Elior Nataf LackritzandGitHub
36a505ac65
Depends on #8535 `libs/checkpoint-conformance/tests/` only validates `InMemorySaver`. `checkpoint-sqlite` has had a `test_conformance_delta.py` for a while, but it guards on `importorskip("langgraph.checkpoint.conformance")` and the package was never in its test environment — so it has been skipping silently every run. `checkpoint-postgres` had no runner at all. Net effect: the shared checkpointer contract was effectively unenforced everywhere except in-memory. ### Change Adds `langgraph-checkpoint-conformance` to the `test` dependency group of both packages, with a path source like the existing `langgraph-checkpoint` entry. That alone is what makes sqlite's runner start executing. Postgres gets the equivalent runner. Both pass the `delta_channel_history` capability. ### Why it's stacked Against `main`'s Postgres, the new runner fails: ``` Capability delta_channel_history failed: test_history_migration_plain_value_as_seed ``` That is exactly the bug #8535 fixes, and it had been failing unnoticed precisely because nothing ran the suite there. So this is based on that branch rather than `main` — the diff here is the one conformance commit, and it will retarget once #8535 lands. Reasonable to read that as the change justifying itself: the first thing turning the suite on did was catch a real bug that had been sitting in `main`. ### Verified `checkpoint-postgres` 270 passed on PG 15 and 16, `checkpoint-sqlite` 118 passed, lint and `ty` clean in both. The `uv.lock` updates are the conformance package entry only. ### Note The sync `PostgresSaver` and `SqliteSaver` aren't covered — the conformance harness reports every capability as `detected=False` for them, so only the async savers are exercised. Pre-existing and not addressed here, but worth knowing the coverage isn't total.
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.