedelta(minutes=5),
+    ),
+    # Or configure per-node at plugin level:
+    activity_options={
+        "expensive_node": activity_options(
+            start_to_close_timeout=timedelta(minutes=30),
+        ),
+    },
+)
+
+# Functional API - same plugin, same activity_options parameter
+plugin = LangGraphPlugin(
+    graphs={"my_entrypoint": entrypoint_func},
+    default_activity_options=activity_options(
+        start_to_close_timeout=timedelta(minutes=5),
+    ),
+    activity_options={
+        "expensive_task": activity_options(
+            start_to_close_timeout=timedelta(minutes=30),
+        ),
+    },
+)
+
+# Or configure in workflow via compile()
+app = compile(
+    "my_entrypoint",
+    activity_options={
+        "my_task": activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+            retry_policy=RetryPolicy(maximum_attempts=3),
+        ),
+    },
+)
+```
+
+### Adapting LangGraph Functional API for Temporal
+
+When adapting a standard LangGraph functional API graph to run with Temporal, there are a few key changes required:
+
+#### 1. Use `await` When Calling Tasks
+
+In standard LangGraph, you can call tasks with `.result()`:
+
+```python
+# Standard LangGraph (won't work with Temporal)
+@entrypoint()
+def my_workflow(input: str) -> dict:
+    result = my_task(input).result()  # ❌ Blocks - not allowed in Temporal
+    return {"output": result}
+```
+
+With Temporal, you must use `await` because blocking is not allowed in workflows:
+
+```python
+# Temporal-compatible
+@entrypoint()
+async def my_workflow(input: str) -> dict:
+    result = await my_task(input)  # ✅ Async await
+    return {"output": result}
+```
+
+#### 2. Entrypoints Must Be Async
+
+Since tasks require `await`, your entrypoint function must be `async`:
+
+```python
+# Standard LangGraph
+@entrypoint()
+def process(data: str) -> dict:  # sync function
+    ...
+
+# Temporal-compatible
+@entrypoint()
+async def process(data: str) -> dict:  # async function
+    ...
+```
+
+#### 3. Tasks Must Be Importable
+
+Tasks run as Temporal activities in a separate context. They must be defined at module level (not inside functions or classes) so they can be imported by the worker:
+
+```python
+# ✅ Correct - module level
+@task
+def analyze(text: str) -> str:
+    return llm.invoke(text)
+
+# ❌ Wrong - can't be imported
+def make_workflow():
+    @task
+    def analyze(text: str) -> str:  # Closure - not importable
+        return llm.invoke(text)
+```
+
+#### 4. Parallel Task Execution
+
+Standard LangGraph parallel execution works the same way, just use `await`:
+
+```python
+@entrypoint()
+async def research(topic: str) -> dict:
+    # Start multiple tasks concurrently
+    futures = [search(query) for query in queries]
+
+    # Wait for all results
+    results = [await f for f in futures]
+
+    return {"results": results}
+```
+
+#### Complete Migration Example
+
+**Before (Standard LangGraph):**
+
+```python
+from langgraph.func import task, entrypoint
+
+@task
+def fetch_data(url: str) -> dict:
+    return requests.get(url).json()
+
+@task
+def process_data(data: dict) -> str:
+    return transform(data)
+
+@entrypoint()
+def pipeline(url: str) -> dict:
+    data = fetch_data(url).result()      # Blocking call
+    output = process_data(data).result()  # Blocking call
+    return {"output": output}
+
+# Direct invocation
+result = pipeline.invoke("https://api.example.com")
+```
+
+**After (Temporal-compatible):**
+
+```python
+from langgraph.func import task, entrypoint
+from temporalio import workflow
+from temporalio.contrib.langgraph import LangGraphPlugin, compile
+
+@task
+def fetch_data(url: str) -> dict:
+    return requests.get(url).json()
+
+@task
+def process_data(data: dict) -> str:
+    return transform(data)
+
+@entrypoint()
+async def pipeline(url: str) -> dict:       # Made async
+    data = await fetch_data(url)             # Use await
+    output = await process_data(data)        # Use await
+    return {"output": output}
+
+# Workflow wrapper
+@workflow.defn
+class PipelineWorkflow:
+    @workflow.run
+    async def run(self, url: str) -> dict:
+        app = compile("pipeline")
+        return await app.ainvoke(url)
+
+# Plugin registration
+plugin = LangGraphPlugin(graphs={"pipeline": pipeline})
+
+# Worker setup
+async with Worker(client, task_queue="q", workflows=[PipelineWorkflow], plugins=[plugin]):
+    result = await client.execute_workflow(PipelineWorkflow.run, "https://api.example.com", ...)
+```
+
+#### Summary of Changes
+
+| Aspect | Standard LangGraph | Temporal Integration |
+|--------|-------------------|---------------------|
+| Task calls | `task(x).result()` | `await task(x)` |
+| Entrypoint | `def func():` (sync OK) | `async def func():` (must be async) |
+| Task location | Anywhere | Module level only |
+| Invocation | `entrypoint.invoke(x)` | `compile("id").ainvoke(x)` |
+| Execution | Direct | Via Temporal workflow |
diff --git a/langgraph_plugin/__init__.py b/langgraph_plugin/__init__.py
new file mode 100644
index 00000000..c30a7488
--- /dev/null
+++ b/langgraph_plugin/__init__.py
@@ -0,0 +1 @@
+# Temporal LangGraph Samples
diff --git a/langgraph_plugin/functional_api/__init__.py b/langgraph_plugin/functional_api/__init__.py
new file mode 100644
index 00000000..434789f1
--- /dev/null
+++ b/langgraph_plugin/functional_api/__init__.py
@@ -0,0 +1 @@
+"""LangGraph Functional API + Temporal Integration Proposal."""
diff --git a/langgraph_plugin/functional_api/agentic_rag/README.md b/langgraph_plugin/functional_api/agentic_rag/README.md
new file mode 100644
index 00000000..e8fa9df3
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/README.md
@@ -0,0 +1,134 @@
+# Agentic RAG (Functional API)
+
+Retrieval-Augmented Generation with document grading and query rewriting for improved answer quality.
+
+## Overview
+
+Unlike simple RAG, agentic RAG evaluates retrieved documents and adapts:
+
+1. **Retrieve** - Fetch documents for the query
+2. **Grade** - Evaluate document relevance
+3. **Rewrite** - If not relevant, reformulate query
+4. **Generate** - Create answer from relevant documents
+
+## Architecture
+
+```
+User Question
+      │
+      ▼
+┌─────────────────┐
+│retrieve_documents│◄─────────┐
+│     (task)       │          │
+└────────┬─────────┘          │
+         │                    │
+         ▼                    │
+┌─────────────────┐           │
+│ grade_documents │           │
+│     (task)      │           │
+└────────┬────────┘           │
+         │                    │
+         ▼                    │
+    Relevant?                 │
+         │                    │
+    YES  │    NO              │
+         │     │              │
+         │     ▼              │
+         │  ┌──────────────┐  │
+         │  │ rewrite_query│──┘
+         │  │    (task)    │
+         │  └──────────────┘
+         ▼
+┌─────────────────┐
+│ generate_answer │
+│     (task)      │
+└─────────────────┘
+```
+
+## Key Code
+
+### Adaptive Retrieval Loop
+
+```python
+@entrypoint()
+async def agentic_rag_entrypoint(question: str, max_retries: int = 2) -> dict:
+    current_query = question
+
+    for attempt in range(max_retries + 1):
+        # Retrieve documents
+        documents = await retrieve_documents(current_query)
+
+        # Grade for relevance
+        grade_result = await grade_documents(question, documents)
+
+        if grade_result["relevant"]:
+            # Generate answer with relevant docs
+            answer = await generate_answer(question, documents)
+            return {"answer": answer, "status": "success"}
+
+        # Rewrite query and retry
+        if attempt < max_retries:
+            current_query = await rewrite_query(current_query)
+
+    # Best-effort answer with all retrieved docs
+    return {"answer": await generate_answer(question, all_docs), "status": "max_retries"}
+```
+
+### Document Grading
+
+```python
+@task
+def grade_documents(question: str, documents: list) -> dict:
+    """Evaluate if documents are relevant to the question."""
+    relevant_docs = [doc for doc in documents if is_relevant(doc, question)]
+    return {
+        "relevant": len(relevant_docs) >= threshold,
+        "relevant_count": len(relevant_docs)
+    }
+```
+
+## Why Temporal?
+
+- **Reliability**: Multiple retrieval attempts complete reliably
+- **Observability**: See which queries succeeded/failed
+- **Retries**: Handle API failures in retrieval/LLM calls
+- **Audit trail**: Track query rewrites in workflow history
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run with API key:
+   ```bash
+   export OPENAI_API_KEY=your-key
+   uv run langgraph_plugin/functional_api/agentic_rag/run_worker.py
+   ```
+
+3. Execute a question:
+   ```bash
+   uv run langgraph_plugin/functional_api/agentic_rag/run_workflow.py
+   ```
+
+## Customization
+
+### Adjust Relevance Threshold
+
+```python
+@task
+def grade_documents(question: str, documents: list) -> dict:
+    # Require more relevant documents
+    return {"relevant": relevant_count >= 3}
+```
+
+### Add Query Transformation Strategies
+
+```python
+@task
+def rewrite_query(query: str) -> str:
+    strategies = ["simplify", "expand", "rephrase"]
+    # Try different rewriting approaches
+    ...
+```
diff --git a/langgraph_plugin/functional_api/agentic_rag/__init__.py b/langgraph_plugin/functional_api/agentic_rag/__init__.py
new file mode 100644
index 00000000..cca96ff7
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/__init__.py
@@ -0,0 +1 @@
+"""Agentic RAG - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/agentic_rag/entrypoint.py b/langgraph_plugin/functional_api/agentic_rag/entrypoint.py
new file mode 100644
index 00000000..e7747eb4
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/entrypoint.py
@@ -0,0 +1,79 @@
+"""Agentic RAG Entrypoint Definition.
+
+The @entrypoint function implements an agentic RAG pattern:
+1. Retrieve documents based on query
+2. Grade documents for relevance
+3. If not relevant, rewrite query and retry
+4. Generate answer using relevant documents
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.agentic_rag.tasks import (
+    generate_answer,
+    grade_documents,
+    retrieve_documents,
+    rewrite_query,
+)
+
+
+@entrypoint()
+async def agentic_rag_entrypoint(question: str, max_retries: int = 2) -> dict[str, Any]:
+    """Run an agentic RAG system.
+
+    The system will:
+    1. Retrieve documents
+    2. Grade them for relevance
+    3. Rewrite query and retry if not relevant
+    4. Generate answer from relevant documents
+
+    Each @task call runs as a Temporal activity with automatic retries.
+
+    Args:
+        question: The user's question.
+        max_retries: Maximum query rewrite attempts.
+
+    Returns:
+        Dict with answer, documents used, and metadata.
+    """
+    current_query = question
+    all_documents: list[dict[str, Any]] = []
+
+    for attempt in range(max_retries + 1):
+        # Step 1: Retrieve documents
+        documents = await retrieve_documents(current_query)
+        all_documents.extend(documents)
+
+        # Step 2: Grade documents
+        grade_result = await grade_documents(question, documents)
+
+        if grade_result["relevant"]:
+            # Step 3: Generate answer with relevant documents
+            answer = await generate_answer(question, documents)
+
+            return {
+                "question": question,
+                "answer": answer,
+                "documents_used": len(documents),
+                "query_rewrites": attempt,
+                "final_query": current_query,
+                "status": "success",
+            }
+
+        # Documents not relevant - rewrite query if retries left
+        if attempt < max_retries:
+            current_query = await rewrite_query(current_query)
+
+    # Max retries reached - generate best-effort answer
+    answer = await generate_answer(question, all_documents)
+
+    return {
+        "question": question,
+        "answer": answer,
+        "documents_used": len(all_documents),
+        "query_rewrites": max_retries,
+        "final_query": current_query,
+        "status": "max_retries_reached",
+    }
diff --git a/langgraph_plugin/functional_api/agentic_rag/run_worker.py b/langgraph_plugin/functional_api/agentic_rag/run_worker.py
new file mode 100644
index 00000000..8073549f
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/run_worker.py
@@ -0,0 +1,60 @@
+"""Worker for the Agentic RAG Functional API sample.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.agentic_rag.entrypoint import (
+    agentic_rag_entrypoint,
+)
+from langgraph_plugin.functional_api.agentic_rag.workflow import AgenticRagWorkflow
+
+
+async def main() -> None:
+    plugin = LangGraphPlugin(
+        graphs={"agentic_rag_entrypoint": agentic_rag_entrypoint},
+        activity_options={
+            "retrieve_documents": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+            "grade_documents": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+            "rewrite_query": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+            "generate_answer": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+        },
+    )
+
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-agentic-rag",
+        workflows=[AgenticRagWorkflow],
+    )
+
+    print("Agentic RAG worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/agentic_rag/run_workflow.py b/langgraph_plugin/functional_api/agentic_rag/run_workflow.py
new file mode 100644
index 00000000..c665f885
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/run_workflow.py
@@ -0,0 +1,33 @@
+"""Execute the Agentic RAG Functional API workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.agentic_rag.workflow import AgenticRagWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    question = "What is LangGraph and how does it work with Temporal?"
+
+    result = await client.execute_workflow(
+        AgenticRagWorkflow.run,
+        question,
+        id="agentic-rag-functional-workflow",
+        task_queue="langgraph-functional-agentic-rag",
+    )
+
+    print(f"Question: {result.get('question')}")
+    print(f"Status: {result.get('status')}")
+    print(f"Query Rewrites: {result.get('query_rewrites')}")
+    print(f"Documents Used: {result.get('documents_used')}")
+    print(f"\nAnswer:\n{result.get('answer')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/agentic_rag/tasks.py b/langgraph_plugin/functional_api/agentic_rag/tasks.py
new file mode 100644
index 00000000..b6d3d70c
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/tasks.py
@@ -0,0 +1,213 @@
+"""Task definitions for the Agentic RAG system.
+
+Each @task runs as a Temporal activity with automatic retries.
+Demonstrates intelligent document retrieval with grading and query rewriting.
+"""
+
+import os
+from typing import Any, Literal
+
+from langchain_core.documents import Document
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langgraph.func import task
+from pydantic import BaseModel, Field
+
+# Sample documents about AI agents and LangGraph for the knowledge base
+SAMPLE_DOCUMENTS = [
+    Document(
+        page_content="""LangGraph is a library for building stateful, multi-actor applications with LLMs.
+        It extends LangChain with the ability to coordinate multiple chains across
+        multiple steps of computation in a cyclic manner. Key features include:
+        - Support for cycles and branching in agent workflows
+        - Built-in persistence for pausing and resuming
+        - Human-in-the-loop support with interrupts
+        - Streaming support for real-time updates""",
+        metadata={"source": "langgraph_overview", "topic": "langgraph"},
+    ),
+    Document(
+        page_content="""Temporal is a durable execution platform that enables developers to build
+        applications that are reliable by default. Key concepts include:
+        - Workflows: Long-running, reliable processes that survive failures
+        - Activities: Individual units of work that can be retried
+        - Signals: External events that can be sent to running workflows
+        - Queries: Read-only operations to inspect workflow state""",
+        metadata={"source": "temporal_overview", "topic": "temporal"},
+    ),
+    Document(
+        page_content="""The ReAct (Reasoning and Acting) pattern is an approach where an LLM
+        alternates between thinking about what to do and taking actions. The loop is:
+        1. Think: The LLM reasons about the current state and what action to take
+        2. Act: Execute the chosen action (e.g., call a tool)
+        3. Observe: Process the result of the action
+        4. Repeat until the task is complete""",
+        metadata={"source": "react_pattern", "topic": "agents"},
+    ),
+    Document(
+        page_content="""Agentic RAG is an advanced pattern where an AI agent decides when and how
+        to retrieve information. Unlike basic RAG which always retrieves, agentic RAG can:
+        - Decide if retrieval is needed based on the query
+        - Grade retrieved documents for relevance
+        - Rewrite queries if initial retrieval fails
+        - Combine multiple retrieval strategies""",
+        metadata={"source": "agentic_rag_overview", "topic": "rag"},
+    ),
+]
+
+
+class DocumentGrade(BaseModel):
+    """Grade for document relevance."""
+
+    binary_score: Literal["yes", "no"] = Field(
+        description="Documents are relevant to the question, 'yes' or 'no'"
+    )
+
+
+def _create_retriever() -> Any:
+    """Create a retriever with sample documents."""
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    vectorstore = InMemoryVectorStore(embeddings)
+    vectorstore.add_documents(SAMPLE_DOCUMENTS)
+    return vectorstore.as_retriever(search_kwargs={"k": 2})
+
+
+@task
+async def retrieve_documents(query: str) -> list[dict[str, Any]]:
+    """Retrieve relevant documents from the knowledge base.
+
+    Args:
+        query: The search query.
+
+    Returns:
+        List of retrieved documents with content and metadata.
+    """
+    retriever = _create_retriever()
+    docs = await retriever.ainvoke(query)
+
+    return [{"content": doc.page_content, "metadata": doc.metadata} for doc in docs]
+
+
+@task
+async def grade_documents(
+    question: str, documents: list[dict[str, Any]]
+) -> dict[str, Any]:
+    """Grade retrieved documents for relevance.
+
+    Args:
+        question: The user's question.
+        documents: Retrieved documents to grade.
+
+    Returns:
+        Dict with relevance flag and graded documents.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    if not documents:
+        return {"relevant": False, "documents": []}
+
+    # Combine document content for grading
+    docs_content = "\n\n".join(d["content"] for d in documents)
+
+    grade_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a grader assessing relevance of retrieved documents to a user question.\n"
+                "If the documents contain information related to the question, grade as relevant.\n"
+                "Give a binary score 'yes' or 'no' to indicate relevance.",
+            ),
+            (
+                "human",
+                "Retrieved documents:\n{documents}\n\nUser question: {question}",
+            ),
+        ]
+    )
+
+    grader = grade_prompt | model.with_structured_output(DocumentGrade)
+    result: Any = await grader.ainvoke(
+        {"documents": docs_content, "question": question}
+    )
+
+    return {
+        "relevant": result.binary_score == "yes",
+        "documents": documents,
+    }
+
+
+@task
+async def rewrite_query(original_query: str) -> str:
+    """Rewrite the query to improve retrieval.
+
+    Args:
+        original_query: The original question.
+
+    Returns:
+        An improved query.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    rewrite_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a question rewriter. Look at the input question and try to reason "
+                "about the underlying intent. Reformulate the question to be more specific "
+                "and likely to retrieve relevant documents about LangGraph, Temporal, AI agents, "
+                "or related topics.",
+            ),
+            (
+                "human",
+                "Original question: {question}\n\nFormulate an improved question:",
+            ),
+        ]
+    )
+
+    chain = rewrite_prompt | model | StrOutputParser()
+    improved = await chain.ainvoke({"question": original_query})
+
+    return improved
+
+
+@task
+async def generate_answer(question: str, documents: list[dict[str, Any]]) -> str:
+    """Generate an answer using the retrieved documents.
+
+    Args:
+        question: The user's question.
+        documents: Relevant documents to use for answering.
+
+    Returns:
+        The generated answer.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.3,
+    )
+
+    docs_content = "\n\n".join(d["content"] for d in documents)
+
+    rag_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are an assistant answering questions using the provided context.\n"
+                "Use the following retrieved documents to answer the question.\n"
+                "If the documents don't contain enough information, say so.\n"
+                "Keep your answer concise and well-structured.",
+            ),
+            ("human", "Context:\n{context}\n\nQuestion: {question}"),
+        ]
+    )
+
+    chain = rag_prompt | model | StrOutputParser()
+    answer = await chain.ainvoke({"context": docs_content, "question": question})
+
+    return answer
diff --git a/langgraph_plugin/functional_api/agentic_rag/workflow.py b/langgraph_plugin/functional_api/agentic_rag/workflow.py
new file mode 100644
index 00000000..50f2b03c
--- /dev/null
+++ b/langgraph_plugin/functional_api/agentic_rag/workflow.py
@@ -0,0 +1,37 @@
+"""Agentic RAG Workflow.
+
+Temporal workflow for the agentic RAG system.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class AgenticRagWorkflow:
+    """Temporal workflow for agentic RAG.
+
+    The system:
+    1. Retrieves documents based on query
+    2. Grades documents for relevance
+    3. Rewrites query and retries if not relevant
+    4. Generates answer using relevant documents
+
+    Each task runs as a Temporal activity with automatic retries.
+    """
+
+    @workflow.run
+    async def run(self, question: str) -> dict[str, Any]:
+        """Run the agentic RAG system.
+
+        Args:
+            question: The user's question.
+
+        Returns:
+            Answer with metadata.
+        """
+        app = compile("agentic_rag_entrypoint")
+        result = await app.ainvoke(question)
+        return result
diff --git a/langgraph_plugin/functional_api/continue_as_new/README.md b/langgraph_plugin/functional_api/continue_as_new/README.md
new file mode 100644
index 00000000..134f38ab
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/README.md
@@ -0,0 +1,123 @@
+# Continue-as-New with Task Caching (Functional API)
+
+Demonstrates task result caching across continue-as-new boundaries using the LangGraph Functional API.
+
+## Overview
+
+Long-running workflows may need to use continue-as-new to avoid unbounded event history growth. This sample shows how to preserve task results across continue-as-new boundaries so previously-completed tasks don't re-execute.
+
+## How It Works
+
+1. **Task results are cached** - When a `@task` completes, its result is stored in an in-memory cache
+2. **`get_state()` serializes the cache** - Before continue-as-new, call `app.get_state()` to get a checkpoint
+3. **`compile(checkpoint=...)` restores the cache** - In the new workflow execution, pass the checkpoint to restore cached results
+4. **Cache hits return immediately** - Tasks with matching inputs return cached results without re-execution
+
+## The Pipeline
+
+This sample runs a 5-step pipeline in two phases:
+
+```
+Phase 1: step_1 → step_2 → step_3 → [continue-as-new]
+Phase 2: step_1* → step_2* → step_3* → step_4 → step_5
+         (* = cached, no re-execution)
+```
+
+For input value `10`:
+- step_1: 10 × 2 = 20
+- step_2: 20 + 5 = 25
+- step_3: 25 × 3 = 75
+- step_4: 75 - 10 = 65
+- step_5: 65 + 100 = **165**
+
+## Key Code
+
+### Workflow with Checkpoint
+
+```python
+@workflow.defn
+class ContinueAsNewWorkflow:
+    @workflow.run
+    async def run(self, input_data: PipelineInput) -> dict:
+        # Restore cache from checkpoint if continuing
+        app = compile("pipeline_entrypoint", checkpoint=input_data.checkpoint)
+
+        if input_data.phase == 1:
+            # Phase 1: run first 3 tasks
+            result = await app.ainvoke({"value": input_data.value, "stop_after": 3})
+
+            # Capture cache state before continue-as-new
+            checkpoint = app.get_state()
+
+            workflow.continue_as_new(PipelineInput(
+                value=input_data.value,
+                checkpoint=checkpoint,  # Pass cached results
+                phase=2,
+            ))
+
+        # Phase 2: run all 5 (1-3 cached)
+        return await app.ainvoke({"value": input_data.value, "stop_after": 5})
+```
+
+### Entrypoint with Partial Execution
+
+```python
+@entrypoint()
+async def pipeline_entrypoint(input_data: dict) -> dict:
+    value = input_data["value"]
+    stop_after = input_data.get("stop_after", 5)
+
+    result = await step_1(value)
+    if stop_after == 1: return {"result": result}
+
+    result = await step_2(result)
+    if stop_after == 2: return {"result": result}
+
+    # ... more tasks ...
+```
+
+## Running the Sample
+
+1. Start Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run the worker:
+   ```bash
+   uv run langgraph_plugin/functional_api/continue_as_new/run_worker.py
+   ```
+
+3. Execute the workflow:
+   ```bash
+   uv run langgraph_plugin/functional_api/continue_as_new/run_workflow.py
+   ```
+
+4. **Check the worker logs** - You should see:
+   - `step_1`, `step_2`, `step_3` logged once (phase 1)
+   - `step_4`, `step_5` logged once (phase 2)
+   - No duplicate task execution!
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `tasks.py` | `@task` functions with logging |
+| `entrypoint.py` | `@entrypoint` with stop_after support |
+| `workflow.py` | Workflow with continue-as-new logic |
+| `run_worker.py` | Worker startup script |
+| `run_workflow.py` | Workflow execution script |
+
+## Key Differences from Graph API
+
+| Aspect | Graph API | Functional API |
+|--------|-----------|----------------|
+| Checkpoint content | Full channel state | Cached task results |
+| Resume behavior | Resume mid-graph | Re-execute from start, skip cached tasks |
+| Granularity | Node-level | Task-level |
+
+## When to Use
+
+- Long-running pipelines that may hit event history limits
+- Workflows with expensive tasks that shouldn't re-execute
+- Multi-phase processing where each phase builds on previous results
diff --git a/langgraph_plugin/functional_api/continue_as_new/__init__.py b/langgraph_plugin/functional_api/continue_as_new/__init__.py
new file mode 100644
index 00000000..d444c57e
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/__init__.py
@@ -0,0 +1 @@
+"""Continue-as-New sample for LangGraph Functional API."""
diff --git a/langgraph_plugin/functional_api/continue_as_new/entrypoint.py b/langgraph_plugin/functional_api/continue_as_new/entrypoint.py
new file mode 100644
index 00000000..97dbe05e
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/entrypoint.py
@@ -0,0 +1,58 @@
+"""Continue-as-New Entrypoint Definition.
+
+Demonstrates a pipeline that can be interrupted via should_continue callback.
+The entrypoint itself has NO knowledge of continue-as-new - it just runs
+all tasks. The workflow controls when to checkpoint via the callback.
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.continue_as_new.tasks import (
+    step_1,
+    step_2,
+    step_3,
+    step_4,
+    step_5,
+)
+
+
+@entrypoint()
+async def pipeline_entrypoint(input_data: dict[str, Any]) -> dict[str, Any]:
+    """Pipeline entrypoint with 5 sequential tasks.
+
+    This entrypoint runs all 5 tasks. If should_continue callback is provided
+    to ainvoke() and returns False after a task completes, execution will
+    stop early and return a checkpoint for continue-as-new.
+
+    Args:
+        input_data: Dict with:
+            - value: Starting integer value
+
+    Returns:
+        Dict with result and number of completed tasks.
+
+    Calculation for value=10, all 5 tasks:
+        10 * 2 = 20 -> 20 + 5 = 25 -> 25 * 3 = 75 -> 75 - 10 = 65 -> 65 + 100 = 165
+    """
+    value = input_data["value"]
+
+    result = value
+
+    # Task 1: multiply by 2
+    result = await step_1(result)
+
+    # Task 2: add 5
+    result = await step_2(result)
+
+    # Task 3: multiply by 3
+    result = await step_3(result)
+
+    # Task 4: subtract 10
+    result = await step_4(result)
+
+    # Task 5: add 100
+    result = await step_5(result)
+
+    return {"result": result, "completed_tasks": 5}
diff --git a/langgraph_plugin/functional_api/continue_as_new/run_worker.py b/langgraph_plugin/functional_api/continue_as_new/run_worker.py
new file mode 100644
index 00000000..cdae0e3f
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/run_worker.py
@@ -0,0 +1,56 @@
+"""Worker for the Continue-as-New LangGraph Functional API example.
+
+Starts a Temporal worker that can execute ContinueAsNewWorkflow.
+"""
+
+import asyncio
+import logging
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.continue_as_new.entrypoint import (
+    pipeline_entrypoint,
+)
+from langgraph_plugin.functional_api.continue_as_new.workflow import (
+    ContinueAsNewWorkflow,
+)
+
+# Configure logging to see task execution
+logging.basicConfig(level=logging.INFO)
+
+
+async def main() -> None:
+    # Create the plugin with our entrypoint registered
+    plugin = LangGraphPlugin(
+        graphs={"pipeline_entrypoint": pipeline_entrypoint},
+        default_activity_options=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-continue-as-new",
+        workflows=[ContinueAsNewWorkflow],
+    )
+
+    print("Worker started. Ctrl+C to exit.")
+    print("Task execution will be logged - watch for cache hits!")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/continue_as_new/run_workflow.py b/langgraph_plugin/functional_api/continue_as_new/run_workflow.py
new file mode 100644
index 00000000..4b78a1e7
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/run_workflow.py
@@ -0,0 +1,52 @@
+"""Execute the Continue-as-New workflow.
+
+Runs the ContinueAsNewWorkflow which demonstrates should_continue callback
+and task caching across continue-as-new boundaries.
+"""
+
+import asyncio
+import uuid
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.continue_as_new.workflow import (
+    ContinueAsNewWorkflow,
+    PipelineInput,
+)
+
+
+async def main() -> None:
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Start the workflow
+    workflow_id = f"continue-as-new-{uuid.uuid4()}"
+    print(f"Starting workflow: {workflow_id}")
+    print("Input value: 10, tasks_per_execution: 3")
+    print()
+    print("Expected execution:")
+    print("  Execution 1: step_1(10)=20, step_2(20)=25, step_3(25)=75")
+    print("  [should_continue returns False -> checkpoint -> continue-as-new]")
+    print("  Execution 2: step_1-3 CACHED, step_4(75)=65, step_5(65)=165")
+    print()
+
+    result = await client.execute_workflow(
+        ContinueAsNewWorkflow.run,
+        PipelineInput(value=10, tasks_per_execution=3),
+        id=workflow_id,
+        task_queue="langgraph-functional-continue-as-new",
+    )
+
+    print(f"Result: {result}")
+    print()
+    print("Check the worker logs to verify:")
+    print("  - step_1, step_2, step_3 logged only ONCE (in execution 1)")
+    print("  - step_4, step_5 logged only in execution 2")
+    print("  - No re-execution of cached tasks!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/continue_as_new/tasks.py b/langgraph_plugin/functional_api/continue_as_new/tasks.py
new file mode 100644
index 00000000..beb076fe
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/tasks.py
@@ -0,0 +1,49 @@
+"""Continue-as-New Task Definitions.
+
+Tasks that demonstrate caching across continue-as-new boundaries.
+Each task tracks execution count to verify caching works correctly.
+"""
+
+import logging
+
+from langgraph.func import task
+
+logger = logging.getLogger(__name__)
+
+
+@task
+def step_1(x: int) -> int:
+    """Step 1: multiply by 2.
+
+    This task logs when it executes to help verify caching.
+    """
+    logger.info(f"step_1 executing with input {x}")
+    return x * 2
+
+
+@task
+def step_2(x: int) -> int:
+    """Step 2: add 5."""
+    logger.info(f"step_2 executing with input {x}")
+    return x + 5
+
+
+@task
+def step_3(x: int) -> int:
+    """Step 3: multiply by 3."""
+    logger.info(f"step_3 executing with input {x}")
+    return x * 3
+
+
+@task
+def step_4(x: int) -> int:
+    """Step 4: subtract 10."""
+    logger.info(f"step_4 executing with input {x}")
+    return x - 10
+
+
+@task
+def step_5(x: int) -> int:
+    """Step 5: add 100."""
+    logger.info(f"step_5 executing with input {x}")
+    return x + 100
diff --git a/langgraph_plugin/functional_api/continue_as_new/workflow.py b/langgraph_plugin/functional_api/continue_as_new/workflow.py
new file mode 100644
index 00000000..aac940ac
--- /dev/null
+++ b/langgraph_plugin/functional_api/continue_as_new/workflow.py
@@ -0,0 +1,98 @@
+"""Continue-as-New Workflow for LangGraph Functional API.
+
+This workflow uses should_continue callback to stop execution after
+a configurable number of tasks, then uses continue-as-new to resume from
+where it left off. The entrypoint itself has NO knowledge of continue-as-new.
+"""
+
+from dataclasses import dataclass
+from typing import Any, cast
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import (
+    CHECKPOINT_KEY,
+    TemporalFunctionalRunner,
+    compile,
+)
+
+
+@dataclass
+class PipelineInput:
+    """Input for the pipeline workflow."""
+
+    value: int
+    # Number of tasks to execute before continue-as-new
+    tasks_per_execution: int = 3
+    # Checkpoint from previous execution (None for first execution)
+    checkpoint: dict[str, Any] | None = None
+
+
+@workflow.defn
+class ContinueAsNewWorkflow:
+    """Workflow demonstrating continue-as-new with should_continue callback.
+
+    The workflow uses should_continue callback to stop execution after
+    a configurable number of tasks, then continues-as-new to resume.
+    This demonstrates how to use continue-as-new with Functional API without
+    the entrypoint needing any knowledge of Temporal.
+
+    This demonstrates that:
+    - Task results are cached in memory
+    - should_continue callback controls when to checkpoint
+    - get_state() serializes the cache for continue-as-new
+    - compile(checkpoint=...) restores the cache in the new execution
+    - Cached tasks return immediately without re-execution
+    """
+
+    @workflow.run
+    async def run(self, input_data: PipelineInput) -> dict[str, Any]:
+        """Execute the pipeline with continue-as-new checkpoint.
+
+        Args:
+            input_data: Pipeline input with value, tasks_per_execution, and checkpoint.
+
+        Returns:
+            Final result after all tasks complete.
+        """
+        # Track tasks executed in this execution
+        tasks_executed = 0
+
+        def should_continue() -> bool:
+            """Stop after configured number of tasks."""
+            nonlocal tasks_executed
+            tasks_executed += 1
+            return tasks_executed <= input_data.tasks_per_execution
+
+        # Create runner, restoring from checkpoint if provided
+        # Cast to TemporalFunctionalRunner since we know this is a Functional API entrypoint
+        app = cast(
+            TemporalFunctionalRunner,
+            compile("pipeline_entrypoint", checkpoint=input_data.checkpoint),
+        )
+
+        # Execute the entrypoint with should_continue callback
+        result = await app.ainvoke(
+            {"value": input_data.value},
+            should_continue=should_continue,
+        )
+
+        # Check if we stopped for checkpointing (more work to do)
+        if CHECKPOINT_KEY in result:
+            checkpoint = result[CHECKPOINT_KEY]
+
+            workflow.logger.info(
+                f"Stopping after {tasks_executed} tasks for checkpointing"
+            )
+
+            # Continue-as-new with checkpoint
+            workflow.continue_as_new(
+                PipelineInput(
+                    value=input_data.value,
+                    tasks_per_execution=input_data.tasks_per_execution,
+                    checkpoint=checkpoint,
+                )
+            )
+
+        # Entrypoint completed - return final result
+        workflow.logger.info(f"Pipeline completed. Result: {result}")
+        return result
diff --git a/langgraph_plugin/functional_api/deep_research/README.md b/langgraph_plugin/functional_api/deep_research/README.md
new file mode 100644
index 00000000..e2438dec
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/README.md
@@ -0,0 +1,133 @@
+# Deep Research Agent (Functional API)
+
+A multi-step research agent that plans queries, executes parallel searches, and synthesizes findings into a comprehensive report.
+
+## Overview
+
+The deep research pattern:
+
+1. **Plan** - Generate research queries based on topic
+2. **Search** - Execute searches in parallel
+3. **Evaluate** - Check if results are sufficient
+4. **Iterate** - Refine and search again if needed
+5. **Synthesize** - Create final report
+
+## Architecture
+
+```
+Research Topic
+      │
+      ▼
+┌──────────────┐
+│ plan_research│
+│    (task)    │
+└──────┬───────┘
+       │
+       ▼
+┌──────────────────────────────┐
+│     execute_search (task)    │
+│  ┌────┐ ┌────┐ ┌────┐ ┌────┐│  ← Parallel execution
+│  │ Q1 │ │ Q2 │ │ Q3 │ │ Q4 ││
+│  └────┘ └────┘ └────┘ └────┘│
+└──────────────┬───────────────┘
+               │
+               ▼
+        Enough results?
+               │
+          NO   │   YES
+               │    │
+        ┌──────┘    │
+        │           ▼
+        │    ┌──────────────────┐
+        │    │ synthesize_report│
+        │    │      (task)      │
+        │    └──────────────────┘
+        │
+        └──► Plan more queries...
+```
+
+## Key Code
+
+### Parallel Search Execution
+
+```python
+@entrypoint()
+async def deep_research_entrypoint(topic: str, max_iterations: int = 2) -> dict:
+    all_results = []
+
+    for iteration in range(1, max_iterations + 1):
+        # Plan research queries
+        queries = await plan_research(topic)
+
+        # Execute searches IN PARALLEL
+        search_futures = [execute_search(q["query"], q["purpose"]) for q in queries]
+        search_results = [await future for future in search_futures]
+        all_results.extend(search_results)
+
+        # Check if we have enough relevant results
+        relevant_count = sum(1 for r in search_results if r.get("relevant"))
+        if relevant_count >= 2:
+            break
+
+    # Synthesize final report
+    report = await synthesize_report(topic, all_results)
+    return {"report": report, "total_searches": len(all_results)}
+```
+
+### Parallel Pattern
+
+```python
+# Start all tasks concurrently (non-blocking)
+futures = [execute_search(q) for q in queries]
+
+# Wait for all to complete
+results = [await f for f in futures]
+```
+
+This is the key difference from Graph API - parallel execution uses simple Python patterns.
+
+## Why Temporal?
+
+- **Parallel durability**: All concurrent searches complete reliably
+- **Cost efficiency**: Parallel execution reduces total time
+- **Progress tracking**: See individual search completions
+- **Resume**: Continue from last completed search if interrupted
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run with API key:
+   ```bash
+   export OPENAI_API_KEY=your-key
+   uv run langgraph_plugin/functional_api/deep_research/run_worker.py
+   ```
+
+3. Research a topic:
+   ```bash
+   uv run langgraph_plugin/functional_api/deep_research/run_workflow.py
+   ```
+
+## Customization
+
+### Adjust Search Breadth
+
+```python
+@task
+def plan_research(topic: str) -> list[dict]:
+    # Generate more or fewer queries
+    return generate_queries(topic, count=6)  # Default might be 4
+```
+
+### Add Source Filtering
+
+```python
+@task
+def execute_search(query: str, purpose: str) -> dict:
+    # Filter to specific sources
+    results = search(query, sources=["academic", "news"])
+    ...
+```
diff --git a/langgraph_plugin/functional_api/deep_research/__init__.py b/langgraph_plugin/functional_api/deep_research/__init__.py
new file mode 100644
index 00000000..70e98e98
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/__init__.py
@@ -0,0 +1 @@
+"""Deep Research Agent - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/deep_research/entrypoint.py b/langgraph_plugin/functional_api/deep_research/entrypoint.py
new file mode 100644
index 00000000..056d9358
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/entrypoint.py
@@ -0,0 +1,70 @@
+"""Deep Research Agent Entrypoint Definition.
+
+The @entrypoint function orchestrates multi-step research:
+1. Plan research queries
+2. Execute searches in parallel
+3. Evaluate and iterate if needed
+4. Synthesize findings into a report
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.deep_research.tasks import (
+    execute_search,
+    plan_research,
+    synthesize_report,
+)
+
+
+@entrypoint()
+async def deep_research_entrypoint(
+    topic: str, max_iterations: int = 2
+) -> dict[str, Any]:
+    """Perform deep research on a topic.
+
+    Demonstrates:
+    - Planning research queries
+    - Parallel task execution for multiple searches
+    - Iterative research refinement
+    - Report synthesis
+
+    Args:
+        topic: The research topic.
+        max_iterations: Maximum research iterations.
+
+    Returns:
+        Dict with research report and metadata.
+    """
+    all_results: list[dict[str, Any]] = []
+
+    for iteration in range(1, max_iterations + 1):
+        # Step 1: Plan research queries
+        queries = await plan_research(topic)
+
+        # Step 2: Execute searches in parallel
+        # Start all search tasks concurrently
+        search_futures = [execute_search(q["query"], q["purpose"]) for q in queries]
+
+        # Wait for all searches to complete
+        search_results = [await future for future in search_futures]
+        all_results.extend(search_results)
+
+        # Step 3: Evaluate results
+        relevant_count = sum(1 for r in search_results if r.get("relevant", False))
+
+        # Check if we have enough relevant results
+        if relevant_count >= 2 or iteration >= max_iterations:
+            break
+
+    # Step 4: Synthesize report
+    report = await synthesize_report(topic, all_results)
+
+    return {
+        "topic": topic,
+        "report": report,
+        "iterations": iteration,
+        "total_searches": len(all_results),
+        "relevant_results": sum(1 for r in all_results if r.get("relevant", False)),
+    }
diff --git a/langgraph_plugin/functional_api/deep_research/run_worker.py b/langgraph_plugin/functional_api/deep_research/run_worker.py
new file mode 100644
index 00000000..2a52155f
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/run_worker.py
@@ -0,0 +1,57 @@
+"""Worker for the Deep Research Functional API sample.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.deep_research.entrypoint import (
+    deep_research_entrypoint,
+)
+from langgraph_plugin.functional_api.deep_research.workflow import DeepResearchWorkflow
+
+
+async def main() -> None:
+    plugin = LangGraphPlugin(
+        graphs={"deep_research_entrypoint": deep_research_entrypoint},
+        activity_options={
+            "plan_research": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+            "execute_search": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+            "synthesize_report": activity_options(
+                start_to_close_timeout=timedelta(minutes=3),
+            ),
+        },
+    )
+
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-deep-research",
+        workflows=[DeepResearchWorkflow],
+    )
+
+    print("Deep Research worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/deep_research/run_workflow.py b/langgraph_plugin/functional_api/deep_research/run_workflow.py
new file mode 100644
index 00000000..d913266c
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/run_workflow.py
@@ -0,0 +1,33 @@
+"""Execute the Deep Research Functional API workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.deep_research.workflow import DeepResearchWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    topic = "LangGraph multi-agent orchestration patterns"
+
+    result = await client.execute_workflow(
+        DeepResearchWorkflow.run,
+        topic,
+        id="deep-research-functional-workflow",
+        task_queue="langgraph-functional-deep-research",
+    )
+
+    print(f"Topic: {result.get('topic')}")
+    print(f"Iterations: {result.get('iterations')}")
+    print(f"Total Searches: {result.get('total_searches')}")
+    print(f"Relevant Results: {result.get('relevant_results')}")
+    print(f"\nReport:\n{result.get('report')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/deep_research/tasks.py b/langgraph_plugin/functional_api/deep_research/tasks.py
new file mode 100644
index 00000000..2ec38e1b
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/tasks.py
@@ -0,0 +1,153 @@
+"""Task definitions for the Deep Research Agent.
