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## Summary Replaces the single-roundtrip `UNION ALL` DeltaChannel read with a two-stage query that avoids fetching unused snapshot blobs, then removes the old combined path entirely. ### Problem `_get_channel_writes_history` used a single `UNION ALL` query that fetched **all** checkpoint metadata, writes, and blobs for a `(thread_id, channel)` in one shot. With `snapshot_frequency=N`, this pulled back O(N/freq) full-size snapshot blobs even though only the nearest one is needed to seed reconstruction. At 500 turns with `snapshot_frequency=10`, this meant fetching ~100 complete message-history snapshots per read. ### Solution Two-stage read: - **Stage 1** — lightweight scan of `checkpoints` only (no blob bytes): walks the parent chain from the target checkpoint and stops at the first ancestor with a snapshot, returning `chain_cids` and `seed_version` - **Stage 2** — targeted fetch: only the writes for `chain_cids` and the single seed blob at `seed_version` The two-stage path is now unconditional — the old combined query and `LG_DELTA_TWO_STAGE_QUERY` env-var gate have been removed. ### Sentinel cleanup `DELTA_SENTINEL` is now a pure in-memory signal and is never written to storage: - Postgres `put()` already stripped it from `channel_values` before writing blobs - Memory saver `put()` now stores `"empty"` instead of serializing the sentinel - `EXT_DELTA_SENTINEL` (msgpack ext code 8) removed from `JsonPlusSerializer` - `DELTA_SENTINEL` is kept as an in-memory marker: `DeltaChannel.checkpoint()` returns it so savers know to skip it, and `_ChannelWritesHistory.seed` uses it to mean "no snapshot found, start from empty" ## Performance Benchmarked at `snapshot_frequency=10` on Postgres (`~100 tok/msg`): | turns | old combined query | two-stage | |------:|-------------------:|----------:| | 50 | 6.0ms | 2.8ms (2.1x faster) | | 100 | 10.1ms | 5.6ms (1.8x faster) | | 500 | **216.1ms** | 15.3ms (**14x faster**) | The old query's read time grew super-linearly with turn count because each read fetched O(N/freq) full snapshot blobs. Two-stage keeps read depth bounded by `snapshot_frequency` regardless of thread length. ## Test plan - `make test` in `libs/checkpoint`, `libs/checkpoint-postgres`, `libs/langgraph` - Removed `test_delta_sentinel_serde_round_trip` (sentinel no longer serializable) - Updated `test_memory.py` — delta channel blobs stored as `"empty"`, not serialized sentinel - Updated `test_channels.py` — `channel_values` no longer contains sentinel key for DeltaChannels - Deleted `test_delta_channel_two_stage_benchmark.py` (one-stage vs two-stage comparison; path no longer exists) --------- Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 (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.