Elior Nataf LackritzandGitHub
524b320e30
A `DeltaChannel` with no version at a checkpoint was never written, so
its value is empty. `channels_from_checkpoint` still asked the saver for
its history, and with no snapshot to stop at, the walk reads every
ancestor of the thread, on every run and every `get_state`. A channel
that never gets written is common: deepagents' `files` channel when an
agent doesn't use files.
On SQLite the walk fails outright once the thread is long enough,
because stage 2 binds one variable per ancestor. With one never-written
channel on a 33,000-step thread:
```
main: get_state -> OperationalError: too many SQL variables
next run -> OperationalError: too many SQL variables
branch: get_state ok in 1 ms, next run ok
```
## Fix
Only channels that have a version at the checkpoint are walked. A
channel without one hydrates empty without asking the saver.
A version is assigned whenever a channel is written (by a task, an
input, `update_state` or a migration from a plain channel), so no
version means no write on this branch. The walk could only have found
writes this branch never applied.
Snapshotting such a channel instead would also bound the walk, but it
writes a blob for an empty value on every thread. Today
`create_checkpoint` skips any channel without a version, so even the
forced `DELTA_MAX_SUPERSTEPS_SINCE_SNAPSHOT` snapshot never lands for
it, and the walk has no bound.
## Tests
`test_delta_channel_supersteps_bound.py`: a graph whose second delta
channel is never written asks the saver only for the written channel's
history, sync and async. Both fail on main (`asked for [['a', 'b'],
['a', 'b']]`). A third test covers the case the skip must not break: a
delta channel's first write, left pending on an interrupted head before
the channel has a version, lands exactly once on resume. It passes on
main too.
JS twin: langchain-ai/langgraphjs#2927.
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.