+
+Each @task runs as a Temporal activity with automatic retries.
+Demonstrates parallel task execution for multiple web searches.
+"""
+
+import os
+from typing import Any
+
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.func import task
+from pydantic import BaseModel, Field
+
+
+class SearchQuery(BaseModel):
+    """A search query to execute."""
+
+    query: str = Field(description="The search query to execute")
+    purpose: str = Field(description="What information this query aims to find")
+
+
+class ResearchPlan(BaseModel):
+    """Research plan with multiple search queries."""
+
+    queries: list[SearchQuery] = Field(
+        description="List of search queries to execute", min_length=1, max_length=5
+    )
+
+
+# Initialize DuckDuckGo search tool
+search_tool = DuckDuckGoSearchRun()
+
+
+@task
+async def plan_research(topic: str) -> list[dict[str, str]]:
+    """Plan the research by generating search queries.
+
+    Args:
+        topic: The research topic.
+
+    Returns:
+        List of search queries with purposes.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    plan_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a research planner. Given a research topic, generate 2-4 search
+queries that will help gather comprehensive information. Each query should
+target a different aspect of the topic.
+
+Consider:
+- Core concepts and definitions
+- Key features and capabilities
+- Use cases and applications
+- Comparisons or alternatives""",
+            ),
+            ("human", "Research topic: {topic}"),
+        ]
+    )
+
+    planner = plan_prompt | model.with_structured_output(ResearchPlan)
+    plan: Any = await planner.ainvoke({"topic": topic})
+
+    return [{"query": q.query, "purpose": q.purpose} for q in plan.queries]
+
+
+@task
+async def execute_search(query: str, purpose: str) -> dict[str, Any]:
+    """Execute a single web search query using DuckDuckGo.
+
+    Each search runs as a separate Temporal activity for durability.
+
+    Args:
+        query: The search query.
+        purpose: What information this query aims to find.
+
+    Returns:
+        Dict with query, purpose, results, and relevance flag.
+    """
+    try:
+        results = search_tool.invoke(query)
+        has_results = bool(results and len(results.strip()) > 0)
+    except Exception as e:
+        results = f"Search failed: {e}"
+        has_results = False
+
+    return {
+        "query": query,
+        "purpose": purpose,
+        "results": results,
+        "relevant": has_results,
+    }
+
+
+@task
+async def synthesize_report(topic: str, search_results: list[dict[str, Any]]) -> str:
+    """Synthesize research findings into a comprehensive report.
+
+    Args:
+        topic: The research topic.
+        search_results: Results from all searches.
+
+    Returns:
+        The synthesized research report.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.3,
+    )
+
+    # Compile all findings
+    findings = []
+    for result in search_results:
+        if result.get("relevant", False):
+            findings.append(f"### {result['purpose']}\n{result['results']}")
+
+    if not findings:
+        findings_text = "Limited information was found on this topic."
+    else:
+        findings_text = "\n\n".join(findings)
+
+    synthesis_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a research synthesizer. Create a comprehensive research report
+from the gathered findings. Structure the report with:
+1. Executive Summary - Key takeaways in 2-3 sentences
+2. Main Findings - Detailed information organized by topic
+3. Conclusions - Synthesis of the research
+
+Be thorough but concise. Cite the source topics when presenting findings.""",
+            ),
+            (
+                "human",
+                "Research Topic: {topic}\n\nGathered Findings:\n{findings}",
+            ),
+        ]
+    )
+
+    chain = synthesis_prompt | model | StrOutputParser()
+    report = await chain.ainvoke({"topic": topic, "findings": findings_text})
+
+    return report
diff --git a/langgraph_plugin/functional_api/deep_research/workflow.py b/langgraph_plugin/functional_api/deep_research/workflow.py
new file mode 100644
index 00000000..588b49cf
--- /dev/null
+++ b/langgraph_plugin/functional_api/deep_research/workflow.py
@@ -0,0 +1,36 @@
+"""Deep Research Agent Workflow.
+
+Temporal workflow for the deep research agent.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class DeepResearchWorkflow:
+    """Temporal workflow for deep research.
+
+    Demonstrates long-running research workflows with:
+    - Parallel search execution
+    - Iterative research refinement
+    - Report synthesis
+
+    Each task runs as a Temporal activity with automatic retries.
+    """
+
+    @workflow.run
+    async def run(self, topic: str) -> dict[str, Any]:
+        """Run deep research on a topic.
+
+        Args:
+            topic: The research topic.
+
+        Returns:
+            Research report with metadata.
+        """
+        app = compile("deep_research_entrypoint")
+        result = await app.ainvoke(topic)
+        return result
diff --git a/langgraph_plugin/functional_api/hello_world/README.md b/langgraph_plugin/functional_api/hello_world/README.md
new file mode 100644
index 00000000..9fdd183d
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/README.md
@@ -0,0 +1,81 @@
+# Hello World (Functional API)
+
+A minimal example demonstrating the LangGraph Functional API with Temporal integration.
+
+## Overview
+
+This sample shows the basic pattern for using `@task` and `@entrypoint` decorators with Temporal:
+
+1. **`@task`** - Defines a function that runs as a Temporal activity
+2. **`@entrypoint`** - Orchestrates tasks, runs in the Temporal workflow
+
+## Key Concepts
+
+### Task as Activity
+
+```python
+@task
+def process_query(query: str) -> str:
+    """This runs as a Temporal activity with automatic retries."""
+    return f"Processed: {query}"
+```
+
+### Entrypoint as Orchestrator
+
+```python
+@entrypoint()
+async def hello_world_entrypoint(query: str) -> dict:
+    # Use await to call tasks (required for Temporal)
+    result = await process_query(query)
+    return {"query": query, "result": result}
+```
+
+### Workflow Wrapper
+
+```python
+@workflow.defn
+class HelloWorldWorkflow:
+    @workflow.run
+    async def run(self, query: str) -> dict:
+        app = compile("hello_world")
+        return await app.ainvoke(query)
+```
+
+## Running the Sample
+
+1. Start Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run the worker:
+   ```bash
+   uv run langgraph_plugin/functional_api/hello_world/run_worker.py
+   ```
+
+3. Execute the workflow:
+   ```bash
+   uv run langgraph_plugin/functional_api/hello_world/run_workflow.py
+   ```
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `tasks.py` | `@task` function definitions |
+| `entrypoint.py` | `@entrypoint` orchestration logic |
+| `workflow.py` | Temporal workflow wrapper |
+| `run_worker.py` | Worker startup script |
+| `run_workflow.py` | Workflow execution script |
+
+## Adapting from Standard LangGraph
+
+```python
+# Standard LangGraph
+result = my_task(input).result()  # Blocking
+
+# Temporal-compatible
+result = await my_task(input)  # Async await
+```
+
+See the main README for the complete migration guide.
diff --git a/langgraph_plugin/functional_api/hello_world/__init__.py b/langgraph_plugin/functional_api/hello_world/__init__.py
new file mode 100644
index 00000000..4e62e5bc
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/__init__.py
@@ -0,0 +1 @@
+"""Hello World - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/hello_world/entrypoint.py b/langgraph_plugin/functional_api/hello_world/entrypoint.py
new file mode 100644
index 00000000..363b2813
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/entrypoint.py
@@ -0,0 +1,34 @@
+"""Hello World Entrypoint Definition.
+
+The @entrypoint function runs directly in the Temporal workflow sandbox.
+It must be deterministic - non-deterministic operations belong in @task functions.
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.hello_world.tasks import process_query
+
+
+@entrypoint()
+async def hello_world_entrypoint(query: str) -> dict[str, Any]:
+    """Simple hello world entrypoint.
+
+    Demonstrates:
+    - Single task execution
+    - Task result handling
+
+    Args:
+        query: The input query to process.
+
+    Returns:
+        Dict containing the query and processed result.
+    """
+    # Task call becomes a Temporal activity
+    result = await process_query(query)
+
+    return {
+        "query": query,
+        "result": result,
+    }
diff --git a/langgraph_plugin/functional_api/hello_world/run_worker.py b/langgraph_plugin/functional_api/hello_world/run_worker.py
new file mode 100644
index 00000000..9b2ddc5e
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/run_worker.py
@@ -0,0 +1,54 @@
+"""Worker for the Hello World LangGraph Functional API example.
+
+Starts a Temporal worker that can execute HelloWorldWorkflow.
+The LangGraphPlugin registers the entrypoint and handles activity registration.
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.hello_world.entrypoint import (
+    hello_world_entrypoint,
+)
+from langgraph_plugin.functional_api.hello_world.workflow import HelloWorldWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with our entrypoint registered by name
+    # Use activity_options() to configure task-specific timeouts
+    plugin = LangGraphPlugin(
+        graphs={"hello_world_entrypoint": hello_world_entrypoint},
+        activity_options={
+            "process_query": activity_options(
+                start_to_close_timeout=timedelta(seconds=30),
+            ),
+        },
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: The dynamic task activity is automatically registered by the plugin
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-hello-world",
+        workflows=[HelloWorldWorkflow],
+    )
+
+    print("Worker started. Ctrl+C to exit.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/hello_world/run_workflow.py b/langgraph_plugin/functional_api/hello_world/run_workflow.py
new file mode 100644
index 00000000..0611f41a
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/run_workflow.py
@@ -0,0 +1,31 @@
+"""Execute the Hello World LangGraph Functional API workflow.
+
+Connects to Temporal and starts the HelloWorldWorkflow.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.hello_world.workflow import HelloWorldWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Execute the workflow
+    result = await client.execute_workflow(
+        HelloWorldWorkflow.run,
+        "Hello, Temporal!",
+        id="hello-world-functional-workflow",
+        task_queue="langgraph-functional-hello-world",
+    )
+
+    print(f"Result: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/hello_world/tasks.py b/langgraph_plugin/functional_api/hello_world/tasks.py
new file mode 100644
index 00000000..77c81309
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/tasks.py
@@ -0,0 +1,23 @@
+"""Hello World Task Definitions.
+
+Tasks are @task-decorated functions that run as Temporal activities.
+They can contain non-deterministic operations (API calls, I/O, randomness).
+"""
+
+from langgraph.func import task
+
+
+@task
+def process_query(query: str) -> str:
+    """Process the query and return a result.
+
+    In a real application, this could call an LLM, database, or external API.
+    Each @task runs as a Temporal activity with automatic retries.
+
+    Args:
+        query: The input query to process.
+
+    Returns:
+        The processed result string.
+    """
+    return f"Processed: {query}"
diff --git a/langgraph_plugin/functional_api/hello_world/workflow.py b/langgraph_plugin/functional_api/hello_world/workflow.py
new file mode 100644
index 00000000..4e7ec4a7
--- /dev/null
+++ b/langgraph_plugin/functional_api/hello_world/workflow.py
@@ -0,0 +1,41 @@
+"""Hello World LangGraph Functional API Workflow.
+
+Minimal example demonstrating LangGraph Functional API with Temporal integration.
+
+Note: This module only contains the workflow definition. The entrypoint and tasks
+are in separate files and are imported by the worker.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class HelloWorldWorkflow:
+    """Temporal workflow that executes the hello world LangGraph entrypoint.
+
+    This workflow demonstrates:
+    - Using compile() to get an entrypoint runner by name
+    - Executing the entrypoint with ainvoke()
+    - Each @task runs as a Temporal activity with durability guarantees
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the hello world entrypoint.
+
+        Args:
+            query: The input query to process.
+
+        Returns:
+            The final state containing the processed result.
+        """
+        # Get the compiled entrypoint runner by name
+        app = compile("hello_world_entrypoint")
+
+        # Execute the entrypoint - the "process_query" task runs as a Temporal activity
+        result = await app.ainvoke(query)
+
+        return result
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/README.md b/langgraph_plugin/functional_api/human_in_the_loop/README.md
new file mode 100644
index 00000000..67b296ec
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/README.md
@@ -0,0 +1,194 @@
+# Human-in-the-Loop (Functional API)
+
+A workflow that pauses for human approval using `interrupt()` before executing actions.
+
+## Overview
+
+The human-in-the-loop pattern demonstrates how to pause a workflow for external input:
+
+1. **Process** - Validate and assess the request
+2. **Interrupt** - Pause workflow, wait for human approval
+3. **Execute** - Complete action based on approval decision
+
+## Architecture
+
+```
+Approval Request
+      │
+      ▼
+┌─────────────────┐
+│ process_request │
+│     (task)      │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│   interrupt()   │ ◄── Workflow pauses here
+│  (wait signal)  │
+└────────┬────────┘
+         │
+    Signal received
+         │
+         ▼
+┌─────────────────┐
+│ execute_action  │
+│     (task)      │
+└─────────────────┘
+```
+
+## Key Code
+
+### Using interrupt()
+
+```python
+@entrypoint()
+async def approval_entrypoint(
+    request_type: str,
+    amount: float,
+    request_data: dict | None = None,
+) -> dict:
+    # Step 1: Validate the request
+    validation = await process_request(request_type, amount, request_data)
+
+    # Step 2: Pause for human approval
+    approval_request = {
+        "request_type": request_type,
+        "amount": amount,
+        "risk_level": validation["risk_level"],
+        "message": f"Please approve {request_type} for ${amount:.2f}",
+    }
+
+    # interrupt() pauses the workflow and returns when signal is received
+    approval_response = interrupt(approval_request)
+
+    # Step 3: Execute based on approval
+    approved = approval_response.get("approved", False)
+    result = await execute_action(request_type, amount, approved, ...)
+
+    return {"approved": approved, **result}
+```
+
+### Temporal Workflow Wrapper
+
+```python
+@workflow.defn
+class ApprovalWorkflow:
+    def __init__(self):
+        self._pending_approval: dict | None = None
+        self._approval_response: dict | None = None
+
+    @workflow.run
+    async def run(self, request: ApprovalRequest) -> dict:
+        runner = compile("approval_entrypoint")
+
+        async for event in runner.astream_events(...):
+            if event["event"] == "on_interrupt":
+                self._pending_approval = event["data"]
+                await workflow.wait_condition(
+                    lambda: self._approval_response is not None
+                )
+                # Resume with approval response
+                runner.update_state(..., {"resuming": self._approval_response})
+
+    @workflow.query
+    def get_pending_approval(self) -> dict | None:
+        return self._pending_approval
+
+    @workflow.signal
+    def provide_approval(self, response: dict) -> None:
+        self._approval_response = response
+```
+
+### Client Interaction
+
+```python
+# Start workflow
+handle = await client.start_workflow(ApprovalWorkflow.run, request, ...)
+
+# Query pending approval
+pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+print(f"Needs approval: {pending['message']}")
+
+# Provide approval via signal
+await handle.signal(ApprovalWorkflow.provide_approval, {
+    "approved": True,
+    "approver": "manager@example.com",
+    "reason": "Approved for vendor payment",
+})
+
+# Get result
+result = await handle.result()
+```
+
+## Why Temporal?
+
+- **Durable pause**: Workflow survives restarts while waiting for approval
+- **Query state**: Check pending approvals without database
+- **Signal resume**: Resume with approval via Temporal signals
+- **Timeout handling**: Add deadlines for approvals
+- **Audit trail**: Full history of approval workflow
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run the worker:
+   ```bash
+   uv run langgraph_plugin/functional_api/human_in_the_loop/run_worker.py
+   ```
+
+3. Execute workflow with approval:
+   ```bash
+   uv run langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py
+   ```
+
+## Customization
+
+### Add Approval Timeout
+
+```python
+@workflow.run
+async def run(self, request: ApprovalRequest) -> dict:
+    # Wait for approval with timeout
+    try:
+        await workflow.wait_condition(
+            lambda: self._approval_response is not None,
+            timeout=timedelta(hours=24),
+        )
+    except asyncio.TimeoutError:
+        return {"status": "expired", "message": "Approval timed out"}
+```
+
+### Multi-Level Approval
+
+```python
+@entrypoint()
+async def multi_approval_entrypoint(request: dict) -> dict:
+    # First level: Manager approval
+    manager_response = interrupt({"level": "manager", ...})
+
+    if request["amount"] > 10000:
+        # Second level: Director approval for large amounts
+        director_response = interrupt({"level": "director", ...})
+
+    return await execute_action(...)
+```
+
+### Risk-Based Routing
+
+```python
+@entrypoint()
+async def risk_based_entrypoint(request: dict) -> dict:
+    validation = await assess_risk(request)
+
+    if validation["risk_level"] == "low":
+        # Auto-approve low risk
+        return await execute_action(request, auto_approved=True)
+    else:
+        # Require human approval for medium/high risk
+        approval = interrupt({"risk_level": validation["risk_level"], ...})
+        return await execute_action(request, approved=approval["approved"])
+```
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/__init__.py b/langgraph_plugin/functional_api/human_in_the_loop/__init__.py
new file mode 100644
index 00000000..996c3d78
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/__init__.py
@@ -0,0 +1 @@
+"""Human-in-the-Loop - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/entrypoint.py b/langgraph_plugin/functional_api/human_in_the_loop/entrypoint.py
new file mode 100644
index 00000000..7938ead8
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/entrypoint.py
@@ -0,0 +1,84 @@
+"""Human-in-the-Loop Entrypoint Definition.
+
+The @entrypoint function demonstrates interrupt() for human approval:
+1. Process and validate the request
+2. Call interrupt() to pause for human approval
+3. Execute based on approval decision
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+from langgraph.types import interrupt
+
+from langgraph_plugin.functional_api.human_in_the_loop.tasks import (
+    execute_action,
+    process_request,
+)
+
+
+@entrypoint()
+async def approval_entrypoint(
+    request_type: str,
+    amount: float,
+    request_data: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Run an approval workflow with human-in-the-loop.
+
+    The workflow:
+    1. Processes and validates the request
+    2. Pauses for human approval using interrupt()
+    3. Executes the approved action or rejects
+
+    The interrupt() call pauses the Temporal workflow until a signal
+    is received with the human's decision.
+
+    Args:
+        request_type: Type of request (e.g., "payment", "refund").
+        amount: The monetary amount.
+        request_data: Additional request details.
+
+    Returns:
+        Dict with final status and execution result.
+    """
+    request_data = request_data or {}
+
+    # Step 1: Process and validate the request
+    validation = await process_request(request_type, amount, request_data)
+
+    # Step 2: Request human approval via interrupt
+    # This pauses the workflow and waits for a signal
+    approval_request = {
+        "request_type": request_type,
+        "amount": amount,
+        "risk_level": validation["risk_level"],
+        "request_data": request_data,
+        "message": f"Please approve {request_type} for ${amount:.2f} "
+        f"(Risk: {validation['risk_level']})",
+    }
+
+    # interrupt() pauses here and returns the human's response when resumed
+    approval_response = interrupt(approval_request)
+
+    # Step 3: Execute based on approval
+    approved = approval_response.get("approved", False)
+    approver = approval_response.get("approver", "unknown")
+    reason = approval_response.get("reason", "No reason provided")
+
+    result = await execute_action(
+        request_type=request_type,
+        amount=amount,
+        approved=approved,
+        approver=approver,
+        reason=reason,
+    )
+
+    return {
+        "request_type": request_type,
+        "amount": amount,
+        "risk_level": validation["risk_level"],
+        "approved": approved,
+        "approver": approver,
+        "reason": reason,
+        **result,
+    }
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py b/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py
new file mode 100644
index 00000000..ad12d7d1
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py
@@ -0,0 +1,52 @@
+"""Worker for the Human-in-the-Loop Functional API sample.
+
+Prerequisites:
+    - Temporal server running locally
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.human_in_the_loop.entrypoint import (
+    approval_entrypoint,
+)
+from langgraph_plugin.functional_api.human_in_the_loop.workflow import ApprovalWorkflow
+
+
+async def main() -> None:
+    plugin = LangGraphPlugin(
+        graphs={"approval_entrypoint": approval_entrypoint},
+        activity_options={
+            "process_request": activity_options(
+                start_to_close_timeout=timedelta(seconds=30),
+            ),
+            "execute_action": activity_options(
+                start_to_close_timeout=timedelta(seconds=30),
+            ),
+        },
+    )
+
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-approval",
+        workflows=[ApprovalWorkflow],
+    )
+
+    print("Human-in-the-Loop worker started. Ctrl+C to exit.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py
new file mode 100644
index 00000000..0087be1c
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py
@@ -0,0 +1,74 @@
+"""Execute the Human-in-the-Loop Functional API workflow.
+
+This script demonstrates the approval workflow:
+1. Starts the workflow with a payment request
+2. Queries for the pending approval
+3. Sends a signal with the approval decision
+4. Gets the final result
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.human_in_the_loop.workflow import (
+    ApprovalRequest,
+    ApprovalWorkflow,
+)
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Create approval request
+    request = ApprovalRequest(
+        request_type="payment",
+        amount=500.00,
+        request_data={
+            "recipient": "vendor@example.com",
+            "description": "Invoice #1234",
+        },
+    )
+
+    # Start the workflow
+    handle = await client.start_workflow(
+        ApprovalWorkflow.run,
+        request,
+        id="approval-functional-workflow",
+        task_queue="langgraph-functional-approval",
+    )
+
+    print(f"Started workflow: {handle.id}")
+
+    # Wait a moment for the workflow to hit the interrupt
+    await asyncio.sleep(1)
+
+    # Query the pending approval
+    pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+    if pending:
+        print(f"\nPending approval: {pending.get('message')}")
+        print(f"Risk level: {pending.get('risk_level')}")
+
+    # Simulate human approval
+    print("\nProviding approval...")
+    await handle.signal(
+        ApprovalWorkflow.provide_approval,
+        {
+            "approved": True,
+            "approver": "manager@example.com",
+            "reason": "Approved for vendor payment",
+        },
+    )
+
+    # Wait for completion
+    result = await handle.result()
+
+    print(f"\nResult: {result.get('result')}")
+    print(f"Executed: {result.get('executed')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/tasks.py b/langgraph_plugin/functional_api/human_in_the_loop/tasks.py
new file mode 100644
index 00000000..dfc9aba5
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/tasks.py
@@ -0,0 +1,72 @@
+"""Task definitions for the Human-in-the-Loop workflow.
+
+Each @task runs as a Temporal activity with automatic retries.
+Demonstrates interrupt() for human approval patterns.
+"""
+
+from typing import Any
+
+from langgraph.func import task
+
+
+@task
+def process_request(
+    request_type: str, amount: float, request_data: dict[str, Any]
+) -> dict[str, Any]:
+    """Validate and assess the request before approval.
+
+    Args:
+        request_type: Type of request (e.g., "payment", "refund").
+        amount: The monetary amount.
+        request_data: Additional request details.
+
+    Returns:
+        Dict with validation status and risk assessment.
+    """
+    # Determine risk level based on amount
+    if amount < 100:
+        risk_level = "low"
+    elif amount < 1000:
+        risk_level = "medium"
+    else:
+        risk_level = "high"
+
+    return {
+        "validated": True,
+        "risk_level": risk_level,
+        "amount": amount,
+        "request_type": request_type,
+    }
+
+
+@task
+def execute_action(
+    request_type: str,
+    amount: float,
+    approved: bool,
+    approver: str,
+    reason: str,
+) -> dict[str, Any]:
+    """Execute or reject the action based on approval status.
+
+    Args:
+        request_type: Type of request.
+        amount: The monetary amount.
+        approved: Whether the request was approved.
+        approver: Who approved/rejected.
+        reason: Reason for the decision.
+
+    Returns:
+        Dict with execution result.
+    """
+    if approved:
+        return {
+            "executed": True,
+            "result": f"Successfully processed {request_type} for ${amount:.2f}. "
+            f"Approved by {approver}: {reason}",
+        }
+    else:
+        return {
+            "executed": False,
+            "result": f"Request rejected by {approver}: {reason}",
+        }
diff --git a/langgraph_plugin/functional_api/human_in_the_loop/workflow.py b/langgraph_plugin/functional_api/human_in_the_loop/workflow.py
new file mode 100644
index 00000000..032defdb
--- /dev/null
+++ b/langgraph_plugin/functional_api/human_in_the_loop/workflow.py
@@ -0,0 +1,154 @@
+"""Human-in-the-Loop Workflow.
+
+Temporal workflow that demonstrates interrupt() handling with signals and queries.
+"""
+
+from dataclasses import dataclass
+from datetime import timedelta
+from typing import Any
+
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    from langgraph.types import Command
+    from temporalio.contrib.langgraph import (
+        TemporalFunctionalRunner,
+        activity_options,
+        compile,
+    )
+
+
+@dataclass
+class ApprovalRequest:
+    """Input for the approval workflow."""
+
+    request_type: str
+    amount: float
+    request_data: dict[str, Any] | None = None
+
+
+@workflow.defn
+class ApprovalWorkflow:
+    """Workflow that pauses for human approval before executing actions.
+
+    This demonstrates the full interrupt flow with the functional API:
+    1. Entrypoint runs until interrupt() is called
+    2. Workflow receives __interrupt__ in result with approval request details
+    3. Workflow waits for signal with human input (with optional timeout)
+    4. Workflow resumes entrypoint with Command(resume=response)
+    5. Entrypoint completes execution
+    """
+
+    def __init__(self) -> None:
+        self._approval_response: dict[str, Any] | None = None
+        self._interrupt_value: dict[str, Any] | None = None
+
+    @workflow.signal
+    def provide_approval(self, response: dict[str, Any]) -> None:
+        """Signal to provide approval response.
+
+        Args:
+            response: Dict with 'approved' (bool), 'reason' (str), 'approver' (str)
+        """
+        self._approval_response = response
+
+    @workflow.query
+    def get_pending_approval(self) -> dict[str, Any] | None:
+        """Query to get the current pending approval request."""
+        return self._interrupt_value
+
+    @workflow.query
+    def get_status(self) -> str:
+        """Query to get the current workflow status."""
+        if self._interrupt_value is None:
+            return "processing"
+        elif self._approval_response is None:
+            return "waiting_for_approval"
+        else:
+            return "approved" if self._approval_response.get("approved") else "rejected"
+
+    @workflow.run
+    async def run(
+        self,
+        request: ApprovalRequest,
+        approval_timeout: timedelta | None = None,
+    ) -> dict[str, Any]:
+        """Run the approval workflow.
+
+        Args:
+            request: The approval request details.
+            approval_timeout: Optional timeout for waiting for approval.
+
+        Returns:
+            The final state containing result and executed status.
+        """
+        # Workflow-level task options override plugin-level settings.
+        # This demonstrates how to customize timeouts per-workflow when needed.
+        # The plugin's activity_options set defaults; compile() can override.
+        app = compile(
+            "approval_entrypoint",
+            activity_options={
+                "process_request": activity_options(
+                    start_to_close_timeout=timedelta(
+                        seconds=10
+                    ),  # Override plugin default
+                ),
+                # execute_action uses plugin default (30s) - not specified here
+            },
+        )
+        assert isinstance(app, TemporalFunctionalRunner)
+
+        # Handle both dataclass and dict input
+        if isinstance(request, dict):
+            request_type = request.get("request_type", "unknown")
+            amount = request.get("amount", 0.0)
+            request_data = request.get("request_data") or {}
+        else:
+            request_type = request.request_type
+            amount = request.amount
+            request_data = request.request_data or {}
+
+        # First invocation - should hit interrupt
+        result = await app.ainvoke(
+            request_type,
+            config={
+                "configurable": {
+                    "amount": amount,
+                    "request_data": request_data,
+                }
+            },
+            on_interrupt=self._handle_interrupt,
+        )
+
+        return result
+
+    async def _handle_interrupt(
+        self, interrupt_value: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Handle interrupt() by waiting for approval signal.
+
+        Args:
+            interrupt_value: The value passed to interrupt().
+
+        Returns:
+            The approval response from the signal.
+        """
+        self._interrupt_value = interrupt_value
+
+        workflow.logger.info(
+            "Workflow paused for approval: %s", interrupt_value.get("message")
+        )
+
+        # Wait for approval signal
+        await workflow.wait_condition(
+            lambda: self._approval_response is not None,
+        )
+
+        workflow.logger.info(
+            "Received approval response: approved=%s",
+            self._approval_response.get("approved")
+            if self._approval_response
+            else None,
+        )
+
+        return self._approval_response  # type: ignore[return-value]
diff --git a/langgraph_plugin/functional_api/plan_and_execute/README.md b/langgraph_plugin/functional_api/plan_and_execute/README.md
new file mode 100644
index 00000000..6a1f9daf
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/README.md
@@ -0,0 +1,147 @@
+# Plan-and-Execute Agent (Functional API)
+
+An agent that creates a structured plan and executes each step sequentially using available tools.
+
+## Overview
+
+The plan-and-execute pattern separates planning from execution:
+
+1. **Plan** - Create a structured plan with specific steps
+2. **Execute** - Run each step using appropriate tools
+3. **Summarize** - Generate final response from results
+
+## Architecture
+
+```
+User Objective
+      │
+      ▼
+┌─────────────┐
+│ create_plan │
+│   (task)    │
+└──────┬──────┘
+       │
+       ▼
+   For each step:
+       │
+       ▼
+┌──────────────┐
+│ execute_step │ ──► Step 1 result
+│   (task)     │
+└──────────────┘
+       │
+       ▼
+┌──────────────┐
+│ execute_step │ ──► Step 2 result
+│   (task)     │
+└──────────────┘
+       │
+       ▼
+      ...
+       │
+       ▼
+┌───────────────────┐
+│ generate_response │
+│      (task)       │
+└───────────────────┘
+```
+
+## Key Code
+
+### Sequential Step Execution
+
+```python
+@entrypoint()
+async def plan_execute_entrypoint(objective: str) -> dict:
+    # Step 1: Create the plan
+    plan = await create_plan(objective)
+
+    # Step 2: Execute each step sequentially
+    step_results = []
+    for step in plan["steps"]:
+        result = await execute_step(
+            step_number=step["step_number"],
+            description=step["description"],
+            tool_hint=step["tool_hint"],
+        )
+        step_results.append(result)
+
+    # Step 3: Generate final response
+    final_response = await generate_response(objective, step_results)
+
+    return {
+        "objective": objective,
+        "plan": plan,
+        "step_results": step_results,
+        "final_response": final_response,
+    }
+```
+
+### Structured Plan
+
+```python
+@task
+def create_plan(objective: str) -> dict:
+    """Generate a structured plan with steps."""
+    return {
+        "objective": objective,
+        "steps": [
+            {"step_number": 1, "description": "...", "tool_hint": "search"},
+            {"step_number": 2, "description": "...", "tool_hint": "calculate"},
+            {"step_number": 3, "description": "...", "tool_hint": "analyze"},
+        ]
+    }
+```
+
+## Why Temporal?
+
+- **Step durability**: Each step execution is durable
+- **Progress visibility**: See plan and completed steps in UI
+- **Partial completion**: Resume from last completed step
+- **Audit trail**: Full execution history preserved
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run with API key:
+   ```bash
+   export OPENAI_API_KEY=your-key
+   uv run langgraph_plugin/functional_api/plan_and_execute/run_worker.py
+   ```
+
+3. Execute an objective:
+   ```bash
+   uv run langgraph_plugin/functional_api/plan_and_execute/run_workflow.py
+   ```
+
+## Customization
+
+### Add Re-planning
+
+```python
+@entrypoint()
+async def plan_execute_entrypoint(objective: str) -> dict:
+    plan = await create_plan(objective)
+    step_results = []
+
+    for step in plan["steps"]:
+        result = await execute_step(step)
+        step_results.append(result)
+
+        # Re-plan if step failed
+        if not result["success"]:
+            plan = await replan(objective, step_results)
+```
+
+### Parallel Step Execution
+
+```python
+# For independent steps, execute in parallel
+independent_steps = [s for s in plan["steps"] if s["can_parallelize"]]
+futures = [execute_step(s) for s in independent_steps]
+results = [await f for f in futures]
+```
diff --git a/langgraph_plugin/functional_api/plan_and_execute/__init__.py b/langgraph_plugin/functional_api/plan_and_execute/__init__.py
new file mode 100644
index 00000000..e7626881
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/__init__.py
@@ -0,0 +1 @@
+"""Plan-and-Execute Agent - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/plan_and_execute/entrypoint.py b/langgraph_plugin/functional_api/plan_and_execute/entrypoint.py
new file mode 100644
index 00000000..ce761a8c
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/entrypoint.py
@@ -0,0 +1,59 @@
+"""Plan-and-Execute Agent Entrypoint Definition.
+
+The @entrypoint function implements a plan-and-execute pattern:
+1. Create a plan with specific steps
+2. Execute each step using available tools
+3. Generate a final response from the results
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.plan_and_execute.tasks import (
+    create_plan,
+    execute_step,
+    generate_response,
+)
+
+
+@entrypoint()
+async def plan_execute_entrypoint(objective: str) -> dict[str, Any]:
+    """Run a plan-and-execute agent.
+
+    The agent will:
+    1. Create a plan with specific steps
+    2. Execute each step sequentially
+    3. Generate a final response
+
+    Each @task call runs as a Temporal activity with automatic retries.
+
+    Args:
+        objective: The task to accomplish.
+
+    Returns:
+        Dict with plan, step results, and final response.
+    """
+    # Step 1: Create the plan
+    plan = await create_plan(objective)
+
+    # Step 2: Execute each step
+    step_results: list[dict[str, Any]] = []
+
+    for step in plan["steps"]:
+        result = await execute_step(
+            step_number=step["step_number"],
+            description=step["description"],
+            tool_hint=step["tool_hint"],
+        )
+        step_results.append(result)
+
+    # Step 3: Generate final response
+    final_response = await generate_response(objective, step_results)
+
+    return {
+        "objective": objective,
+        "plan": plan,
+        "step_results": step_results,
+        "final_response": final_response,
+    }
diff --git a/langgraph_plugin/functional_api/plan_and_execute/run_worker.py b/langgraph_plugin/functional_api/plan_and_execute/run_worker.py
new file mode 100644
index 00000000..248bb9a1
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/run_worker.py
@@ -0,0 +1,59 @@
+"""Worker for the Plan-and-Execute Functional API sample.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.plan_and_execute.entrypoint import (
+    plan_execute_entrypoint,
+)
+from langgraph_plugin.functional_api.plan_and_execute.workflow import (
+    PlanExecuteWorkflow,
+)
+
+
+async def main() -> None:
+    plugin = LangGraphPlugin(
+        graphs={"plan_execute_entrypoint": plan_execute_entrypoint},
+        activity_options={
+            "create_plan": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+            "execute_step": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+            "generate_response": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+        },
+    )
+
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-plan-execute",
+        workflows=[PlanExecuteWorkflow],
+    )
+
+    print("Plan-and-Execute worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/plan_and_execute/run_workflow.py b/langgraph_plugin/functional_api/plan_and_execute/run_workflow.py
new file mode 100644
index 00000000..0002c17b
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/run_workflow.py
@@ -0,0 +1,33 @@
+"""Execute the Plan-and-Execute Functional API workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.plan_and_execute.workflow import (
+    PlanExecuteWorkflow,
+)
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    objective = "Look up information about LangGraph, then calculate what 25 * 4 equals, and analyze the combined findings."
+
+    result = await client.execute_workflow(
+        PlanExecuteWorkflow.run,
+        objective,
+        id="plan-execute-functional-workflow",
+        task_queue="langgraph-functional-plan-execute",
+    )
+
+    print(f"Objective: {result.get('objective')}")
+    print(f"\nPlan Steps: {len(result.get('plan', {}).get('steps', []))}")
+    print(f"\nFinal Response:\n{result.get('final_response')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/plan_and_execute/tasks.py b/langgraph_plugin/functional_api/plan_and_execute/tasks.py
new file mode 100644
index 00000000..ba2ab354
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/tasks.py
@@ -0,0 +1,248 @@
+"""Task definitions for the Plan-and-Execute Agent.
+
+Each @task runs as a Temporal activity with automatic retries.
+Demonstrates step-by-step plan execution with tool usage.
+"""
+
+import os
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.func import task
+from pydantic import BaseModel, Field
+
+
+class PlanStep(BaseModel):
+    """A single step in the execution plan."""
+
+    step_number: int = Field(description="The step number (1-indexed)")
+    description: str = Field(description="What this step should accomplish")
+    tool_hint: str = Field(
+        description="Suggested tool to use (calculate, lookup, or analyze)"
+    )
+
+
+class Plan(BaseModel):
+    """An execution plan with ordered steps."""
+
+    objective: str = Field(description="The main objective to accomplish")
+    steps: list[PlanStep] = Field(
+        description="Ordered list of steps to execute", min_length=1, max_length=5
+    )
+
+
+# --- Tool definitions ---
+
+
+@tool
+def calculate(expression: str) -> str:
+    """Evaluate a mathematical expression.
+
+    Args:
+        expression: A mathematical expression like "2 + 2" or "10 * 5"
+
+    Returns:
+        The result of the calculation
+    """
+    try:
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression"
+        result = eval(expression)  # noqa: S307
+        return f"Result: {result}"
+    except Exception as e:
+        return f"Error calculating: {str(e)}"
+
+
+@tool
+def lookup(topic: str) -> str:
+    """Look up information about a topic.
+
+    Args:
+        topic: The topic to look up information about
+
+    Returns:
+        Information about the topic
+    """
+    knowledge = {
+        "python": "Python is a high-level programming language known for its simplicity.",
+        "temporal": "Temporal is a durable execution platform for reliable workflows.",
+        "langgraph": "LangGraph is a library for building stateful LLM applications.",
+        "agents": "AI agents use LLMs to make decisions and take actions autonomously.",
+    }
+    topic_lower = topic.lower()
+    for key, value in knowledge.items():
+        if key in topic_lower:
+            return value
+    return f"No specific information found about '{topic}'."
+
+
+@tool
+def analyze(data: str) -> str:
+    """Analyze data or text and provide insights.
+
+    Args:
+        data: The data or text to analyze
+
+    Returns:
+        Analysis results
+    """
+    word_count = len(data.split())
+    char_count = len(data)
+    return f"Analysis: {word_count} words, {char_count} characters."
+
+
+@task
+async def create_plan(objective: str) -> dict[str, Any]:
+    """Create an execution plan from the objective.
+
+    Args:
+        objective: The main task objective.
+
+    Returns:
+        Dict with objective and list of steps.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    plan_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a planning agent. Given an objective, create a step-by-step
+plan to accomplish it. Each step should be specific and actionable.
+
+Available tools for execution:
+- calculate: For mathematical computations
+- lookup: For retrieving information about topics
+- analyze: For analyzing data or text
+
+Create 2-4 steps that can be executed sequentially.""",
+            ),
+            ("human", "Objective: {objective}"),
+        ]
+    )
+
+    planner = plan_prompt | model.with_structured_output(Plan)
+    plan: Any = await planner.ainvoke({"objective": objective})
+
+    return {
+        "objective": plan.objective,
+        "steps": [
+            {
+                "step_number": s.step_number,
+                "description": s.description,
+                "tool_hint": s.tool_hint,
+            }
+            for s in plan.steps
+        ],
+    }
+
+
+@task
+async def execute_step(
+    step_number: int, description: str, tool_hint: str
+) -> dict[str, Any]:
+    """Execute a single step using the appropriate tool.
+
+    Args:
+        step_number: The step number.
+        description: What this step should accomplish.
+        tool_hint: Suggested tool to use.
+
+    Returns:
+        Dict with step result and success status.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    tools = [calculate, lookup, analyze]
+    model_with_tools = model.bind_tools(tools)
+
+    # Ask the model to execute the step
+    messages = [
+        SystemMessage(
+            content=f"You are executing step {step_number} of a plan. "
+            f"Complete this step: {description}\n"
+            f"Suggested tool: {tool_hint}"
+        ),
+        HumanMessage(content=description),
+    ]
+
+    response = await model_with_tools.ainvoke(messages)
+
+    # Execute any tool calls
+    result_parts = []
+    if hasattr(response, "tool_calls") and response.tool_calls:
+        tools_by_name = {"calculate": calculate, "lookup": lookup, "analyze": analyze}
+        for tc in response.tool_calls:
+            tool_fn = tools_by_name.get(tc["name"])
+            if tool_fn:
+                try:
+                    tool_result = tool_fn.invoke(tc["args"])
+                    result_parts.append(f"{tc['name']}: {tool_result}")
+                except Exception as e:
+                    result_parts.append(f"{tc['name']}: Error - {e}")
+
+    if response.content:
+        result_parts.append(str(response.content))
+
+    result = "\n".join(result_parts) if result_parts else "Step completed"
+
+    return {
+        "step_number": step_number,
+        "description": description,
+        "result": result,
+        "success": True,
+    }
+
+
+@task
+async def generate_response(objective: str, step_results: list[dict[str, Any]]) -> str:
+    """Generate the final response summarizing the execution.
+
+    Args:
+        objective: The original objective.
+        step_results: Results from all executed steps.
+
+    Returns:
+        The final response.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    results_text = "\n".join(
+        f"{r['step_number']}. {r['description']}: {r['result']}" for r in step_results
+    )
+
+    response_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a summarizer. Given an objective and the results of
+executing a plan, provide a clear, concise final answer.
+
+Focus on directly answering the original objective using the
+gathered information.""",
+            ),
+            (
+                "human",
+                "Objective: {objective}\n\nExecution results:\n{results}\n\nProvide final answer:",
+            ),
+        ]
+    )
+
+    chain = response_prompt | model | StrOutputParser()
+    response = await chain.ainvoke({"objective": objective, "results": results_text})
+
+    return response
diff --git a/langgraph_plugin/functional_api/plan_and_execute/workflow.py b/langgraph_plugin/functional_api/plan_and_execute/workflow.py
new file mode 100644
index 00000000..3d261472
--- /dev/null
+++ b/langgraph_plugin/functional_api/plan_and_execute/workflow.py
@@ -0,0 +1,36 @@
+"""Plan-and-Execute Agent Workflow.
