Fixes langchain-ai/langgraph#8534 `put` splits stored values in two: primitives stay inline in the checkpoint's `channel_values`, everything else moves to `checkpoint_blobs`, and only `_DeltaSnapshot` leaves an inline marker behind when it moves. Stage-1 seed detection tested for that marker, so a plain value — what a thread migrated from `BinaryOperatorAggregate` leaves behind — was invisible to the walk. ### Effect Migrated threads found no seed, walked to the root, and replayed every write on every read. Values still came out correct, because replaying an additive reducer from empty rebuilds the same list, which is why nothing looked wrong. What was lost is early termination — the entire point of `DeltaChannel`: <!-- linear:table-colwidths:266,266,266 --> | thread length | writes replayed, before | after | | -- | -- | -- | | 2 turns | 3 | 1 | | 6 turns | 7 | 1 | | 20 turns | 21 | 1 | Read latency is flat at \~0.6ms across all three after the change. ### Approach Stage 1 now checks both places a value can live rather than trusting the marker. It probes `checkpoint_blobs`: ```sql EXISTS (SELECT 1 FROM checkpoint_blobs b0 WHERE b0.thread_id = checkpoints.thread_id AND b0.checkpoint_ns = checkpoints.checkpoint_ns AND b0.channel = %s AND b0.version = checkpoint -> 'channel_versions' ->> %s AND b0.type <> 'empty') AS hb_0 ``` and selects the inline value alongside it, since `None`, `str`, `int`, `float` and `bool` stay in `channel_values` with no blob row: ```sql checkpoint -> 'channel_values' -> %s AS inline_0 ``` The blob predicate matches `checkpoint_blobs`' primary key `(thread_id, checkpoint_ns, channel, version)` exactly, so it is one index lookup per row per channel, bounded by the 1024-row page. I picked reading storage over the cheaper alternative — also writing the marker for plain values — because **that would not fix any thread already on disk.** Existing checkpoints have no marker and there is nowhere to add one retroactively. The seed resolves to the blob when one exists and the inline value otherwise. That ordering is also what keeps a genuine inline `true` — a `bool` channel holding `True` — distinguishable from the literal `true` marker `put` inlines for a `_DeltaSnapshot`: only the snapshot has a blob. `None` is deliberately not treated as a seed; a JSON null is indistinguishable from "nothing stored" at this layer, so the walk continues and replay from empty is correct. Params go from two to four per channel; both callers updated. The inline half came out of review on this PR — a blob-only probe would have left scalar-aggregate migrations (an integer sum, say) still replaying their full history. ### On the `type <> 'empty'` predicate Being upfront since it isn't demonstrable with a test: `put` does not currently produce `empty` rows on this path — `blob_versions` is filtered to keys present in `channel_values`, so `_dump_blobs`' empty branch is unreachable from it. I confirmed there are no `empty` rows in a populated test database. I kept it because stage 2 already applies the same check when resolving the seed blob. Without it the two stages could disagree: stage 1 terminates the walk on a row stage 2 then discards, producing no seed *and* a truncated write chain — the same failure shape this function exists to avoid. Rationale is in the docstring so the next reader doesn't have to ask. Happy to drop it if you'd rather not carry an unexercised predicate. ### Tests `libs/checkpoint-postgres/tests/test_delta_plain_value_seed.py` — blob-stored plain-value seed, `_DeltaSnapshot` seed, a version bump with nothing stored (which must not stop the walk short of an older real value), inline primitives (`int`, `str`, `float`, `None`), and inline `True` versus the snapshot marker. Each fails against the behaviour it fixes. Verified: postgres suite 269 passed on PG 15 and 16; delta-channel conformance against `AsyncPostgresSaver` went from 6 of 8 to 8 of 8, including the pre-existing `test_history_migration_plain_value_as_seed` failure this was causing; `make lint` clean. ### Not included I wanted a Postgres conformance runner alongside `checkpoint-sqlite`'s, but it needs `langgraph-checkpoint-conformance` as a dev dependency and the contributing guide asks for maintainer sign-off before adding one. The direct tests above cover the same ground without it. Worth flagging separately: **conformance effectively runs against** `InMemorySaver` **only today.** `libs/checkpoint-conformance/tests/` contains just `test_validate_memory.py`, and `checkpoint-sqlite`'s `test_conformance_delta.py` silently skips because the package isn't installed in its test environment (`importorskip`). Wiring it up for sqlite and postgres is what would have caught this bug, and langchain-ai/langgraph#8534 notes it. Sqlite is unaffected by the bug itself — it stores `channel_values` inline and inspects them directly. `langgraph-api` already resolves seeds by version rather than by marker.
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.