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A DeltaChannel replays its saved writes on every read, and
`_messages_delta_reducer` doesn't fill in missing ids, so the loop gives
messages their ids before saving them (`ensure_message_ids`). That
missed several cases:
- Strings and `(role, content)` tuples, alone or in a list (`[("user",
"hi")]`), and messages inside an `Overwrite` were saved with no id, so
nothing could target them by id, for example `RemoveMessage`. A regular
messages channel gives them ids.
- A raw `Pregel` graph's input to a DeltaChannel got its ids after it
was applied, and none with exit durability or on a new thread; a dict
given as input read back with an id its nodes never saw. A
`Command(update=...)` sent to an older checkpoint got none either.
Now, in a DeltaChannel whose reducer is `_messages_delta_reducer`, every
message-like the reducer accepts becomes a message with an id before the
writes are saved or applied: in `put_writes`, before input is applied,
for a forked `Command`, and in `update_state`. Anything that can't
become a message is left as it is, so it fails where it always has.
Other DeltaChannels keep the rule they had (message objects, and message
dicts in a list, get ids), since they may hold other data and
deepagents' messages channel, which uses its own copy of the reducer,
relies on it; the dicts in an `Overwrite` there are left as they are.
Lists and dicts the caller passed in are never changed: a write that
changes is replaced. So the id step can recognize the reducer without an
import cycle, `_messages_delta_reducer` now lives in
`langgraph.pregel._messages`; `langgraph.graph.message` re-exports it.
Visible changes: in a messages DeltaChannel, a node's update streams as
messages with ids, and a string is stored as a message (about 170 bytes
more each).
JS twin: langchain-ai/langgraphjs#2992. Thanks to @HarshShroff for
testing every `Overwrite` form and for the tuple fix.
## Tests
`test_delta_channel_id_stability.py` and
`test_delta_channel_update_state.py`: a write of every shape the reducer
accepts gets an id that every read keeps, as input, from a node and
through `update_state` (sync, async and as input); a list written twice
adds its messages twice and stays as it was; a forked `Command` gets an
id; a message dict the reducer rejects in a list still fails the run
before it's saved; a raw `Pregel` input gets an id on a new thread with
every durability, and its nodes see the ids that are saved; another
DeltaChannel keeps the dicts in an `Overwrite`. 36 of the 87 tests in
these files fail on main. The langgraph and prebuilt suites pass, and
lint is clean.
---------
Co-authored-by: Harsh Shroff <29751900+HarshShroff@users.noreply.github.com>
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.