+
+Temporal workflow for the plan-and-execute agent.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class PlanExecuteWorkflow:
+    """Temporal workflow for plan-and-execute agent.
+
+    The agent:
+    1. Creates a plan with specific steps
+    2. Executes each step using tools
+    3. Generates a final response
+
+    Each task runs as a Temporal activity with automatic retries.
+    """
+
+    @workflow.run
+    async def run(self, objective: str) -> dict[str, Any]:
+        """Run the plan-and-execute agent.
+
+        Args:
+            objective: The task to accomplish.
+
+        Returns:
+            Plan, step results, and final response.
+        """
+        app = compile("plan_execute_entrypoint")
+        result = await app.ainvoke(objective)
+        return result
diff --git a/langgraph_plugin/functional_api/react_agent/README.md b/langgraph_plugin/functional_api/react_agent/README.md
new file mode 100644
index 00000000..e6210903
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/README.md
@@ -0,0 +1,108 @@
+# ReAct Agent (Functional API)
+
+A ReAct (Reasoning and Acting) agent that uses tools to answer questions, implemented with the Functional API.
+
+## Overview
+
+The ReAct pattern alternates between:
+1. **Think** - LLM decides what action to take
+2. **Act** - Execute the chosen tool
+3. **Observe** - Feed tool results back to LLM
+4. **Repeat** - Until the LLM has enough information to answer
+
+## Architecture
+
+```
+User Query
+    │
+    ▼
+┌─────────────┐
+│ call_model  │ ◄──────────────┐
+│   (task)    │                │
+└─────┬───────┘                │
+      │                        │
+      ▼                        │
+  Has tool calls?              │
+      │                        │
+  YES │     NO                 │
+      │      └──► Return Answer
+      ▼                        │
+┌─────────────┐                │
+│execute_tools│                │
+│   (task)    │ ───────────────┘
+└─────────────┘
+```
+
+Each box is a `@task` that runs as a Temporal activity.
+
+## Key Code
+
+### Entrypoint Logic
+
+```python
+@entrypoint()
+async def react_agent_entrypoint(query: str, max_iterations: int = 10) -> dict:
+    messages = [{"role": "user", "content": query}]
+
+    for iteration in range(max_iterations):
+        # Think: Call LLM
+        response = await call_model(messages)
+        messages.append(response)
+
+        # Check if done
+        if not response.get("tool_calls"):
+            return {"final_answer": response["content"]}
+
+        # Act: Execute tools
+        tool_results = await execute_tools(response["tool_calls"])
+        messages.extend(tool_results)
+
+    return {"final_answer": "Max iterations reached"}
+```
+
+### Tasks
+
+```python
+@task
+def call_model(messages: list) -> dict:
+    """LLM call with tool definitions - runs as activity."""
+    return llm.invoke(messages)
+
+@task
+def execute_tools(tool_calls: list) -> list:
+    """Execute requested tools - runs as activity."""
+    return [execute_tool(tc) for tc in tool_calls]
+```
+
+## Why Temporal?
+
+- **Durability**: Long reasoning chains complete reliably
+- **Retries**: API failures are handled automatically
+- **Visibility**: Each think/act step visible in UI
+- **Timeouts**: Control LLM call durations
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Set your API key and run the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key
+   uv run langgraph_plugin/functional_api/react_agent/run_worker.py
+   ```
+
+3. Execute a query:
+   ```bash
+   uv run langgraph_plugin/functional_api/react_agent/run_workflow.py
+   ```
+
+## Comparison with Graph API
+
+| Aspect | Graph API | Functional API |
+|--------|-----------|----------------|
+| Control flow | Conditional edges | Python loop |
+| State | Shared TypedDict | Local variables |
+| Readability | Graph visualization | Linear code |
diff --git a/langgraph_plugin/functional_api/react_agent/__init__.py b/langgraph_plugin/functional_api/react_agent/__init__.py
new file mode 100644
index 00000000..dfecc037
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/__init__.py
@@ -0,0 +1 @@
+"""ReAct Agent - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/react_agent/entrypoint.py b/langgraph_plugin/functional_api/react_agent/entrypoint.py
new file mode 100644
index 00000000..3d1a121e
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/entrypoint.py
@@ -0,0 +1,68 @@
+"""ReAct Agent Entrypoint Definition.
+
+The @entrypoint function implements the ReAct (Reasoning and Acting) pattern:
+1. Think: LLM decides what action to take (call_model task)
+2. Act: Execute the chosen tool (execute_tools task)
+3. Observe: Feed tool results back to LLM
+4. Repeat until done
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.react_agent.tasks import call_model, execute_tools
+
+
+@entrypoint()
+async def react_agent_entrypoint(
+    query: str, max_iterations: int = 10
+) -> dict[str, Any]:
+    """Run a ReAct agent to answer a query.
+
+    The agent will:
+    1. Think about what information is needed
+    2. Use tools to gather information
+    3. Synthesize a final answer
+
+    Each @task call (call_model, execute_tools) runs as a Temporal activity
+    with automatic retries and durability guarantees.
+
+    Args:
+        query: The user's question or request.
+        max_iterations: Maximum number of think/act iterations.
+
+    Returns:
+        Dict containing the conversation messages and final answer.
+    """
+    # Initialize conversation with user query
+    messages: list[dict[str, Any]] = [{"role": "user", "content": query}]
+
+    for iteration in range(max_iterations):
+        # Think: Call LLM to decide action
+        response = await call_model(messages)
+        messages.append(response)
+
+        # Check if model wants to use tools
+        tool_calls = response.get("tool_calls")
+
+        if not tool_calls:
+            # Model is done - return final answer
+            return {
+                "messages": messages,
+                "final_answer": response.get("content", ""),
+                "iterations": iteration + 1,
+            }
+
+        # Act: Execute the requested tools
+        tool_results = await execute_tools(tool_calls)
+
+        # Observe: Add tool results to conversation
+        messages.extend(tool_results)
+
+    # Max iterations reached
+    return {
+        "messages": messages,
+        "final_answer": "Max iterations reached without final answer",
+        "iterations": max_iterations,
+    }
diff --git a/langgraph_plugin/functional_api/react_agent/run_worker.py b/langgraph_plugin/functional_api/react_agent/run_worker.py
new file mode 100644
index 00000000..68136231
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/run_worker.py
@@ -0,0 +1,61 @@
+"""Worker for the ReAct Agent Functional API sample.
+
+Starts a Temporal worker that can execute ReActAgentWorkflow.
+The LangGraphPlugin registers the entrypoint and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.react_agent.entrypoint import (
+    react_agent_entrypoint,
+)
+from langgraph_plugin.functional_api.react_agent.workflow import ReActAgentWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the ReAct agent entrypoint registered
+    plugin = LangGraphPlugin(
+        graphs={"react_agent_entrypoint": react_agent_entrypoint},
+        activity_options={
+            "call_model": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+            "execute_tools": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+        },
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Task activities are automatically registered by the plugin
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-react-agent",
+        workflows=[ReActAgentWorkflow],
+    )
+
+    print("ReAct Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/react_agent/run_workflow.py b/langgraph_plugin/functional_api/react_agent/run_workflow.py
new file mode 100644
index 00000000..107f21e2
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/run_workflow.py
@@ -0,0 +1,34 @@
+"""Execute the ReAct Agent Functional API workflow.
+
+Connects to Temporal and starts the ReActAgentWorkflow.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.react_agent.workflow import ReActAgentWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    query = "What's the weather in New York? Then calculate: if it's 72°F, what is that in Celsius? Use the formula (F-32)*5/9"
+
+    # Execute the workflow
+    result = await client.execute_workflow(
+        ReActAgentWorkflow.run,
+        query,
+        id="react-agent-functional-workflow",
+        task_queue="langgraph-functional-react-agent",
+    )
+
+    print(f"Final Answer: {result.get('final_answer')}")
+    print(f"Iterations: {result.get('iterations')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/react_agent/tasks.py b/langgraph_plugin/functional_api/react_agent/tasks.py
new file mode 100644
index 00000000..d6ad0aaf
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/tasks.py
@@ -0,0 +1,121 @@
+"""Task definitions for the ReAct Agent.
+
+Each @task function runs as a Temporal activity, providing automatic retries
+and failure recovery for LLM calls and tool executions.
+"""
+
+import os
+from typing import Any
+
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage
+from langchain_openai import ChatOpenAI
+from langgraph.func import task
+
+from langgraph_plugin.functional_api.react_agent.tools import calculate, get_weather
+
+
+@task
+async def call_model(messages: list[dict[str, Any]]) -> dict[str, Any]:
+    """Call the LLM to decide what action to take.
+
+    This task runs as a Temporal activity, so LLM failures are automatically retried.
+
+    Args:
+        messages: The conversation history as a list of message dicts.
+
+    Returns:
+        Dict containing the AI message response with potential tool calls.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Bind tools to the model
+    tools = [get_weather, calculate]
+    model_with_tools = model.bind_tools(tools)
+
+    # Convert dict messages to LangChain message objects
+    lc_messages: list[BaseMessage] = []
+    for msg in messages:
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+        if role == "user":
+            lc_messages.append(HumanMessage(content=content))
+        elif role == "assistant":
+            tool_calls = msg.get("tool_calls")
+            if tool_calls:
+                lc_messages.append(AIMessage(content=content, tool_calls=tool_calls))
+            else:
+                lc_messages.append(AIMessage(content=content))
+        elif role == "tool":
+            lc_messages.append(
+                ToolMessage(
+                    content=content,
+                    tool_call_id=msg.get("tool_call_id", ""),
+                )
+            )
+
+    # Call the model
+    response = await model_with_tools.ainvoke(lc_messages)
+
+    # Convert response to serializable dict
+    result: dict[str, Any] = {
+        "role": "assistant",
+        "content": response.content,
+    }
+
+    if hasattr(response, "tool_calls") and response.tool_calls:
+        result["tool_calls"] = [
+            {
+                "id": tc["id"],
+                "name": tc["name"],
+                "args": tc["args"],
+            }
+            for tc in response.tool_calls
+        ]
+
+    return result
+
+
+@task
+async def execute_tools(tool_calls: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Execute the requested tools and return results.
+
+    This task runs as a Temporal activity, so tool executions are automatically retried.
+
+    Args:
+        tool_calls: List of tool calls from the model response.
+
+    Returns:
+        List of tool result message dicts.
+    """
+    tools_by_name = {
+        "get_weather": get_weather,
+        "calculate": calculate,
+    }
+
+    results = []
+    for tool_call in tool_calls:
+        tool_name = tool_call["name"]
+        tool_args = tool_call["args"]
+        tool_id = tool_call["id"]
+
+        tool_fn = tools_by_name.get(tool_name)
+        if tool_fn:
+            try:
+                result = tool_fn.invoke(tool_args)
+            except Exception as e:
+                result = f"Error executing {tool_name}: {e}"
+        else:
+            result = f"Unknown tool: {tool_name}"
+
+        results.append(
+            {
+                "role": "tool",
+                "content": str(result),
+                "tool_call_id": tool_id,
+            }
+        )
+
+    return results
diff --git a/langgraph_plugin/functional_api/react_agent/tools.py b/langgraph_plugin/functional_api/react_agent/tools.py
new file mode 100644
index 00000000..d579f547
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/tools.py
@@ -0,0 +1,50 @@
+"""Tool definitions for the ReAct Agent.
+
+These are standard LangChain tools that will be called from @task functions.
+"""
+
+from langchain_core.tools import tool
+
+
+@tool
+def get_weather(city: str) -> str:
+    """Get the current weather for a city.
+
+    Args:
+        city: The city name to get weather for.
+
+    Returns:
+        A string describing the current weather conditions.
+    """
+    # Simulated weather data - in production, call a real weather API
+    weather_data = {
+        "new york": "72°F, Partly cloudy with light winds",
+        "london": "58°F, Overcast with chance of rain",
+        "tokyo": "68°F, Clear skies",
+        "paris": "65°F, Sunny with occasional clouds",
+        "sydney": "77°F, Warm and humid",
+    }
+    city_lower = city.lower()
+    return weather_data.get(city_lower, f"Weather data not available for {city}")
+
+
+@tool
+def calculate(expression: str) -> str:
+    """Perform a mathematical calculation.
+
+    Args:
+        expression: A mathematical expression like "2 + 2" or "10 * 5".
+
+    Returns:
+        The result of the calculation as a string.
+    """
+    try:
+        # Safely evaluate mathematical expressions
+        # Only allow basic math operations
+        allowed_chars = set("0123456789+-*/(). ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Only basic math operations are allowed"
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error calculating: {e}"
diff --git a/langgraph_plugin/functional_api/react_agent/workflow.py b/langgraph_plugin/functional_api/react_agent/workflow.py
new file mode 100644
index 00000000..c5f8075f
--- /dev/null
+++ b/langgraph_plugin/functional_api/react_agent/workflow.py
@@ -0,0 +1,44 @@
+"""ReAct Agent Workflow.
+
+Temporal workflow that executes the ReAct agent with durable execution.
+
+Note: This module only contains the workflow definition. The entrypoint and tasks
+are in separate files and are imported by the worker.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ReActAgentWorkflow:
+    """Temporal workflow that executes a ReAct agent with durable execution.
+
+    This workflow uses the LangGraph Functional API with the Temporal integration.
+    Each @task (LLM call, tool execution) runs as a Temporal activity:
+    - The call_model task calls the LLM to decide what to do
+    - The execute_tools task executes the requested tools
+
+    If any task fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed task.
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the ReAct agent to answer a query.
+
+        Args:
+            query: The user's question or request.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled entrypoint runner by name
+        app = compile("react_agent_entrypoint")
+
+        # Execute the agent
+        result = await app.ainvoke(query)
+
+        return result
diff --git a/langgraph_plugin/functional_api/reflection/README.md b/langgraph_plugin/functional_api/reflection/README.md
new file mode 100644
index 00000000..f590c612
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/README.md
@@ -0,0 +1,123 @@
+# Reflection Agent (Functional API)
+
+An agent that generates content, critiques it, and iteratively improves until quality criteria are met.
+
+## Overview
+
+The reflection pattern implements a generate-critique-revise loop:
+
+1. **Generate** - Create initial content
+2. **Critique** - Evaluate with structured feedback
+3. **Revise** - Improve based on critique
+4. **Loop** - Repeat until satisfactory or max iterations
+
+## Architecture
+
+```
+┌──────────────────┐
+│ generate_content │
+│     (task)       │
+└────────┬─────────┘
+         │
+         ▼
+┌──────────────────┐
+│ critique_content │◄────┐
+│     (task)       │     │
+└────────┬─────────┘     │
+         │               │
+         ▼               │
+    Satisfactory?        │
+         │               │
+    NO   │    YES        │
+         │     └──► Return Final
+         ▼               │
+┌──────────────────┐     │
+│  revise_content  │─────┘
+│     (task)       │
+└──────────────────┘
+```
+
+## Key Code
+
+### Entrypoint with Iteration Loop
+
+```python
+@entrypoint()
+async def reflection_entrypoint(task_description: str, max_iterations: int = 3) -> dict:
+    # Generate initial draft
+    current_draft = await generate_content(task_description)
+    critiques = []
+
+    for iteration in range(1, max_iterations + 1):
+        # Critique current draft
+        critique = await critique_content(task_description, current_draft, iteration)
+        critiques.append(critique)
+
+        # Check if good enough
+        if critique.get("is_satisfactory"):
+            return {"final_content": current_draft, "status": "satisfactory"}
+
+        # Revise based on feedback
+        current_draft = await revise_content(task_description, current_draft, critique)
+
+    return {"final_content": current_draft, "status": "max_iterations_reached"}
+```
+
+### Structured Critique
+
+```python
+@task
+def critique_content(task: str, draft: str, iteration: int) -> dict:
+    """Returns structured critique with score and suggestions."""
+    return {
+        "strengths": [...],
+        "weaknesses": [...],
+        "suggestions": [...],
+        "quality_score": 7,
+        "is_satisfactory": True  # score >= threshold
+    }
+```
+
+## Why Temporal?
+
+- **Durability**: Multi-iteration refinement completes reliably
+- **Visibility**: See each critique/revision in workflow history
+- **Cost tracking**: Monitor LLM calls across iterations
+- **Resume**: Continue from any iteration if interrupted
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run with API key:
+   ```bash
+   export OPENAI_API_KEY=your-key
+   uv run langgraph_plugin/functional_api/reflection/run_worker.py
+   ```
+
+3. Execute:
+   ```bash
+   uv run langgraph_plugin/functional_api/reflection/run_workflow.py
+   ```
+
+## Customization
+
+### Adjust Quality Threshold
+
+```python
+# In critique task, change when content is "satisfactory"
+is_satisfactory = quality_score >= 8  # Stricter threshold
+```
+
+### Add Domain-Specific Criteria
+
+```python
+@task
+def critique_content(task: str, draft: str, iteration: int) -> dict:
+    # Add custom evaluation criteria
+    criteria = ["technical accuracy", "code correctness", "SEO optimization"]
+    ...
+```
diff --git a/langgraph_plugin/functional_api/reflection/__init__.py b/langgraph_plugin/functional_api/reflection/__init__.py
new file mode 100644
index 00000000..b919ebeb
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/__init__.py
@@ -0,0 +1 @@
+"""Reflection Agent - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/reflection/entrypoint.py b/langgraph_plugin/functional_api/reflection/entrypoint.py
new file mode 100644
index 00000000..70f977bf
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/entrypoint.py
@@ -0,0 +1,83 @@
+"""Reflection Agent Entrypoint Definition.
+
+The @entrypoint function implements an iterative improvement workflow:
+1. Generate initial content
+2. Reflect and critique
+3. Revise based on feedback
+4. Check quality and loop or finish
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.reflection.tasks import (
+    critique_content,
+    generate_content,
+    revise_content,
+)
+
+
+@entrypoint()
+async def reflection_entrypoint(
+    task_description: str, max_iterations: int = 3
+) -> dict[str, Any]:
+    """Run a reflection agent to generate and refine content.
+
+    The agent will:
+    1. Generate initial content
+    2. Critique the content
+    3. Revise if not satisfactory
+    4. Repeat until quality threshold or max iterations
+
+    Each @task call runs as a Temporal activity with automatic retries
+    and durability guarantees.
+
+    Args:
+        task_description: The writing/generation task.
+        max_iterations: Maximum number of reflection iterations.
+
+    Returns:
+        Dict containing the final content and iteration history.
+    """
+    # Step 1: Generate initial content
+    current_draft = await generate_content(task_description)
+
+    critiques: list[dict[str, Any]] = []
+
+    for iteration in range(1, max_iterations + 1):
+        # Step 2: Critique the current draft
+        critique = await critique_content(task_description, current_draft, iteration)
+        critiques.append(critique)
+
+        # Check if satisfactory
+        if critique.get("is_satisfactory", False):
+            return {
+                "final_content": current_draft,
+                "iterations": iteration,
+                "final_score": critique.get("quality_score", 0),
+                "critiques": critiques,
+                "status": "satisfactory",
+            }
+
+        # Check if max iterations reached
+        if iteration >= max_iterations:
+            return {
+                "final_content": current_draft,
+                "iterations": iteration,
+                "final_score": critique.get("quality_score", 0),
+                "critiques": critiques,
+                "status": "max_iterations_reached",
+            }
+
+        # Step 3: Revise based on feedback
+        current_draft = await revise_content(task_description, current_draft, critique)
+
+    # Should not reach here, but handle edge case
+    return {
+        "final_content": current_draft,
+        "iterations": max_iterations,
+        "final_score": 0,
+        "critiques": critiques,
+        "status": "completed",
+    }
diff --git a/langgraph_plugin/functional_api/reflection/run_worker.py b/langgraph_plugin/functional_api/reflection/run_worker.py
new file mode 100644
index 00000000..8e013bb7
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/run_worker.py
@@ -0,0 +1,57 @@
+"""Worker for the Reflection Agent Functional API sample.
+
+Starts a Temporal worker that can execute ReflectionWorkflow.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.reflection.entrypoint import reflection_entrypoint
+from langgraph_plugin.functional_api.reflection.workflow import ReflectionWorkflow
+
+
+async def main() -> None:
+    plugin = LangGraphPlugin(
+        graphs={"reflection_entrypoint": reflection_entrypoint},
+        activity_options={
+            "generate_content": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+            "critique_content": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+            "revise_content": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+        },
+    )
+
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-reflection",
+        workflows=[ReflectionWorkflow],
+    )
+
+    print("Reflection Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/reflection/run_workflow.py b/langgraph_plugin/functional_api/reflection/run_workflow.py
new file mode 100644
index 00000000..e0c15712
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/run_workflow.py
@@ -0,0 +1,35 @@
+"""Execute the Reflection Agent Functional API workflow.
+
+Connects to Temporal and starts the ReflectionWorkflow.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.reflection.workflow import ReflectionWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    task = "Write a short paragraph explaining the benefits of using Temporal for AI agent workflows."
+
+    result = await client.execute_workflow(
+        ReflectionWorkflow.run,
+        task,
+        id="reflection-functional-workflow",
+        task_queue="langgraph-functional-reflection",
+    )
+
+    print(f"Status: {result.get('status')}")
+    print(f"Iterations: {result.get('iterations')}")
+    print(f"Final Score: {result.get('final_score')}/10")
+    print(f"\nFinal Content:\n{result.get('final_content')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/reflection/tasks.py b/langgraph_plugin/functional_api/reflection/tasks.py
new file mode 100644
index 00000000..a8e5d39a
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/tasks.py
@@ -0,0 +1,183 @@
+"""Task definitions for the Reflection Agent.
+
+Each @task function runs as a Temporal activity, providing automatic retries
+and failure recovery for LLM calls.
+"""
+
+import os
+from typing import Any
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.func import task
+from pydantic import BaseModel, Field
+
+
+class Critique(BaseModel):
+    """Critique of the generated content."""
+
+    strengths: list[str] = Field(description="What's good about the content")
+    weaknesses: list[str] = Field(description="What needs improvement")
+    suggestions: list[str] = Field(description="Specific suggestions for improvement")
+    quality_score: int = Field(description="Quality score from 1-10", ge=1, le=10)
+    is_satisfactory: bool = Field(
+        description="Whether the content meets quality standards (score >= 7)"
+    )
+
+
+@task
+async def generate_content(task_description: str) -> str:
+    """Generate initial content based on the task.
+
+    Creates the first draft that will be refined through reflection.
+
+    Args:
+        task_description: The writing/generation task.
+
+    Returns:
+        The generated draft content.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.7,  # Slightly higher for creative generation
+    )
+
+    generate_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a skilled writer. Generate high-quality content based on
+the given task. Focus on:
+- Clarity and coherence
+- Relevant information
+- Engaging style
+- Proper structure
+
+Produce your best work on the first try.""",
+            ),
+            ("human", "{task}"),
+        ]
+    )
+
+    chain = generate_prompt | model | StrOutputParser()
+    draft = await chain.ainvoke({"task": task_description})
+
+    return draft
+
+
+@task
+async def critique_content(
+    task_description: str, draft: str, iteration: int
+) -> dict[str, Any]:
+    """Reflect on the current draft and provide critique.
+
+    Analyzes the content for strengths, weaknesses, and
+    provides specific improvement suggestions.
+
+    Args:
+        task_description: The original task.
+        draft: The current draft content.
+        iteration: Current iteration number.
+
+    Returns:
+        Dict with critique details including quality score.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,  # Lower for consistent critique
+    )
+
+    reflect_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a thoughtful critic and editor. Analyze the given content
+and provide constructive feedback.
+
+Consider:
+- Does it fulfill the original task?
+- Is it clear and well-structured?
+- Is the information accurate and relevant?
+- Is the style engaging and appropriate?
+- What specific improvements would make it better?
+
+Be specific and actionable in your feedback.
+Score 7+ means the content is ready for publication.""",
+            ),
+            (
+                "human",
+                "Task: {task}\n\nDraft (iteration {iteration}):\n{draft}\n\nProvide your critique:",
+            ),
+        ]
+    )
+
+    critic = reflect_prompt | model.with_structured_output(Critique)
+    critique: Any = await critic.ainvoke(
+        {"task": task_description, "draft": draft, "iteration": iteration}
+    )
+
+    return {
+        "strengths": critique.strengths,
+        "weaknesses": critique.weaknesses,
+        "suggestions": critique.suggestions,
+        "quality_score": critique.quality_score,
+        "is_satisfactory": critique.is_satisfactory,
+    }
+
+
+@task
+async def revise_content(
+    task_description: str, draft: str, critique: dict[str, Any]
+) -> str:
+    """Revise the content based on critique feedback.
+
+    Incorporates the suggestions to produce an improved version.
+
+    Args:
+        task_description: The original task.
+        draft: The current draft content.
+        critique: The critique with suggestions.
+
+    Returns:
+        The revised content.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.7,
+    )
+
+    # Format the feedback
+    strengths = critique.get("strengths", [])
+    weaknesses = critique.get("weaknesses", [])
+    suggestions = critique.get("suggestions", [])
+
+    feedback = f"""
+Strengths: {', '.join(strengths)}
+Weaknesses: {', '.join(weaknesses)}
+Suggestions: {', '.join(suggestions)}
+"""
+
+    revise_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a skilled writer revising your work based on feedback.
+Carefully address each weakness and incorporate the suggestions while
+preserving the strengths.
+
+Produce an improved version that addresses the critique.""",
+            ),
+            (
+                "human",
+                "Original task: {task}\n\nCurrent draft:\n{draft}\n\nFeedback:\n{feedback}\n\nRevised version:",
+            ),
+        ]
+    )
+
+    chain = revise_prompt | model | StrOutputParser()
+    revised = await chain.ainvoke(
+        {"task": task_description, "draft": draft, "feedback": feedback}
+    )
+
+    return revised
diff --git a/langgraph_plugin/functional_api/reflection/workflow.py b/langgraph_plugin/functional_api/reflection/workflow.py
new file mode 100644
index 00000000..fe923e1f
--- /dev/null
+++ b/langgraph_plugin/functional_api/reflection/workflow.py
@@ -0,0 +1,45 @@
+"""Reflection Agent Workflow.
+
+Temporal workflow that executes the reflection agent with durable execution.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ReflectionWorkflow:
+    """Temporal workflow that executes a reflection agent.
+
+    The reflection pattern iteratively improves content by:
+    1. Generating initial content
+    2. Critiquing and scoring
+    3. Revising based on feedback
+    4. Repeating until quality threshold
+
+    Each @task runs as a Temporal activity, so LLM calls are
+    automatically retried on failure and the workflow survives
+    worker crashes.
+    """
+
+    @workflow.run
+    async def run(
+        self, task_description: str, max_iterations: int = 3
+    ) -> dict[str, Any]:
+        """Run the reflection agent.
+
+        Args:
+            task_description: The writing/generation task.
+            max_iterations: Maximum reflection iterations.
+
+        Returns:
+            The final content with iteration history.
+        """
+        app = compile("reflection_entrypoint")
+
+        # Pass max_iterations through config or as part of input
+        result = await app.ainvoke(task_description)
+
+        return result
diff --git a/langgraph_plugin/functional_api/supervisor/README.md b/langgraph_plugin/functional_api/supervisor/README.md
new file mode 100644
index 00000000..794a8fd7
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/README.md
@@ -0,0 +1,141 @@
+# Supervisor Multi-Agent (Functional API)
+
+A supervisor agent that coordinates multiple specialized agents to complete complex tasks.
+
+## Overview
+
+The supervisor pattern uses a central coordinator:
+
+1. **Supervisor** - Decides which agent should work next
+2. **Researcher** - Gathers information
+3. **Writer** - Creates content
+4. **Analyst** - Performs calculations and analysis
+5. **Loop** - Continue until supervisor says FINISH
+
+## Architecture
+
+```
+User Query
+      │
+      ▼
+┌───────────────────┐
+│ supervisor_decide │◄──────────────────┐
+│      (task)       │                   │
+└─────────┬─────────┘                   │
+          │                             │
+          ▼                             │
+    Route to agent                      │
+          │                             │
+    ┌─────┼─────┬─────────┐             │
+    │     │     │         │             │
+    ▼     ▼     ▼         ▼             │
+┌──────┐┌──────┐┌───────┐ FINISH        │
+│研究者││作家  ││分析师 │    │          │
+│(task)││(task)││(task) │    │          │
+└──┬───┘└──┬───┘└───┬───┘    │          │
+   │       │        │        │          │
+   └───────┴────────┴────────┴──────────┘
+                             │
+                             ▼
+                      Final Summary
+```
+
+## Key Code
+
+### Supervisor Loop
+
+```python
+@entrypoint()
+async def supervisor_entrypoint(query: str, max_iterations: int = 10) -> dict:
+    available_agents = ["researcher", "writer", "analyst"]
+    messages = [{"role": "user", "content": query}]
+    agent_outputs = []
+
+    for iteration in range(max_iterations):
+        # Supervisor decides next step
+        decision = await supervisor_decide(messages, available_agents)
+        next_agent = decision["next_agent"]
+
+        if next_agent == "FINISH":
+            break
+
+        # Route to appropriate agent
+        if next_agent == "researcher":
+            output = await researcher_work(decision["task_for_agent"])
+        elif next_agent == "writer":
+            output = await writer_work(decision["task_for_agent"], context)
+        elif next_agent == "analyst":
+            output = await analyst_work(decision["task_for_agent"], context)
+
+        agent_outputs.append(output)
+        messages.append({"role": next_agent, "content": output})
+
+    # Final summary
+    final_answer = await writer_work("Summarize the work done", all_outputs)
+    return {"final_answer": final_answer, "agent_outputs": agent_outputs}
+```
+
+### Agent Tasks
+
+```python
+@task
+def supervisor_decide(messages: list, available_agents: list) -> dict:
+    """Decide which agent should work next."""
+    return {"next_agent": "researcher", "task_for_agent": "Find data on..."}
+
+@task
+def researcher_work(task: str) -> str:
+    """Gather information on the given task."""
+    return search_and_summarize(task)
+
+@task
+def writer_work(task: str, context: str) -> str:
+    """Create content based on task and context."""
+    return generate_content(task, context)
+```
+
+## Why Temporal?
+
+- **Agent coordination**: Reliable handoffs between agents
+- **Visibility**: See each agent's contribution
+- **Long-running**: Complex tasks with many agents complete reliably
+- **Audit trail**: Full conversation history preserved
+
+## Running the Sample
+
+1. Start Temporal:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. Run with API key:
+   ```bash
+   export OPENAI_API_KEY=your-key
+   uv run langgraph_plugin/functional_api/supervisor/run_worker.py
+   ```
+
+3. Submit a complex task:
+   ```bash
+   uv run langgraph_plugin/functional_api/supervisor/run_workflow.py
+   ```
+
+## Customization
+
+### Add More Agents
+
+```python
+available_agents = ["researcher", "writer", "analyst", "coder", "reviewer"]
+
+# In the routing logic:
+elif next_agent == "coder":
+    output = await coder_work(decision["task_for_agent"], context)
+```
+
+### Parallel Agent Execution
+
+```python
+# For independent agent tasks
+if decision["parallel_agents"]:
+    futures = [agent_work(agent, task) for agent, task in decision["parallel_agents"]]
+    outputs = [await f for f in futures]
+```
diff --git a/langgraph_plugin/functional_api/supervisor/__init__.py b/langgraph_plugin/functional_api/supervisor/__init__.py
new file mode 100644
index 00000000..0a4c2427
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/__init__.py
@@ -0,0 +1 @@
+"""Supervisor Multi-Agent - LangGraph Functional API with Temporal."""
diff --git a/langgraph_plugin/functional_api/supervisor/entrypoint.py b/langgraph_plugin/functional_api/supervisor/entrypoint.py
new file mode 100644
index 00000000..e6d338a6
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/entrypoint.py
@@ -0,0 +1,88 @@
+"""Supervisor Multi-Agent Entrypoint Definition.
+
+The @entrypoint function coordinates multiple specialized agents:
+- Supervisor: Decides which agent to route to
+- Researcher: Gathers information
+- Writer: Creates content
+- Analyst: Performs calculations and analysis
+"""
+
+from typing import Any
+
+from langgraph.func import entrypoint
+
+from langgraph_plugin.functional_api.supervisor.tasks import (
+    analyst_work,
+    researcher_work,
+    supervisor_decide,
+    writer_work,
+)
+
+
+@entrypoint()
+async def supervisor_entrypoint(query: str, max_iterations: int = 10) -> dict[str, Any]:
+    """Run a supervisor multi-agent system.
+
+    The supervisor coordinates specialized agents to complete complex tasks.
+    Each agent's work runs as a separate Temporal activity.
+
+    Args:
+        query: The user's request.
+        max_iterations: Maximum agent handoffs.
+
+    Returns:
+        Dict with conversation history and final result.
+    """
+    available_agents = ["researcher", "writer", "analyst"]
+
+    messages: list[dict[str, Any]] = [{"role": "user", "content": query}]
+    agent_outputs: list[str] = []
+
+    for iteration in range(max_iterations):
+        # Supervisor decides next step
+        decision = await supervisor_decide(messages, available_agents)
+
+        next_agent = decision["next_agent"]
+        task_for_agent = decision["task_for_agent"]
+
+        # Add supervisor's decision to messages
+        messages.append(
+            {
+                "role": "supervisor",
+                "content": f"Routing to {next_agent}: {task_for_agent}",
+            }
+        )
+
+        if next_agent == "FINISH":
+            break
+
+        # Route to appropriate agent
+        context = "\n".join(agent_outputs[-3:])  # Last 3 outputs for context
+
+        if next_agent == "researcher":
+            output = await researcher_work(task_for_agent)
+        elif next_agent == "writer":
+            output = await writer_work(task_for_agent, context)
+        elif next_agent == "analyst":
+            output = await analyst_work(task_for_agent, context)
+        else:
+            output = f"Unknown agent: {next_agent}"
+
+        agent_outputs.append(output)
+        messages.append({"role": next_agent, "content": output})
+
+    # Get final summary from writer
+    if agent_outputs:
+        final_summary = await writer_work(
+            "Summarize the work done and provide a final answer to the user's original query.",
+            "\n".join(agent_outputs),
+        )
+    else:
+        final_summary = "No agent work was performed."
+
+    return {
+        "messages": messages,
+        "agent_outputs": agent_outputs,
+        "final_answer": final_summary,
+        "iterations": iteration + 1 if "iteration" in dir() else 0,
+    }
diff --git a/langgraph_plugin/functional_api/supervisor/run_worker.py b/langgraph_plugin/functional_api/supervisor/run_worker.py
new file mode 100644
index 00000000..00b73a0c
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/run_worker.py
@@ -0,0 +1,58 @@
+"""Worker for the Supervisor Multi-Agent Functional API sample.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import (
+    LangGraphPlugin,
+    activity_options,
+)
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.supervisor.entrypoint import supervisor_entrypoint
+from langgraph_plugin.functional_api.supervisor.workflow import SupervisorWorkflow
+
+
+async def main() -> None:
+    plugin = LangGraphPlugin(
+        graphs={"supervisor_entrypoint": supervisor_entrypoint},
+        activity_options={
+            "supervisor_decide": activity_options(
+                start_to_close_timeout=timedelta(minutes=1),
+            ),
+            "researcher_work": activity_options(
+                start_to_close_timeout=timedelta(minutes=3),
+            ),
+            "writer_work": activity_options(
+                start_to_close_timeout=timedelta(minutes=3),
+            ),
+            "analyst_work": activity_options(
+                start_to_close_timeout=timedelta(minutes=2),
+            ),
+        },
+    )
+
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    worker = Worker(
+        client,
+        task_queue="langgraph-functional-supervisor",
+        workflows=[SupervisorWorkflow],
+    )
+
+    print("Supervisor Multi-Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/supervisor/run_workflow.py b/langgraph_plugin/functional_api/supervisor/run_workflow.py
new file mode 100644
index 00000000..2f452b46
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/run_workflow.py
@@ -0,0 +1,30 @@
+"""Execute the Supervisor Multi-Agent Functional API workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.functional_api.supervisor.workflow import SupervisorWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    query = "Research AI trends in 2024, then write a brief summary, and calculate what percentage of companies are adopting AI agents (assume 35 out of 100)."
+
+    result = await client.execute_workflow(
+        SupervisorWorkflow.run,
+        query,
+        id="supervisor-functional-workflow",
+        task_queue="langgraph-functional-supervisor",
+    )
+
+    print(f"Final Answer:\n{result.get('final_answer')}")
+    print(f"\nIterations: {result.get('iterations')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/functional_api/supervisor/tasks.py b/langgraph_plugin/functional_api/supervisor/tasks.py
new file mode 100644
index 00000000..b1dcb2ab
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/tasks.py
@@ -0,0 +1,232 @@
+"""Task definitions for the Supervisor Multi-Agent system.
+
+Each agent's work runs as a @task (Temporal activity), providing automatic
+retries and failure recovery for the entire multi-agent system.
+"""
+
+import os
+from typing import Any, Literal
+
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.func import task
+from pydantic import BaseModel, Field
+
+
+class SupervisorDecision(BaseModel):
+    """Supervisor's decision on which agent to use next."""
+
+    next_agent: Literal["researcher", "writer", "analyst", "FINISH"] = Field(
+        description="The next agent to route to, or FINISH if task is complete"
+    )
+    task_for_agent: str = Field(
+        description="The specific task to give to the next agent"
+    )
+    reasoning: str = Field(description="Brief explanation of the routing decision")
+
+
+# --- Tool implementations for agents ---
+
+
+def web_search(query: str) -> str:
+    """Mock web search for demonstration."""
+    mock_results = {
+        "ai trends 2024": (
+            "Key AI trends in 2024:\n"
+            "1. Multimodal AI models combining text, image, and video\n"
+            "2. AI agents and autonomous systems\n"
+            "3. Small language models for edge deployment"
+        ),
+        "temporal workflow": (
+            "Temporal is a durable execution platform:\n"
+            "- Workflows survive failures and can run for years\n"
+            "- Activities are retried automatically on failure"
+        ),
+    }
+    query_lower = query.lower()
+    for key, result in mock_results.items():
+        if any(word in query_lower for word in key.split()):
+            return result
+    return f"Search results for '{query}': General information found."
+
+
+def calculate(expression: str) -> str:
+    """Safely evaluate mathematical expressions."""
+    try:
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters"
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error: {e}"
+
+
+@task
+async def supervisor_decide(
+    messages: list[dict[str, Any]], available_agents: list[str]
+) -> dict[str, Any]:
+    """Supervisor decides which agent should handle the next step.
+
+    Args:
+        messages: Conversation history.
+        available_agents: List of available agent names.
+
+    Returns:
+        Dict with next_agent, task_for_agent, and reasoning.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    agents_desc = """
+- researcher: Has web search capabilities. Use for gathering information, facts, or research tasks.
+- writer: Specializes in content creation and summarization.
+- analyst: Expert in calculations and data analysis.
+"""
+
+    supervisor_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                f"""You are a team supervisor managing specialized agents:
+{agents_desc}
+
+Based on the conversation, decide which agent should work next.
+If the task is complete, respond with FINISH.
+Always provide a specific task for the chosen agent.""",
+            ),
+            ("human", "Conversation so far:\n{conversation}\n\nDecide the next step:"),
+        ]
+    )
+
+    # Format conversation
+    conv_text = "\n".join(
+        f"{m.get('role', 'unknown')}: {m.get('content', '')}" for m in messages
+    )
+
+    chain = supervisor_prompt | model.with_structured_output(SupervisorDecision)
+    decision: Any = await chain.ainvoke({"conversation": conv_text})
+
+    return {
+        "next_agent": decision.next_agent,
+        "task_for_agent": decision.task_for_agent,
+        "reasoning": decision.reasoning,
+    }
+
+
+@task
+async def researcher_work(task_description: str) -> str:
+    """Researcher agent performs web search and information gathering.
+
+    Args:
+        task_description: The research task to perform.
+
+    Returns:
+        Research findings as a string.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Perform search
+    search_results = web_search(task_description)
+
+    # Synthesize findings
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a research specialist. Synthesize the search results into a clear summary.",
+            ),
+            (
+                "human",
+                f"Task: {task_description}\n\nSearch Results:\n{search_results}\n\nProvide your research summary:",
+            ),
+        ]
+    )
+
+    chain = prompt | model | StrOutputParser()
+    summary = await chain.ainvoke({})
+
+    return f"[Researcher] {summary}"
+
+
+@task
+async def writer_work(task_description: str, context: str = "") -> str:
+    """Writer agent creates content or summaries.
+
+    Args:
+        task_description: The writing task to perform.
+        context: Additional context from previous agents.
+
+    Returns:
+        Written content as a string.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.7,
+    )
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a skilled writer. Create clear, engaging content based on the task.",
+            ),
+            (
+                "human",
+                f"Task: {task_description}\n\nContext:\n{context}\n\nWrite your content:",
+            ),
+        ]
+    )
+
+    chain = prompt | model | StrOutputParser()
+    content = await chain.ainvoke({})
+
+    return f"[Writer] {content}"
+
+
+@task
+async def analyst_work(task_description: str, data: str = "") -> str:
+    """Analyst agent performs calculations and data analysis.
+
+    Args:
+        task_description: The analysis task to perform.
+        data: Data to analyze.
+
+    Returns:
+        Analysis results as a string.
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Check if there's a calculation needed
+    analysis = ""
+    if any(op in task_description for op in ["+", "-", "*", "/", "calculate"]):
+        # Extract and evaluate expression
+        analysis = f"Calculation: {calculate(task_description)}\n"
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a data analyst. Provide clear analysis and insights.",
+            ),
+            (
+                "human",
+                f"Task: {task_description}\n\nData:\n{data}\n\nPrevious analysis:\n{analysis}\n\nProvide your analysis:",
+            ),
+        ]
+    )
+
+    chain = prompt | model | StrOutputParser()
+    result = await chain.ainvoke({})
+
+    return f"[Analyst] {result}"
diff --git a/langgraph_plugin/functional_api/supervisor/workflow.py b/langgraph_plugin/functional_api/supervisor/workflow.py
new file mode 100644
index 00000000..a948159f
--- /dev/null
+++ b/langgraph_plugin/functional_api/supervisor/workflow.py
@@ -0,0 +1,36 @@
+"""Supervisor Multi-Agent Workflow.
