Elior Nataf LackritzandGitHub
799c2f04fd
An exit-durability run that starts from a checkpoint the thread has
moved past, addressed by `checkpoint_id`, stores its DeltaChannel writes
on that checkpoint. The branch that already grew from it reads the
writes stored there, so its checkpoints picked up this run's writes too:
```python
graph.invoke({"log": ["in1"]}, config) # nodes a and b add "a1", "b1"
turn_1 = graph.get_state(config).config
graph.invoke({"log": ["in2"]}, config)
turn_2 = graph.get_state(config).config
graph.invoke({"log": ["in3"]}, turn_1, durability="exit")
graph.get_state(turn_2).values["log"]
# ["in1", "a1", "b1", "in3", "a3", "b3", "in2", "a2", "b2"], was ["in1", "a1", "b1", "in2", "a2", "b2"]
```
Sync and async durability store each step's writes on the run's own
checkpoints, so they weren't affected. The run now snapshots the
DeltaChannels it wrote instead, as a run's input already does on a
checkpoint a `checkpoint_id` addressed (#9260). That costs one snapshot
of each channel the run wrote, even when the addressed checkpoint is the
latest one. Threads that already hit this keep the wrong values on the
existing branch.
Found by @hntrl in review of langchain-ai/langgraphjs#2974
([repro](https://gist.github.com/hntrl/2185e489abe22147b3f6f77010c18264)).
JS twin: langchain-ai/langgraphjs#2989
## Tests
`test_delta_channel_exit_mode.py`: one and two exit runs from the end of
turn 1, after turn 2, leave every checkpoint's DeltaChannel equal to a
plain reducer channel. Both fail on main on every saver in the fixture.
The `langgraph` suite passes (without Redis), and lint is clean.
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
Tip
If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.