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## Summary The sqlite delta history silently drops a parent checkpoint whose id sorts above its child's, losing that parent's stored value and its pending writes. The channel hydrates short with no error. Fixes #8550 ## Problem Stage 1 walked ancestors with: ```sql WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id <= ? ORDER BY checkpoint_id DESC ``` Ancestry is defined by the `parent_checkpoint_id` column. These two predicates add a second requirement: that every child's id sorts above its parent's. The contract promises monotonic ids, but that only holds within one process, so ids from processes with different clocks can break it. When the requirement is violated the parent is excluded from the stream and its seed and writes go with it. Dropping the range filter alone does not fix it: in `checkpoint_id DESC` order that parent arrives *before* the target, so the walk streams past it before it has started. ## Fix A recursive CTE anchored at the target, following `parent_checkpoint_id`: ```sql WITH RECURSIVE ancestors(checkpoint_id, parent_checkpoint_id, type, checkpoint) AS ( SELECT ... FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ? UNION ALL SELECT c.... FROM ancestors a CROSS JOIN checkpoints c ON c.checkpoint_id = a.parent_checkpoint_id WHERE c.thread_id = ? AND c.checkpoint_ns = ? ) SELECT checkpoint_id, type, checkpoint FROM ancestors ``` Rows now arrive in walk order (target, parent, grandparent, ...), so `step_walk_with_row` no longer needs its off-path skip or its `parent_cid` tracking; both are removed. The query reads only true ancestors, where the old one read every row at or below the target including sibling branches. `CROSS JOIN` pins the join order. The saver never runs `ANALYZE`, and with a plain `JOIN` sqlite put `checkpoints` as the outer loop, scanning the whole thread on every recursion step. With `ancestors` outside, each step is one primary key lookup. Through `get_delta_channel_history`: | chain length | plain `JOIN` | `CROSS JOIN` | | -- | -- | -- | | 1000 | 0.032s | 0.001s | | 2000 | 0.124s | 0.003s | | 4000 | 0.475s | 0.006s | ## Cycle guard Following pointers can loop where a bounded id scan could not, and a loop is reachable through `put` alone: `put` writes with `INSERT OR REPLACE`, so re-putting an existing checkpoint id under a descendant's config repoints that checkpoint at its own descendant. The walk stops on a repeated `checkpoint_id` (one set insert per row, no depth ceiling that could truncate a long migrated thread). sqlite yields recursive rows lazily, so abandoning the cursor ends the recursion. `test_walk_terminates_when_put_makes_the_parent_chain_cycle` fails by hanging, not by asserting, if the guard regresses (confirmed by deleting the guard locally). The package has no `pytest-timeout`, so the CI job timeout is the backstop. ## Postgres No equivalent change needed. It pages the whole thread with no id bound and follows parent pointers in Python, and its upsert never rewrites `parent_checkpoint_id`, so it can neither miss this parent nor form the loop. `BaseCheckpointSaver` and `InMemorySaver` also walk parent pointers. ## Test plan New `libs/checkpoint-sqlite/tests/test_delta_parent_walk.py`: - [x] Sync and async, parametrised over both id orders; the sync case also asserts equality with `BaseCheckpointSaver` on the same rows. `parent_id_sorts_above_child` is the bug, `parent_id_sorts_below_child` the control. - [x] `test_walk_reaches_root_of_long_chain_with_descending_ids`: 40 checkpoints, only stored value at the root. - [x] `test_walk_terminates_when_put_makes_the_parent_chain_cycle`. - [x] `test_walk_step_looks_up_the_parent_by_primary_key`: asserts the recursive step's `EXPLAIN QUERY PLAN` is a key lookup, so a plain `JOIN` can't come back. Fails with it. - [x] On `main`: 3 of the 6 walk tests fail (both `parent_id_sorts_above_child` cases and the long chain). The cycle test passes on `main` too, since the old bounded scan could not loop; it guards the new path. - [x] #8550's repro returns `{'writes': [('task', 'ch', 'write-root')], 'seed': 'seed'}` sync and async (was `{'writes': []}` on `main`). - [x] `libs/checkpoint-sqlite`: `make format`, `make lint` clean; full suite 125 passed, 2 skipped. - [x] `libs/langgraph`: `-k "delta or sqlite"` 739 passed, 1 skipped. Thanks to @lylelllll for the report, the minimal repro, the base-saver comparison that isolated it to the fast path, and for suggesting the recursive CTE. Co-authored-by: lylelllll <59271327+lylelllll@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.