+
+Temporal workflow that executes the supervisor multi-agent system.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class SupervisorWorkflow:
+    """Temporal workflow that executes a supervisor multi-agent system.
+
+    The supervisor coordinates three specialized agents:
+    - Researcher: Gathers information
+    - Writer: Creates content
+    - Analyst: Performs calculations
+
+    Each agent's work runs as a Temporal activity with automatic retries.
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the supervisor multi-agent system.
+
+        Args:
+            query: The user's request.
+
+        Returns:
+            The final result with conversation history.
+        """
+        app = compile("supervisor_entrypoint")
+        result = await app.ainvoke(query)
+        return result
diff --git a/langgraph_plugin/graph_api/__init__.py b/langgraph_plugin/graph_api/__init__.py
new file mode 100644
index 00000000..721ac9c8
--- /dev/null
+++ b/langgraph_plugin/graph_api/__init__.py
@@ -0,0 +1 @@
+"""LangGraph Graph API examples using StateGraph."""
diff --git a/langgraph_plugin/graph_api/activity_from_node/README.md b/langgraph_plugin/graph_api/activity_from_node/README.md
new file mode 100644
index 00000000..9dc57201
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/README.md
@@ -0,0 +1,51 @@
+# Activity from Node
+
+Demonstrates calling Temporal activities directly from a LangGraph node using the `run_in_workflow` feature.
+
+## What This Sample Demonstrates
+
+- **run_in_workflow nodes**: Using `temporal_node_metadata(run_in_workflow=True)` to run a node in the workflow context
+- **Activity orchestration**: Calling multiple Temporal activities from within a graph node
+- **Mixed execution modes**: Combining run_in_workflow nodes with regular activity nodes
+- **Sandbox enforcement**: Node code is sandboxed to ensure deterministic execution
+
+## How It Works
+
+1. **Orchestrator Node**: Runs directly in the workflow (not as an activity) with `run_in_workflow=True`
+   - Calls `validate_data` activity to validate input
+   - Calls `enrich_data` activity to enrich valid data
+   - Implements orchestration logic (conditional activity calls)
+
+2. **Finalize Node**: Runs as a regular Temporal activity (default behavior)
+   - Processes the enriched data
+
+3. **Activities**: Standard Temporal activities called from the orchestrator
+   - `validate_data`: Validates input data
+   - `enrich_data`: Enriches data with additional information
+
+## When to Use run_in_workflow
+
+Use `run_in_workflow=True` when your node needs to:
+- Call Temporal activities, child workflows, or other Temporal operations
+- Use workflow features like timers, signals, or queries
+- Implement complex orchestration logic with multiple activity calls
+
+**Important**: Code in run_in_workflow nodes is sandboxed to ensure deterministic execution. Non-deterministic operations (like `random.randint()`) will be blocked.
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_plugin/graph_api/activity_from_node/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_plugin/graph_api/activity_from_node/run_workflow.py
+```
+
+## Expected Output
+
+```
+Result: {'data': 'Hello from LangGraph', 'validated': True, 'enriched_data': 'Hello from LangGraph [enriched at activity]', 'final_result': 'Processed: Hello from LangGraph [enriched at activity]'}
+```
diff --git a/langgraph_plugin/graph_api/activity_from_node/__init__.py b/langgraph_plugin/graph_api/activity_from_node/__init__.py
new file mode 100644
index 00000000..9b2b2e3a
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/__init__.py
@@ -0,0 +1 @@
+"""Activity from Node sample."""
diff --git a/langgraph_plugin/graph_api/activity_from_node/activities.py b/langgraph_plugin/graph_api/activity_from_node/activities.py
new file mode 100644
index 00000000..a9898154
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/activities.py
@@ -0,0 +1,42 @@
+"""Activity from Node - Activity Definitions.
+
+Activities that are called from a run_in_workflow node.
+"""
+
+from temporalio import activity
+
+
+@activity.defn
+async def validate_data(data: str) -> bool:
+    """Validate the input data.
+
+    In a real application, this could:
+    - Check data format and schema
+    - Verify required fields
+    - Call external validation services
+    """
+    activity.logger.info(f"Validating data: {data}")
+
+    # Simple validation - check if data is non-empty
+    is_valid = bool(data and data.strip())
+
+    activity.logger.info(f"Validation result: {is_valid}")
+    return is_valid
+
+
+@activity.defn
+async def enrich_data(data: str) -> str:
+    """Enrich the input data with additional information.
+
+    In a real application, this could:
+    - Call external APIs for data enrichment
+    - Lookup data from databases
+    - Apply transformations
+    """
+    activity.logger.info(f"Enriching data: {data}")
+
+    # Simple enrichment - add metadata
+    enriched = f"{data} [enriched at activity]"
+
+    activity.logger.info(f"Enriched data: {enriched}")
+    return enriched
diff --git a/langgraph_plugin/graph_api/activity_from_node/graph.py b/langgraph_plugin/graph_api/activity_from_node/graph.py
new file mode 100644
index 00000000..adcaa890
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/graph.py
@@ -0,0 +1,113 @@
+"""Activity from Node - Graph Definition.
+
+This module defines a graph where a node runs in the workflow context
+and calls Temporal activities directly.
+"""
+
+from datetime import timedelta
+from typing import Any
+
+from langgraph.graph import END, START, StateGraph
+from typing_extensions import TypedDict
+
+# =============================================================================
+# State Definition
+# =============================================================================
+
+
+class ProcessingState(TypedDict, total=False):
+    """State for the processing graph."""
+
+    data: str
+    validated: bool
+    enriched_data: str
+    final_result: str
+
+
+# =============================================================================
+# Node Functions
+# =============================================================================
+
+
+async def orchestrator_node(state: ProcessingState) -> ProcessingState:
+    """Node that orchestrates multiple activity calls from the workflow.
+
+    This node runs directly in the workflow (run_in_workflow=True) so it can:
+    - Call multiple Temporal activities
+    - Use workflow features like timers, signals, queries
+    - Implement complex orchestration logic
+
+    The node is sandboxed, ensuring deterministic code.
+    """
+    from temporalio import workflow
+
+    data = state.get("data", "")
+
+    # Call validation activity
+    is_valid = await workflow.execute_activity(
+        "validate_data",
+        data,
+        start_to_close_timeout=timedelta(seconds=30),
+    )
+
+    if not is_valid:
+        return {"validated": False, "final_result": "Validation failed"}
+
+    # Call enrichment activity
+    enriched = await workflow.execute_activity(
+        "enrich_data",
+        data,
+        start_to_close_timeout=timedelta(seconds=30),
+    )
+
+    return {"validated": True, "enriched_data": enriched}
+
+
+def finalize_node(state: ProcessingState) -> ProcessingState:
+    """Final processing node - runs as a regular activity.
+
+    This demonstrates mixing run_in_workflow nodes with regular activity nodes.
+    """
+    if not state.get("validated"):
+        return state
+
+    enriched = state.get("enriched_data", "")
+    return {"final_result": f"Processed: {enriched}"}
+
+
+# =============================================================================
+# Graph Builder
+# =============================================================================
+
+
+def build_activity_from_node_graph() -> Any:
+    """Build a graph with a node that calls activities from the workflow.
+
+    The orchestrator node uses run_in_workflow=True to execute directly
+    in the workflow context, allowing it to call Temporal activities.
+    """
+    from temporalio.contrib.langgraph import activity_options, temporal_node_metadata
+
+    graph = StateGraph(ProcessingState)
+
+    # Orchestrator runs in workflow to call activities
+    graph.add_node(
+        "orchestrator",
+        orchestrator_node,
+        metadata=temporal_node_metadata(run_in_workflow=True),
+    )
+
+    # Finalize runs as a regular activity with timeout config
+    graph.add_node(
+        "finalize",
+        finalize_node,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    graph.add_edge(START, "orchestrator")
+    graph.add_edge("orchestrator", "finalize")
+    graph.add_edge("finalize", END)
+
+    return graph.compile()
diff --git a/langgraph_plugin/graph_api/activity_from_node/run_worker.py b/langgraph_plugin/graph_api/activity_from_node/run_worker.py
new file mode 100644
index 00000000..150e45b8
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/run_worker.py
@@ -0,0 +1,50 @@
+"""Worker for the Activity from Node example.
+
+Starts a Temporal worker that can execute ActivityFromNodeWorkflow.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.activity_from_node.activities import (
+    enrich_data,
+    validate_data,
+)
+from langgraph_plugin.graph_api.activity_from_node.graph import (
+    build_activity_from_node_graph,
+)
+from langgraph_plugin.graph_api.activity_from_node.workflow import (
+    ActivityFromNodeWorkflow,
+)
+
+
+async def main() -> None:
+    # Create the plugin with our graph registered
+    plugin = LangGraphPlugin(
+        graphs={"activity_from_node_graph": build_activity_from_node_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: We register our custom activities alongside LangGraph's auto-registered ones
+    worker = Worker(
+        client,
+        task_queue="langgraph-activity-from-node",
+        workflows=[ActivityFromNodeWorkflow],
+        activities=[validate_data, enrich_data],
+    )
+
+    print("Worker started. Ctrl+C to exit.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/activity_from_node/run_workflow.py b/langgraph_plugin/graph_api/activity_from_node/run_workflow.py
new file mode 100644
index 00000000..744b4c65
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/run_workflow.py
@@ -0,0 +1,35 @@
+"""Run the Activity from Node workflow.
+
+Starts a workflow execution and waits for the result.
+"""
+
+import asyncio
+import uuid
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.activity_from_node.workflow import (
+    ActivityFromNodeWorkflow,
+)
+
+
+async def main() -> None:
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Run the workflow
+    result = await client.execute_workflow(
+        ActivityFromNodeWorkflow.run,
+        "Hello from LangGraph",
+        id=f"activity-from-node-{uuid.uuid4()}",
+        task_queue="langgraph-activity-from-node",
+    )
+
+    print(f"Result: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/activity_from_node/workflow.py b/langgraph_plugin/graph_api/activity_from_node/workflow.py
new file mode 100644
index 00000000..8b67e1fa
--- /dev/null
+++ b/langgraph_plugin/graph_api/activity_from_node/workflow.py
@@ -0,0 +1,39 @@
+"""Activity from Node - Workflow Definition.
+
+Demonstrates calling Temporal activities from a LangGraph node.
+
+Note: This module only contains the workflow definition. The graph and
+activities are defined separately and imported only by the worker.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ActivityFromNodeWorkflow:
+    """Workflow that runs a graph with activity-calling nodes.
+
+    This demonstrates:
+    - Using run_in_workflow=True for nodes that need workflow context
+    - Calling Temporal activities from within graph nodes
+    - Mixing run_in_workflow nodes with regular activity nodes
+    """
+
+    @workflow.run
+    async def run(self, data: str) -> dict[str, Any]:
+        """Run the processing graph.
+
+        Args:
+            data: The input data to process.
+
+        Returns:
+            The final state with processing results.
+        """
+        app = compile("activity_from_node_graph")
+
+        result = await app.ainvoke({"data": data})
+
+        return result
diff --git a/langgraph_plugin/graph_api/agentic_rag/README.md b/langgraph_plugin/graph_api/agentic_rag/README.md
new file mode 100644
index 00000000..e85b5786
--- /dev/null
+++ b/langgraph_plugin/graph_api/agentic_rag/README.md
@@ -0,0 +1,111 @@
+# Agentic RAG
+
+An intelligent RAG (Retrieval Augmented Generation) agent that decides when to retrieve documents, grades their relevance, and rewrites queries when needed.
+
+## What This Sample Demonstrates
+
+- **`create_agent` pattern**: Uses LangChain's `create_agent` for the retrieval step
+- **Document grading**: Retrieved documents are evaluated for relevance before generating answers
+- **Query rewriting**: If documents aren't relevant, the query is reformulated and retrieval is retried
+- **Durable execution**: Graph nodes run as Temporal activities with automatic retries
+- **Crash recovery**: If the worker fails, execution resumes from the last completed node
+
+## How It Works
+
+The graph implements an agentic RAG pattern with `create_agent` for retrieval:
+
+```
+                              +-------+
+                              | START |
+                              +---+---+
+                                  |
+                                  v
+                        +-----------------+
+             +--------->| retrieve_agent  |<--------+
+             |          | (create_agent)  |         |
+             |          +-----------------+         |
+             |                  |                   |
+             |     "not relevant"   "relevant"      |
+             |          |                |          |
+         +---+---+      |                |     +----+
+         |rewrite|<-----+                +---->| END|
+         +-------+                             +----+
+                                                 ^
+                                                 |
+                                           +-----+----+
+                                           | generate |
+                                           +----------+
+```
+
+1. **retrieve_agent**: Uses `create_agent` to decide whether to retrieve and fetch documents (runs as single activity)
+2. **grade_documents**: Conditional edge that evaluates document relevance
+3. **generate**: Produces the final answer using relevant documents (runs as activity)
+4. **rewrite**: Reformulates the query if documents weren't relevant, then retries (runs as activity)
+
+### Subgraph Behavior
+
+The Temporal LangGraph plugin automatically detects subgraphs (like `create_agent`) and executes their **inner nodes as separate activities**. This means:
+
+- The retrieve_agent subgraph's `model` and `tools` nodes run as separate Temporal activities
+- Each node has its own retry/timeout configuration
+- If the worker crashes during retrieval, execution resumes from the last completed inner node
+- You get full durability without manually adding separate nodes
+
+The sample includes a knowledge base with documents about:
+- LangGraph features and capabilities
+- Temporal concepts (workflows, activities, signals, queries)
+- ReAct pattern for AI agents
+- Agentic RAG patterns
+- Human-in-the-loop workflows
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_plugin/graph_api/agentic_rag/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_plugin/graph_api/agentic_rag/run_workflow.py
+```
+
+## Expected Output
+
+```
+The ReAct (Reasoning and Acting) pattern is an approach where an LLM alternates
+between thinking about what to do and taking actions. The loop consists of:
+1. Think - The LLM reasons about the current state and what action to take
+2. Act - Execute the chosen action (e.g., call a tool)
+3. Observe - Process the result of the action
+4. Repeat until the task is complete
+
+When combined with Temporal, the ReAct pattern gains durability because each
+graph node runs as a Temporal activity. This means progress is saved after each
+node completes, and if the worker crashes, execution resumes from the last
+completed node.
+```
+
+## Sample Queries
+
+You can modify the query in `run_workflow.py` to test different scenarios:
+
+- **Retrieval needed**: "What is LangGraph and how does it work?"
+- **Multi-topic**: "How do Temporal signals work with human-in-the-loop workflows?"
+- **Off-topic (no retrieval)**: "What's 2 + 2?" (agent responds directly)
+- **Needs rewriting**: "Tell me about durable stuff" (vague query gets rewritten)
+
+## Architecture Notes
+
+The sample uses an in-memory vector store with sample documents for simplicity. In production, you would:
+
+1. Use a persistent vector store (Pinecone, Weaviate, Chroma, etc.)
+2. Load documents from your actual knowledge base
+3. Configure appropriate chunk sizes and embedding models
+
+The document grading step adds latency but significantly improves answer quality by ensuring the RAG system only uses relevant context.
diff --git a/langgraph_plugin/graph_api/agentic_rag/__init__.py b/langgraph_plugin/graph_api/agentic_rag/__init__.py
new file mode 100644
index 00000000..28df7238
--- /dev/null
+++ b/langgraph_plugin/graph_api/agentic_rag/__init__.py
@@ -0,0 +1 @@
+"""Agentic RAG sample with document grading and query rewriting."""
diff --git a/langgraph_plugin/graph_api/agentic_rag/graph.py b/langgraph_plugin/graph_api/agentic_rag/graph.py
new file mode 100644
index 00000000..ce32611f
--- /dev/null
+++ b/langgraph_plugin/graph_api/agentic_rag/graph.py
@@ -0,0 +1,358 @@
+"""Agentic RAG Graph Definition.
+
+This module implements an agentic RAG (Retrieval Augmented Generation) system
+that intelligently decides when to retrieve documents, grades their relevance,
+and can rewrite queries when documents aren't helpful.
+
+The graph uses create_agent for the retrieval step, which provides a convenient
+way to build a ReAct-style agent. The outer graph adds document grading and
+query rewriting logic.
+
+Flow:
+1. retrieve_agent - Uses create_agent to decide and fetch documents
+2. grade_documents - Evaluates document relevance (conditional routing)
+3. generate - Produces final answer using relevant documents
+4. rewrite - Reformulates query if documents aren't relevant, then retries
+
+Architecture Note:
+The Temporal LangGraph plugin automatically detects subgraphs (like create_agent)
+and executes their inner nodes as separate activities. This means:
+- The retrieve_agent subgraph's 'model' and 'tools' nodes run as separate activities
+- Each tool call has its own retry/timeout configuration
+- If the worker crashes, execution resumes from the last completed inner node
+- The generate, rewrite nodes in the outer graph also run as separate activities
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from datetime import timedelta
+from typing import Annotated, Any, Literal, Sequence, cast
+
+from langchain_core.documents import Document
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools.retriever import create_retriever_tool
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+from langchain.agents import create_agent
+
+# Sample documents about AI agents and LangGraph for the knowledge base
+SAMPLE_DOCUMENTS = [
+    Document(
+        page_content="""LangGraph is a library for building stateful, multi-actor applications with LLMs.
+        It extends LangChain with the ability to coordinate multiple chains (or actors) across
+        multiple steps of computation in a cyclic manner. Key features include:
+        - Support for cycles and branching in agent workflows
+        - Built-in persistence for pausing and resuming
+        - Human-in-the-loop support with interrupts
+        - Streaming support for real-time updates""",
+        metadata={"source": "langgraph_overview", "topic": "langgraph"},
+    ),
+    Document(
+        page_content="""Temporal is a durable execution platform that enables developers to build
+        applications that are reliable by default. Key concepts include:
+        - Workflows: Long-running, reliable processes that survive failures
+        - Activities: Individual units of work that can be retried
+        - Signals: External events that can be sent to running workflows
+        - Queries: Read-only operations to inspect workflow state
+        The Temporal LangGraph integration runs each graph node as a Temporal activity.""",
+        metadata={"source": "temporal_overview", "topic": "temporal"},
+    ),
+    Document(
+        page_content="""The ReAct (Reasoning and Acting) pattern is an approach where an LLM
+        alternates between thinking about what to do and taking actions. The loop is:
+        1. Think: The LLM reasons about the current state and what action to take
+        2. Act: Execute the chosen action (e.g., call a tool)
+        3. Observe: Process the result of the action
+        4. Repeat until the task is complete
+        This pattern allows LLMs to effectively use tools and handle multi-step tasks.""",
+        metadata={"source": "react_pattern", "topic": "agents"},
+    ),
+    Document(
+        page_content="""Agentic RAG (Retrieval Augmented Generation) is an advanced pattern where
+        an AI agent decides when and how to retrieve information. Unlike basic RAG which always
+        retrieves, agentic RAG can:
+        - Decide if retrieval is needed based on the query
+        - Grade retrieved documents for relevance
+        - Rewrite queries if initial retrieval fails
+        - Combine multiple retrieval strategies
+        This leads to more efficient and accurate responses.""",
+        metadata={"source": "agentic_rag_overview", "topic": "rag"},
+    ),
+    Document(
+        page_content="""Human-in-the-loop workflows allow AI systems to pause and wait for
+        human input at critical decision points. In LangGraph, this is implemented using
+        the interrupt() function. When combined with Temporal:
+        - The workflow pauses durably while waiting for human input
+        - Temporal signals are used to receive the human response
+        - The workflow resumes exactly where it left off
+        This enables approval workflows, human review of AI outputs, and collaborative AI.""",
+        metadata={"source": "hitl_overview", "topic": "human_in_loop"},
+    ),
+]
+
+
+class AgentState(TypedDict):
+    """State schema for the agentic RAG graph.
+
+    Attributes:
+        messages: Conversation history with add_messages reducer for merging.
+        docs_relevant: Whether retrieved documents are relevant (set by grade_documents).
+    """
+
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+    docs_relevant: bool
+
+
+class DocumentGrade(BaseModel):
+    """Grade for document relevance."""
+
+    binary_score: Literal["yes", "no"] = Field(
+        description="Documents are relevant to the question, 'yes' or 'no'"
+    )
+
+
+def _create_retriever() -> Any:
+    """Create a retriever with sample documents.
+
+    Returns:
+        A retriever that can search the sample knowledge base.
+    """
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    vectorstore = InMemoryVectorStore(embeddings)
+    vectorstore.add_documents(SAMPLE_DOCUMENTS)
+    return vectorstore.as_retriever(search_kwargs={"k": 2})
+
+
+def build_agentic_rag_graph() -> Any:
+    """Build an agentic RAG graph with document grading.
+
+    Uses create_agent for the retrieval step. The outer graph adds document
+    grading and query rewriting logic around the retrieval agent.
+
+    Flow:
+    1. retrieve_agent decides whether to retrieve and fetches docs
+    2. grade_documents checks relevance (conditional edge)
+    3. generate produces answer OR rewrite reformulates query
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    # Create the model
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Create retriever and tool
+    retriever = _create_retriever()
+    retriever_tool = create_retriever_tool(
+        retriever,
+        "retrieve_knowledge",
+        "Search the knowledge base for information about LangGraph, Temporal, "
+        "AI agents, RAG patterns, and human-in-the-loop workflows. "
+        "Use this when the user asks about these topics.",
+    )
+    tools = [retriever_tool]
+
+    # Build retrieval agent using create_agent
+    # Note: create_agent returns a compiled graph. The Temporal plugin automatically
+    # detects this subgraph and runs its inner nodes (model, tools) as separate activities.
+    retrieve_agent: Any = create_agent(model, tools)
+
+    # Grade documents for relevance (as a node/activity, not conditional edge)
+    def grade_documents(state: AgentState) -> dict[str, Any]:
+        """Grade retrieved documents for relevance.
+
+        This is a node (activity) that examines the retrieved documents and
+        determines if they contain information relevant to answering the
+        user's question. The result is stored in state for routing.
+
+        Note: This must be a node (not conditional edge) because it makes
+        an LLM call. LLM calls should run as Temporal activities for proper
+        durability and retry handling.
+        """
+        # Set up structured output for grading
+        grader = model.with_structured_output(DocumentGrade)
+
+        # Get the question (first human message) and docs (last message with tool results)
+        messages = state["messages"]
+        question = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)), ""
+        )
+
+        # Find the retrieved documents in the messages
+        docs_content = ""
+        for msg in reversed(messages):
+            if hasattr(msg, "content") and isinstance(msg.content, str):
+                # Tool messages contain the retrieved documents
+                if hasattr(msg, "tool_call_id"):
+                    docs_content = msg.content
+                    break
+
+        if not docs_content:
+            return {"docs_relevant": False}
+
+        # Grade the documents
+        grade_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are a grader assessing relevance of retrieved documents to a user question.\n"
+                    "If the documents contain information related to the question, grade as relevant.\n"
+                    "Give a binary score 'yes' or 'no' to indicate relevance.",
+                ),
+                (
+                    "human",
+                    "Retrieved documents:\n{documents}\n\nUser question: {question}",
+                ),
+            ]
+        )
+
+        chain = grade_prompt | grader
+        result = cast(
+            DocumentGrade,
+            chain.invoke({"documents": docs_content, "question": question}),
+        )
+
+        return {"docs_relevant": result.binary_score == "yes"}
+
+    # Simple routing function (no LLM call - safe for workflow context)
+    def route_after_grading(state: AgentState) -> Literal["generate", "rewrite"]:
+        """Route based on document relevance grade.
+
+        This is a pure function that just checks the grade result.
+        Safe to run in workflow context since it makes no external calls.
+        """
+        return "generate" if state.get("docs_relevant", False) else "rewrite"
+
+    # Generate answer using retrieved documents
+    def generate(state: AgentState) -> dict[str, Any]:
+        """Generate answer using the retrieved documents.
+
+        Takes the relevant documents and user question to produce
+        a well-grounded response.
+        """
+        messages = state["messages"]
+        question = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)), ""
+        )
+
+        # Find the retrieved documents
+        docs_content = ""
+        for msg in reversed(messages):
+            if hasattr(msg, "tool_call_id") and hasattr(msg, "content"):
+                content = msg.content
+                docs_content = content if isinstance(content, str) else str(content)
+                break
+
+        # Generate response using RAG prompt
+        rag_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are an assistant answering questions using the provided context.\n"
+                    "Use the following retrieved documents to answer the question.\n"
+                    "If the documents don't contain enough information, say so.\n"
+                    "Keep your answer concise and well-structured.",
+                ),
+                ("human", "Context:\n{context}\n\nQuestion: {question}"),
+            ]
+        )
+
+        chain = rag_prompt | model | StrOutputParser()
+        response = chain.invoke({"context": docs_content, "question": question})
+
+        return {"messages": [{"role": "assistant", "content": response}]}
+
+    # Rewrite query for better retrieval
+    def rewrite(state: AgentState) -> dict[str, Any]:
+        """Rewrite the query to improve retrieval.
+
+        Analyzes the original question and formulates an improved version
+        that may yield more relevant documents.
+        """
+        messages = state["messages"]
+        question = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)), ""
+        )
+
+        rewrite_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are a question rewriter. Look at the input question and try to reason "
+                    "about the underlying intent. Reformulate the question to be more specific "
+                    "and likely to retrieve relevant documents about LangGraph, Temporal, AI agents, "
+                    "or related topics.",
+                ),
+                (
+                    "human",
+                    "Original question: {question}\n\nFormulate an improved question:",
+                ),
+            ]
+        )
+
+        chain = rewrite_prompt | model | StrOutputParser()
+        improved_question = chain.invoke({"question": question})
+
+        return {"messages": [HumanMessage(content=improved_question)]}
+
+    # Build the outer graph with grading logic
+    workflow = StateGraph(AgentState)
+
+    # Add nodes with activity options
+    # retrieve_agent is a compiled graph from create_agent - inner nodes run as separate activities
+    # Use per_node_activity_options in LangGraphPlugin to configure its inner nodes
+    workflow.add_node("retrieve_agent", retrieve_agent)
+    workflow.add_node(
+        "grade_documents",
+        grade_documents,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=1),
+        ),
+    )
+    workflow.add_node(
+        "generate",
+        generate,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    workflow.add_node(
+        "rewrite",
+        rewrite,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=1),
+        ),
+    )
+
+    # Add edges
+    workflow.add_edge(START, "retrieve_agent")
+
+    # After retrieval, grade documents (as activity)
+    workflow.add_edge("retrieve_agent", "grade_documents")
+
+    # Route based on grade result (pure function, no LLM call)
+    workflow.add_conditional_edges(
+        "grade_documents",
+        route_after_grading,
+        {"generate": "generate", "rewrite": "rewrite"},
+    )
+
+    # Generate produces final answer
+    workflow.add_edge("generate", END)
+
+    # Rewrite goes back to retrieve_agent to try again
+    workflow.add_edge("rewrite", "retrieve_agent")
+
+    return workflow.compile()
diff --git a/langgraph_plugin/graph_api/agentic_rag/run_worker.py b/langgraph_plugin/graph_api/agentic_rag/run_worker.py
new file mode 100644
index 00000000..3ea097b0
--- /dev/null
+++ b/langgraph_plugin/graph_api/agentic_rag/run_worker.py
@@ -0,0 +1,56 @@
+"""Worker for the Agentic RAG sample.
+
+Starts a Temporal worker that can execute AgenticRAGWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+import logging
+import sys
+
+# Configure logging to see workflow.logger output
+logging.basicConfig(
+    stream=sys.stdout,
+    level=logging.INFO,
+    format="%(asctime)s  %(levelname)-8s  %(name)s  %(message)s",
+)
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.agentic_rag.graph import build_agentic_rag_graph
+from langgraph_plugin.graph_api.agentic_rag.workflow import AgenticRAGWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the agentic RAG graph registered
+    plugin = LangGraphPlugin(
+        graphs={"agentic_rag": build_agentic_rag_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities (LLM calls, retrieval, grading) are automatically registered
+    worker = Worker(
+        client,
+        task_queue="langgraph-agentic-rag",
+        workflows=[AgenticRAGWorkflow],
+    )
+
+    print("Agentic RAG worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/agentic_rag/run_workflow.py b/langgraph_plugin/graph_api/agentic_rag/run_workflow.py
new file mode 100644
index 00000000..ae5621a1
--- /dev/null
+++ b/langgraph_plugin/graph_api/agentic_rag/run_workflow.py
@@ -0,0 +1,33 @@
+"""Execute the Agentic RAG workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.agentic_rag.workflow import AgenticRAGWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # This query requires retrieval from the knowledge base
+    # The agent will:
+    # 1. Decide to retrieve documents about the topic
+    # 2. Grade the retrieved documents for relevance
+    # 3. Generate an answer using the relevant documents
+    result = await client.execute_workflow(
+        AgenticRAGWorkflow.run,
+        "What is the ReAct pattern and how does it work with Temporal?",
+        id="agentic-rag-workflow",
+        task_queue="langgraph-agentic-rag",
+    )
+
+    # Print only the text response
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/agentic_rag/workflow.py b/langgraph_plugin/graph_api/agentic_rag/workflow.py
new file mode 100644
index 00000000..ec979216
--- /dev/null
+++ b/langgraph_plugin/graph_api/agentic_rag/workflow.py
@@ -0,0 +1,59 @@
+"""Agentic RAG Workflow.
+
+Temporal workflow that executes the agentic RAG agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class AgenticRAGWorkflow:
+    """Temporal workflow that executes an agentic RAG agent.
+
+    This workflow uses a LangGraph graph that intelligently decides:
+    - Whether to retrieve documents or respond directly
+    - If retrieved documents are relevant (with grading)
+    - Whether to rewrite the query for better retrieval
+
+    Each graph node runs as a Temporal activity:
+    - The "agent" node decides whether to retrieve
+    - The "retrieve" node fetches documents
+    - The "generate" node produces answers
+    - The "rewrite" node reformulates queries
+
+    If any node fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed node.
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the agentic RAG agent to answer a query.
+
+        The agent will:
+        1. Decide if retrieval is needed based on the query
+        2. Retrieve relevant documents if needed
+        3. Grade documents for relevance
+        4. Generate answer or rewrite query as appropriate
+
+        Args:
+            query: The user's question or request.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("agentic_rag")
+
+        # Execute the agent
+        # The input format is a dict with "messages"
+        result = await app.ainvoke({"messages": [{"role": "user", "content": query}]})
+
+        return result
diff --git a/langgraph_plugin/graph_api/continue_as_new/README.md b/langgraph_plugin/graph_api/continue_as_new/README.md
new file mode 100644
index 00000000..117c6984
--- /dev/null
+++ b/langgraph_plugin/graph_api/continue_as_new/README.md
@@ -0,0 +1,117 @@
+# Continue-as-New with Graph API
+
+This sample demonstrates how to use Temporal's continue-as-new feature with LangGraph's Graph API. The key design principle is that **the graph is completely unaware of Temporal or continue-as-new** - all checkpoint logic is handled by the workflow.
+
+## Overview
+
+The sample includes:
+
+- **`graph.py`**: A pure 5-step linear graph that knows nothing about Temporal
+- **`workflow.py`**: A workflow that uses `should_continue` callback to control execution and implements continue-as-new
+
+## How It Works
+
+### The Pure Graph
+
+The graph is a simple linear pipeline:
+
+```
+START -> step_1 -> step_2 -> step_3 -> step_4 -> step_5 -> END
+```
+
+Each step adds a result to the state. The graph has NO knowledge of:
+- Temporal
+- Continue-as-new
+- Checkpointing
+
+### The Workflow
+
+The workflow controls when to stop and checkpoint using the `should_continue` callback:
+
+```python
+def should_continue() -> bool:
+    nonlocal steps_executed
+    steps_executed += 1
+    return steps_executed <= input.steps_per_execution
+```
+
+When `should_continue()` returns `False`, the graph execution stops and returns a checkpoint containing:
+- Current state values
+- Next nodes to execute
+- Completed nodes list
+- Cached writes for trigger injection
+
+The workflow then calls `continue_as_new()` with this checkpoint to resume in a new execution.
+
+### Execution Flow
+
+With `steps_per_execution=2`, the 5-step graph requires 3 workflow executions:
+
+1. **Execution 1**: Runs step_1, step_2 → continue-as-new
+2. **Execution 2**: Runs step_3, step_4 → continue-as-new
+3. **Execution 3**: Runs step_5 → completes
+
+## Running the Sample
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   cd samples-python
+   uv run python -m langgraph_plugin.graph_api.continue_as_new.run_worker
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   cd samples-python
+   uv run python -m langgraph_plugin.graph_api.continue_as_new.run_workflow
+   ```
+
+## Expected Output
+
+The workflow will output:
+```
+Pipeline completed!
+Results: ['step_1: initialized', 'step_2: processed', 'step_3: transformed', 'step_4: validated', 'step_5: finalized']
+```
+
+In the Temporal UI, you'll see the workflow has multiple runs connected by continue-as-new.
+
+## Key Concepts
+
+### `should_continue` Callback
+
+The `should_continue` callback is passed to `ainvoke()` and is called after each step (tick) of the graph:
+
+```python
+result = await app.ainvoke(input_state, should_continue=should_continue)
+```
+
+When it returns `False`, execution stops and the result contains `CHECKPOINT_KEY` with state snapshot.
+
+### Checkpoint Contents
+
+The checkpoint (`StateSnapshot`) contains:
+- `values`: Current state values
+- `next`: Tuple of next node names to execute
+- `metadata`: Step counter, completed nodes, cached writes
+- `tasks`: Empty for should_continue stops (used for interrupt resume)
+
+### Restoring from Checkpoint
+
+When creating a runner with a checkpoint, the runner:
+1. Restores completed nodes list
+2. Caches writes from last executed nodes
+3. On resume, injects cached writes to trigger next nodes
+4. Skips re-executing completed nodes
+
+## Comparison with Functional API
+
+| Aspect | Graph API | Functional API |
+|--------|-----------|----------------|
+| Checkpoint content | Full graph state + writes | Task result cache |
+| Resume mechanism | Trigger injection | Cache lookup |
+| Graph changes | Supports continue mid-graph | Re-executes from start |
diff --git a/langgraph_plugin/graph_api/deep_research/README.md b/langgraph_plugin/graph_api/deep_research/README.md
new file mode 100644
index 00000000..fd07172c
--- /dev/null
+++ b/langgraph_plugin/graph_api/deep_research/README.md
@@ -0,0 +1,144 @@
+# Deep Research Agent
+
+A multi-step research agent that performs iterative research using real web search to produce comprehensive reports. This sample demonstrates how Temporal's durable execution enables long-running research workflows with LangGraph's parallel execution.
+
+## Overview
+
+The Deep Research Agent:
+1. **Plans research** - Analyzes the topic and generates targeted search queries using OpenAI
+2. **Parallel search** - Executes multiple DuckDuckGo web searches concurrently using LangGraph's Send API
+3. **Evaluates results** - Grades search results for relevance and coverage
+4. **Iterates** - Continues researching if more information is needed
+5. **Synthesizes** - Produces a comprehensive research report using OpenAI
+
+## Why Temporal?
+
+Research workflows are ideal for Temporal because:
+- **Long-running**: Research can take minutes to hours
+- **Parallel execution**: Multiple web searches run as separate activities
+- **Fault tolerance**: If a search fails, only that activity retries
+- **Visibility**: Progress is tracked in Temporal's UI
+- **Resumable**: Interrupted research continues where it left off
+
+## Architecture
+
+```
+[Plan] --> [Search 1] -\
+       --> [Search 2] ----> [Evaluate] --> [Synthesize] --> END
+       --> [Search 3] -/         |
+                                 v
+                            [Continue?]
+                                 |
+                           [More Plan] --> ...
+```
+
+Each search runs as a separate Temporal activity, enabling parallel execution.
+
+## Running the Sample
+
+### Prerequisites
+
+- Temporal server running locally
+- OpenAI API key
+
+### Steps
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key-here
+   uv run langgraph_plugin/graph_api/deep_research/run_worker.py
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   uv run langgraph_plugin/graph_api/deep_research/run_workflow.py
+   ```
+
+## Sample Output
+
+```
+============================================================
+RESEARCH REPORT
+============================================================
+
+## Executive Summary
+LangGraph and Temporal combine to create durable AI agents that survive
+failures and can handle long-running workflows...
+
+## Main Findings
+### Core Concepts
+- LangGraph provides the agent graph structure
+- Temporal provides durable execution...
+
+### Integration Benefits
+- Each graph node runs as a Temporal activity
+- Automatic retries for failed operations...
+
+## Conclusions
+The LangGraph-Temporal integration is ideal for production AI agents...
+```
+
+## Key Features Demonstrated
+
+### 1. Parallel Search with Send API
+```python
+def fan_out_searches(state: ResearchState) -> list[Send]:
+    """Fan out to parallel search executions."""
+    return [
+        Send("search", {"query": q.query, "purpose": q.purpose})
+        for q in state.get("search_queries", [])
+    ]
+```
+
+### 2. Research Iteration Loop
+```python
+def should_continue(state: ResearchState) -> Literal["synthesize", "plan"]:
+    """Decide whether to continue researching."""
+    if iteration < max_iterations and coverage < threshold:
+        return "plan"  # Continue researching
+    return "synthesize"  # Ready to report
+```
+
+### 3. Result Aggregation
+Results from parallel searches are aggregated using a reducer:
+```python
+search_results: Annotated[list[SearchResult], lambda x, y: x + y]
+```
+
+## Customization
+
+### Using Alternative Search APIs
+
+The sample uses DuckDuckGo by default. To use other search providers:
+
+```python
+# Tavily (requires TAVILY_API_KEY)
+from langchain_community.tools import TavilySearchResults
+search_tool = TavilySearchResults()
+
+# Or use any LangChain-compatible search tool
+```
+
+### Adjusting Research Depth
+
+Control iteration depth when starting the workflow:
+
+```python
+result = await client.execute_workflow(
+    DeepResearchWorkflow.run,
+    args=["Your research topic", 3],  # max 3 iterations
+    ...
+)
+```
+
+## Next Steps
+
+- Integrate additional search providers (Tavily, Exa, Google)
+- Implement continue-as-new for very long research sessions
+- Add human review checkpoints for important findings
+- Store research artifacts in Temporal's data store
diff --git a/langgraph_plugin/graph_api/deep_research/__init__.py b/langgraph_plugin/graph_api/deep_research/__init__.py
new file mode 100644
index 00000000..cc527396
--- /dev/null
+++ b/langgraph_plugin/graph_api/deep_research/__init__.py
@@ -0,0 +1 @@
+"""Deep Research Agent sample for Temporal LangGraph integration."""
diff --git a/langgraph_plugin/graph_api/deep_research/graph.py b/langgraph_plugin/graph_api/deep_research/graph.py
new file mode 100644
index 00000000..34b91c73
--- /dev/null
+++ b/langgraph_plugin/graph_api/deep_research/graph.py
@@ -0,0 +1,340 @@
+"""Deep Research Agent Graph Definition.
+
+This module implements a multi-step research agent that performs iterative
+research to produce comprehensive reports. It demonstrates:
+- Parallel search execution using LangGraph's Send API
+- Real web search using DuckDuckGo
+- Result evaluation and iteration
+- Long-running research workflows (showcasing Temporal's durability)
+- Report synthesis from multiple sources
+
+The research flow:
+1. plan_research - Analyze the topic and generate search queries
+2. search (parallel) - Execute multiple web searches concurrently
+3. evaluate_results - Grade search results for relevance
+4. decide_next - Either continue researching or synthesize
+5. synthesize - Produce final research report
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+
+Prerequisites:
+    - OPENAI_API_KEY environment variable set
+"""
+
+import os
+from datetime import timedelta
+from typing import Annotated, Any, Literal, cast
+
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.constants import Send
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+
+class SearchQuery(BaseModel):
+    """A search query to execute."""
+
+    query: str = Field(description="The search query to execute")
+    purpose: str = Field(description="What information this query aims to find")
+
+
+class ResearchPlan(BaseModel):
+    """Research plan with multiple search queries."""
+
+    queries: list[SearchQuery] = Field(
+        description="List of search queries to execute", min_length=1, max_length=5
+    )
+
+
+class SearchResult(TypedDict):
+    """Result from a single search."""
+
+    query: str
+    purpose: str
+    results: str
+    relevant: bool
+
+
+class ResearchState(TypedDict):
+    """State for the deep research graph.
+
+    Attributes:
+        messages: Conversation history with the research question.
+        topic: The main research topic extracted from the question.
+        search_queries: Planned search queries from the planner.
+        search_results: Results from executed searches.
+        iteration: Current research iteration (for limiting loops).
+        max_iterations: Maximum number of research iterations.
+    """
+
+    messages: Annotated[list[BaseMessage], add_messages]
+    topic: str
+    search_queries: list[SearchQuery]
+    search_results: Annotated[list[SearchResult], lambda x, y: x + y]
+    iteration: int
+    max_iterations: int
+
+
+class SearchTaskState(TypedDict):
+    """State for individual search tasks (used with Send)."""
+
+    query: str
+    purpose: str
+
+
+# Initialize DuckDuckGo search tool
+search_tool = DuckDuckGoSearchRun()
+
+
+def build_deep_research_graph() -> Any:
+    """Build a deep research agent graph.
+
+    The graph implements a research workflow that:
+    1. Plans research by generating targeted search queries
+    2. Executes searches in parallel using Send
+    3. Evaluates results for relevance
+    4. Iterates if more research is needed
+    5. Synthesizes findings into a final report
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    def plan_research(state: ResearchState) -> dict[str, Any]:
+        """Plan the research by generating search queries.
+
+        Analyzes the research topic and creates a set of targeted
+        queries to gather comprehensive information.
