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Nick HollonandGitHub
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## Summary
- Adds `stream_events(version="v3")` and `astream_events(version="v3")`
to `RemoteGraph`, matching the local `CompiledStateGraph` surface and
unblocking polymorphic v3 streaming over `Graph | RemoteGraph`.
- Implementation is a thin adapter
(`libs/langgraph/langgraph/pregel/_remote_run_stream.py`) that wraps the
v3 SDK's `AsyncThreadStream` / `SyncThreadStream` and duck-types
`GraphRunStream` / `AsyncGraphRunStream`. No coupling to local v3 mux
internals.
- `v1` / `v2` paths unchanged. `astream_events(version='v1'|'v2')` still
raises `NotImplementedError` (separate gap).
### Scope decisions baked into this PR
- Unsupported v3 kwargs hard-reject at dispatch with
`NotImplementedError`: `control`, `transformers`, `interrupt_before`,
`interrupt_after`, and any unknown `**kwargs`. Server / SDK don't plumb
these through v3 yet; easy to lift later.
- Sync `interleave()` raises `NotImplementedError` pointing callers at
`astream_events`. Real sync interleave would need drainer threads;
deferred since most sync RemoteGraph callers just iterate raw events.
- Async `interleave()` is best-effort ordering (client receive order),
documented as a divergence from local v3's monotonic stamp ordering.
- Adapter `interrupted` / `interrupts` properties are **non-blocking**
snapshots of the SDK's current state. This differs from local
`(Async)GraphRunStream.interrupted`, which pump-drives the run to
terminal before returning. Callers needing a wait-for-interrupt pattern
should drain a projection (e.g., `interleave('values')`) until the SDK's
paused sentinel fires. Documented in the adapter docstrings.
### Audit of impact
Existing RemoteGraph callers in this org all use the v2 `.stream()` /
`.astream()` path (deepagents production wrapper, langgraph-api test
graphs, langgraph-supervisor TS type guard). **Zero callers** use
`stream_events` / `astream_events` on RemoteGraph today, so the new v3
methods are net-new surface — no risk of breaking existing consumers.
### Out of scope (follow-ups)
- Bumping `libs/langgraph/pyproject.toml`'s `langgraph-sdk` constraint
from `<0.4.0` to `<0.5.0`. Deferred until 0.4.0 publishes to PyPI; dev
resolution unaffected via the editable workspace dep.
- Real `astream_events(version='v1'|'v2')` implementation.
- Server-side plumbing for `control` / `interrupt_before` /
`interrupt_after` on v3 runs.
- Sync `interleave()` via drainer threads.
## Test plan
- [x] \`make test\` in \`libs/langgraph/\`: 1874 passed, 4 skipped (43
new in \`test_remote_graph_v3.py\`)
- [x] \`make lint\` in \`libs/langgraph/\`: ruff + mypy clean
- [x] \`pytest -m integration
tests/integration/test_remote_graph_v3.py\` in \`libs/sdk-py/\` against
the docker stack: 4/4 passed in 1.35s
- [x] Manual smoke: \`RemoteGraph('tools_agent',
url='http://localhost:2024').astream_events(..., version='v3')\`
end-to-end against the v3 integration api
- [x] Existing RemoteGraph v2 tests untouched (31 passed, 3 skipped with
docker up)
- [x] Will need rebase after \`langgraph-sdk 0.4.0\` lands on PyPI and
the version constraint is bumped in a separate PR
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.
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- 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.
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- 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.