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Fixes langchain-ai/langgraph#8384 `InMemorySaver.get_delta_channel_history` skipped the writes stored at the ancestor it seeded from whenever that ancestor's blob was a plain value rather than a `_DeltaSnapshot`, silently dropping the first write made after migrating a thread to `DeltaChannel`. ### Why the old rule was wrong A stored blob is the value *entering* its checkpoint; the writes stored under that same checkpoint are what produce its child. That's true for `_DeltaSnapshot` blobs and pre-delta plain values alike, so there was never a reason to treat them differently. Writes at ancestors *older* than the seed genuinely are subsumed by the seed value — but that's already guaranteed by terminating the walk, since the channel leaves `remaining` once its seed is found. The removed check re-solved that and overreached by one checkpoint. `BaseCheckpointSaver`, `SqliteSaver` and `PostgresSaver` never had this check. `InMemorySaver` was the only outlier. ### How I verified it Built a differential harness running the same migration scenarios through `InMemorySaver`, the `BaseCheckpointSaver` reference walk, and `SqliteSaver`. **4 of 11 scenarios agreed before this change; 11 of 11 after.** The loss is wider than one write — on the `add_messages` → `DeltaChannel` path it drops a real user message. Suites: `libs/checkpoint` 156 passed, `libs/langgraph` 1972 passed, `libs/checkpoint-sqlite` 117 passed, `libs/checkpoint-postgres` passed against PG 16. `make format`, `make lint` clean in each. ### Two things worth a closer look in review **1. I inverted two existing assertions** in `TestPreDeltaBlobTerminator` (`libs/checkpoint/tests/test_memory.py`). They encoded the old rule. Their fixture is the real migration shape — a plain-value blob carrying pending writes, with a delta-era child — which I confirmed against a dumped checkpoint chain from the issue's repro, so the assertions were wrong rather than the fixture being unrealistic. I added an ancestor *older* than the seed so the terminator still guards what it legitimately should: older writes stay excluded, the seed's own writes replay. **2. The new conformance test fails against Postgres**, for a reason unrelated to this change. Postgres `aput` leaves an inline `True` marker in `channel_values` only for `_DeltaSnapshot`; plain non-primitive values are popped with no marker, and seed detection is `(checkpoint -> 'channel_values' -> ch) IS NOT NULL`. So Postgres can't locate a plain-value seed at all: ``` seed stored as plain list: InMemorySaver -> [10, 20] AsyncPostgresSaver -> no seed key seed stored as _DeltaSnapshot: InMemorySaver -> found AsyncPostgresSaver -> found ``` The pre-existing `test_history_migration_plain_value_as_seed` already fails there too — conformance CI only validates `InMemorySaver`, so nobody was watching. Values still come out correct today (with no seed the walk runs to the root and replays everything), but early termination is lost: 1 write replayed on `InMemorySaver` vs 7 on Postgres for the same 6-turn thread. Filing separately rather than folding a write-path/format decision into this PR. ### Note on scope This touches three packages: the fix in `libs/checkpoint`, graph-level regression tests in `libs/langgraph` (the bug is only observable through a graph read), and the contract test in `libs/checkpoint-conformance` so third-party savers are covered too. --------- Co-authored-by: PiedPiper911 <32931126+PiedPiper911@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.