+        """
+        messages = state["messages"]
+        topic = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)),
+            "AI agents",
+        )
+        topic_str = str(topic)
+
+        plan_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a research planner. Given a research topic, generate 2-4 search
+queries that will help gather comprehensive information. Each query should
+target a different aspect of the topic.
+
+Consider:
+- Core concepts and definitions
+- Key features and capabilities
+- Use cases and applications
+- Comparisons or alternatives""",
+                ),
+                ("human", "Research topic: {topic}"),
+            ]
+        )
+
+        planner = plan_prompt | model.with_structured_output(ResearchPlan)
+        plan = cast(ResearchPlan, planner.invoke({"topic": topic_str}))
+
+        return {
+            "topic": topic_str,
+            "search_queries": plan.queries,
+            "iteration": state.get("iteration", 0) + 1,
+            "max_iterations": state.get("max_iterations", 2),
+        }
+
+    def execute_search(state: SearchTaskState) -> dict[str, Any]:
+        """Execute a single web search query using DuckDuckGo.
+
+        Performs real web search and returns the results.
+        Each search runs as a separate Temporal activity for durability.
+        """
+        query = state["query"]
+        purpose = state["purpose"]
+
+        try:
+            # Execute real web search
+            results = search_tool.invoke(query)
+            has_results = bool(results and len(results.strip()) > 0)
+        except Exception as e:
+            results = f"Search failed: {e}"
+            has_results = False
+
+        return {
+            "search_results": [
+                {
+                    "query": query,
+                    "purpose": purpose,
+                    "results": results,
+                    "relevant": has_results,
+                }
+            ]
+        }
+
+    def fan_out_searches(
+        state: ResearchState,
+    ) -> list[Send]:
+        """Fan out to parallel search executions.
+
+        Creates a Send for each search query, enabling parallel execution
+        of searches as separate Temporal activities.
+        """
+        sends = []
+        for query in state.get("search_queries", []):
+            sends.append(
+                Send(
+                    "search",
+                    {"query": query.query, "purpose": query.purpose},
+                )
+            )
+        return sends
+
+    def evaluate_results(state: ResearchState) -> dict[str, Any]:
+        """Evaluate search results for coverage and quality.
+
+        Determines if we have enough relevant information to synthesize
+        a comprehensive report, or if more research is needed.
+        """
+        results = state.get("search_results", [])
+        relevant_count = sum(1 for r in results if r.get("relevant", False))
+
+        # Simple evaluation: check if we have enough relevant results
+        coverage = relevant_count / max(len(results), 1)
+
+        return {
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Research iteration {state['iteration']}: "
+                    f"Found {relevant_count}/{len(results)} relevant results. "
+                    f"Coverage: {coverage:.0%}",
+                }
+            ]
+        }
+
+    def should_continue(state: ResearchState) -> Literal["synthesize", "plan"]:
+        """Decide whether to continue researching or synthesize.
+
+        Routes to synthesis if:
+        - We have reached max iterations
+        - We have sufficient relevant results
+
+        Otherwise, continues with another research iteration.
+        """
+        iteration = state.get("iteration", 1)
+        max_iterations = state.get("max_iterations", 2)
+        results = state.get("search_results", [])
+        relevant_count = sum(1 for r in results if r.get("relevant", False))
+
+        # Continue if under max iterations and low coverage
+        if iteration < max_iterations and relevant_count < 2:
+            return "plan"
+
+        return "synthesize"
+
+    def synthesize_report(state: ResearchState) -> dict[str, Any]:
+        """Synthesize research findings into a comprehensive report.
+
+        Combines all relevant search results into a well-structured
+        research report that answers the original question.
+        """
+        topic = state.get("topic", "the research topic")
+        results = state.get("search_results", [])
+
+        # Compile all findings
+        findings = []
+        for result in results:
+            if result.get("relevant", False):
+                findings.append(f"### {result['purpose']}\n{result['results']}")
+
+        if not findings:
+            findings_text = "Limited information was found on this topic."
+        else:
+            findings_text = "\n\n".join(findings)
+
+        synthesis_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a research synthesizer. Create a comprehensive research report
+from the gathered findings. Structure the report with:
+1. Executive Summary - Key takeaways in 2-3 sentences
+2. Main Findings - Detailed information organized by topic
+3. Conclusions - Synthesis of the research
+
+Be thorough but concise. Cite the source topics when presenting findings.""",
+                ),
+                (
+                    "human",
+                    "Research Topic: {topic}\n\nGathered Findings:\n{findings}",
+                ),
+            ]
+        )
+
+        chain = synthesis_prompt | model | StrOutputParser()
+        report = chain.invoke({"topic": topic, "findings": findings_text})
+
+        return {"messages": [{"role": "assistant", "content": report}]}
+
+    # Build the research graph
+    workflow = StateGraph(ResearchState)
+
+    # Add nodes with activity options
+    workflow.add_node(
+        "plan",
+        plan_research,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    workflow.add_node(
+        "search",
+        execute_search,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=1),
+        ),
+    )
+    workflow.add_node(
+        "evaluate",
+        evaluate_results,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+    workflow.add_node(
+        "synthesize",
+        synthesize_report,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=3),
+        ),
+    )
+
+    # Add edges
+    workflow.add_edge(START, "plan")
+
+    # Fan out from plan to parallel searches
+    workflow.add_conditional_edges("plan", fan_out_searches, ["search"])
+
+    # All searches converge at evaluate
+    workflow.add_edge("search", "evaluate")
+
+    # Decide whether to continue or synthesize
+    workflow.add_conditional_edges(
+        "evaluate",
+        should_continue,
+        {"synthesize": "synthesize", "plan": "plan"},
+    )
+
+    workflow.add_edge("synthesize", END)
+
+    return workflow.compile()
diff --git a/langgraph_plugin/graph_api/deep_research/run_worker.py b/langgraph_plugin/graph_api/deep_research/run_worker.py
new file mode 100644
index 00000000..854a7388
--- /dev/null
+++ b/langgraph_plugin/graph_api/deep_research/run_worker.py
@@ -0,0 +1,46 @@
+"""Worker for the Deep Research Agent sample.
+
+Starts a Temporal worker that can execute DeepResearchWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.deep_research.graph import build_deep_research_graph
+from langgraph_plugin.graph_api.deep_research.workflow import DeepResearchWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the deep research graph registered
+    plugin = LangGraphPlugin(
+        graphs={"deep_research": build_deep_research_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-deep-research",
+        workflows=[DeepResearchWorkflow],
+    )
+
+    print("Deep Research Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/deep_research/run_workflow.py b/langgraph_plugin/graph_api/deep_research/run_workflow.py
new file mode 100644
index 00000000..e5b6a91c
--- /dev/null
+++ b/langgraph_plugin/graph_api/deep_research/run_workflow.py
@@ -0,0 +1,37 @@
+"""Execute the Deep Research workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.deep_research.workflow import DeepResearchWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Research topic that will trigger multiple search queries
+    # The agent will:
+    # 1. Plan queries about LangGraph, Temporal, durable execution
+    # 2. Execute searches in parallel
+    # 3. Evaluate results and possibly iterate
+    # 4. Synthesize a comprehensive research report
+    result = await client.execute_workflow(
+        DeepResearchWorkflow.run,
+        "How do LangGraph and Temporal work together for durable AI agents?",
+        id="deep-research-workflow",
+        task_queue="langgraph-deep-research",
+    )
+
+    # Print the research report (last message)
+    print("\n" + "=" * 60)
+    print("RESEARCH REPORT")
+    print("=" * 60 + "\n")
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/deep_research/workflow.py b/langgraph_plugin/graph_api/deep_research/workflow.py
new file mode 100644
index 00000000..2f86a576
--- /dev/null
+++ b/langgraph_plugin/graph_api/deep_research/workflow.py
@@ -0,0 +1,56 @@
+"""Deep Research Agent Workflow.
+
+Temporal workflow that executes the deep research agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class DeepResearchWorkflow:
+    """Temporal workflow that executes deep research with durable execution.
+
+    This workflow performs multi-step research on a topic:
+    1. Plans research queries
+    2. Executes parallel searches
+    3. Evaluates and iterates if needed
+    4. Synthesizes a comprehensive report
+
+    The Temporal integration ensures:
+    - Each search runs as a separate activity (parallel execution)
+    - Research survives worker crashes
+    - Long-running research (minutes to hours) completes reliably
+    - Progress is visible through workflow history
+    """
+
+    @workflow.run
+    async def run(self, topic: str, max_iterations: int = 2) -> dict[str, Any]:
+        """Run deep research on a topic.
+
+        Args:
+            topic: The research topic or question.
+            max_iterations: Maximum research iterations (default 2).
+
+        Returns:
+            The final state containing the research report.
+        """
+        app = compile("deep_research")
+
+        result = await app.ainvoke(
+            {
+                "messages": [{"role": "user", "content": topic}],
+                "search_results": [],
+                "iteration": 0,
+                "max_iterations": max_iterations,
+            }
+        )
+
+        return result
diff --git a/langgraph_plugin/graph_api/hello_world/README.md b/langgraph_plugin/graph_api/hello_world/README.md
new file mode 100644
index 00000000..b1b126b9
--- /dev/null
+++ b/langgraph_plugin/graph_api/hello_world/README.md
@@ -0,0 +1,36 @@
+# Hello World LangGraph Agent
+
+Minimal LangGraph agent with Temporal integration. A single-node graph that processes a query.
+
+## What This Sample Demonstrates
+
+- **Basic plugin setup**: Registering a LangGraph graph with `LangGraphPlugin`
+- **Graph registration**: Creating a graph builder function and registering it by name
+- **Simple workflow invocation**: Using `compile()` to get a graph runner within a workflow
+- **Activity-based node execution**: Each graph node runs as a Temporal activity
+
+## How It Works
+
+1. **State Definition**: A simple `TypedDict` defines the workflow state with `query` and `result` fields
+2. **Node Function**: A single node processes the query and returns a result
+3. **Graph Builder**: Creates and compiles a minimal StateGraph with one node
+4. **Workflow**: Uses `compile("graph_name")` to get a runner and `ainvoke()` to execute
+5. **Plugin**: Registers the graph builder so it's available to workflows
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_plugin/graph_api/hello_world/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_plugin/graph_api/hello_world/run_workflow.py
+```
+
+## Expected Output
+
+```
+Result: {'query': 'Hello, Temporal!', 'result': 'Processed: Hello, Temporal!'}
+```
diff --git a/langgraph_plugin/graph_api/hello_world/__init__.py b/langgraph_plugin/graph_api/hello_world/__init__.py
new file mode 100644
index 00000000..988bb5c8
--- /dev/null
+++ b/langgraph_plugin/graph_api/hello_world/__init__.py
@@ -0,0 +1 @@
+# Hello World LangGraph Example
diff --git a/langgraph_plugin/graph_api/hello_world/graph.py b/langgraph_plugin/graph_api/hello_world/graph.py
new file mode 100644
index 00000000..4db5a275
--- /dev/null
+++ b/langgraph_plugin/graph_api/hello_world/graph.py
@@ -0,0 +1,67 @@
+"""Hello World LangGraph Graph Definition.
+
+This module defines the graph structure and node functions.
+It is imported only by the worker (not by the workflow).
+"""
+
+from datetime import timedelta
+from typing import Any
+
+from langgraph.graph import END, START, StateGraph
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+# =============================================================================
+# State Definition
+# =============================================================================
+
+
+class HelloState(TypedDict, total=False):
+    """Simple state for the hello world agent."""
+
+    query: str
+    result: str
+
+
+# =============================================================================
+# Node Functions
+# =============================================================================
+
+
+def process_query(state: HelloState) -> HelloState:
+    """Process the query and return a result.
+
+    In a real application, this could call an LLM, database, or external API.
+    Each node function runs as a Temporal activity with automatic retries.
+    """
+    query = state.get("query", "")
+    return {"result": f"Processed: {query}"}
+
+
+# =============================================================================
+# Graph Builder
+# =============================================================================
+
+
+def build_hello_graph() -> Any:
+    """Build a minimal single-node graph.
+
+    This function is registered with LangGraphPlugin and called to create
+    the compiled graph when needed.
+    """
+    graph = StateGraph(HelloState)
+
+    # Add a single processing node with activity options
+    graph.add_node(
+        "process",
+        process_query,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Define edges: START -> process -> END
+    graph.add_edge(START, "process")
+    graph.add_edge("process", END)
+
+    return graph.compile()
diff --git a/langgraph_plugin/graph_api/hello_world/run_worker.py b/langgraph_plugin/graph_api/hello_world/run_worker.py
new file mode 100644
index 00000000..15c8430b
--- /dev/null
+++ b/langgraph_plugin/graph_api/hello_world/run_worker.py
@@ -0,0 +1,42 @@
+"""Worker for the Hello World LangGraph example.
+
+Starts a Temporal worker that can execute HelloWorldWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.hello_world.graph import build_hello_graph
+from langgraph_plugin.graph_api.hello_world.workflow import HelloWorldWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with our graph registered by name
+    plugin = LangGraphPlugin(
+        graphs={"hello_graph": build_hello_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities are automatically registered by the plugin
+    worker = Worker(
+        client,
+        task_queue="langgraph-hello-world",
+        workflows=[HelloWorldWorkflow],
+    )
+
+    print("Worker started. Ctrl+C to exit.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/hello_world/run_workflow.py b/langgraph_plugin/graph_api/hello_world/run_workflow.py
new file mode 100644
index 00000000..e6c4c4cc
--- /dev/null
+++ b/langgraph_plugin/graph_api/hello_world/run_workflow.py
@@ -0,0 +1,32 @@
+"""Execute the Hello World LangGraph workflow.
+
+Connects to Temporal and starts the HelloWorldWorkflow.
+Note: The graph is only needed by the worker, not by the workflow starter.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.hello_world.workflow import HelloWorldWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Execute the workflow
+    result = await client.execute_workflow(
+        HelloWorldWorkflow.run,
+        "Hello, Temporal!",
+        id="hello-world-workflow",
+        task_queue="langgraph-hello-world",
+    )
+
+    print(f"Result: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/hello_world/workflow.py b/langgraph_plugin/graph_api/hello_world/workflow.py
new file mode 100644
index 00000000..cc8b1902
--- /dev/null
+++ b/langgraph_plugin/graph_api/hello_world/workflow.py
@@ -0,0 +1,43 @@
+"""Hello World LangGraph Workflow.
+
+Minimal example demonstrating LangGraph with Temporal integration.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because langgraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class HelloWorldWorkflow:
+    """Temporal workflow that executes the hello world LangGraph agent.
+
+    This workflow demonstrates:
+    - Using compile() to get a graph runner by name
+    - Executing the graph with ainvoke()
+    - Each node runs as a Temporal activity with durability guarantees
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the hello world agent.
+
+        Args:
+            query: The input query to process.
+
+        Returns:
+            The final state containing the processed result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("hello_graph")
+
+        # Execute the graph - the "process" node runs as a Temporal activity
+        result = await app.ainvoke({"query": query})
+
+        return result
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/README.md b/langgraph_plugin/graph_api/human_in_the_loop/README.md
new file mode 100644
index 00000000..f8d0dd1e
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/README.md
@@ -0,0 +1,72 @@
+# Human-in-the-Loop Samples
+
+Two approaches to implementing human-in-the-loop approval workflows with LangGraph and Temporal.
+
+## Samples
+
+### [approval_graph_interrupt](./approval_graph_interrupt/)
+
+Uses LangGraph's `interrupt()` function to pause graph execution.
+
+- Graph calls `interrupt(request_data)` to pause
+- Workflow detects `__interrupt__` in result, waits for signal
+- Workflow resumes graph with `Command(resume=response)`
+
+**Best for:** Standard LangGraph patterns, portable graph definitions.
+
+### [approval_wait_condition](./approval_wait_condition/)
+
+Uses `run_in_workflow=True` to access Temporal operations directly in graph nodes.
+
+- Node marked with `metadata={"run_in_workflow": True}`
+- Node uses `workflow.instance()` to access workflow
+- Node waits directly with `workflow.wait_condition()`
+
+**Best for:** Keeping all wait logic encapsulated in the graph, simpler workflow code.
+
+## Quick Comparison
+
+| Aspect | graph_interrupt | wait_condition |
+|--------|-----------------|----------------|
+| Wait logic location | Workflow | Graph node |
+| Graph portability | Higher | Lower |
+| Workflow complexity | More code | Less code |
+| Temporal API access | Indirect | Direct |
+
+## Available Queries
+
+Both samples expose these queries for monitoring workflow progress:
+
+```bash
+# Get workflow status
+temporal workflow query --workflow-id <id> --type get_status
+# Returns: "processing" | "waiting_for_approval" | "approved" | "rejected"
+
+# Get pending approval details
+temporal workflow query --workflow-id <id> --type get_pending_approval
+
+# Get ASCII diagram of graph execution progress
+temporal workflow query --workflow-id <id> --type get_graph_ascii
+# Output:
+# ┌───────────────────┐
+# │       START       │ ✓
+# └─────────┬─────────┘
+#           │
+#           ▼
+# ┌───────────────────┐
+# │  request_approval │ ▶ INTERRUPTED
+# └─────────┬─────────┘
+#           │
+#           ▼
+# ┌───────────────────┐
+# │        END        │ ○
+# └───────────────────┘
+# Legend: ✓ completed  ▶ current/interrupted  ○ pending
+
+# Get Mermaid diagram (renders in GitHub, Notion, etc.)
+temporal workflow query --workflow-id <id> --type get_graph_mermaid
+
+# Get full typed state
+temporal workflow query --workflow-id <id> --type get_graph_state
+# Returns: { values: ApprovalState, next: [...], step: N, interrupted: bool, ... }
+```
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/__init__.py b/langgraph_plugin/graph_api/human_in_the_loop/__init__.py
new file mode 100644
index 00000000..ec207c4e
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/__init__.py
@@ -0,0 +1,8 @@
+"""Human-in-the-Loop Samples.
+
+This directory contains samples demonstrating different approaches
+to implementing human-in-the-loop patterns with LangGraph and Temporal:
+
+- approval_graph_interrupt: Uses LangGraph's interrupt() function
+- approval_wait_condition: Uses run_in_workflow=True with workflow.wait_condition()
+"""
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/README.md b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/README.md
new file mode 100644
index 00000000..a78b8793
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/README.md
@@ -0,0 +1,101 @@
+# Human-in-the-Loop Approval Workflow
+
+A workflow demonstrating human-in-the-loop approval using LangGraph's `interrupt()` function with Temporal signals, queries, and notification activities.
+
+## What This Sample Demonstrates
+
+- **LangGraph interrupt**: Using `interrupt()` to pause graph execution for human input
+- **Notification activity**: Alerting approvers when approval is needed
+- **Temporal signals**: Receiving approval/rejection decisions from external systems
+- **Temporal queries**: Checking pending approval status and workflow state
+- **CLI response tool**: Separate script for approvers to respond to requests
+- **Timeout handling**: Optional approval deadlines with auto-rejection
+
+## How It Works
+
+1. **process_request**: Validates the request and determines risk level based on amount
+2. **request_approval**: Calls `interrupt()` to pause execution and wait for human input
+3. **notify_approver** (activity): Sends notification with instructions on how to respond
+4. **execute_action**: Processes the approved request or handles rejection
+
+The workflow flow:
+```
+Request → [Process & Assess Risk] → [Interrupt] → [Notify Approver] → [Wait for Signal] → [Execute or Reject] → Result
+```
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+
+## Running the Example
+
+**Terminal 1 - Start the worker:**
+```bash
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_worker.py
+```
+
+**Terminal 2 - Start a workflow:**
+```bash
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_workflow.py
+```
+
+The worker will print notification instructions like:
+```
+*** APPROVAL NEEDED ***
+Workflow ID: approval-abc12345
+Request: Please approve purchase for $500.00 (Risk: medium)
+
+To respond, run:
+  Approve: uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py approval-abc12345 --approve --reason 'Your reason'
+  Reject:  uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py approval-abc12345 --reject --reason 'Your reason'
+```
+
+**Terminal 3 - Respond to the approval request:**
+```bash
+# Check status
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py approval-abc12345 --status
+
+# Approve
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py approval-abc12345 --approve --reason "Within budget"
+
+# Or reject
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py approval-abc12345 --reject --reason "Needs manager approval"
+```
+
+## Response Script Options
+
+```
+usage: run_respond.py [-h] [--approve] [--reject] [--status] [--reason REASON] [--approver APPROVER] workflow_id
+
+positional arguments:
+  workflow_id          The workflow ID to respond to
+
+options:
+  --approve            Approve the request
+  --reject             Reject the request
+  --status             Check workflow status
+  --reason REASON      Reason for approval/rejection
+  --approver APPROVER  Approver identifier (default: cli-user)
+```
+
+## Expected Output
+
+**Workflow starter (Terminal 2):**
+```
+Starting approval workflow: approval-abc12345
+Workflow started. Waiting for result...
+
+To approve/reject, use the run_respond script (see worker output for commands)
+
+============================================================
+Result: Successfully processed purchase for $500.00. Approved by cli-user: Within budget
+Executed: True
+```
+
+## Key APIs Used
+
+- `interrupt(value)`: Pauses graph execution and returns `value` in `__interrupt__`
+- `Command(resume=response)`: Resumes graph execution with the given response
+- `@activity.defn`: Notification activity to alert approvers
+- `@workflow.signal`: Receives external input (approval response)
+- `@workflow.query`: Exposes workflow state (pending approval, status)
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/__init__.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/__init__.py
new file mode 100644
index 00000000..17c0c106
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/__init__.py
@@ -0,0 +1,11 @@
+"""Approval Workflow Sample.
+
+Demonstrates human-in-the-loop approval pattern using LangGraph's interrupt()
+with Temporal signals for receiving human input.
+
+Components:
+- graph.py: LangGraph graph with approval node using interrupt()
+- workflow.py: Temporal workflow that handles interrupts and signals
+- run_worker.py: Worker setup with LangGraphPlugin
+- run_workflow.py: Client that starts workflow and sends approval signal
+"""
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/activities.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/activities.py
new file mode 100644
index 00000000..f3af3ab4
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/activities.py
@@ -0,0 +1,47 @@
+"""Activities for the approval workflow."""
+
+from temporalio import activity
+
+
+@activity.defn
+async def notify_approver(request_info: dict) -> str:
+    """Notify the approver about a pending approval request.
+
+    In a real implementation, this could:
+    - Send an email
+    - Post to Slack
+    - Create a ticket in a ticketing system
+    - Send a push notification
+
+    Args:
+        request_info: Information about the approval request.
+
+    Returns:
+        Confirmation message.
+    """
+    workflow_id = activity.info().workflow_id
+    message = request_info.get("message", "Approval needed")
+
+    # Log notification (simulating sending notification)
+    activity.logger.info(
+        f"NOTIFICATION: {message}\n"
+        f"  Workflow ID: {workflow_id}\n"
+        f"  To respond, run:\n"
+        f"    python -m langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.run_respond {workflow_id} --approve --reason 'Approved'\n"
+        f"    python -m langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.run_respond {workflow_id} --reject --reason 'Rejected'"
+    )
+
+    # In production, you would send actual notification here
+    print("\n*** APPROVAL NEEDED ***")
+    print(f"Workflow ID: {workflow_id}")
+    print(f"Request: {message}")
+    print("\nTo respond, run:")
+    print(
+        f"  Approve: uv run python -m langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.run_respond {workflow_id} --approve --reason 'Your reason'"
+    )
+    print(
+        f"  Reject:  uv run python -m langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.run_respond {workflow_id} --reject --reason 'Your reason'"
+    )
+    print()
+
+    return f"Notification sent for workflow {workflow_id}"
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/graph.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/graph.py
new file mode 100644
index 00000000..c61254f4
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/graph.py
@@ -0,0 +1,148 @@
+"""Approval Workflow Graph Definition.
+
+This module builds a graph that demonstrates human-in-the-loop approval
+using LangGraph's interrupt() function.
+
+The graph flow:
+1. process_request: Validates and prepares the request
+2. request_approval: Calls interrupt() to pause for human approval
+3. execute_action: Processes the approved request (or rejects it)
+"""
+
+from datetime import timedelta
+from typing import Any
+
+from langgraph.graph import END, START, StateGraph
+from langgraph.types import interrupt
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+
+class ApprovalState(TypedDict, total=False):
+    """State for the approval workflow."""
+
+    # Input
+    request_type: str
+    request_data: dict[str, Any]
+    amount: float
+
+    # Processing state
+    validated: bool
+    risk_level: str
+
+    # Approval state
+    approved: bool
+    approval_reason: str
+    approver: str
+
+    # Output
+    result: str
+    executed: bool
+
+
+def process_request(state: ApprovalState) -> ApprovalState:
+    """Validate and assess the request before approval.
+
+    This node analyzes the request and determines risk level.
+    """
+    amount = state.get("amount", 0)
+
+    # Determine risk level based on amount
+    if amount < 100:
+        risk_level = "low"
+    elif amount < 1000:
+        risk_level = "medium"
+    else:
+        risk_level = "high"
+
+    return {
+        "validated": True,
+        "risk_level": risk_level,
+    }
+
+
+def request_approval(state: ApprovalState) -> ApprovalState:
+    """Request human approval via interrupt.
+
+    This node pauses execution and waits for human input.
+    The interrupt() call returns the human's response when resumed.
+    """
+    # Create the approval request with context for the human
+    approval_request = {
+        "request_type": state.get("request_type", "unknown"),
+        "amount": state.get("amount", 0),
+        "risk_level": state.get("risk_level", "unknown"),
+        "request_data": state.get("request_data", {}),
+        "message": f"Please approve {state.get('request_type', 'request')} "
+        f"for ${state.get('amount', 0):.2f} (Risk: {state.get('risk_level', 'unknown')})",
+    }
+
+    # This pauses the graph and returns control to the workflow.
+    # The workflow will wait for a signal with the human's response.
+    # When resumed with Command(resume=response), interrupt() returns that response.
+    approval_response = interrupt(approval_request)
+
+    return {
+        "approved": approval_response.get("approved", False),
+        "approval_reason": approval_response.get("reason", ""),
+        "approver": approval_response.get("approver", "unknown"),
+    }
+
+
+def execute_action(state: ApprovalState) -> ApprovalState:
+    """Execute or reject the action based on approval status."""
+    if state.get("approved"):
+        return {
+            "executed": True,
+            "result": f"Successfully processed {state.get('request_type', 'request')} "
+            f"for ${state.get('amount', 0):.2f}. "
+            f"Approved by {state.get('approver', 'unknown')}: {state.get('approval_reason', '')}",
+        }
+    else:
+        return {
+            "executed": False,
+            "result": f"Request rejected by {state.get('approver', 'unknown')}: "
+            f"{state.get('approval_reason', 'No reason provided')}",
+        }
+
+
+def build_approval_graph() -> Any:
+    """Build the approval workflow graph.
+
+    Flow:
+    START -> process_request -> request_approval -> execute_action -> END
+
+    The request_approval node uses interrupt() to pause for human input.
+    """
+    graph = StateGraph(ApprovalState)
+
+    # Add nodes with activity options
+    graph.add_node(
+        "process_request",
+        process_request,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+    graph.add_node(
+        "request_approval",
+        request_approval,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+    graph.add_node(
+        "execute_action",
+        execute_action,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Define edges
+    graph.add_edge(START, "process_request")
+    graph.add_edge("process_request", "request_approval")
+    graph.add_edge("request_approval", "execute_action")
+    graph.add_edge("execute_action", END)
+
+    return graph.compile()
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py
new file mode 100644
index 00000000..674aa2e7
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_respond.py
@@ -0,0 +1,78 @@
+"""Respond to an approval request.
+
+This script allows an approver to approve or reject a pending approval workflow.
+"""
+
+import argparse
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.workflow import (
+    ApprovalWorkflow,
+)
+
+
+async def main() -> None:
+    parser = argparse.ArgumentParser(description="Respond to an approval workflow")
+    parser.add_argument("workflow_id", help="The workflow ID to respond to")
+    parser.add_argument("--approve", action="store_true", help="Approve the request")
+    parser.add_argument("--reject", action="store_true", help="Reject the request")
+    parser.add_argument("--status", action="store_true", help="Check workflow status")
+    parser.add_argument("--reason", default="", help="Reason for approval/rejection")
+    parser.add_argument(
+        "--approver", default="cli-user", help="Approver identifier (default: cli-user)"
+    )
+
+    args = parser.parse_args()
+
+    # Validate arguments
+    if not args.status and not args.approve and not args.reject:
+        parser.error("Must specify --approve, --reject, or --status")
+    if args.approve and args.reject:
+        parser.error("Cannot specify both --approve and --reject")
+
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Get workflow handle
+    handle = client.get_workflow_handle(args.workflow_id)
+
+    if args.status:
+        # Query the workflow status
+        try:
+            status = await handle.query(ApprovalWorkflow.get_status)
+            pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+            print(f"Workflow ID: {args.workflow_id}")
+            print(f"Status: {status}")
+            if pending:
+                print(f"Pending approval: {pending.get('message', 'No message')}")
+                print(f"  Request type: {pending.get('request_type')}")
+                print(f"  Amount: ${pending.get('amount', 0):.2f}")
+                print(f"  Risk level: {pending.get('risk_level')}")
+        except Exception as e:
+            print(f"Error querying workflow: {e}")
+    else:
+        # Send approval/rejection signal
+        approved = args.approve
+        response = {
+            "approved": approved,
+            "reason": args.reason,
+            "approver": args.approver,
+        }
+
+        try:
+            await handle.signal(ApprovalWorkflow.provide_approval, response)
+            action = "Approved" if approved else "Rejected"
+            print(f"{action} workflow {args.workflow_id}")
+            if args.reason:
+                print(f"Reason: {args.reason}")
+        except Exception as e:
+            print(f"Error sending signal: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_worker.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_worker.py
new file mode 100644
index 00000000..6d85a09b
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_worker.py
@@ -0,0 +1,56 @@
+"""Run the Approval Workflow worker.
+
+Starts a Temporal worker that can execute the approval workflow.
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin, activity_options
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.activities import (
+    notify_approver,
+)
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.graph import (
+    build_approval_graph,
+)
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.workflow import (
+    ApprovalWorkflow,
+)
+
+TASK_QUEUE = "langgraph-approval-interrupt"
+
+
+async def main() -> None:
+    # Create the LangGraph plugin with the approval graph
+    plugin = LangGraphPlugin(
+        graphs={"approval_workflow": build_approval_graph},
+        default_activity_options=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run worker
+    worker = Worker(
+        client,
+        task_queue=TASK_QUEUE,
+        workflows=[ApprovalWorkflow],
+        activities=[notify_approver],
+    )
+
+    print(f"Starting approval workflow worker on task queue: {TASK_QUEUE}")
+    print("Press Ctrl+C to stop")
+
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_workflow.py
new file mode 100644
index 00000000..2b8cdb81
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/run_workflow.py
@@ -0,0 +1,56 @@
+"""Execute the Approval Workflow.
+
+Starts an approval workflow that pauses for human approval.
+The worker will print instructions for how to approve/reject.
+"""
+
+import asyncio
+import uuid
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.workflow import (
+    ApprovalRequest,
+    ApprovalWorkflow,
+)
+
+TASK_QUEUE = "langgraph-approval-interrupt"
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Generate a unique workflow ID
+    workflow_id = f"approval-{uuid.uuid4().hex[:8]}"
+
+    print(f"Starting approval workflow: {workflow_id}")
+
+    handle = await client.start_workflow(
+        ApprovalWorkflow.run,
+        ApprovalRequest(
+            request_type="purchase",
+            amount=500.00,
+            request_data={"item": "Office supplies", "vendor": "Acme Corp"},
+        ),
+        id=workflow_id,
+        task_queue=TASK_QUEUE,
+    )
+
+    print("Workflow started. Waiting for result...")
+    print(
+        "\nTo approve/reject, use the run_respond script (see worker output for commands)"
+    )
+
+    # Wait for the workflow to complete
+    result = await handle.result()
+
+    print(f"\n{'='*60}")
+    print(f"Result: {result.get('result')}")
+    print(f"Executed: {result.get('executed')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/workflow.py
new file mode 100644
index 00000000..f2785258
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_graph_interrupt/workflow.py
@@ -0,0 +1,213 @@
+"""Approval Workflow Definition.
+
+This workflow demonstrates human-in-the-loop approval using:
+- LangGraph's interrupt() for pausing execution
+- Temporal signals for receiving human input
+- Temporal queries for checking pending approvals
+- Notification activity to alert approvers
+- Timeout handling for approval deadlines
+"""
+
+from dataclasses import dataclass
+from datetime import timedelta
+from typing import Any, cast
+
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    from langgraph.types import Command
+    from temporalio.contrib.langgraph import compile as lg_compile
+    from temporalio.contrib.langgraph import TemporalLangGraphRunner, TemporalFunctionalRunner
+
+    from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.activities import (
+        notify_approver,
+    )
+    from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.graph import (
+        ApprovalState,
+    )
+
+
+@dataclass
+class ApprovalRequest:
+    """Input for the approval workflow."""
+
+    request_type: str
+    amount: float
+    request_data: dict[str, Any] | None = None
+
+
+@dataclass
+class GraphStateResponse:
+    """Response from get_graph_state query."""
+
+    values: ApprovalState
+    """Current state values from the graph."""
+
+    next: list[str]
+    """Next node(s) to execute."""
+
+    step: int
+    """Current execution step count."""
+
+    interrupted: bool
+    """Whether the graph is currently interrupted."""
+
+    interrupt_node: str | None
+    """Node that triggered the interrupt, if any."""
+
+    interrupt_value: dict[str, Any] | None
+    """Value passed to interrupt(), if any."""
+
+
+@workflow.defn
+class ApprovalWorkflow:
+    """Workflow that pauses for human approval before executing actions.
+
+    This demonstrates the full interrupt flow:
+    1. Graph runs until interrupt() is called in request_approval node
+    2. Workflow receives __interrupt__ in result with approval request details
+    3. Workflow waits for signal with human input
+    4. Workflow resumes graph with Command(resume=response)
+    5. Graph completes with execute_action node
+    """
+
+    def __init__(self) -> None:
+        self._approval_response: dict[str, Any] | None = None
+        self._interrupt_value: dict[str, Any] | None = None
+        self._app = lg_compile("approval_workflow") # Store runner for visualization queries
+
+    @workflow.signal
+    def provide_approval(self, response: dict[str, Any]) -> None:
+        """Signal to provide approval response.
+
+        Args:
+            response: Dict with 'approved' (bool), 'reason' (str), 'approver' (str)
+        """
+        self._approval_response = response
+
+    @workflow.query
+    def get_pending_approval(self) -> dict[str, Any] | None:
+        """Query to get the current pending approval request.
+
+        Returns:
+            The interrupt value containing approval request details, or None.
+        """
+        return self._interrupt_value
+
+    @workflow.query
+    def get_status(self) -> str:
+        """Query to get the current workflow status."""
+        if self._interrupt_value is None:
+            return "processing"
+        elif self._approval_response is None:
+            return "waiting_for_approval"
+        else:
+            return "approved" if self._approval_response.get("approved") else "rejected"
+
+    @workflow.query
+    def get_graph_ascii(self) -> str:
+        """Query to get ASCII art visualization of graph execution progress.
+
+        Returns an ASCII diagram showing which nodes have completed,
+        which is currently executing/interrupted, and which are pending.
+        """
+        return self._app.get_graph_ascii()
+
+    @workflow.query
+    def get_graph_mermaid(self) -> str:
+        """Query to get Mermaid diagram of graph execution progress.
+
+        Returns a Mermaid flowchart with nodes colored by status:
+        - Green: completed nodes
+        - Yellow: current/interrupted node
+        - Gray: pending nodes
+
+        Can be rendered in GitHub, Notion, or any Mermaid-compatible viewer.
+        """
+        return self._app.get_graph_mermaid()
+
+    @workflow.query
+    def get_graph_state(self) -> GraphStateResponse:
+        """Query to get the current graph execution state.
+
+        Returns a GraphStateResponse with typed ApprovalState values.
+        """
+        snapshot = self._app.get_state()
+        interrupt_task = snapshot.tasks[0] if snapshot.tasks else None
+        return GraphStateResponse(
+            values=cast(ApprovalState, snapshot.values),
+            next=list(snapshot.next),
+            step=snapshot.metadata.get("step", 0) if snapshot.metadata else 0,
+            interrupted=bool(snapshot.tasks),
+            interrupt_node=interrupt_task.get("interrupt_node")
+            if interrupt_task
+            else None,
+            interrupt_value=interrupt_task.get("interrupt_value")
+            if interrupt_task
+            else None,
+        )
+
+    @workflow.run
+    async def run(
+        self,
+        request: ApprovalRequest,
+    ) -> dict[str, Any]:
+        """Run the approval workflow.
+
+        Args:
+            request: The approval request details.
+            approval_timeout: Optional timeout for waiting for approval.
+                             If None, waits indefinitely.
+
+        Returns:
+            The final state containing result and executed status.
+        """
+        # Prepare initial state
+        initial_state = {
+            "request_type": request.request_type,
+            "amount": request.amount,
+            "request_data": request.request_data or {},
+        }
+
+        # First invocation - should hit interrupt at request_approval node
+        result = await self._app.ainvoke(initial_state)
+
+        # Check for interrupt
+        if "__interrupt__" in result:
+            # Store the interrupt value for queries
+            self._interrupt_value = result["__interrupt__"][0].value
+
+            workflow.logger.info(
+                "Workflow paused for approval: %s", self._interrupt_value.get("message")
+            )
+
+            # Notify the approver via activity
+            await workflow.execute_activity(
+                notify_approver,
+                self._interrupt_value,
+                start_to_close_timeout=timedelta(seconds=30),
+            )
+
+            # Wait for approval signal (with optional timeout)
+            try:
+                await workflow.wait_condition(lambda: self._approval_response is not None)
+            except TimeoutError:
+                # Timeout - auto-reject
+                workflow.logger.warning("Approval timeout - auto-rejecting")
+                self._approval_response = {
+                    "approved": False,
+                    "reason": "Approval timeout exceeded",
+                    "approver": "system",
+                }
+
+            workflow.logger.info(
+                "Received approval response: approved=%s",
+                self._approval_response.get("approved")
+                if self._approval_response
+                else None,
+            )
+
+            # Resume with the approval response
+            result = await self._app.ainvoke(Command(resume=self._approval_response))
+
+        return result
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/README.md b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/README.md
new file mode 100644
index 00000000..69027cf2
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/README.md
@@ -0,0 +1,116 @@
+# Human-in-the-Loop Approval Workflow (Condition-based)
+
+A workflow demonstrating human-in-the-loop approval using `run_in_workflow=True` with `workflow.wait_condition()` to wait for signals directly in graph nodes.
+
+## What This Sample Demonstrates
+
+- **run_in_workflow=True**: Using this metadata to run graph nodes inside the workflow
+- **workflow.instance()**: Accessing the workflow instance from graph nodes
+- **Temporal signals**: Receiving approval/rejection decisions directly in graph nodes
+- **Temporal queries**: Checking pending approval status and workflow state
+- **Notification activity**: Alerting approvers when approval is needed
+- **CLI response tool**: Separate script for approvers to respond to requests
+
+## How It Works
+
+1. **process_request**: Validates the request and determines risk level based on amount
+2. **request_approval** (`run_in_workflow=True`): Accesses Temporal operations directly:
+   - Calls notification activity
+   - Waits for approval signal using `workflow.wait_condition()`
+3. **execute_action**: Processes the approved request or handles rejection
+
+The workflow flow:
+```
+Request → [Process & Assess Risk] → [Wait for Signal in Node] → [Execute or Reject] → Result
+```
+
+## Comparison with interrupt() Approach
+
+This sample uses `run_in_workflow=True` instead of `interrupt()`:
+
+| Aspect | interrupt() | run_in_workflow=True |
+|--------|-------------|---------------------|
+| Signal handling | In workflow | In graph node |
+| Activity calls | In workflow | In graph node |
+| Workflow complexity | More complex | Simpler |
+| Graph encapsulation | Logic split | All logic in graph |
+
+Use `run_in_workflow=True` when you want to keep all the waiting and signaling logic within the graph itself.
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+
+## Running the Example
+
+**Terminal 1 - Start the worker:**
+```bash
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_worker.py
+```
+
+**Terminal 2 - Start a workflow:**
+```bash
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_workflow.py
+```
+
+The worker will print notification instructions like:
+```
+*** APPROVAL NEEDED ***
+Workflow ID: approval-condition-abc12345
+Request: Please approve purchase for $500.00 (Risk: medium)
+
+To respond, run:
+  Approve: uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py approval-condition-abc12345 --approve --reason 'Your reason'
+  Reject:  uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py approval-condition-abc12345 --reject --reason 'Your reason'
+```
+
+**Terminal 3 - Respond to the approval request:**
+```bash
+# Check status
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py approval-condition-abc12345 --status
+
+# Approve
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py approval-condition-abc12345 --approve --reason "Within budget"
+
+# Or reject
+uv run langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py approval-condition-abc12345 --reject --reason "Needs manager approval"
+```
+
+## Response Script Options
+
+```
+usage: run_respond.py [-h] [--approve] [--reject] [--status] [--reason REASON] [--approver APPROVER] workflow_id
+
+positional arguments:
+  workflow_id          The workflow ID to respond to
+
+options:
+  --approve            Approve the request
+  --reject             Reject the request
+  --status             Check workflow status
+  --reason REASON      Reason for approval/rejection
+  --approver APPROVER  Approver identifier (default: cli-user)
+```
+
+## Expected Output
+
+**Workflow starter (Terminal 2):**
+```
+Starting approval workflow: approval-condition-abc12345
+Workflow started. Waiting for result...
