Sydney RunkleandClaude Sonnet 4.6
ba12c8264d
Restores DeltaChannel as a standalone class in channels/delta.py and adds
a snapshot_frequency parameter that writes a full snapshot blob every N writes,
bounding the ancestor replay walk depth while preserving O(N) storage for
large N.
Key design decisions:
- Write-count based (not step-based): snapshot fires every N writes to the
channel, tracked via _write_count incremented in both update() and
replay_writes(). This ensures the snapshot always coincides with an actual
channel write (i.e., a new_versions entry in put()), so it is always stored.
- Snapshot blob format: {"__delta_v__": value, "__delta_wc__": n} embeds the
write count so from_checkpoint() can restore it across invocations, keeping
the cadence correct without any external state.
- _checkpoint.py simplified: DeltaChannel.checkpoint() now returns the right
thing (sentinel or snapshot dict) so create_checkpoint needs no special logic.
- _needs_replay updated: triggers on DELTA_SENTINEL / MISSING; snapshot dicts
and plain values (migration) resolve directly via from_checkpoint().
Benchmark shows correct tradeoffs across frequencies (500 turns):
freq=1 → 296 MB storage, ~7ms reads
freq=5 → 60 MB storage, ~4ms reads
freq=10 → 30 MB storage, ~4ms reads
freq=50 → 6.5 MB storage, ~3ms reads
freq=inf→ 290 KB storage, ~114ms reads
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.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
If you're looking to quickly build agents with LangChain's create_agent (built on LangGraph), check out the LangChain Agents documentation.
Note
Looking for the 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 (new!) – 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.