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.

Since the LangGraph integration is currently in a branch, you need to install from the branch repositories.

Clone this samples repository:

git clone -b langgraph_plugin https://github.com/mfateev/samples-python.git
cd samples-python

Install the SDK from the langgraph-plugin branch:

uv pip install "temporalio @ git+https://github.com/mfateev/sdk-python.git@langgraph-plugin"

Each directory contains a complete example with its own README for detailed instructions:

Sample	Description
hello_world	Simple starter example demonstrating basic plugin setup and graph registration
activity_from_node	Calling Temporal activities from a graph node using run_in_workflow
react_agent	ReAct agent pattern with tool calling and multi-step reasoning
human_in_the_loop	Human-in-the-loop approval workflows using two approaches
↳ approval_graph_interrupt	Uses LangGraph's `interrupt()` function
↳ approval_wait_condition	Uses `run_in_workflow=True` with `workflow.wait_condition()`
supervisor	Multi-agent supervisor pattern coordinating specialized agents
agentic_rag	Retrieval-augmented generation with document grading and query rewriting
deep_research	Multi-step research with web search and iterative refinement
plan_and_execute	Plan-and-execute pattern with structured step execution
reflection	Self-reflection pattern for iterative improvement