+
+To approve/reject, use the run_respond script (see worker output for commands)
+
+============================================================
+Result: Successfully processed purchase for $500.00. Approved by cli-user: Within budget
+Executed: True
+```
+
+## Key APIs Used
+
+- `run_in_workflow=True`: Metadata to run node inside workflow context
+- `workflow.instance()`: Get access to the current workflow instance
+- `workflow.wait_condition()`: Wait for signal in graph node
+- `workflow.execute_activity()`: Call activities from graph node
+- `@workflow.signal`: Receives external input (approval response)
+- `@workflow.query`: Exposes workflow state (pending approval, status)
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/__init__.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/__init__.py
new file mode 100644
index 00000000..1764b0ce
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/__init__.py
@@ -0,0 +1,12 @@
+"""Approval Workflow Sample (Condition-based).
+
+Demonstrates human-in-the-loop approval pattern using run_in_workflow=True
+with workflow.wait_condition() to wait for signals directly in graph nodes.
+
+Components:
+- graph.py: LangGraph graph with approval node using run_in_workflow=True
+- workflow.py: Temporal workflow definition
+- run_worker.py: Worker setup with LangGraphPlugin
+- run_workflow.py: Client that starts workflow
+- run_respond.py: Script to send approval signals
+"""
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/graph.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/graph.py
new file mode 100644
index 00000000..2c906adf
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/graph.py
@@ -0,0 +1,222 @@
+"""Approval Workflow Graph Definition (Condition-based).
+
+This module builds a graph that demonstrates human-in-the-loop approval
+using run_in_workflow=True with workflow.wait_condition().
+
+The graph flow:
+1. process_request: Validates and prepares the request
+2. request_approval: Uses run_in_workflow=True to wait for Temporal signal
+3. execute_action: Processes the approved request (or rejects it)
+"""
+
+from datetime import timedelta
+from typing import Any
+
+from langgraph.graph import END, START, StateGraph
+from temporalio import activity
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+
+class ApprovalState(TypedDict, total=False):
+    """State for the approval workflow."""
+
+    # Input
+    request_type: str
+    request_data: dict[str, Any]
+    amount: float
+
+    # Processing state
+    validated: bool
+    risk_level: str
+
+    # Approval state
+    approved: bool
+    approval_reason: str
+    approver: str
+
+    # Output
+    result: str
+    executed: bool
+
+
+def process_request(state: ApprovalState) -> ApprovalState:
+    """Validate and assess the request before approval.
+
+    This node analyzes the request and determines risk level.
+    """
+    amount = state.get("amount", 0)
+
+    # Determine risk level based on amount
+    if amount < 100:
+        risk_level = "low"
+    elif amount < 1000:
+        risk_level = "medium"
+    else:
+        risk_level = "high"
+
+    return {
+        "validated": True,
+        "risk_level": risk_level,
+    }
+
+
+@activity.defn
+async def notify_approver(request_info: dict) -> str:
+    """Notify the approver about a pending approval request.
+
+    In a real implementation, this could:
+    - Send an email
+    - Post to Slack
+    - Create a ticket in a ticketing system
+    - Send a push notification
+
+    Args:
+        request_info: Information about the approval request.
+
+    Returns:
+        Confirmation message.
+    """
+    workflow_id = activity.info().workflow_id
+    message = request_info.get("message", "Approval needed")
+
+    # Log notification (simulating sending notification)
+    activity.logger.info(
+        f"NOTIFICATION: {message}\n"
+        f"  Workflow ID: {workflow_id}\n"
+        f"  To respond, run:\n"
+        f"    python -m langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.run_respond {workflow_id} --approve --reason 'Approved'\n"
+        f"    python -m langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.run_respond {workflow_id} --reject --reason 'Rejected'"
+    )
+
+    # In production, you would send actual notification here
+    print("\n*** APPROVAL NEEDED ***")
+    print(f"Workflow ID: {workflow_id}")
+    print(f"Request: {message}")
+    print("\nTo respond, run:")
+    print(
+        f"  Approve: uv run python -m langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.run_respond {workflow_id} --approve --reason 'Your reason'"
+    )
+    print(
+        f"  Reject:  uv run python -m langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.run_respond {workflow_id} --reject --reason 'Your reason'"
+    )
+    print()
+
+    return f"Notification sent for workflow {workflow_id}"
+
+
+async def request_approval(state: ApprovalState) -> ApprovalState:
+    """Request human approval using Temporal signals directly.
+
+    This node runs inside the workflow (run_in_workflow=True) and can
+    access Temporal operations directly via workflow.instance().
+
+    It waits for an approval signal using workflow.wait_condition().
+    """
+    from datetime import timedelta
+
+    from temporalio import workflow
+
+    # Get access to the workflow instance
+    wf = workflow.instance()
+
+    # Create the approval request with context for the human
+    approval_request = {
+        "request_type": state.get("request_type", "unknown"),
+        "amount": state.get("amount", 0),
+        "risk_level": state.get("risk_level", "unknown"),
+        "request_data": state.get("request_data", {}),
+        "message": f"Please approve {state.get('request_type', 'request')} "
+        f"for ${state.get('amount', 0):.2f} (Risk: {state.get('risk_level', 'unknown')})",
+    }
+
+    # Store approval request for queries
+    wf._pending_approval = approval_request
+
+    workflow.logger.info(
+        "Workflow paused for approval: %s", approval_request["message"]
+    )
+
+    # Notify the approver via activity
+    await workflow.execute_activity(
+        notify_approver,
+        approval_request,
+        start_to_close_timeout=timedelta(seconds=30),
+    )
+
+    # Wait for approval signal
+    await workflow.wait_condition(lambda: wf._approval_response is not None)
+
+    approval_response = wf._approval_response
+
+    workflow.logger.info(
+        "Received approval response: approved=%s",
+        approval_response.get("approved") if approval_response else None,
+    )
+
+    return {
+        "approved": approval_response.get("approved", False),
+        "approval_reason": approval_response.get("reason", ""),
+        "approver": approval_response.get("approver", "unknown"),
+    }
+
+
+def execute_action(state: ApprovalState) -> ApprovalState:
+    """Execute or reject the action based on approval status."""
+    if state.get("approved"):
+        return {
+            "executed": True,
+            "result": f"Successfully processed {state.get('request_type', 'request')} "
+            f"for ${state.get('amount', 0):.2f}. "
+            f"Approved by {state.get('approver', 'unknown')}: {state.get('approval_reason', '')}",
+        }
+    else:
+        return {
+            "executed": False,
+            "result": f"Request rejected by {state.get('approver', 'unknown')}: "
+            f"{state.get('approval_reason', 'No reason provided')}",
+        }
+
+
+def build_approval_graph() -> Any:
+    """Build the approval workflow graph.
+
+    Flow:
+    START -> process_request -> request_approval -> execute_action -> END
+
+    The request_approval node uses run_in_workflow=True to access
+    Temporal operations directly (signals, activities, etc.).
+    """
+    from temporalio.contrib.langgraph import temporal_node_metadata
+
+    graph = StateGraph(ApprovalState)
+
+    # Add nodes with activity options
+    graph.add_node(
+        "process_request",
+        process_request,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+    # Mark request_approval as run_in_workflow - it can access Temporal operations
+    graph.add_node(
+        "request_approval",
+        request_approval,
+        metadata=temporal_node_metadata(run_in_workflow=True),
+    )
+    graph.add_node(
+        "execute_action",
+        execute_action,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Define edges
+    graph.add_edge(START, "process_request")
+    graph.add_edge("process_request", "request_approval")
+    graph.add_edge("request_approval", "execute_action")
+    graph.add_edge("execute_action", END)
+
+    return graph.compile()
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py
new file mode 100644
index 00000000..9ccd4747
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_respond.py
@@ -0,0 +1,78 @@
+"""Respond to an approval request (Condition-based).
+
+This script allows an approver to approve or reject a pending approval workflow.
+"""
+
+import argparse
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.workflow import (
+    ApprovalWorkflow,
+)
+
+
+async def main() -> None:
+    parser = argparse.ArgumentParser(description="Respond to an approval workflow")
+    parser.add_argument("workflow_id", help="The workflow ID to respond to")
+    parser.add_argument("--approve", action="store_true", help="Approve the request")
+    parser.add_argument("--reject", action="store_true", help="Reject the request")
+    parser.add_argument("--status", action="store_true", help="Check workflow status")
+    parser.add_argument("--reason", default="", help="Reason for approval/rejection")
+    parser.add_argument(
+        "--approver", default="cli-user", help="Approver identifier (default: cli-user)"
+    )
+
+    args = parser.parse_args()
+
+    # Validate arguments
+    if not args.status and not args.approve and not args.reject:
+        parser.error("Must specify --approve, --reject, or --status")
+    if args.approve and args.reject:
+        parser.error("Cannot specify both --approve and --reject")
+
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Get workflow handle
+    handle = client.get_workflow_handle(args.workflow_id)
+
+    if args.status:
+        # Query the workflow status
+        try:
+            status = await handle.query(ApprovalWorkflow.get_status)
+            pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+            print(f"Workflow ID: {args.workflow_id}")
+            print(f"Status: {status}")
+            if pending:
+                print(f"Pending approval: {pending.get('message', 'No message')}")
+                print(f"  Request type: {pending.get('request_type')}")
+                print(f"  Amount: ${pending.get('amount', 0):.2f}")
+                print(f"  Risk level: {pending.get('risk_level')}")
+        except Exception as e:
+            print(f"Error querying workflow: {e}")
+    else:
+        # Send approval/rejection signal
+        approved = args.approve
+        response = {
+            "approved": approved,
+            "reason": args.reason,
+            "approver": args.approver,
+        }
+
+        try:
+            await handle.signal(ApprovalWorkflow.provide_approval, response)
+            action = "Approved" if approved else "Rejected"
+            print(f"{action} workflow {args.workflow_id}")
+            if args.reason:
+                print(f"Reason: {args.reason}")
+        except Exception as e:
+            print(f"Error sending signal: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_worker.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_worker.py
new file mode 100644
index 00000000..09827426
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_worker.py
@@ -0,0 +1,54 @@
+"""Run the Approval Workflow worker (Condition-based).
+
+Starts a Temporal worker that can execute the approval workflow.
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin, activity_options
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.graph import (
+    build_approval_graph,
+    notify_approver,
+)
+from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.workflow import (
+    ApprovalWorkflow,
+)
+
+TASK_QUEUE = "langgraph-approval-condition"
+
+
+async def main() -> None:
+    # Create the LangGraph plugin with the approval graph
+    plugin = LangGraphPlugin(
+        graphs={"approval_workflow": build_approval_graph},
+        default_activity_options=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run worker
+    worker = Worker(
+        client,
+        task_queue=TASK_QUEUE,
+        workflows=[ApprovalWorkflow],
+        activities=[notify_approver],
+    )
+
+    print(f"Starting approval workflow worker on task queue: {TASK_QUEUE}")
+    print("Press Ctrl+C to stop")
+
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_workflow.py
new file mode 100644
index 00000000..9fca0e67
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/run_workflow.py
@@ -0,0 +1,56 @@
+"""Execute the Approval Workflow (Condition-based).
+
+Starts an approval workflow that pauses for human approval.
+The worker will print instructions for how to approve/reject.
+"""
+
+import asyncio
+import uuid
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.workflow import (
+    ApprovalRequest,
+    ApprovalWorkflow,
+)
+
+TASK_QUEUE = "langgraph-approval-condition"
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Generate a unique workflow ID
+    workflow_id = f"approval-condition-{uuid.uuid4().hex[:8]}"
+
+    print(f"Starting approval workflow: {workflow_id}")
+
+    handle = await client.start_workflow(
+        ApprovalWorkflow.run,
+        ApprovalRequest(
+            request_type="purchase",
+            amount=500.00,
+            request_data={"item": "Office supplies", "vendor": "Acme Corp"},
+        ),
+        id=workflow_id,
+        task_queue=TASK_QUEUE,
+    )
+
+    print("Workflow started. Waiting for result...")
+    print(
+        "\nTo approve/reject, use the run_respond script (see worker output for commands)"
+    )
+
+    # Wait for the workflow to complete
+    result = await handle.result()
+
+    print(f"\n{'='*60}")
+    print(f"Result: {result.get('result')}")
+    print(f"Executed: {result.get('executed')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/workflow.py
new file mode 100644
index 00000000..93994c0f
--- /dev/null
+++ b/langgraph_plugin/graph_api/human_in_the_loop/approval_wait_condition/workflow.py
@@ -0,0 +1,187 @@
+"""Approval Workflow Definition (Signal-based).
+
+This workflow demonstrates human-in-the-loop approval using:
+- run_in_workflow=True to access Temporal operations from graph nodes
+- Temporal signals for receiving human input
+- Temporal queries for checking pending approvals
+"""
+
+from dataclasses import dataclass
+from typing import Any, cast
+
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    from temporalio.contrib.langgraph import compile as lg_compile
+
+    from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.graph import (
+        ApprovalState,
+    )
+
+
+@dataclass
+class ApprovalRequest:
+    """Input for the approval workflow."""
+
+    request_type: str
+    amount: float
+    request_data: dict[str, Any] | None = None
+
+
+@dataclass
+class GraphStateResponse:
+    """Response from get_graph_state query."""
+
+    values: ApprovalState
+    """Current state values from the graph."""
+
+    next: list[str]
+    """Next node(s) to execute."""
+
+    step: int
+    """Current execution step count."""
+
+    interrupted: bool
+    """Whether the graph is currently interrupted."""
+
+    interrupt_node: str | None
+    """Node that triggered the interrupt, if any."""
+
+    interrupt_value: dict[str, Any] | None
+    """Value passed to interrupt(), if any."""
+
+
+@workflow.defn
+class ApprovalWorkflow:
+    """Workflow that pauses for human approval before executing actions.
+
+    This demonstrates using run_in_workflow=True to access Temporal
+    operations directly from graph nodes:
+    1. Graph runs until request_approval node
+    2. request_approval node (run_in_workflow=True) waits for signal
+    3. Signal received, approval response stored
+    4. Graph continues with execute_action node
+    """
+
+    def __init__(self) -> None:
+        self._approval_response: dict[str, Any] | None = None
+        self._pending_approval: dict[str, Any] | None = None
+        self._app: Any = None  # Store runner for visualization queries
+
+    @workflow.signal
+    def provide_approval(self, response: dict[str, Any]) -> None:
+        """Signal to provide approval response.
+
+        Args:
+            response: Dict with 'approved' (bool), 'reason' (str), 'approver' (str)
+        """
+        self._approval_response = response
+
+    @workflow.query
+    def get_pending_approval(self) -> dict[str, Any] | None:
+        """Query to get the current pending approval request.
+
+        Returns:
+            The pending approval request details, or None.
+        """
+        return self._pending_approval
+
+    @workflow.query
+    def get_status(self) -> str:
+        """Query to get the current workflow status."""
+        if self._pending_approval is None:
+            return "processing"
+        elif self._approval_response is None:
+            return "waiting_for_approval"
+        else:
+            return "approved" if self._approval_response.get("approved") else "rejected"
+
+    @workflow.query
+    def get_graph_ascii(self) -> str:
+        """Query to get ASCII art visualization of graph execution progress.
+
+        Returns an ASCII diagram showing which nodes have completed,
+        which is currently executing/interrupted, and which are pending.
+        """
+        if self._app is None:
+            return "Graph not yet initialized"
+        return self._app.get_graph_ascii()
+
+    @workflow.query
+    def get_graph_mermaid(self) -> str:
+        """Query to get Mermaid diagram of graph execution progress.
+
+        Returns a Mermaid flowchart with nodes colored by status:
+        - Green: completed nodes
+        - Yellow: current/interrupted node
+        - Gray: pending nodes
+
+        Can be rendered in GitHub, Notion, or any Mermaid-compatible viewer.
+        """
+        if self._app is None:
+            return "Graph not yet initialized"
+        return self._app.get_graph_mermaid()
+
+    @workflow.query
+    def get_graph_state(self) -> GraphStateResponse:
+        """Query to get the current graph execution state.
+
+        Returns a GraphStateResponse with typed ApprovalState values.
+        """
+        if self._app is None:
+            return GraphStateResponse(
+                values=cast(ApprovalState, {}),
+                next=[],
+                step=0,
+                interrupted=False,
+                interrupt_node=None,
+                interrupt_value=None,
+            )
+        snapshot = self._app.get_state()
+        interrupt_task = snapshot.tasks[0] if snapshot.tasks else None
+        return GraphStateResponse(
+            values=cast(ApprovalState, snapshot.values),
+            next=list(snapshot.next),
+            step=snapshot.metadata.get("step", 0) if snapshot.metadata else 0,
+            interrupted=bool(snapshot.tasks),
+            interrupt_node=interrupt_task.get("interrupt_node")
+            if interrupt_task
+            else None,
+            interrupt_value=interrupt_task.get("interrupt_value")
+            if interrupt_task
+            else None,
+        )
+
+    @workflow.run
+    async def run(self, request: ApprovalRequest) -> dict[str, Any]:
+        """Run the approval workflow.
+
+        Args:
+            request: The approval request details.
+
+        Returns:
+            The final state containing result and executed status.
+        """
+        self._app = lg_compile("approval_workflow")
+
+        # Handle both dataclass and dict input (Temporal deserializes to dict)
+        if isinstance(request, dict):
+            request_type = request.get("request_type", "unknown")
+            amount = request.get("amount", 0.0)
+            request_data = request.get("request_data") or {}
+        else:
+            request_type = request.request_type
+            amount = request.amount
+            request_data = request.request_data or {}
+
+        # Prepare initial state
+        initial_state = {
+            "request_type": request_type,
+            "amount": amount,
+            "request_data": request_data,
+        }
+
+        # Run the graph - the request_approval node will wait for signal internally
+        result = await self._app.ainvoke(initial_state)
+
+        return result
diff --git a/langgraph_plugin/graph_api/plan_and_execute/README.md b/langgraph_plugin/graph_api/plan_and_execute/README.md
new file mode 100644
index 00000000..8b47d54e
--- /dev/null
+++ b/langgraph_plugin/graph_api/plan_and_execute/README.md
@@ -0,0 +1,175 @@
+# Plan-and-Execute Agent
+
+An agent that separates planning from execution, creating a high-level plan first and then executing each step sequentially with the ability to replan based on results.
+
+## Overview
+
+The Plan-and-Execute pattern:
+1. **Planning phase** - Analyzes the objective and creates a step-by-step plan
+2. **Execution phase** - Runs each step using available tools
+3. **Evaluation** - Checks progress after each step
+4. **Replanning** - Adjusts the plan if steps fail or new information emerges
+5. **Response** - Synthesizes results into a final answer
+
+## Why This Pattern?
+
+Plan-and-Execute offers advantages over pure ReAct:
+- **Structured approach**: Clear separation of thinking and doing
+- **Visibility**: The plan is visible before execution starts
+- **Resumability**: Temporal can resume from any step
+- **Adaptability**: Dynamic replanning when things change
+
+## Architecture
+
+```
+[Plan] --> [Execute Step 1] --> [Evaluate] --> [Execute Step 2] --> ...
+                                    |
+                                    v
+                               [Replan?] --> [New Plan] --> [Execute] --> ...
+                                    |
+                                    v
+                               [Respond] --> END
+```
+
+Each step runs as a Temporal activity with its own retry logic.
+
+## Available Tools
+
+The executor agent has access to:
+- `calculate`: Evaluate mathematical expressions
+- `lookup`: Retrieve information about topics
+- `analyze`: Analyze data or text
+
+## Running the Sample
+
+### Prerequisites
+
+- Temporal server running locally
+- OpenAI API key
+
+### Steps
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key-here
+   uv run langgraph_plugin/graph_api/plan_and_execute/run_worker.py
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   uv run langgraph_plugin/graph_api/plan_and_execute/run_workflow.py
+   ```
+
+## Sample Output
+
+```
+============================================================
+EXECUTION RESULT
+============================================================
+
+Steps Executed:
+  1. Look up information about LangGraph
+     Result: LangGraph is a library for building stateful, multi-actor LLM applications...
+  2. Look up information about other agent frameworks
+     Result: AI agents are autonomous systems that use LLMs...
+  3. Calculate the feature comparison
+     Result: Result: 5...
+
+Final Answer:
+LangGraph is a library for building stateful, multi-actor LLM applications with
+support for cycles, branches, and persistence. Compared to a basic ReAct agent,
+LangGraph offers 5 additional key features: state persistence, human-in-the-loop
+support, parallel execution, conditional branching, and streaming...
+```
+
+## Key Features Demonstrated
+
+### 1. Plan Generation
+```python
+class Plan(BaseModel):
+    objective: str
+    steps: list[PlanStep]
+
+# LLM generates structured plan
+planner = prompt | model.with_structured_output(Plan)
+plan = planner.invoke({"objective": objective})
+```
+
+### 2. Step-by-Step Execution
+```python
+def execute_step(state: PlanExecuteState) -> dict[str, Any]:
+    step = plan.steps[current_step]
+    result = executor_agent.invoke({"messages": [HumanMessage(step.description)]})
+    return {"step_results": [...], "current_step": current_step + 1}
+```
+
+### 3. Dynamic Routing
+```python
+def should_continue(state) -> Literal["execute", "replan", "respond"]:
+    if state.get("needs_replan"):
+        return "replan"
+    if current_step < len(plan.steps):
+        return "execute"
+    return "respond"
+```
+
+### 4. Replanning
+```python
+def replan(state: PlanExecuteState) -> dict[str, Any]:
+    # Consider what's been done and create new steps
+    new_plan = replanner.invoke({
+        "objective": objective,
+        "completed": completed_work
+    })
+    return {"plan": new_plan, "current_step": 0}
+```
+
+## Customization
+
+### Adding Custom Tools
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def search_web(query: str) -> str:
+    """Search the web for information."""
+    # Implement actual web search
+    pass
+
+tools = [calculate, lookup, analyze, search_web]
+executor_agent = create_agent(model, tools)
+```
+
+### Adjusting Plan Constraints
+
+Modify the planner prompt to control plan characteristics:
+
+```python
+plan_prompt = ChatPromptTemplate.from_messages([
+    ("system", "Create a plan with 3-5 specific steps. Each step should take 1-2 minutes to execute..."),
+    ...
+])
+```
+
+## Comparison with ReAct
+
+| Aspect | ReAct | Plan-and-Execute |
+|--------|-------|------------------|
+| Approach | Think-act in each iteration | Plan all steps, then execute |
+| Visibility | Actions emerge dynamically | Full plan visible upfront |
+| Control | Less predictable | More structured |
+| Flexibility | High adaptability | Requires explicit replanning |
+| Best for | Exploratory tasks | Well-defined objectives |
+
+## Next Steps
+
+- Add more sophisticated tools (web search, code execution)
+- Implement plan approval (human-in-the-loop before execution)
+- Add parallel step execution for independent steps
+- Implement plan caching for similar objectives
diff --git a/langgraph_plugin/graph_api/plan_and_execute/__init__.py b/langgraph_plugin/graph_api/plan_and_execute/__init__.py
new file mode 100644
index 00000000..c8ada293
--- /dev/null
+++ b/langgraph_plugin/graph_api/plan_and_execute/__init__.py
@@ -0,0 +1 @@
+"""Plan-and-Execute Agent sample for Temporal LangGraph integration."""
diff --git a/langgraph_plugin/graph_api/plan_and_execute/graph.py b/langgraph_plugin/graph_api/plan_and_execute/graph.py
new file mode 100644
index 00000000..fe0afd2d
--- /dev/null
+++ b/langgraph_plugin/graph_api/plan_and_execute/graph.py
@@ -0,0 +1,481 @@
+"""Plan-and-Execute Agent Graph Definition.
+
+This module implements a plan-and-execute agent that first creates a plan,
+then executes each step sequentially with the ability to replan based on results.
+
+The agent pattern separates planning from execution:
+1. Planner creates a high-level plan with specific steps
+2. Executor runs each step using available tools
+3. After each step, the agent can replan if needed
+4. Progress is tracked and visible through Temporal
+
+Flow:
+1. plan - Create initial plan from the objective
+2. execute_step - Run the next step using tools
+3. evaluate - Check if more steps needed or replanning required
+4. replan (optional) - Adjust remaining steps based on results
+5. respond - Generate final response when complete
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from datetime import timedelta
+from typing import Annotated, Any, Literal
+
+from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+from langchain.agents import create_agent
+
+
+class PlanStep(BaseModel):
+    """A single step in the execution plan."""
+
+    step_number: int = Field(description="The step number (1-indexed)")
+    description: str = Field(description="What this step should accomplish")
+    tool_hint: str = Field(
+        description="Suggested tool to use (calculate, lookup, or analyze)"
+    )
+
+
+class Plan(BaseModel):
+    """An execution plan with ordered steps."""
+
+    objective: str = Field(description="The main objective to accomplish")
+    steps: list[PlanStep] = Field(
+        description="Ordered list of steps to execute", min_length=1, max_length=5
+    )
+
+
+class StepResult(BaseModel):
+    """Result of executing a single step."""
+
+    step_number: int
+    description: str
+    result: str
+    success: bool
+
+
+class PlanExecuteState(TypedDict):
+    """State for the plan-and-execute graph.
+
+    Attributes:
+        messages: Conversation history with messages.
+        objective: The main task objective.
+        plan: The current execution plan.
+        step_results: Results from completed steps.
+        current_step: Index of the current step (0-indexed).
+        needs_replan: Whether replanning is needed.
+    """
+
+    messages: Annotated[list[BaseMessage], add_messages]
+    objective: str
+    plan: Plan | None
+    step_results: list[StepResult]
+    current_step: int
+    needs_replan: bool
+
+
+# Helper functions to handle dict/object access after Temporal serialization
+def _get_plan_steps(plan: Plan | dict[str, Any]) -> list[Any]:
+    """Get steps from Plan, handling both object and dict forms."""
+    if isinstance(plan, dict):
+        return plan.get("steps", [])
+    return list(plan.steps)
+
+
+def _get_step_attr(
+    step: PlanStep | dict[str, Any], attr: str, default: Any = ""
+) -> Any:
+    """Get attribute from PlanStep, handling both object and dict forms."""
+    if isinstance(step, dict):
+        return step.get(attr, default)
+    return getattr(step, attr, default)
+
+
+def _get_result_success(result: StepResult | dict[str, Any]) -> bool:
+    """Get success from StepResult, handling both object and dict forms."""
+    if isinstance(result, dict):
+        return result.get("success", False)
+    return result.success
+
+
+# Define tools for the executor agent
+@tool
+def calculate(expression: str) -> str:
+    """Evaluate a mathematical expression.
+
+    Args:
+        expression: A mathematical expression like "2 + 2" or "10 * 5"
+
+    Returns:
+        The result of the calculation
+    """
+    try:
+        # Safe evaluation of mathematical expressions
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression"
+        result = eval(expression)  # noqa: S307
+        return f"Result: {result}"
+    except Exception as e:
+        return f"Error calculating: {str(e)}"
+
+
+@tool
+def lookup(topic: str) -> str:
+    """Look up information about a topic.
+
+    Args:
+        topic: The topic to look up information about
+
+    Returns:
+        Information about the topic
+    """
+    # Mock knowledge base
+    knowledge = {
+        "python": "Python is a high-level programming language known for its simplicity and readability.",
+        "temporal": "Temporal is a durable execution platform for running reliable workflows.",
+        "langgraph": "LangGraph is a library for building stateful, multi-actor LLM applications.",
+        "agents": "AI agents are autonomous systems that use LLMs to make decisions and take actions.",
+        "planning": "Planning agents first create a plan, then execute steps sequentially.",
+    }
+
+    topic_lower = topic.lower()
+    for key, value in knowledge.items():
+        if key in topic_lower:
+            return value
+
+    return f"No specific information found about '{topic}'. Consider breaking down the query."
+
+
+@tool
+def analyze(data: str) -> str:
+    """Analyze data or text and provide insights.
+
+    Args:
+        data: The data or text to analyze
+
+    Returns:
+        Analysis results
+    """
+    # Simple mock analysis
+    word_count = len(data.split())
+    char_count = len(data)
+    return f"Analysis: {word_count} words, {char_count} characters. The content discusses: {data[:100]}..."
+
+
+def build_plan_and_execute_graph() -> Any:
+    """Build a plan-and-execute agent graph.
+
+    The graph implements a planning workflow that:
+    1. Creates a plan with specific steps
+    2. Executes each step using available tools
+    3. Evaluates progress and replans if needed
+    4. Generates a final response
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Build executor agent with tools
+    tools = [calculate, lookup, analyze]
+    executor_agent: Any = create_agent(model, tools)
+
+    def create_plan(state: PlanExecuteState) -> dict[str, Any]:
+        """Create an execution plan from the objective.
+
+        Analyzes the objective and breaks it down into
+        specific, actionable steps.
+        """
+        messages = state["messages"]
+        objective = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)),
+            "Complete the task",
+        )
+
+        plan_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a planning agent. Given an objective, create a step-by-step
+plan to accomplish it. Each step should be specific and actionable.
+
+Available tools for execution:
+- calculate: For mathematical computations
+- lookup: For retrieving information about topics
+- analyze: For analyzing data or text
+
+Create 2-4 steps that can be executed sequentially.""",
+                ),
+                ("human", "Objective: {objective}"),
+            ]
+        )
+
+        planner = plan_prompt | model.with_structured_output(Plan)
+        plan = planner.invoke({"objective": str(objective)})
+
+        return {
+            "objective": str(objective),
+            "plan": plan,
+            "current_step": 0,
+            "step_results": [],
+            "needs_replan": False,
+        }
+
+    def execute_step(state: PlanExecuteState) -> dict[str, Any]:
+        """Execute the current step using the executor agent.
+
+        Runs the executor agent with the step description as input.
+        The agent uses available tools to complete the step.
+        """
+        plan = state.get("plan")
+        current_step = state.get("current_step", 0)
+
+        steps = _get_plan_steps(plan) if plan else []
+        if not plan or current_step >= len(steps):
+            return {"needs_replan": False}
+
+        step = steps[current_step]
+        step_number = _get_step_attr(step, "step_number", 0)
+        step_desc = _get_step_attr(step, "description", "")
+        tool_hint = _get_step_attr(step, "tool_hint", "")
+
+        # Run the executor agent for this step
+        result = executor_agent.invoke(
+            {
+                "messages": [
+                    SystemMessage(
+                        content=f"You are executing step {step_number} of a plan. "
+                        f"Complete this step: {step_desc}\n"
+                        f"Suggested tool: {tool_hint}"
+                    ),
+                    HumanMessage(content=step_desc),
+                ]
+            }
+        )
+
+        # Extract the result from the agent's response
+        result_content = ""
+        if result.get("messages"):
+            last_msg = result["messages"][-1]
+            result_content = (
+                last_msg.content if hasattr(last_msg, "content") else str(last_msg)
+            )
+
+        step_result = StepResult(
+            step_number=step_number,
+            description=step_desc,
+            result=result_content,
+            success=True,
+        )
+
+        return {
+            "step_results": state.get("step_results", []) + [step_result],
+            "current_step": current_step + 1,
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Step {step_number} completed: {result_content[:200]}...",
+                }
+            ],
+        }
+
+    def evaluate_progress(state: PlanExecuteState) -> dict[str, Any]:
+        """Evaluate if we should continue, replan, or finish.
+
+        Checks completion status and determines next action.
+        """
+        plan = state.get("plan")
+        current_step = state.get("current_step", 0)
+        step_results = state.get("step_results", [])
+
+        if not plan:
+            return {"needs_replan": True}
+
+        # Check if all steps completed
+        steps = _get_plan_steps(plan)
+        all_complete = current_step >= len(steps)
+
+        # Check if any step failed
+        failed_steps = [r for r in step_results if not _get_result_success(r)]
+
+        return {
+            "needs_replan": len(failed_steps) > 0,
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Progress: {current_step}/{len(steps)} steps complete. "
+                    f"Failures: {len(failed_steps)}",
+                }
+            ]
+            if not all_complete
+            else [],
+        }
+
+    def should_continue(
+        state: PlanExecuteState,
+    ) -> Literal["execute", "replan", "respond"]:
+        """Decide whether to continue execution, replan, or respond.
+
+        Routes based on:
+        - needs_replan flag -> replan
+        - more steps remaining -> execute
+        - all steps complete -> respond
+        """
+        if state.get("needs_replan", False):
+            return "replan"
+
+        plan = state.get("plan")
+        current_step = state.get("current_step", 0)
+
+        if plan and current_step < len(_get_plan_steps(plan)):
+            return "execute"
+
+        return "respond"
+
+    def replan(state: PlanExecuteState) -> dict[str, Any]:
+        """Adjust the plan based on execution results.
+
+        Creates a new plan considering what has been accomplished
+        and what failed, to better achieve the objective.
+        """
+        objective = state.get("objective", "Complete the task")
+        step_results = state.get("step_results", [])
+
+        # Format completed work
+        completed = "\n".join(
+            f"- Step {r.step_number}: {r.description} -> {r.result[:100]}"
+            for r in step_results
+        )
+
+        replan_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a replanning agent. Given the original objective and
+work completed so far, create a new plan for remaining work.
+
+Available tools: calculate, lookup, analyze
+
+Consider what has already been accomplished and create 1-3 new steps
+to complete the objective.""",
+                ),
+                (
+                    "human",
+                    "Objective: {objective}\n\nCompleted work:\n{completed}\n\nCreate new plan:",
+                ),
+            ]
+        )
+
+        planner = replan_prompt | model.with_structured_output(Plan)
+        new_plan = planner.invoke({"objective": objective, "completed": completed})
+
+        return {
+            "plan": new_plan,
+            "current_step": 0,
+            "needs_replan": False,
+        }
+
+    def respond(state: PlanExecuteState) -> dict[str, Any]:
+        """Generate the final response summarizing the execution.
+
+        Compiles all step results into a coherent final answer.
+        """
+        objective = state.get("objective", "")
+        step_results = state.get("step_results", [])
+
+        # Format all results
+        results_text = "\n".join(
+            f"{r.step_number}. {r.description}: {r.result}" for r in step_results
+        )
+
+        response_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a summarizer. Given an objective and the results of
+executing a plan, provide a clear, concise final answer.
+
+Focus on directly answering the original objective using the
+gathered information.""",
+                ),
+                (
+                    "human",
+                    "Objective: {objective}\n\nExecution results:\n{results}\n\nProvide final answer:",
+                ),
+            ]
+        )
+
+        chain = response_prompt | model | StrOutputParser()
+        response = chain.invoke({"objective": objective, "results": results_text})
+
+        return {"messages": [{"role": "assistant", "content": response}]}
+
+    # Build the plan-and-execute graph
+    workflow = StateGraph(PlanExecuteState)
+
+    # Add nodes with activity options
+    workflow.add_node(
+        "plan",
+        create_plan,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    # execute uses create_agent internally - configure via per_node_activity_options
+    workflow.add_node("execute", execute_step)
+    workflow.add_node(
+        "evaluate",
+        evaluate_progress,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+    workflow.add_node(
+        "replan",
+        replan,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    workflow.add_node(
+        "respond",
+        respond,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=1),
+        ),
+    )
+
+    # Add edges
+    workflow.add_edge(START, "plan")
+    workflow.add_edge("plan", "execute")
+    workflow.add_edge("execute", "evaluate")
+
+    # Conditional routing after evaluation
+    workflow.add_conditional_edges(
+        "evaluate",
+        should_continue,
+        {"execute": "execute", "replan": "replan", "respond": "respond"},
+    )
+
+    # After replanning, continue execution
+    workflow.add_edge("replan", "execute")
+
+    workflow.add_edge("respond", END)
+
+    return workflow.compile()
diff --git a/langgraph_plugin/graph_api/plan_and_execute/run_worker.py b/langgraph_plugin/graph_api/plan_and_execute/run_worker.py
new file mode 100644
index 00000000..3f9f61e8
--- /dev/null
+++ b/langgraph_plugin/graph_api/plan_and_execute/run_worker.py
@@ -0,0 +1,50 @@
+"""Worker for the Plan-and-Execute Agent sample.
+
+Starts a Temporal worker that can execute PlanAndExecuteWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.plan_and_execute.graph import (
+    build_plan_and_execute_graph,
+)
+from langgraph_plugin.graph_api.plan_and_execute.workflow import (
+    PlanAndExecuteWorkflow,
+)
+
+
+async def main() -> None:
+    # Create the plugin with the plan-and-execute graph registered
+    plugin = LangGraphPlugin(
+        graphs={"plan_and_execute": build_plan_and_execute_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-plan-execute",
+        workflows=[PlanAndExecuteWorkflow],
+    )
+
+    print("Plan-and-Execute Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/plan_and_execute/run_workflow.py b/langgraph_plugin/graph_api/plan_and_execute/run_workflow.py
new file mode 100644
index 00000000..0aa482b9
--- /dev/null
+++ b/langgraph_plugin/graph_api/plan_and_execute/run_workflow.py
@@ -0,0 +1,50 @@
+"""Execute the Plan-and-Execute workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.plan_and_execute.workflow import (
+    PlanAndExecuteWorkflow,
+)
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # A task that requires planning and multiple steps
+    # The agent will:
+    # 1. Create a plan with specific steps
+    # 2. Execute each step using tools (lookup, calculate, analyze)
+    # 3. Compile results into a final answer
+    result = await client.execute_workflow(
+        PlanAndExecuteWorkflow.run,
+        "What is LangGraph and how does it compare to other agent frameworks? "
+        "Calculate how many more features it has than a basic ReAct agent.",
+        id="plan-execute-workflow",
+        task_queue="langgraph-plan-execute",
+    )
+
+    # Print the final response
+    print("\n" + "=" * 60)
+    print("EXECUTION RESULT")
+    print("=" * 60 + "\n")
+
+    # Print step results if available
+    if result.get("step_results"):
+        print("Steps Executed:")
+        for step in result["step_results"]:
+            print(f"  {step.step_number}. {step.description}")
+            print(f"     Result: {step.result[:100]}...")
+        print()
+
+    # Print final message
+    print("Final Answer:")
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/plan_and_execute/workflow.py b/langgraph_plugin/graph_api/plan_and_execute/workflow.py
new file mode 100644
index 00000000..9f4a5c13
--- /dev/null
+++ b/langgraph_plugin/graph_api/plan_and_execute/workflow.py
@@ -0,0 +1,55 @@
+"""Plan-and-Execute Agent Workflow.
+
+Temporal workflow that executes the plan-and-execute agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class PlanAndExecuteWorkflow:
+    """Temporal workflow that executes a plan-and-execute agent.
+
+    This workflow:
+    1. Creates a plan from the objective
+    2. Executes each step sequentially
+    3. Replans if steps fail
+    4. Returns the final result
+
+    The Temporal integration ensures:
+    - Each step runs as a separate activity
+    - Failed steps can be retried or replanned
+    - Progress is checkpointed after each step
+    - Long-running plans complete reliably
+    """
+
+    @workflow.run
+    async def run(self, objective: str) -> dict[str, Any]:
+        """Run the plan-and-execute agent.
+
+        Args:
+            objective: The task to accomplish.
+
+        Returns:
+            The final state containing the execution results.
+        """
+        app = compile("plan_and_execute")
+
+        result = await app.ainvoke(
+            {
+                "messages": [{"role": "user", "content": objective}],
+                "step_results": [],
+                "current_step": 0,
+                "needs_replan": False,
+            }
+        )
+
+        return result
diff --git a/langgraph_plugin/graph_api/react_agent/README.md b/langgraph_plugin/graph_api/react_agent/README.md
new file mode 100644
index 00000000..fc91d7fa
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/README.md
@@ -0,0 +1,61 @@
+# ReAct Agent with Tools
+
+A ReAct (Reasoning + Acting) agent using LangChain's `create_agent` with Temporal for durable execution.
+
+## What This Sample Demonstrates
+
+- **ReAct pattern**: The think-act-observe loop where the LLM decides actions and observes results
+- **Multi-step reasoning**: The agent makes multiple tool calls to gather information and compute results
+- **Durable execution**: Each graph node runs as a Temporal activity with automatic retries
+- **Crash recovery**: If the worker fails, execution resumes from the last completed node
+- **Cyclic graph execution**: The agent loops between thinking and acting until it has an answer
+
+## How It Works
+
+1. **Tools**: Two LangChain tools (`get_weather`, `calculate`) provide weather data and math operations
+2. **Agent**: `create_agent()` builds a cyclic graph with "agent" and "tools" nodes
+3. **Temporal integration**: Each node runs as a separate activity, providing durability
+4. **Workflow**: Invokes the agent and returns the final conversation state
+
+The ReAct pattern:
+```
+User Query → [Agent Node] → [Tools Node] → [Agent Node] → [Tools Node] → ... → Final Answer
+```
+
+The sample query "What's the weather in Tokyo? Convert the temperature to Celsius." demonstrates multiple agentic loops:
+1. Agent decides to call `get_weather("Tokyo")` → gets "68°F, Clear skies"
+2. Agent decides to call `calculate("(68-32)*5/9")` → gets the Celsius conversion
+3. Agent synthesizes final answer
+
+Each node execution is:
+- **Durable**: Progress is saved after each node completes
+- **Retryable**: Failed nodes can be automatically retried
+- **Recoverable**: If the worker crashes, execution resumes from the last completed node
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_plugin/graph_api/react_agent/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_plugin/graph_api/react_agent/run_workflow.py
+```
+
+## Expected Output
+
+```
+The weather in Tokyo is 68°F (20°C) with clear skies.
+```
+
+You can modify the query in `run_workflow.py` to test different scenarios:
+- **Weather only**: "What's the weather like in Paris?"
+- **Calculator only**: "What is 25 * 4 + 10?"
+- **Multi-step**: "What's the weather in New York and London? Which city is warmer?"
diff --git a/langgraph_plugin/graph_api/react_agent/__init__.py b/langgraph_plugin/graph_api/react_agent/__init__.py
new file mode 100644
index 00000000..1e145783
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/__init__.py
@@ -0,0 +1 @@
+"""ReAct Agent with Tools sample."""
diff --git a/langgraph_plugin/graph_api/react_agent/graph.py b/langgraph_plugin/graph_api/react_agent/graph.py
new file mode 100644
index 00000000..51582633
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/graph.py
@@ -0,0 +1,69 @@
+"""ReAct Agent Graph Definition.
+
+This module builds an agent using LangChain's create_agent.
+Each node (agent reasoning, tool execution) runs as a Temporal activity,
+providing automatic retries and failure recovery.
