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Elior Nataf LackritzandGitHub
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Closes #8531. `PostgresSaver` and `AsyncPostgresSaver` inherited `prune` / `aprune` from the base class, which raises `NotImplementedError`, so long-lived threads had no supported way to drop old checkpoints. This adds both strategies. Thanks to @wangjiawei-vegetable for proposing this scope in #8531 and #9247. - **`delete`** removes every checkpoint, write and blob of the threads, in one transaction. - **`keep_latest`** keeps the latest checkpoint of each namespace (the one `get_tuple` loads), with its pending writes and the blobs it references, and deletes the rest, including blobs only the deleted checkpoints used. A kept checkpoint whose parent is deleted is left with no parent. A subgraph call's namespace goes with the root checkpoint the call started from; a `checkpointer=True` subgraph keeps its latest checkpoint, since its state outlives each call. - **Each thread is pruned on one snapshot.** Its transaction runs at `REPEATABLE READ` and deletes everything that snapshot sees except the kept checkpoints, so a run appending to the thread at the same time is safe, whatever its checkpoint IDs. - **`keep_latest` refuses threads that use a `DeltaChannel`** (`NotImplementedError` naming them, nothing pruned). A `DeltaChannel` rebuilds its latest value from older checkpoints, so deleting them would empty it. The saver can't see channel types, so it detects one by what only a `DeltaChannel` leaves: `counters_since_delta_snapshot` in metadata, a snapshot (an inline `true` with a blob behind it), or `delta_writes_versioned`, which LangGraph.js sets on threads its `DeltaChannel` graphs start. The check runs for the whole batch first and again in each thread's transaction, so a thread that starts using a `DeltaChannel` mid-batch is refused at its turn. Unlike the `prune` in #6883, `keep_latest` here deletes the blobs older checkpoints no longer share with the kept ones (most of the storage on a long thread), and guards `DeltaChannel` threads. Measured locally, with or without planner statistics: a 6,000-checkpoint thread prunes in 100 to 200 ms, and one with 1,500 finished subgraph calls in about 110 ms. ## Known limitations - Two prunes of the same thread at once can fail one of them with a serialization failure or a deadlock; retrying is safe. - Forking or replaying from a checkpoint while it's being pruned is not safe. - Threads written by older releases can escape the `DeltaChannel` check: before #9142 (Python 1.2.13) and langchain-ai/langgraphjs#2973 (JS 1.5.0), some `update_state` paths saved checkpoints with neither counters nor a snapshot. - Postgres reuses the freed space but doesn't shrink the table files; that takes `VACUUM FULL` or `pg_repack`. - `DeltaChannel`-aware pruning (keeping ancestors back to the last snapshot) is a follow-up. ## Test plan - [x] `libs/checkpoint-postgres/tests/test_prune.py`: 71 tests over the sync and async savers (single connection, pool, pipeline), including the conformance `prune` capability. Passes on Postgres 15 and 16. - [x] `libs/langgraph/tests/test_prune_postgres.py`: 39 graph tests. A pruned thread loads the same state and keeps running like an unpruned twin (sync, async and exit durability, interrupts, a failed parallel step, interrupted and `checkpointer=True` subgraphs), plus deterministic races: a turn saved mid-prune, and threads switching to a `DeltaChannel` before and after their own check. - [x] Each guard (the snapshot, the writes and blob scopes, the subgraph call rules, the parent reset, the `DeltaChannel` checks, the single-string check, the pre-v4 pending sends, the pipeline sync) broken one at a time: every break fails at least one test. - [x] A thread pruned in a loop while it runs, in each durability mode, ends identical to its twin; threads written by LangGraph.js 1.5.0 and pruned from Python behave like their twins.
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.