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Sydney RunkleandClaude Opus 4.7
3a7ed5b454
Restructure DeltaChannel reconstruction so the hydration path matches pregel's storage axes (blobs + writes) without leaking internal DTOs into the public checkpoint contract. Key changes: * Deleted `DeltaChannelWrites` dataclass and `SEED_UNSET` sentinel. Reconstruction data no longer flows through `Checkpoint.channel_values` as a wrapped DTO — that field now carries a value or `DELTA_SENTINEL`, never a reconstruction shape. * Added private `_ChannelWritesHistory(seed: Any, writes: list[PendingWrite])` NamedTuple as the return type for the new storage-level query. * Added private, experimental `_get_channel_writes_history` / `_aget_channel_writes_history` on `BaseCheckpointSaver` — reference impl via `get_tuple` + `parent_config` walk, overridden on `InMemorySaver` / `PostgresSaver` / `AsyncPostgresSaver` for perf. Fixes a latent migration bug in the base fallback (now inspects ancestor `channel_values` for pre-delta seed). * `DeltaChannel.from_checkpoint(seed)` simplified to two cases (sentinel/MISSING → empty, else → seed). New `replay_writes` method folds `list[PendingWrite]` through the reducer. * Delta hydration consolidated inside `channels_from_checkpoint` via optional `saver` + `config` kwargs (+ async mirror `achannels_from_checkpoint`). All six pregel call sites updated. `get_tuple` no longer patches `channel_values` — removed `_resolve_delta_channels` (memory) and per-tuple reconstruction from `_load_checkpoint_tuple` (postgres sync + async). * Hydration short-circuits on the target's own blob: if `channel_values[k]` is a real value (pre-migration tip, `update_state` result), use it directly. Only walks ancestors when the target holds sentinel or is missing. Fixes a correctness bug where migration-tip and `update_state` values would be lost. * New test_delta_channel_migration.py: 10 scenarios covering BinaryOperatorAggregate → DeltaChannel migration (basic + async, time-travel, fork, `update_state`, tip-of-pre-migration, base-saver fallback parity, cross-thread isolation). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.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
If you're looking to quickly build agents with LangChain's create_agent (built on LangGraph), check out the LangChain Agents documentation.
Note
Looking for the 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 (new!) – 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.