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Any
+
+from langchain_openai import ChatOpenAI
+
+from langchain.agents import create_agent
+from langgraph_plugin.graph_api.react_agent.tools import calculate, get_weather
+
+
+def build_react_agent() -> Any:
+    """Build an agent with durable execution.
+
+    Uses LangChain's create_agent which creates a graph with:
+    - An "agent" node that calls the LLM to decide actions
+    - A "tools" node that executes the requested tools
+
+    The Temporal integration runs each node as a separate activity:
+    - Progress is saved after each node completes
+    - Failed nodes can be automatically retried
+    - If the worker crashes, execution resumes from the last completed node
+
+    The agent follows the ReAct pattern:
+    1. Think: LLM decides what action to take (agent node)
+    2. Act: Execute the chosen tool (tools node)
+    3. Observe: Feed tool results back to LLM
+    4. Repeat until done
+
+    Activity Configuration:
+        Since create_agent() returns a pre-built graph, configure activity
+        options at the plugin level using per_node_activity_options:
+
+        ```python
+        from temporalio.contrib.langgraph import LangGraphPlugin, activity_options
+
+        plugin = LangGraphPlugin(
+            graphs={"react_agent": build_react_agent},
+            per_node_activity_options={
+                "agent": activity_options(start_to_close_timeout=timedelta(minutes=2)),
+                "tools": activity_options(start_to_close_timeout=timedelta(minutes=1)),
+            },
+        )
+        ```
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    # Create the model
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Define the tools - get_weather and calculate
+    # The sample query requires both: get weather, then calculate F to C conversion
+    tools = [get_weather, calculate]
+
+    # Create the agent using LangChain's create_agent
+    # The Temporal integration will run each node as an activity
+    return create_agent(model, tools)
diff --git a/langgraph_plugin/graph_api/react_agent/run_worker.py b/langgraph_plugin/graph_api/react_agent/run_worker.py
new file mode 100644
index 00000000..002067c2
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/run_worker.py
@@ -0,0 +1,47 @@
+"""Worker for the ReAct Agent sample.
+
+Starts a Temporal worker that can execute ReActAgentWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.react_agent.graph import build_react_agent
+from langgraph_plugin.graph_api.react_agent.workflow import ReActAgentWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the ReAct agent graph registered
+    plugin = LangGraphPlugin(
+        graphs={"react_agent": build_react_agent},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities (LLM calls, tool executions) are automatically registered
+    worker = Worker(
+        client,
+        task_queue="langgraph-react-agent",
+        workflows=[ReActAgentWorkflow],
+    )
+
+    print("ReAct Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/react_agent/run_workflow.py b/langgraph_plugin/graph_api/react_agent/run_workflow.py
new file mode 100644
index 00000000..39ebe77c
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/run_workflow.py
@@ -0,0 +1,31 @@
+"""Execute the ReAct Agent workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.react_agent.workflow import ReActAgentWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # This query requires multiple tool calls:
+    # 1. First, get_weather to find the temperature in Tokyo
+    # 2. Then, calculate to convert Fahrenheit to Celsius
+    result = await client.execute_workflow(
+        ReActAgentWorkflow.run,
+        "What's the weather in Tokyo? Convert the temperature to Celsius.",
+        id="react-agent-workflow",
+        task_queue="langgraph-react-agent",
+    )
+
+    # Print only the text response
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/react_agent/tools.py b/langgraph_plugin/graph_api/react_agent/tools.py
new file mode 100644
index 00000000..76fb7e1e
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/tools.py
@@ -0,0 +1,52 @@
+"""Tool definitions for the ReAct Agent.
+
+These are standard LangChain tools. When used with the Temporal LangGraph
+integration, they execute inside the "tools" node activity, which provides
+automatic retries and failure recovery.
+"""
+
+from langchain_core.tools import tool
+
+
+@tool
+def get_weather(city: str) -> str:
+    """Get the current weather for a city.
+
+    Args:
+        city: The city name to get weather for.
+
+    Returns:
+        A string describing the current weather conditions.
+    """
+    # Simulated weather data - in production, call a real weather API
+    weather_data = {
+        "new york": "72°F, Partly cloudy with light winds",
+        "london": "58°F, Overcast with chance of rain",
+        "tokyo": "68°F, Clear skies",
+        "paris": "65°F, Sunny with occasional clouds",
+        "sydney": "77°F, Warm and humid",
+    }
+    city_lower = city.lower()
+    return weather_data.get(city_lower, f"Weather data not available for {city}")
+
+
+@tool
+def calculate(expression: str) -> str:
+    """Perform a mathematical calculation.
+
+    Args:
+        expression: A mathematical expression like "2 + 2" or "10 * 5".
+
+    Returns:
+        The result of the calculation as a string.
+    """
+    try:
+        # Safely evaluate mathematical expressions
+        # Only allow basic math operations
+        allowed_chars = set("0123456789+-*/(). ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Only basic math operations are allowed"
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error calculating: {e}"
diff --git a/langgraph_plugin/graph_api/react_agent/workflow.py b/langgraph_plugin/graph_api/react_agent/workflow.py
new file mode 100644
index 00000000..f28192b5
--- /dev/null
+++ b/langgraph_plugin/graph_api/react_agent/workflow.py
@@ -0,0 +1,52 @@
+"""ReAct Agent Workflow.
+
+Temporal workflow that executes the ReAct agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ReActAgentWorkflow:
+    """Temporal workflow that executes a ReAct agent with durable execution.
+
+    This workflow uses LangGraph's create_react_agent with the Temporal
+    integration. Each graph node runs as a Temporal activity:
+    - The "agent" node calls the LLM to decide what to do
+    - The "tools" node executes the requested tools
+
+    If any node fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed node.
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the ReAct agent to answer a query.
+
+        The agent will:
+        1. Think about what information is needed
+        2. Use tools to gather information
+        3. Synthesize a final answer
+
+        Args:
+            query: The user's question or request.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("react_agent")
+
+        # Execute the agent
+        # The input format for create_react_agent is a dict with "messages"
+        result = await app.ainvoke({"messages": [{"role": "user", "content": query}]})
+
+        return result
diff --git a/langgraph_plugin/graph_api/reflection/README.md b/langgraph_plugin/graph_api/reflection/README.md
new file mode 100644
index 00000000..f8134832
--- /dev/null
+++ b/langgraph_plugin/graph_api/reflection/README.md
@@ -0,0 +1,193 @@
+# Reflection Agent
+
+An agent that generates content, critiques it, and iteratively improves until quality criteria are met. This demonstrates the self-reflection pattern for producing higher-quality outputs.
+
+## Overview
+
+The Reflection Agent implements a generate-critique-revise loop:
+1. **Generate** - Create initial content based on the task
+2. **Reflect** - Critique the content with structured feedback
+3. **Revise** - Improve content based on the critique
+4. **Evaluate** - Check if quality criteria are met
+5. **Loop** - Repeat until satisfied or max iterations reached
+
+## Why Reflection?
+
+The reflection pattern improves output quality by:
+- **Self-correction**: Catching and fixing issues automatically
+- **Structured feedback**: Using specific criteria for evaluation
+- **Iterative refinement**: Multiple passes produce better results
+- **Visibility**: Each iteration's critique is recorded
+
+## Why Temporal?
+
+Temporal enhances reflection workflows with:
+- **Durability**: Long refinement sessions complete reliably
+- **Visibility**: See each iteration in the workflow history
+- **Checkpointing**: Resume from any iteration if interrupted
+- **Cost tracking**: Monitor LLM calls across iterations
+
+## Architecture
+
+```
+[Generate] --> [Reflect] --> [Score >= 7?] --> YES --> [Finalize] --> END
+                   ^              |
+                   |              NO
+                   |              v
+                   +-------- [Revise]
+```
+
+Each node runs as a Temporal activity with automatic retry.
+
+## Running the Sample
+
+### Prerequisites
+
+- Temporal server running locally
+- OpenAI API key
+
+### Steps
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key-here
+   uv run langgraph_plugin/graph_api/reflection/run_worker.py
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   uv run langgraph_plugin/graph_api/reflection/run_workflow.py
+   ```
+
+## Sample Output
+
+```
+============================================================
+REFLECTION JOURNEY
+============================================================
+
+Iteration 1 Critique:
+  Score: 5/10
+  Strengths: Clear structure, Good opening hook
+  Weaknesses: Too generic, Lacks specific examples
+
+Iteration 2 Critique:
+  Score: 7/10
+  Strengths: Specific examples added, Better flow
+  Weaknesses: Could be more concise
+
+============================================================
+FINAL CONTENT
+============================================================
+
+In the world of AI agents, failure is not just possible—it's inevitable.
+Network requests timeout, APIs rate-limit, and worker processes crash.
+Without durable execution, these failures mean lost progress, corrupted
+state, and frustrated users...
+
+[Rest of the refined blog post]
+```
+
+## Key Features Demonstrated
+
+### 1. Structured Critique
+```python
+class Critique(BaseModel):
+    strengths: list[str]
+    weaknesses: list[str]
+    suggestions: list[str]
+    quality_score: int  # 1-10
+    is_satisfactory: bool  # True if score >= 7
+```
+
+### 2. Quality-Based Routing
+```python
+def should_revise(state) -> Literal["revise", "finalize"]:
+    if critique.is_satisfactory or iteration >= max_iterations:
+        return "finalize"
+    return "revise"
+```
+
+### 3. Feedback-Driven Revision
+```python
+def revise(state: ReflectionState) -> dict[str, Any]:
+    feedback = format_critique(latest_critique)
+    revised = revision_chain.invoke({
+        "task": task,
+        "draft": draft,
+        "feedback": feedback
+    })
+    return {"current_draft": revised, "iteration": iteration + 1}
+```
+
+## Customization
+
+### Adjusting Quality Threshold
+
+Change when content is considered "good enough":
+
+```python
+class Critique(BaseModel):
+    is_satisfactory: bool = Field(
+        description="Whether content meets standards (score >= 8)"  # Stricter
+    )
+```
+
+### Adding Domain-Specific Criteria
+
+Customize the critique for specific content types:
+
+```python
+reflect_prompt = ChatPromptTemplate.from_messages([
+    ("system", """You are a technical writing editor. Evaluate:
+    - Technical accuracy
+    - Code example correctness
+    - Appropriate complexity level
+    - SEO optimization
+    ..."""),
+    ...
+])
+```
+
+### Different Models for Generation vs Critique
+
+Use different models for each role:
+
+```python
+# Creative model for generation
+generator = ChatOpenAI(model="gpt-4", temperature=0.8)
+
+# Precise model for critique
+critic = ChatOpenAI(model="gpt-4", temperature=0)
+```
+
+## Use Cases
+
+The reflection pattern works well for:
+- **Writing tasks**: Blog posts, documentation, emails
+- **Code generation**: Self-reviewing generated code
+- **Problem-solving**: Iteratively refining solutions
+- **Summarization**: Improving summary quality
+- **Translation**: Self-correcting translations
+
+## Comparison with Single-Pass Generation
+
+| Aspect | Single-Pass | Reflection |
+|--------|-------------|------------|
+| Quality | Variable | Consistently higher |
+| Cost | 1 LLM call | 3-6+ LLM calls |
+| Latency | Fast | Slower |
+| Transparency | Low | High (visible iterations) |
+| Best for | Quick drafts | Final outputs |
+
+## Next Steps
+
+- Add human-in-the-loop for critique approval
+- Implement specialized critics (grammar, style, accuracy)
+- Add A/B testing of different revision strategies
+- Integrate with external quality metrics
diff --git a/langgraph_plugin/graph_api/reflection/__init__.py b/langgraph_plugin/graph_api/reflection/__init__.py
new file mode 100644
index 00000000..1baeab1d
--- /dev/null
+++ b/langgraph_plugin/graph_api/reflection/__init__.py
@@ -0,0 +1 @@
+"""Reflection Agent sample for Temporal LangGraph integration."""
diff --git a/langgraph_plugin/graph_api/reflection/graph.py b/langgraph_plugin/graph_api/reflection/graph.py
new file mode 100644
index 00000000..aacd7fc2
--- /dev/null
+++ b/langgraph_plugin/graph_api/reflection/graph.py
@@ -0,0 +1,358 @@
+"""Reflection Agent Graph Definition.
+
+This module implements a reflection agent that generates content, critiques it,
+and iteratively improves until quality criteria are met.
+
+The reflection pattern:
+1. generate - Create initial content based on the prompt
+2. reflect - Critique the content and identify improvements
+3. revise - Incorporate feedback to improve the content
+4. evaluate - Check if quality criteria are met
+5. Loop until satisfied or max iterations reached
+
+This pattern is useful for:
+- Writing tasks (essays, code, documentation)
+- Problem-solving (iterative refinement)
+- Quality assurance (self-checking outputs)
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from datetime import timedelta
+from typing import Annotated, Any, Literal
+
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from temporalio.contrib.langgraph import activity_options
+from typing_extensions import TypedDict
+
+
+class Critique(BaseModel):
+    """Critique of the generated content."""
+
+    strengths: list[str] = Field(description="What's good about the content")
+    weaknesses: list[str] = Field(description="What needs improvement")
+    suggestions: list[str] = Field(description="Specific suggestions for improvement")
+    quality_score: int = Field(description="Quality score from 1-10", ge=1, le=10)
+    is_satisfactory: bool = Field(
+        description="Whether the content meets quality standards (score >= 7)"
+    )
+
+
+class ReflectionState(TypedDict):
+    """State for the reflection agent graph.
+
+    Attributes:
+        messages: Conversation history.
+        task: The writing/generation task.
+        current_draft: The current version of the content.
+        critiques: History of critiques for each iteration.
+        iteration: Current iteration number.
+        max_iterations: Maximum allowed iterations.
+        final_content: The final approved content.
+    """
+
+    messages: Annotated[list[BaseMessage], add_messages]
+    task: str
+    current_draft: str
+    critiques: list[Critique]
+    iteration: int
+    max_iterations: int
+    final_content: str
+
+
+def build_reflection_graph() -> Any:
+    """Build a reflection agent graph.
+
+    The graph implements an iterative improvement workflow:
+    1. Generate initial content
+    2. Reflect and critique
+    3. Revise based on feedback
+    4. Check quality and loop or finish
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.7,  # Slightly higher for creative generation
+    )
+
+    critic_model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,  # Lower for consistent critique
+    )
+
+    def generate(state: ReflectionState) -> dict[str, Any]:
+        """Generate initial content based on the task.
+
+        Creates the first draft that will be refined through reflection.
+        """
+        messages = state["messages"]
+        task = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)),
+            "Write a short paragraph",
+        )
+
+        generate_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a skilled writer. Generate high-quality content based on
+the given task. Focus on:
+- Clarity and coherence
+- Relevant information
+- Engaging style
+- Proper structure
+
+Produce your best work on the first try.""",
+                ),
+                ("human", "{task}"),
+            ]
+        )
+
+        chain = generate_prompt | model | StrOutputParser()
+        draft = chain.invoke({"task": str(task)})
+
+        return {
+            "task": str(task),
+            "current_draft": draft,
+            "iteration": 1,
+            "max_iterations": state.get("max_iterations", 3),
+            "critiques": [],
+        }
+
+    def reflect(state: ReflectionState) -> dict[str, Any]:
+        """Reflect on the current draft and provide critique.
+
+        Analyzes the content for strengths, weaknesses, and
+        provides specific improvement suggestions.
+        """
+        task = state.get("task", "")
+        draft = state.get("current_draft", "")
+        iteration = state.get("iteration", 1)
+
+        reflect_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a thoughtful critic and editor. Analyze the given content
+and provide constructive feedback.
+
+Consider:
+- Does it fulfill the original task?
+- Is it clear and well-structured?
+- Is the information accurate and relevant?
+- Is the style engaging and appropriate?
+- What specific improvements would make it better?
+
+Be specific and actionable in your feedback.
+Score 7+ means the content is ready for publication.""",
+                ),
+                (
+                    "human",
+                    "Task: {task}\n\nDraft (iteration {iteration}):\n{draft}\n\nProvide your critique:",
+                ),
+            ]
+        )
+
+        critic = reflect_prompt | critic_model.with_structured_output(Critique)
+        critique: Any = critic.invoke(
+            {"task": task, "draft": draft, "iteration": iteration}
+        )
+
+        return {
+            "critiques": state.get("critiques", []) + [critique],
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Iteration {iteration} critique: Score {critique.quality_score}/10. "
+                    f"{'Satisfactory!' if critique.is_satisfactory else 'Needs improvement.'}",
+                }
+            ],
+        }
+
+    def should_revise(state: ReflectionState) -> Literal["revise", "finalize"]:
+        """Decide whether to revise or finalize.
+
+        Routes to finalize if:
+        - Content is satisfactory (score >= 7)
+        - Max iterations reached
+
+        Otherwise continues to revise.
+        """
+        critiques = state.get("critiques", [])
+        iteration = state.get("iteration", 1)
+        max_iterations = state.get("max_iterations", 3)
+
+        if not critiques:
+            return "revise"
+
+        latest_critique = critiques[-1]
+
+        # Finalize if satisfactory or max iterations reached
+        # Note: After Temporal serialization, Critique objects become dicts
+        is_satisfactory = (
+            latest_critique.get("is_satisfactory", False)
+            if isinstance(latest_critique, dict)
+            else latest_critique.is_satisfactory
+        )
+        if is_satisfactory or iteration >= max_iterations:
+            return "finalize"
+
+        return "revise"
+
+    def revise(state: ReflectionState) -> dict[str, Any]:
+        """Revise the content based on critique feedback.
+
+        Incorporates the suggestions to produce an improved version.
+        """
+        task = state.get("task", "")
+        draft = state.get("current_draft", "")
+        critiques = state.get("critiques", [])
+        iteration = state.get("iteration", 1)
+
+        if not critiques:
+            return {"iteration": iteration + 1}
+
+        latest_critique = critiques[-1]
+
+        # Format the feedback
+        # Note: After Temporal serialization, Critique objects become dicts
+        if isinstance(latest_critique, dict):
+            strengths = latest_critique.get("strengths", [])
+            weaknesses = latest_critique.get("weaknesses", [])
+            suggestions = latest_critique.get("suggestions", [])
+        else:
+            strengths = latest_critique.strengths
+            weaknesses = latest_critique.weaknesses
+            suggestions = latest_critique.suggestions
+
+        feedback = f"""
+Strengths: {', '.join(strengths)}
+Weaknesses: {', '.join(weaknesses)}
+Suggestions: {', '.join(suggestions)}
+"""
+
+        revise_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a skilled writer revising your work based on feedback.
+Carefully address each weakness and incorporate the suggestions while
+preserving the strengths.
+
+Produce an improved version that addresses the critique.""",
+                ),
+                (
+                    "human",
+                    "Original task: {task}\n\nCurrent draft:\n{draft}\n\nFeedback:\n{feedback}\n\nRevised version:",
+                ),
+            ]
+        )
+
+        chain = revise_prompt | model | StrOutputParser()
+        revised = chain.invoke({"task": task, "draft": draft, "feedback": feedback})
+
+        return {
+            "current_draft": revised,
+            "iteration": iteration + 1,
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Revised draft (iteration {iteration + 1}) created.",
+                }
+            ],
+        }
+
+    def finalize(state: ReflectionState) -> dict[str, Any]:
+        """Finalize the content and prepare the response.
+
+        Marks the current draft as final and creates the summary.
+        """
+        draft = state.get("current_draft", "")
+        critiques = state.get("critiques", [])
+        iteration = state.get("iteration", 1)
+
+        # Get final score
+        # Note: After Temporal serialization, Critique objects become dicts
+        if critiques:
+            last_critique = critiques[-1]
+            final_score = (
+                last_critique.get("quality_score", 0)
+                if isinstance(last_critique, dict)
+                else last_critique.quality_score
+            )
+        else:
+            final_score = 0
+
+        summary = f"""
+Content finalized after {iteration} iteration(s).
+Final quality score: {final_score}/10
+
+--- FINAL CONTENT ---
+{draft}
+"""
+
+        return {
+            "final_content": draft,
+            "messages": [{"role": "assistant", "content": summary}],
+        }
+
+    # Build the reflection graph
+    workflow = StateGraph(ReflectionState)
+
+    # Add nodes with activity options for LLM calls
+    workflow.add_node(
+        "generate",
+        generate,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    workflow.add_node(
+        "reflect",
+        reflect,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    workflow.add_node(
+        "revise",
+        revise,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+    )
+    workflow.add_node(
+        "finalize",
+        finalize,
+        metadata=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    # Add edges
+    workflow.add_edge(START, "generate")
+    workflow.add_edge("generate", "reflect")
+
+    # Conditional: revise or finalize based on critique
+    workflow.add_conditional_edges(
+        "reflect",
+        should_revise,
+        {"revise": "revise", "finalize": "finalize"},
+    )
+
+    # After revision, reflect again
+    workflow.add_edge("revise", "reflect")
+
+    workflow.add_edge("finalize", END)
+
+    return workflow.compile()
diff --git a/langgraph_plugin/graph_api/reflection/run_worker.py b/langgraph_plugin/graph_api/reflection/run_worker.py
new file mode 100644
index 00000000..86c919fd
--- /dev/null
+++ b/langgraph_plugin/graph_api/reflection/run_worker.py
@@ -0,0 +1,46 @@
+"""Worker for the Reflection Agent sample.
+
+Starts a Temporal worker that can execute ReflectionWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.reflection.graph import build_reflection_graph
+from langgraph_plugin.graph_api.reflection.workflow import ReflectionWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the reflection graph registered
+    plugin = LangGraphPlugin(
+        graphs={"reflection": build_reflection_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-reflection",
+        workflows=[ReflectionWorkflow],
+    )
+
+    print("Reflection Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/reflection/run_workflow.py b/langgraph_plugin/graph_api/reflection/run_workflow.py
new file mode 100644
index 00000000..6974aa18
--- /dev/null
+++ b/langgraph_plugin/graph_api/reflection/run_workflow.py
@@ -0,0 +1,56 @@
+"""Execute the Reflection workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.reflection.workflow import ReflectionWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # A writing task that benefits from iterative refinement
+    # The agent will:
+    # 1. Generate initial content
+    # 2. Critique and identify improvements
+    # 3. Revise based on feedback
+    # 4. Repeat until quality score >= 7 or max iterations
+    result = await client.execute_workflow(
+        ReflectionWorkflow.run,
+        args=[
+            "Write a short technical blog post introduction about why "
+            "durable execution is important for AI agents. Target audience: "
+            "software engineers. Length: 2-3 paragraphs.",
+            3,  # max iterations
+        ],
+        id="reflection-workflow",
+        task_queue="langgraph-reflection",
+    )
+
+    # Print the refinement journey
+    print("\n" + "=" * 60)
+    print("REFLECTION JOURNEY")
+    print("=" * 60 + "\n")
+
+    # Show critique history
+    if result.get("critiques"):
+        for i, critique in enumerate(result["critiques"], 1):
+            print(f"Iteration {i} Critique:")
+            print(f"  Score: {critique.quality_score}/10")
+            print(f"  Strengths: {', '.join(critique.strengths[:2])}")
+            print(f"  Weaknesses: {', '.join(critique.weaknesses[:2])}")
+            print()
+
+    # Print final content
+    print("=" * 60)
+    print("FINAL CONTENT")
+    print("=" * 60 + "\n")
+    print(result.get("final_content", result["current_draft"]))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/reflection/workflow.py b/langgraph_plugin/graph_api/reflection/workflow.py
new file mode 100644
index 00000000..63c79af7
--- /dev/null
+++ b/langgraph_plugin/graph_api/reflection/workflow.py
@@ -0,0 +1,56 @@
+"""Reflection Agent Workflow.
+
+Temporal workflow that executes the reflection agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ReflectionWorkflow:
+    """Temporal workflow that executes a reflection agent.
+
+    This workflow:
+    1. Generates initial content
+    2. Reflects and critiques the content
+    3. Revises based on feedback
+    4. Loops until quality criteria met or max iterations
+
+    The Temporal integration ensures:
+    - Each generation/reflection/revision runs as an activity
+    - Progress is saved after each iteration
+    - Long refinement sessions complete reliably
+    - Quality improvement is visible in workflow history
+    """
+
+    @workflow.run
+    async def run(self, task: str, max_iterations: int = 3) -> dict[str, Any]:
+        """Run the reflection agent on a writing task.
+
+        Args:
+            task: The content generation task.
+            max_iterations: Maximum refinement iterations (default 3).
+
+        Returns:
+            The final state containing the refined content.
+        """
+        app = compile("reflection")
+
+        result = await app.ainvoke(
+            {
+                "messages": [{"role": "user", "content": task}],
+                "critiques": [],
+                "iteration": 0,
+                "max_iterations": max_iterations,
+            }
+        )
+
+        return result
diff --git a/langgraph_plugin/graph_api/supervisor/README.md b/langgraph_plugin/graph_api/supervisor/README.md
new file mode 100644
index 00000000..b95397f8
--- /dev/null
+++ b/langgraph_plugin/graph_api/supervisor/README.md
@@ -0,0 +1,115 @@
+# Supervisor Multi-Agent System
+
+A supervisor agent that coordinates specialized worker agents (researcher, writer, analyst) using LangGraph with Temporal for durable execution.
+
+## What This Sample Demonstrates
+
+- **Multi-agent orchestration**: A supervisor coordinates multiple specialized agents
+- **Task routing**: The supervisor intelligently routes tasks to the appropriate agent
+- **Agent specialization**: Each agent has specific tools and expertise
+- **Durable multi-agent execution**: All agent interactions run as Temporal activities
+- **Crash recovery**: If any agent fails or the worker crashes, execution resumes seamlessly
+
+## How It Works
+
+### Architecture
+
+```
+                    ┌─────────────┐
+                    │  Supervisor │
+                    │ (Coordinator)│
+                    └──────┬──────┘
+                           │
+           ┌───────────────┼───────────────┐
+           │               │               │
+           ▼               ▼               ▼
+    ┌────────────┐  ┌────────────┐  ┌────────────┐
+    │ Researcher │  │   Writer   │  │  Analyst   │
+    │            │  │            │  │            │
+    │ web_search │  │write_content│ │ calculate  │
+    │            │  │ summarize  │  │analyze_data│
+    └────────────┘  └────────────┘  └────────────┘
+```
+
+### Agents
+
+1. **Supervisor**: Central coordinator that analyzes requests, decides which agent(s) to use, routes tasks, and synthesizes final responses
+
+2. **Researcher**: Specialized in information gathering with web search capabilities. Use for research tasks, fact-finding, and current events
+
+3. **Writer**: Specialized in content creation and summarization. Use for writing articles, reports, summaries, or any content generation
+
+4. **Analyst**: Specialized in calculations and data analysis. Use for math problems, data interpretation, or analytical tasks
+
+### Flow
+
+1. User sends a request to the supervisor
+2. Supervisor analyzes the request and determines which agent(s) are needed
+3. Supervisor routes the task to the appropriate agent
+4. Agent completes its work and returns results to supervisor
+5. Supervisor may route to additional agents or synthesize final response
+6. Final response is returned to the user
+
+### Temporal Integration
+
+With Temporal, the multi-agent system gains:
+- **Durability**: Each agent's execution is checkpointed
+- **Retries**: Failed agent calls are automatically retried
+- **Recovery**: If the worker crashes, execution resumes from the last completed agent interaction
+- **Visibility**: Track agent interactions through Temporal's UI
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+- Install dependencies: `uv sync --group langgraph`
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_plugin/graph_api/supervisor/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_plugin/graph_api/supervisor/run_workflow.py
+```
+
+## Expected Output
+
+The supervisor will coordinate the agents to:
+1. Use the researcher to find AI trends information
+2. Use the analyst to evaluate which trends are most impactful
+3. Use the writer to create an executive summary
+
+You'll see the final synthesized response incorporating work from multiple agents.
+
+## Example Queries
+
+The default query demonstrates multi-agent collaboration:
+```
+Research the latest AI trends in 2024, analyze which trends are most
+impactful for enterprise software development, and write a brief
+executive summary (2-3 paragraphs).
+```
+
+You can modify `run_workflow.py` to test different scenarios:
+
+- **Research only**: "What are the key features of Temporal workflows?"
+- **Analysis only**: "Calculate the compound interest on $10,000 at 5% for 3 years"
+- **Writing only**: "Write a brief introduction to LangGraph multi-agent systems"
+- **Multi-agent**: "Research LangGraph patterns, analyze their trade-offs, and summarize recommendations"
+
+## Key Files
+
+- `graph.py`: Defines the supervisor and specialized agents using `langgraph-supervisor`
+- `workflow.py`: Temporal workflow that executes the multi-agent system
+- `run_worker.py`: Starts the Temporal worker with the LangGraph plugin
+- `run_workflow.py`: Executes the workflow with a sample request
+
+## References
+
+- [LangGraph Supervisor Library](https://github.com/langchain-ai/langgraph-supervisor-py)
+- [Multi-Agent Systems in LangGraph](https://langchain-ai.github.io/langgraph/concepts/multi_agent/)
+- [Temporal LangGraph Integration](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md)
diff --git a/langgraph_plugin/graph_api/supervisor/__init__.py b/langgraph_plugin/graph_api/supervisor/__init__.py
new file mode 100644
index 00000000..85a56917
--- /dev/null
+++ b/langgraph_plugin/graph_api/supervisor/__init__.py
@@ -0,0 +1 @@
+# Supervisor multi-agent sample
diff --git a/langgraph_plugin/graph_api/supervisor/graph.py b/langgraph_plugin/graph_api/supervisor/graph.py
new file mode 100644
index 00000000..0423291d
--- /dev/null
+++ b/langgraph_plugin/graph_api/supervisor/graph.py
@@ -0,0 +1,231 @@
+"""Supervisor Multi-Agent Graph Definition.
+
+This module implements a supervisor agent that coordinates specialized worker agents.
+The supervisor routes tasks to appropriate agents (researcher, writer, analyst) and
+aggregates their results.
+
+Architecture:
+- Supervisor: Central coordinator that decides which agent should handle each task
+- Researcher: Agent with web search capabilities for gathering information
+- Writer: Agent specialized in content creation and summarization
+- Analyst: Agent for data analysis and mathematical operations
+
+The Temporal LangGraph plugin runs each agent's nodes as separate activities,
+providing durability and automatic retries for the entire multi-agent system.
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Any
+
+from langchain_openai import ChatOpenAI
+from langgraph_supervisor import create_supervisor
+
+from langchain.agents import create_agent
+
+# --- Tools for specialized agents ---
+
+
+def web_search(query: str) -> str:
+    """Search the web for information.
+
+    This is a mock implementation. In production, you would integrate
+    with a real search API like Tavily, SerpAPI, or similar.
+    """
+    # Mock search results for demonstration
+    mock_results = {
+        "ai trends 2024": (
+            "Key AI trends in 2024:\n"
+            "1. Multimodal AI models combining text, image, and video\n"
+            "2. AI agents and autonomous systems\n"
+            "3. Small language models for edge deployment\n"
+            "4. AI in scientific discovery\n"
+            "5. Responsible AI and governance frameworks"
+        ),
+        "temporal workflow": (
+            "Temporal is a durable execution platform:\n"
+            "- Workflows survive failures and can run for years\n"
+            "- Activities are retried automatically on failure\n"
+            "- Built-in support for signals, queries, and timers\n"
+            "- Used by companies like Netflix, Snap, and Stripe"
+        ),
+        "langgraph multi-agent": (
+            "LangGraph multi-agent patterns:\n"
+            "- Supervisor: Central coordinator routes tasks to specialists\n"
+            "- Swarm: Agents hand off to each other dynamically\n"
+            "- Hierarchical: Team supervisors under a top-level coordinator\n"
+            "- Collaborative: Agents work together on shared state"
+        ),
+    }
+
+    # Try to match query to mock results
+    query_lower = query.lower()
+    for key, result in mock_results.items():
+        if any(word in query_lower for word in key.split()):
+            return result
+
+    return f"Search results for '{query}': No specific results found. Please try a different query."
+
+
+def write_content(topic: str, style: str = "informative") -> str:
+    """Write content on a given topic.
+
+    This is a mock implementation. The actual content generation
+    would be handled by the LLM in the agent's reasoning.
+    """
+    return f"[Content draft on '{topic}' in {style} style - LLM will generate actual content]"
+
+
+def summarize(text: str, max_length: int = 100) -> str:
+    """Summarize the given text.
+
+    This is a mock implementation. The actual summarization
+    would be handled by the LLM in the agent's reasoning.
+    """
+    return f"[Summary of {len(text)} characters, max {max_length} words - LLM will generate]"
+
+
+def calculate(expression: str) -> str:
+    """Evaluate a mathematical expression safely.
+
+    Supports basic arithmetic operations.
+    """
+    try:
+        # Only allow safe mathematical operations
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression. Only numbers and +-*/.() are allowed."
+
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error evaluating '{expression}': {e}"
+
+
+def analyze_data(data: str, analysis_type: str = "summary") -> str:
+    """Analyze data and provide insights.
+
+    Args:
+        data: The data to analyze (numbers, text, etc.)
+        analysis_type: Type of analysis (summary, trend, comparison)
+    """
+    return f"[{analysis_type.title()} analysis of data: '{data[:50]}...' - LLM will provide detailed analysis]"
+
+
+# --- Build the multi-agent system ---
+
+
+def build_supervisor_graph() -> Any:
+    """Build a supervisor multi-agent system with durable execution.
+
+    Creates a supervisor that coordinates three specialized agents:
+    1. Researcher: Gathers information via web search
+    2. Writer: Creates and summarizes content
+    3. Analyst: Performs calculations and data analysis
+
+    The supervisor decides which agent(s) to use based on the task,
+    routes work appropriately, and synthesizes final results.
+
+    With Temporal integration:
+    - Each agent's execution runs as Temporal activities
+    - Agent failures are automatically retried
+    - Progress is checkpointed between agent handoffs
+    - The entire multi-agent workflow survives worker crashes
+
+    Activity Configuration:
+        Since create_supervisor() and create_agent() return pre-built graphs,
+        configure activity options at the plugin level using per_node_activity_options:
+
+        ```python
+        from temporalio.contrib.langgraph import LangGraphPlugin, activity_options
+
+        plugin = LangGraphPlugin(
+            graphs={"supervisor": build_supervisor_graph},
+            per_node_activity_options={
+                # Supervisor node makes routing decisions
+                "supervisor": activity_options(start_to_close_timeout=timedelta(minutes=1)),
+                # Agent nodes make LLM calls
+                "researcher": activity_options(start_to_close_timeout=timedelta(minutes=3)),
+                "writer": activity_options(start_to_close_timeout=timedelta(minutes=3)),
+                "analyst": activity_options(start_to_close_timeout=timedelta(minutes=2)),
+            },
+        )
+        ```
+
+    Returns:
+        A compiled LangGraph supervisor that can be executed with ainvoke().
+    """
+    # Create the model
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Create specialized agents
+
+    # Researcher agent - gathers information
+    researcher: Any = create_agent(
+        model=model,
+        tools=[web_search],
+        name="researcher",
+        system_prompt=(
+            "You are a research specialist with web search capabilities. "
+            "Your job is to find accurate, relevant information on any topic. "
+            "Always cite what you find and be thorough in your research. "
+            "If you can't find specific information, say so clearly."
+        ),
+    )
+
+    # Writer agent - creates content
+    writer: Any = create_agent(
+        model=model,
+        tools=[write_content, summarize],
+        name="writer",
+        system_prompt=(
+            "You are a skilled writer and content creator. "
+            "You excel at writing clear, engaging content and summarizing complex topics. "
+            "Adapt your writing style to the requested format (blog, report, summary, etc.). "
+            "Always structure your content logically with clear sections."
+        ),
+    )
+
+    # Analyst agent - data analysis and calculations
+    analyst: Any = create_agent(
+        model=model,
+        tools=[calculate, analyze_data],
+        name="analyst",
+        system_prompt=(
+            "You are a data analyst expert in mathematical calculations and data interpretation. "
+            "Use the calculate tool for any mathematical operations. "
+            "Provide clear explanations of your analysis and methodology. "
+            "Always double-check your calculations."
+        ),
+    )
+
+    # Create the supervisor to coordinate the agents
+    workflow = create_supervisor(
+        agents=[researcher, writer, analyst],
+        model=model,
+        prompt=(
+            "You are a team supervisor managing three specialized agents:\n"
+            "- researcher: Has web search capabilities. Use for gathering information, "
+            "facts, current events, or research tasks.\n"
+            "- writer: Specializes in content creation and summarization. Use for "
+            "writing articles, reports, summaries, or any content generation.\n"
+            "- analyst: Expert in calculations and data analysis. Use for math problems, "
+            "data interpretation, or analytical tasks.\n\n"
+            "Your job is to:\n"
+            "1. Understand the user's request\n"
+            "2. Break it down into subtasks if needed\n"
+            "3. Assign tasks to the appropriate agent(s)\n"
+            "4. Synthesize the final response from agent outputs\n\n"
+            "Assign work to one agent at a time. Do not do the work yourself - "
+            "always delegate to the appropriate specialist."
+        ),
+        # Include full conversation history for context
+        output_mode="full_history",
+    )
+
+    return workflow.compile()
diff --git a/langgraph_plugin/graph_api/supervisor/run_worker.py b/langgraph_plugin/graph_api/supervisor/run_worker.py
new file mode 100644
index 00000000..738f9407
--- /dev/null
+++ b/langgraph_plugin/graph_api/supervisor/run_worker.py
@@ -0,0 +1,53 @@
+"""Worker for the Supervisor Multi-Agent sample.
+
+Starts a Temporal worker that can execute SupervisorWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.supervisor.graph import build_supervisor_graph
+from langgraph_plugin.graph_api.supervisor.workflow import SupervisorWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the supervisor graph registered
+    plugin = LangGraphPlugin(
+        graphs={"supervisor": build_supervisor_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities (LLM calls, tool executions, agent handoffs) are automatically registered
+    worker = Worker(
+        client,
+        task_queue="langgraph-supervisor",
+        workflows=[SupervisorWorkflow],
+    )
+
+    print("Supervisor Multi-Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    print()
+    print("This worker handles:")
+    print("  - Supervisor: Coordinates and routes tasks")
+    print("  - Researcher: Web search and information gathering")
+    print("  - Writer: Content creation and summarization")
+    print("  - Analyst: Calculations and data analysis")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/supervisor/run_workflow.py b/langgraph_plugin/graph_api/supervisor/run_workflow.py
new file mode 100644
index 00000000..da657676
--- /dev/null
+++ b/langgraph_plugin/graph_api/supervisor/run_workflow.py
@@ -0,0 +1,60 @@
+"""Execute the Supervisor Multi-Agent workflow."""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_plugin.graph_api.supervisor.workflow import SupervisorWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # This request requires multiple agents:
+    # 1. Researcher to find information about AI trends
+    # 2. Analyst to process/analyze the data
+    # 3. Writer to create a summary
+    request = (
+        "Research the latest AI trends in 2024, analyze which trends are most "
+        "impactful for enterprise software development, and write a brief "
+        "executive summary (2-3 paragraphs)."
+    )
+
+    print("Executing supervisor multi-agent workflow...")
+    print(f"Request: {request}")
+    print()
+
+    result = await client.execute_workflow(
+        SupervisorWorkflow.run,
+        request,
+        id="supervisor-workflow",
+        task_queue="langgraph-supervisor",
+    )
+
+    # Print the final response
+    print("=" * 60)
+    print("FINAL RESPONSE:")
+    print("=" * 60)
+
+    # Get the last assistant message
+    messages = result.get("messages", [])
+    for msg in reversed(messages):
+        # Handle both dict and object formats
+        if isinstance(msg, dict):
+            if msg.get("role") == "assistant" or msg.get("type") == "ai":
+                content = msg.get("content", "")
+                if content and not content.startswith("["):  # Skip tool outputs
+                    print(content)
+                    break
+        elif hasattr(msg, "content"):
+            msg_type = getattr(msg, "type", "")
+            if msg_type == "ai" and msg.content:
+                print(msg.content)
+                break
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_plugin/graph_api/supervisor/workflow.py b/langgraph_plugin/graph_api/supervisor/workflow.py
new file mode 100644
index 00000000..138176ed
--- /dev/null
+++ b/langgraph_plugin/graph_api/supervisor/workflow.py
@@ -0,0 +1,55 @@
+"""Supervisor Multi-Agent Workflow.
+
+Temporal workflow that executes the supervisor multi-agent system with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class SupervisorWorkflow:
+    """Temporal workflow that executes a supervisor multi-agent system.
+
+    This workflow uses a supervisor pattern where a central coordinator
+    routes tasks to specialized agents (researcher, writer, analyst).
+    Each agent's execution runs as Temporal activities:
+    - The supervisor's routing decisions run as activities
+    - Each specialized agent's work runs as activities
+    - Agent handoffs are durably recorded
+
+    If any agent fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed agent interaction.
+    """
+
+    @workflow.run
+    async def run(self, request: str) -> dict[str, Any]:
+        """Run the supervisor multi-agent system to handle a request.
+
+        The supervisor will:
+        1. Analyze the request to determine which agent(s) are needed
+        2. Route tasks to appropriate specialized agents
+        3. Coordinate agent interactions and handoffs
+        4. Synthesize a final response from agent outputs
+
+        Args:
+            request: The user's request or task description.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("supervisor")
+
+        # Execute the multi-agent system
+        # The supervisor will coordinate the specialized agents
+        result = await app.ainvoke({"messages": [{"role": "user", "content": request}]})
+
+        return result
diff --git a/pyproject.toml b/pyproject.toml
index caae123c..498e37ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,13 +31,13 @@ dsl = ["pyyaml>=6.0.1,<7", "types-pyyaml>=6.0.12,<7", "dacite>=1.8.1,<2"]
 encryption = ["cryptography>=38.0.1,<39", "aiohttp>=3.8.1,<4"]
 gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"]
 langchain = [
-    "langchain>=0.1.7,<0.2 ; python_version >= '3.8.1' and python_version < '4.0'",
-    "langchain-openai>=0.0.6,<0.0.7 ; python_version >= '3.8.1' and python_version < '4.0'",
-    "langsmith>=0.1.22,<0.2 ; python_version >= '3.8.1' and python_version < '4.0'",
+    "langchain>=0.3.0",
+    "langchain-openai>=0.2.0",
+    "langsmith>=0.1.22",
     "openai>=1.4.0,<2",
     "fastapi>=0.115.12",
     "tqdm>=4.62.0,<5",
-    "uvicorn[standard]>=0.24.0.post1,<0.25",
+    "uvicorn[standard]>=0.24.0",
 ]
 nexus = ["nexus-rpc>=1.1.0,<2"]
 open-telemetry = [
@@ -58,6 +58,17 @@ cloud-export-to-parquet = [
     "boto3>=1.34.89,<2",
     "pyarrow>=19.0.1",
 ]
+langgraph = [
+    "langgraph>=0.2.0",
+    "langgraph-supervisor>=0.0.1",
+    "langchain>=0.3.0",
+    "langchain-core>=0.1.0",
+    "langchain-openai>=0.2.0",
+    "langchain-community>=0.3.0",
+    "duckduckgo-search>=6.0.0",
+    "ddgs>=7.0.0",
+    "numpy>=1.26.0,<3",
+]
 
 [tool.hatch.metadata]
 allow-direct-references = true
@@ -80,6 +91,7 @@ packages = [
     "gevent_async",
     "hello",
     "langchain",
+    "langgraph_plugin",
     "message_passing",
     "nexus",
     "open_telemetry",
diff --git a/temporal_functional_architecture.png b/temporal_functional_architecture.png
new file mode 100644
index 00000000..e6f3d717
Binary files /dev/null and b/temporal_functional_architecture.png differ
diff --git a/temporal_functional_architecture.svg b/temporal_functional_architecture.svg
new file mode 100644
index 00000000..88348b4e
--- /dev/null
+++ b/temporal_functional_architecture.svg
@@ -0,0 +1,132 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 900 500" width="900" height="500">
+  <defs>
+    <linearGradient id="workflowGrad" x1="0%" y1="0%" x2="0%" y2="100%">
+      <stop offset="0%" style="stop-color:#1a365d"/>
+      <stop offset="100%" style="stop-color:#2c5282"/>
+    </linearGradient>
+    <linearGradient id="activityGrad" x1="0%" y1="0%" x2="0%" y2="100%">
+      <stop offset="0%" style="stop-color:#744210"/>
+      <stop offset="100%" style="stop-color:#975a16"/>
+    </linearGradient>
+    <marker id="arrow" markerWidth="10" markerHeight="7" refX="9" refY="3.5" orient="auto">
+      <polygon points="0 0, 10 3.5, 0 7" fill="#4a5568"/>
+    </marker>
+  </defs>
+
+  <!-- Background -->
+  <rect width="900" height="500" fill="#f7fafc"/>
+
+  <!-- Title -->
+  <text x="450" y="40" font-family="Arial, sans-serif" font-size="24" font-weight="bold" fill="#1a202c" text-anchor="middle">
+    Temporal + LangGraph Functional API
+  </text>
+
+  <!-- Temporal Workflow Box -->
+  <rect x="40" y="80" width="340" height="380" rx="12" fill="none" stroke="#2c5282" stroke-width="3"/>
+  <rect x="40" y="80" width="340" height="45" rx="12" fill="url(#workflowGrad)"/>
+  <rect x="40" y="113" width="340" height="12" fill="url(#workflowGrad)"/>
+  <text x="210" y="112" font-family="Arial, sans-serif" font-size="18" font-weight="bold" fill="white" text-anchor="middle">
+    Temporal Workflow
+  </text>
+
+  <!-- TemporalFunctionalRunner -->
+  <rect x="70" y="150" width="280" height="70" rx="8" fill="#3182ce"/>
+  <text x="210" y="190" font-family="Arial, sans-serif" font-size="16" font-weight="bold" fill="white" text-anchor="middle">
+    TemporalFunctionalRunner
+  </text>
+
+  <!-- @entrypoint execution -->
+  <rect x="70" y="250" width="280" height="70" rx="8" fill="#38a169"/>
+  <text x="210" y="282" font-family="Arial, sans-serif" font-size="16" font-weight="bold" fill="white" text-anchor="middle">
+    @entrypoint
+  </text>
+  <text x="210" y="302" font-family="Arial, sans-serif" font-size="12" fill="#c6f6d5" text-anchor="middle">
+    Direct function execution
+  </text>
+
+  <!-- Task Result Cache -->
+  <rect x="70" y="350" width="280" height="70" rx="8" fill="#4a5568"/>
+  <text x="210" y="382" font-family="Arial, sans-serif" font-size="16" font-weight="bold" fill="white" text-anchor="middle">
+    Task Result Cache
+  </text>
+  <text x="210" y="402" font-family="Arial, sans-serif" font-size="12" fill="#e2e8f0" text-anchor="middle">
+    For continue-as-new
+  </text>
+
+  <!-- Arrows inside workflow -->
+  <line x1="210" y1="220" x2="210" y2="248" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+  <line x1="210" y1="320" x2="210" y2="348" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+
+  <!-- Temporal Activities Box -->
+  <rect x="520" y="80" width="340" height="380" rx="12" fill="none" stroke="#975a16" stroke-width="3"/>
+  <rect x="520" y="80" width="340" height="45" rx="12" fill="url(#activityGrad)"/>
+  <rect x="520" y="113" width="340" height="12" fill="url(#activityGrad)"/>
+  <text x="690" y="112" font-family="Arial, sans-serif" font-size="18" font-weight="bold" fill="white" text-anchor="middle">
+    Temporal Activities
+  </text>
+
+  <!-- @task Activity boxes -->
+  <rect x="550" y="150" width="130" height="90" rx="8" fill="#805ad5"/>
+  <text x="615" y="185" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    @task LLM
+  </text>
+  <text x="615" y="210" font-family="Arial, sans-serif" font-size="12" fill="#e9d8fd" text-anchor="middle">
+    ChatGPT, Claude
+  </text>
+  <text x="615" y="228" font-family="Arial, sans-serif" font-size="12" fill="#e9d8fd" text-anchor="middle">
+    Embeddings
+  </text>
+
+  <rect x="700" y="150" width="130" height="90" rx="8" fill="#dd6b20"/>
+  <text x="765" y="185" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    @task Tool
+  </text>
+  <text x="765" y="210" font-family="Arial, sans-serif" font-size="12" fill="#feebc8" text-anchor="middle">
+    API Calls
+  </text>
+  <text x="765" y="228" font-family="Arial, sans-serif" font-size="12" fill="#feebc8" text-anchor="middle">
+    Database
+  </text>
+
+  <rect x="550" y="260" width="130" height="90" rx="8" fill="#319795"/>
+  <text x="615" y="295" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    @task Agent
+  </text>
+  <text x="615" y="320" font-family="Arial, sans-serif" font-size="12" fill="#b2f5ea" text-anchor="middle">
+    ReAct Loop
+  </text>
+  <text x="615" y="338" font-family="Arial, sans-serif" font-size="12" fill="#b2f5ea" text-anchor="middle">
+    Multi-step
+  </text>
+
+  <rect x="700" y="260" width="130" height="90" rx="8" fill="#e53e3e"/>
+  <text x="765" y="295" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    @task Human
+  </text>
+  <text x="765" y="320" font-family="Arial, sans-serif" font-size="12" fill="#fed7d7" text-anchor="middle">
+    interrupt()
+  </text>
+  <text x="765" y="338" font-family="Arial, sans-serif" font-size="12" fill="#fed7d7" text-anchor="middle">
+    Approval
+  </text>
+
+  <rect x="625" y="370" width="130" height="70" rx="8" fill="#4a5568"/>
+  <text x="690" y="405" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    @task Transform
+  </text>
+  <text x="690" y="428" font-family="Arial, sans-serif" font-size="12" fill="#e2e8f0" text-anchor="middle">
+    Pure functions
+  </text>
+
+  <!-- Arrows between workflow and activities -->
+  <line x1="350" y1="285" x2="518" y2="285" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+  <text x="434" y="275" font-family="Arial, sans-serif" font-size="12" fill="#718096" text-anchor="middle">
+    execute
+  </text>
+
+  <line x1="518" y1="310" x2="352" y2="310" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+  <text x="434" y="330" font-family="Arial, sans-serif" font-size="12" fill="#718096" text-anchor="middle">
+    results
+  </text>
+</svg>
diff --git a/temporal_langgraph_architecture.png b/temporal_langgraph_architecture.png
new file mode 100644
index 00000000..14a41200
Binary files /dev/null and b/temporal_langgraph_architecture.png differ
diff --git a/temporal_langgraph_architecture.svg b/temporal_langgraph_architecture.svg
new file mode 100644
index 00000000..42a272e0
--- /dev/null
+++ b/temporal_langgraph_architecture.svg
@@ -0,0 +1,126 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 900 500" width="900" height="500">
+  <defs>
+    <linearGradient id="workflowGrad" x1="0%" y1="0%" x2="0%" y2="100%">
+      <stop offset="0%" style="stop-color:#1a365d"/>
+      <stop offset="100%" style="stop-color:#2c5282"/>
+    </linearGradient>
+    <linearGradient id="activityGrad" x1="0%" y1="0%" x2="0%" y2="100%">
+      <stop offset="0%" style="stop-color:#744210"/>
+      <stop offset="100%" style="stop-color:#975a16"/>
+    </linearGradient>
+    <marker id="arrow" markerWidth="10" markerHeight="7" refX="9" refY="3.5" orient="auto">
+      <polygon points="0 0, 10 3.5, 0 7" fill="#4a5568"/>
+    </marker>
+  </defs>
+
+  <!-- Background -->
+  <rect width="900" height="500" fill="#f7fafc"/>
+
+  <!-- Title -->
+  <text x="450" y="40" font-family="Arial, sans-serif" font-size="24" font-weight="bold" fill="#1a202c" text-anchor="middle">
+    Temporal + LangGraph Architecture
+  </text>
+
+  <!-- Temporal Workflow Box -->
+  <rect x="40" y="80" width="340" height="380" rx="12" fill="none" stroke="#2c5282" stroke-width="3"/>
+  <rect x="40" y="80" width="340" height="45" rx="12" fill="url(#workflowGrad)"/>
+  <rect x="40" y="113" width="340" height="12" fill="url(#workflowGrad)"/>
+  <text x="210" y="112" font-family="Arial, sans-serif" font-size="18" font-weight="bold" fill="white" text-anchor="middle">
+    Temporal Workflow
+  </text>
+
+  <!-- TemporalLangGraphRunner -->
+  <rect x="70" y="150" width="280" height="70" rx="8" fill="#3182ce"/>
+  <text x="210" y="190" font-family="Arial, sans-serif" font-size="16" font-weight="bold" fill="white" text-anchor="middle">
+    TemporalLangGraphRunner
+  </text>
+
+  <!-- AsyncPregelLoop -->
+  <rect x="70" y="250" width="280" height="70" rx="8" fill="#38a169"/>
+  <text x="210" y="290" font-family="Arial, sans-serif" font-size="16" font-weight="bold" fill="white" text-anchor="middle">
+    AsyncPregelLoop
+  </text>
+
+  <!-- State -->
+  <rect x="70" y="350" width="280" height="70" rx="8" fill="#4a5568"/>
+  <text x="210" y="390" font-family="Arial, sans-serif" font-size="16" font-weight="bold" fill="white" text-anchor="middle">
+    Graph State / Channels
+  </text>
+
+  <!-- Arrows inside workflow -->
+  <line x1="210" y1="220" x2="210" y2="248" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+  <line x1="210" y1="320" x2="210" y2="348" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+
+  <!-- Temporal Activities Box -->
+  <rect x="520" y="80" width="340" height="380" rx="12" fill="none" stroke="#975a16" stroke-width="3"/>
+  <rect x="520" y="80" width="340" height="45" rx="12" fill="url(#activityGrad)"/>
+  <rect x="520" y="113" width="340" height="12" fill="url(#activityGrad)"/>
+  <text x="690" y="112" font-family="Arial, sans-serif" font-size="18" font-weight="bold" fill="white" text-anchor="middle">
+    Temporal Activities
+  </text>
+
+  <!-- Node Activity boxes -->
+  <rect x="550" y="150" width="130" height="90" rx="8" fill="#805ad5"/>
+  <text x="615" y="185" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    LLM Node
+  </text>
+  <text x="615" y="210" font-family="Arial, sans-serif" font-size="12" fill="#e9d8fd" text-anchor="middle">
+    ChatGPT, Claude
+  </text>
+  <text x="615" y="228" font-family="Arial, sans-serif" font-size="12" fill="#e9d8fd" text-anchor="middle">
+    Embeddings
+  </text>
+
+  <rect x="700" y="150" width="130" height="90" rx="8" fill="#dd6b20"/>
+  <text x="765" y="185" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    Tool Node
+  </text>
+  <text x="765" y="210" font-family="Arial, sans-serif" font-size="12" fill="#feebc8" text-anchor="middle">
+    API Calls
+  </text>
+  <text x="765" y="228" font-family="Arial, sans-serif" font-size="12" fill="#feebc8" text-anchor="middle">
+    Database
+  </text>
+
+  <rect x="550" y="260" width="130" height="90" rx="8" fill="#319795"/>
+  <text x="615" y="295" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    Agent Node
+  </text>
+  <text x="615" y="320" font-family="Arial, sans-serif" font-size="12" fill="#b2f5ea" text-anchor="middle">
+    ReAct Loop
+  </text>
+  <text x="615" y="338" font-family="Arial, sans-serif" font-size="12" fill="#b2f5ea" text-anchor="middle">
+    Multi-step
+  </text>
+
+  <rect x="700" y="260" width="130" height="90" rx="8" fill="#e53e3e"/>
+  <text x="765" y="295" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    Human Node
+  </text>
+  <text x="765" y="320" font-family="Arial, sans-serif" font-size="12" fill="#fed7d7" text-anchor="middle">
+    interrupt()
+  </text>
+  <text x="765" y="338" font-family="Arial, sans-serif" font-size="12" fill="#fed7d7" text-anchor="middle">
+    Approval
+  </text>
+
+  <rect x="625" y="370" width="130" height="70" rx="8" fill="#4a5568"/>
+  <text x="690" y="405" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">
+    Transform Node
+  </text>
+  <text x="690" y="428" font-family="Arial, sans-serif" font-size="12" fill="#e2e8f0" text-anchor="middle">
+    Pure functions
+  </text>
+
+  <!-- Arrows between workflow and activities -->
+  <line x1="350" y1="285" x2="518" y2="285" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+  <text x="434" y="275" font-family="Arial, sans-serif" font-size="12" fill="#718096" text-anchor="middle">
+    execute
+  </text>
+
+  <line x1="518" y1="310" x2="352" y2="310" stroke="#4a5568" stroke-width="2" marker-end="url(#arrow)"/>
+  <text x="434" y="330" font-family="Arial, sans-serif" font-size="12" fill="#718096" text-anchor="middle">
+    results
+  </text>
+</svg>
diff --git a/tests/langgraph_plugin/__init__.py b/tests/langgraph_plugin/__init__.py
new file mode 100644
index 00000000..f5a6846d
--- /dev/null
+++ b/tests/langgraph_plugin/__init__.py
@@ -0,0 +1 @@
+# LangGraph samples tests
diff --git a/tests/langgraph_plugin/agentic_rag_test.py b/tests/langgraph_plugin/agentic_rag_test.py
new file mode 100644
index 00000000..ae28614a
--- /dev/null
+++ b/tests/langgraph_plugin/agentic_rag_test.py
@@ -0,0 +1,40 @@
+"""Tests for the agentic_rag LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.agentic_rag.graph import build_agentic_rag_graph
+from langgraph_plugin.graph_api.agentic_rag.workflow import AgenticRAGWorkflow
+
+from .conftest import requires_openai
+
+
+@requires_openai
+async def test_agentic_rag_workflow(client: Client) -> None:
+    """Test agentic RAG workflow with a knowledge base query.
+
+    This test requires OPENAI_API_KEY to be set.
+    """
+    task_queue = f"agentic-rag-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"agentic_rag": build_agentic_rag_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[AgenticRAGWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            AgenticRAGWorkflow.run,
+            "What are AI agents?",
+            id=f"agentic-rag-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # The result should contain messages with retrieved context
+        assert "messages" in result
+        assert len(result["messages"]) > 0
diff --git a/tests/langgraph_plugin/approval_graph_interrupt_test.py b/tests/langgraph_plugin/approval_graph_interrupt_test.py
new file mode 100644
index 00000000..f872ef53
--- /dev/null
+++ b/tests/langgraph_plugin/approval_graph_interrupt_test.py
@@ -0,0 +1,119 @@
+"""Tests for the approval_graph_interrupt LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client, WorkflowHandle
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.activities import (
+    notify_approver,
+)
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.graph import (
+    build_approval_graph,
+)
+from langgraph_plugin.graph_api.human_in_the_loop.approval_graph_interrupt.workflow import (
+    ApprovalRequest,
+    ApprovalWorkflow,
+)
+
+
+async def test_approval_graph_interrupt_approved(client: Client) -> None:
+    """Test approval workflow when request is approved."""
+    task_queue = f"approval-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"approval_workflow": build_approval_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ApprovalWorkflow],
+        activities=[notify_approver],
+        plugins=[plugin],
+    ):
+        # Start the workflow
+        handle: WorkflowHandle[ApprovalWorkflow, dict] = await client.start_workflow(
+            ApprovalWorkflow.run,
+            ApprovalRequest(request_type="expense", amount=500.0),
+            id=f"approval-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # Wait for the workflow to reach the approval point
+        import asyncio
+
+        for _ in range(20):
+            status = await handle.query(ApprovalWorkflow.get_status)
+            if status == "waiting_for_approval":
+                break
+            await asyncio.sleep(0.1)
+
+        assert status == "waiting_for_approval"
+
+        # Query the pending approval
+        pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+        assert pending is not None
+        assert pending["amount"] == 500.0
+        assert pending["risk_level"] == "medium"
+
+        # Send approval signal
+        await handle.signal(
+            ApprovalWorkflow.provide_approval,
+            {"approved": True, "reason": "Looks good", "approver": "manager"},
+        )
+
+        # Wait for result
+        result = await handle.result()
+
+        assert result["approved"] is True
+        assert result["executed"] is True
+        assert "Successfully processed" in result["result"]
+        assert "manager" in result["result"]
+
+
+async def test_approval_graph_interrupt_rejected(client: Client) -> None:
+    """Test approval workflow when request is rejected."""
+    task_queue = f"approval-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"approval_workflow": build_approval_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ApprovalWorkflow],
+        activities=[notify_approver],
+        plugins=[plugin],
+    ):
+        handle: WorkflowHandle[ApprovalWorkflow, dict] = await client.start_workflow(
+            ApprovalWorkflow.run,
+            ApprovalRequest(request_type="purchase", amount=5000.0),
+            id=f"approval-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # Wait for approval state
+        import asyncio
+
+        for _ in range(20):
+            status = await handle.query(ApprovalWorkflow.get_status)
+            if status == "waiting_for_approval":
+                break
+            await asyncio.sleep(0.1)
+
+        # Verify high risk level for large amount
+        pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+        assert pending is not None
+        assert pending["risk_level"] == "high"
+
+        # Reject the request
+        await handle.signal(
+            ApprovalWorkflow.provide_approval,
+            {"approved": False, "reason": "Budget exceeded", "approver": "cfo"},
+        )
+
+        result = await handle.result()
+
+        assert result["approved"] is False
+        assert result["executed"] is False
+        assert "rejected" in result["result"]
+        assert "cfo" in result["result"]
diff --git a/tests/langgraph_plugin/approval_wait_condition_test.py b/tests/langgraph_plugin/approval_wait_condition_test.py
new file mode 100644
index 00000000..15dd01b5
--- /dev/null
+++ b/tests/langgraph_plugin/approval_wait_condition_test.py
@@ -0,0 +1,117 @@
+"""Tests for the approval_wait_condition LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client, WorkflowHandle
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.graph import (
+    build_approval_graph,
+    notify_approver,
+)
+from langgraph_plugin.graph_api.human_in_the_loop.approval_wait_condition.workflow import (
+    ApprovalRequest,
+    ApprovalWorkflow,
+)
+
+
+async def test_approval_wait_condition_approved(client: Client) -> None:
+    """Test approval workflow when request is approved."""
+    task_queue = f"approval-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"approval_workflow": build_approval_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ApprovalWorkflow],
+        activities=[notify_approver],
+        plugins=[plugin],
+    ):
+        # Start the workflow
+        handle: WorkflowHandle[ApprovalWorkflow, dict] = await client.start_workflow(
+            ApprovalWorkflow.run,
+            ApprovalRequest(request_type="expense", amount=500.0),
+            id=f"approval-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # Wait for the workflow to reach the approval point
+        import asyncio
+
+        for _ in range(20):
+            status = await handle.query(ApprovalWorkflow.get_status)
+            if status == "waiting_for_approval":
+                break
+            await asyncio.sleep(0.1)
+
+        assert status == "waiting_for_approval"
+
+        # Query the pending approval
+        pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+        assert pending is not None
+        assert pending["amount"] == 500.0
+        assert pending["risk_level"] == "medium"
+
+        # Send approval signal
+        await handle.signal(
+            ApprovalWorkflow.provide_approval,
+            {"approved": True, "reason": "Looks good", "approver": "manager"},
+        )
+
+        # Wait for result
+        result = await handle.result()
+
+        assert result["approved"] is True
+        assert result["executed"] is True
+        assert "Successfully processed" in result["result"]
+        assert "manager" in result["result"]
+
+
+async def test_approval_wait_condition_rejected(client: Client) -> None:
+    """Test approval workflow when request is rejected."""
+    task_queue = f"approval-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"approval_workflow": build_approval_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ApprovalWorkflow],
+        activities=[notify_approver],
+        plugins=[plugin],
+    ):
+        handle: WorkflowHandle[ApprovalWorkflow, dict] = await client.start_workflow(
+            ApprovalWorkflow.run,
+            ApprovalRequest(request_type="purchase", amount=5000.0),
+            id=f"approval-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # Wait for approval state
+        import asyncio
+
+        for _ in range(20):
+            status = await handle.query(ApprovalWorkflow.get_status)
+            if status == "waiting_for_approval":
+                break
+            await asyncio.sleep(0.1)
+
+        # Verify high risk level for large amount
+        pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+        assert pending is not None
+        assert pending["risk_level"] == "high"
+
+        # Reject the request
+        await handle.signal(
+            ApprovalWorkflow.provide_approval,
+            {"approved": False, "reason": "Budget exceeded", "approver": "cfo"},
+        )
+
+        result = await handle.result()
+
+        assert result["approved"] is False
+        assert result["executed"] is False
+        assert "rejected" in result["result"]
+        assert "cfo" in result["result"]
diff --git a/tests/langgraph_plugin/conftest.py b/tests/langgraph_plugin/conftest.py
new file mode 100644
index 00000000..5f1038ec
--- /dev/null
+++ b/tests/langgraph_plugin/conftest.py
@@ -0,0 +1,31 @@
+"""Shared test fixtures for LangGraph samples."""
+
+import os
+
+# Disable LangSmith tracing for tests to avoid rate limit issues
+os.environ["LANGCHAIN_TRACING_V2"] = "false"
+
+import pytest
+from temporalio.contrib.langgraph._functional_registry import (
+    get_global_entrypoint_registry,
+)
+from temporalio.contrib.langgraph._graph_registry import get_global_registry
+
+
+@pytest.fixture(autouse=True)
+def clear_registry() -> None:
+    """Clear the global registries before each test."""
+    get_global_registry().clear()
+    get_global_entrypoint_registry().clear()
+
+
+def has_openai_api_key() -> bool:
+    """Check if OpenAI API key is available."""
+    return bool(os.environ.get("OPENAI_API_KEY"))
+
+
+# Skip marker for tests that require OpenAI API
+requires_openai = pytest.mark.skipif(
+    not has_openai_api_key(),
+    reason="OPENAI_API_KEY environment variable not set",
+)
diff --git a/tests/langgraph_plugin/deep_research_test.py b/tests/langgraph_plugin/deep_research_test.py
new file mode 100644
index 00000000..07a8dd6c
--- /dev/null
+++ b/tests/langgraph_plugin/deep_research_test.py
@@ -0,0 +1,40 @@
+"""Tests for the deep_research LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.deep_research.graph import build_deep_research_graph
+from langgraph_plugin.graph_api.deep_research.workflow import DeepResearchWorkflow
+
+from .conftest import requires_openai
+
+
+@requires_openai
+async def test_deep_research_workflow(client: Client) -> None:
+    """Test deep research workflow with a research topic.
+
+    This test requires OPENAI_API_KEY to be set.
+    Note: This test may take longer due to web searches.
+    """
+    task_queue = f"deep-research-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"deep_research": build_deep_research_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[DeepResearchWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            DeepResearchWorkflow.run,
+            args=["What is Temporal.io?", 1],  # Just 1 iteration for testing
+            id=f"deep-research-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # The result should contain research findings
+        assert "messages" in result
diff --git a/tests/langgraph_plugin/functional_agentic_rag_test.py b/tests/langgraph_plugin/functional_agentic_rag_test.py
new file mode 100644
index 00000000..d5e91225
--- /dev/null
+++ b/tests/langgraph_plugin/functional_agentic_rag_test.py
@@ -0,0 +1,37 @@
+"""Tests for the agentic_rag Functional API sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.agentic_rag.entrypoint import (
+    agentic_rag_entrypoint,
+)
+from langgraph_plugin.functional_api.agentic_rag.workflow import AgenticRagWorkflow
+
+
+async def test_agentic_rag_functional_workflow(client: Client) -> None:
+    """Test that the agentic RAG functional workflow retrieves and generates."""
+    task_queue = f"agentic-rag-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"agentic_rag_entrypoint": agentic_rag_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[AgenticRagWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            AgenticRagWorkflow.run,
+            "What is Temporal?",
+            id=f"agentic-rag-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert "answer" in result
+        assert "documents_used" in result
diff --git a/tests/langgraph_plugin/functional_continue_as_new_test.py b/tests/langgraph_plugin/functional_continue_as_new_test.py
new file mode 100644
index 00000000..d545305c
--- /dev/null
+++ b/tests/langgraph_plugin/functional_continue_as_new_test.py
@@ -0,0 +1,60 @@
+"""Tests for the Continue-as-New Functional API sample."""
+
+import uuid
+from datetime import timedelta
+
+import pytest
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin, activity_options
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.continue_as_new.entrypoint import (
+    pipeline_entrypoint,
+)
+from langgraph_plugin.functional_api.continue_as_new.workflow import (
+    ContinueAsNewWorkflow,
+    PipelineInput,
+)
+
+
+@pytest.mark.asyncio
+async def test_continue_as_new_workflow(client: Client) -> None:
+    """Test that the continue-as-new workflow executes correctly.
+
+    Verifies:
+    1. Phase 1 executes tasks 1-3
+    2. Continue-as-new passes checkpoint
+    3. Phase 2 completes with all 5 tasks
+    4. Final result is correct (165)
+    """
+    plugin = LangGraphPlugin(
+        graphs={"pipeline_entrypoint": pipeline_entrypoint},
+        default_activity_options=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
+    )
+
+    new_config = client.config()
+    existing_plugins = new_config.get("plugins", [])
+    new_config["plugins"] = list(existing_plugins) + [plugin]
+    plugin_client = Client(**new_config)
+
+    task_queue = f"test-continue-as-new-{uuid.uuid4()}"
+
+    async with Worker(
+        plugin_client,
+        task_queue=task_queue,
+        workflows=[ContinueAsNewWorkflow],
+    ):
+        result = await plugin_client.execute_workflow(
+            ContinueAsNewWorkflow.run,
+            PipelineInput(value=10),
+            id=f"test-continue-as-new-{uuid.uuid4()}",
+            task_queue=task_queue,
+            execution_timeout=timedelta(seconds=60),
+        )
+
+        # Verify correct final result
+        # 10 * 2 = 20 -> 20 + 5 = 25 -> 25 * 3 = 75 -> 75 - 10 = 65 -> 65 + 100 = 165
+        assert result["result"] == 165
+        assert result["completed_tasks"] == 5
diff --git a/tests/langgraph_plugin/functional_deep_research_test.py b/tests/langgraph_plugin/functional_deep_research_test.py
new file mode 100644
index 00000000..d16770c3
--- /dev/null
+++ b/tests/langgraph_plugin/functional_deep_research_test.py
@@ -0,0 +1,38 @@
+"""Tests for the deep_research Functional API sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.deep_research.entrypoint import (
+    deep_research_entrypoint,
+)
+from langgraph_plugin.functional_api.deep_research.workflow import DeepResearchWorkflow
+
+
+async def test_deep_research_functional_workflow(client: Client) -> None:
+    """Test that the deep research functional workflow produces a report."""
+    task_queue = f"deep-research-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"deep_research_entrypoint": deep_research_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[DeepResearchWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            DeepResearchWorkflow.run,
+            "Python async programming",
+            id=f"deep-research-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert "report" in result
+        assert "relevant_results" in result
+        assert result["relevant_results"] >= 0
diff --git a/tests/langgraph_plugin/functional_hello_world_test.py b/tests/langgraph_plugin/functional_hello_world_test.py
new file mode 100644
index 00000000..115535ef
--- /dev/null
+++ b/tests/langgraph_plugin/functional_hello_world_test.py
@@ -0,0 +1,62 @@
+"""Tests for the hello_world Functional API sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.hello_world.entrypoint import (
+    hello_world_entrypoint,
+)
+from langgraph_plugin.functional_api.hello_world.workflow import HelloWorldWorkflow
+
+
+async def test_hello_world_functional_workflow(client: Client) -> None:
+    """Test that the hello world functional workflow processes a query correctly."""
+    task_queue = f"hello-world-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"hello_world_entrypoint": hello_world_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[HelloWorldWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            HelloWorldWorkflow.run,
+            "Hello from test",
+            id=f"hello-world-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert result["query"] == "Hello from test"
+        assert result["result"] == "Processed: Hello from test"
+
+
+async def test_hello_world_functional_empty_query(client: Client) -> None:
+    """Test hello world functional workflow with empty query."""
+    task_queue = f"hello-world-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"hello_world_entrypoint": hello_world_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[HelloWorldWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            HelloWorldWorkflow.run,
+            "",
+            id=f"hello-world-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert result["query"] == ""
+        assert result["result"] == "Processed: "
diff --git a/tests/langgraph_plugin/functional_plan_and_execute_test.py b/tests/langgraph_plugin/functional_plan_and_execute_test.py
new file mode 100644
index 00000000..b73ce831
--- /dev/null
+++ b/tests/langgraph_plugin/functional_plan_and_execute_test.py
@@ -0,0 +1,40 @@
+"""Tests for the plan_and_execute Functional API sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.plan_and_execute.entrypoint import (
+    plan_execute_entrypoint,
+)
+from langgraph_plugin.functional_api.plan_and_execute.workflow import (
+    PlanExecuteWorkflow,
+)
+
+
+async def test_plan_execute_functional_workflow(client: Client) -> None:
+    """Test that the plan-and-execute functional workflow creates and executes a plan."""
+    task_queue = f"plan-execute-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"plan_execute_entrypoint": plan_execute_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[PlanExecuteWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            PlanExecuteWorkflow.run,
+            "Build a simple calculator",
+            id=f"plan-execute-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert "final_response" in result
+        assert "step_results" in result
+        assert len(result["step_results"]) > 0
diff --git a/tests/langgraph_plugin/functional_reflection_test.py b/tests/langgraph_plugin/functional_reflection_test.py
new file mode 100644
index 00000000..de8e5ae5
--- /dev/null
+++ b/tests/langgraph_plugin/functional_reflection_test.py
@@ -0,0 +1,36 @@
+"""Tests for the reflection Functional API sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.reflection.entrypoint import reflection_entrypoint
+from langgraph_plugin.functional_api.reflection.workflow import ReflectionWorkflow
+
+
+async def test_reflection_functional_workflow(client: Client) -> None:
+    """Test that the reflection functional workflow improves content."""
+    task_queue = f"reflection-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"reflection_entrypoint": reflection_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ReflectionWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            ReflectionWorkflow.run,
+            "Write about testing",
+            id=f"reflection-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert "final_content" in result
+        assert "iterations" in result
+        assert result["iterations"] >= 1
diff --git a/tests/langgraph_plugin/functional_supervisor_test.py b/tests/langgraph_plugin/functional_supervisor_test.py
new file mode 100644
index 00000000..db5bebd7
--- /dev/null
+++ b/tests/langgraph_plugin/functional_supervisor_test.py
@@ -0,0 +1,36 @@
+"""Tests for the supervisor Functional API sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.functional_api.supervisor.entrypoint import supervisor_entrypoint
+from langgraph_plugin.functional_api.supervisor.workflow import SupervisorWorkflow
+
+
+async def test_supervisor_functional_workflow(client: Client) -> None:
+    """Test that the supervisor functional workflow coordinates agents."""
+    task_queue = f"supervisor-functional-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(
+        graphs={"supervisor_entrypoint": supervisor_entrypoint},
+    )
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[SupervisorWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            SupervisorWorkflow.run,
+            "Analyze market trends",
+            id=f"supervisor-functional-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert "final_answer" in result
+        assert "agent_outputs" in result
+        assert len(result["agent_outputs"]) > 0
diff --git a/tests/langgraph_plugin/hello_world_test.py b/tests/langgraph_plugin/hello_world_test.py
new file mode 100644
index 00000000..3c087bfe
--- /dev/null
+++ b/tests/langgraph_plugin/hello_world_test.py
@@ -0,0 +1,55 @@
+"""Tests for the hello_world LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.hello_world.graph import build_hello_graph
+from langgraph_plugin.graph_api.hello_world.workflow import HelloWorldWorkflow
+
+
+async def test_hello_world_workflow(client: Client) -> None:
+    """Test that the hello world workflow processes a query correctly."""
+    task_queue = f"hello-world-test-{uuid.uuid4()}"
+
+    # Create plugin with the graph
+    plugin = LangGraphPlugin(graphs={"hello_graph": build_hello_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[HelloWorldWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            HelloWorldWorkflow.run,
+            "Hello from test",
+            id=f"hello-world-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert result["result"] == "Processed: Hello from test"
+
+
+async def test_hello_world_empty_query(client: Client) -> None:
+    """Test hello world workflow with empty query."""
+    task_queue = f"hello-world-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"hello_graph": build_hello_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[HelloWorldWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            HelloWorldWorkflow.run,
+            "",
+            id=f"hello-world-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert result["result"] == "Processed: "
diff --git a/tests/langgraph_plugin/plan_and_execute_test.py b/tests/langgraph_plugin/plan_and_execute_test.py
new file mode 100644
index 00000000..f6353072
--- /dev/null
+++ b/tests/langgraph_plugin/plan_and_execute_test.py
@@ -0,0 +1,41 @@
+"""Tests for the plan_and_execute LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.plan_and_execute.graph import (
+    build_plan_and_execute_graph,
+)
+from langgraph_plugin.graph_api.plan_and_execute.workflow import PlanAndExecuteWorkflow
+
+from .conftest import requires_openai
+
+
+@requires_openai
+async def test_plan_and_execute_workflow(client: Client) -> None:
+    """Test plan and execute workflow with a simple task.
+
+    This test requires OPENAI_API_KEY to be set.
+    """
+    task_queue = f"plan-execute-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"plan_and_execute": build_plan_and_execute_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[PlanAndExecuteWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            PlanAndExecuteWorkflow.run,
+            "Calculate 15 * 8 and tell me the result",
+            id=f"plan-execute-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # The result should contain plan steps and results
+        assert "messages" in result
diff --git a/tests/langgraph_plugin/react_agent_test.py b/tests/langgraph_plugin/react_agent_test.py
new file mode 100644
index 00000000..1845945c
--- /dev/null
+++ b/tests/langgraph_plugin/react_agent_test.py
@@ -0,0 +1,70 @@
+"""Tests for the react_agent LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.react_agent.graph import build_react_agent
+from langgraph_plugin.graph_api.react_agent.workflow import ReActAgentWorkflow
+
+from .conftest import requires_openai
+
+
+@requires_openai
+async def test_react_agent_workflow(client: Client) -> None:
+    """Test ReAct agent workflow with a simple query.
+
+    This test requires OPENAI_API_KEY to be set.
+    """
+    task_queue = f"react-agent-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"react_agent": build_react_agent})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ReActAgentWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            ReActAgentWorkflow.run,
+            "What is 2 + 2?",
+            id=f"react-agent-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # The result should contain messages with the answer
+        assert "messages" in result
+        assert len(result["messages"]) > 0
+
+
+@requires_openai
+async def test_react_agent_with_tools(client: Client) -> None:
+    """Test ReAct agent using tools.
+
+    This test requires OPENAI_API_KEY to be set.
+    """
+    task_queue = f"react-agent-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"react_agent": build_react_agent})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ReActAgentWorkflow],
+        plugins=[plugin],
+    ):
+        # Query that should use the get_weather tool
+        result = await client.execute_workflow(
+            ReActAgentWorkflow.run,
+            "What's the weather in New York?",
+            id=f"react-agent-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        assert "messages" in result
+        # Should have used the weather tool and returned a result
+        messages = result["messages"]
+        assert len(messages) > 1  # At least user message and AI response
diff --git a/tests/langgraph_plugin/reflection_test.py b/tests/langgraph_plugin/reflection_test.py
new file mode 100644
index 00000000..2556110c
--- /dev/null
+++ b/tests/langgraph_plugin/reflection_test.py
@@ -0,0 +1,40 @@
+"""Tests for the reflection LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.reflection.graph import build_reflection_graph
+from langgraph_plugin.graph_api.reflection.workflow import ReflectionWorkflow
+
+from .conftest import requires_openai
+
+
+@requires_openai
+async def test_reflection_workflow(client: Client) -> None:
+    """Test reflection workflow with a writing task.
+
+    This test requires OPENAI_API_KEY to be set.
+    """
+    task_queue = f"reflection-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"reflection": build_reflection_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[ReflectionWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            ReflectionWorkflow.run,
+            args=["Write a short haiku about programming", 2],
+            id=f"reflection-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # The result should contain final content
+        assert "final_content" in result or "current_draft" in result
+        assert "messages" in result
diff --git a/tests/langgraph_plugin/supervisor_test.py b/tests/langgraph_plugin/supervisor_test.py
new file mode 100644
index 00000000..826d07ee
--- /dev/null
+++ b/tests/langgraph_plugin/supervisor_test.py
@@ -0,0 +1,40 @@
+"""Tests for the supervisor LangGraph sample."""
+
+import uuid
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_plugin.graph_api.supervisor.graph import build_supervisor_graph
+from langgraph_plugin.graph_api.supervisor.workflow import SupervisorWorkflow
+
+from .conftest import requires_openai
+
+
+@requires_openai
+async def test_supervisor_workflow(client: Client) -> None:
+    """Test supervisor workflow with a research query.
+
+    This test requires OPENAI_API_KEY to be set.
+    """
+    task_queue = f"supervisor-test-{uuid.uuid4()}"
+
+    plugin = LangGraphPlugin(graphs={"supervisor": build_supervisor_graph})
+
+    async with Worker(
+        client,
+        task_queue=task_queue,
+        workflows=[SupervisorWorkflow],
+        plugins=[plugin],
+    ):
+        result = await client.execute_workflow(
+            SupervisorWorkflow.run,
+            "What is the capital of France?",
+            id=f"supervisor-{uuid.uuid4()}",
+            task_queue=task_queue,
+        )
+
+        # The result should contain messages
+        assert "messages" in result
+        assert len(result["messages"]) > 0
diff --git a/uv.lock b/uv.lock
index dd39daa7..ff1dc05e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,9 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version >= '3.12.4' and python_full_version < '3.13'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
     "python_full_version < '3.11'",
 ]
@@ -12,7 +12,7 @@ resolution-markers = [
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
-source = { registry = "https://pypi.org/simple/" }
+source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
     { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
@@ -20,8 +20,8 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.14"
-source = { registry = "https://pypi.org/simple/" }
+version = "3.13.3"
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
@@ -32,82 +32,116 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e6/0b/e39ad954107ebf213a2325038a3e7a506be3d98e1435e1f82086eec4cde2/aiohttp-3.12.14.tar.gz", hash = "sha256:6e06e120e34d93100de448fd941522e11dafa78ef1a893c179901b7d66aa29f2", size = 7822921, upload-time = "2025-07-10T13:05:33.968Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0c/88/f161f429f9de391eee6a5c2cffa54e2ecd5b7122ae99df247f7734dfefcb/aiohttp-3.12.14-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:906d5075b5ba0dd1c66fcaaf60eb09926a9fef3ca92d912d2a0bbdbecf8b1248", size = 702641, upload-time = "2025-07-10T13:02:38.98Z" },
-    { url = "https://files.pythonhosted.org/packages/fe/b5/24fa382a69a25d242e2baa3e56d5ea5227d1b68784521aaf3a1a8b34c9a4/aiohttp-3.12.14-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c875bf6fc2fd1a572aba0e02ef4e7a63694778c5646cdbda346ee24e630d30fb", size = 479005, upload-time = "2025-07-10T13:02:42.714Z" },
-    { url = "https://files.pythonhosted.org/packages/09/67/fda1bc34adbfaa950d98d934a23900918f9d63594928c70e55045838c943/aiohttp-3.12.14-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fbb284d15c6a45fab030740049d03c0ecd60edad9cd23b211d7e11d3be8d56fd", size = 466781, upload-time = "2025-07-10T13:02:44.639Z" },
-    { url = "https://files.pythonhosted.org/packages/36/96/3ce1ea96d3cf6928b87cfb8cdd94650367f5c2f36e686a1f5568f0f13754/aiohttp-3.12.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:38e360381e02e1a05d36b223ecab7bc4a6e7b5ab15760022dc92589ee1d4238c", size = 1648841, upload-time = "2025-07-10T13:02:46.356Z" },
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