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William FHGitHubWill Fu-HinthornClaude Opus 4.7copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>## Summary
Adds per-node `timeout` support to async StateGraph/Pregel nodes and to
the functional API (`@task` / `@entrypoint`). A timeout caps how long a
single node attempt may run, either as a hard wall-clock budget
(`run_timeout`), or as an idle window that resets on observable progress
(`idle_timeout`), or both. When exceeded, LangGraph raises
`NodeTimeoutError`, clears writes from the failed attempt, and lets the
existing retry policy decide whether to retry.
## Public API
A single `timeout=` kwarg on `add_node`, `@task`, `@entrypoint`, and
`NodeBuilder.set_timeout`. Pass a number/`timedelta` for the simple case
(treated as a hard wall-clock cap), or a `TimeoutPolicy` (in
`langgraph.types`) for finer control:
```python
from datetime import timedelta
from langgraph.types import TimeoutPolicy
# simple: hard wall-clock cap on each attempt
builder.add_node("call_model", call_model, timeout=60)
builder.add_node("call_model", call_model, timeout=timedelta(minutes=2))
# full control
builder.add_node(
"call_model",
call_model,
timeout=TimeoutPolicy(
run_timeout=120, # hard wall-clock cap in seconds, never refreshed
idle_timeout=30, # cap on time without observable progress, units in seconds
refresh_on="auto", # "auto" | "heartbeat"
),
)
```
- `run_timeout`: hard wall-clock cap on a single attempt; never
refreshed.
- `idle_timeout`: progress-resetting cap. Refreshed by writes, stream
output, yielded async stream chunks, child-task scheduling, runtime
stream-writer calls, and any LangChain callback event from descendants
of the node's run. `runtime.heartbeat()` is a manual signal for work
that doesn't naturally emit any of these.
- `refresh_on="heartbeat"` narrows the refresh source to explicit
`runtime.heartbeat()` only — useful when you want a strict idle
definition that isn't reset by chatty subordinates.
For long-running async work that doesn't naturally emit progress:
```python
async def call_model(state: State, runtime: Runtime) -> State:
while still_working:
...
runtime.heartbeat()
return {"messages": [response]}
```
`NodeTimeoutError` subclasses `TimeoutError` and carries `node`,
`timeout`, `run_timeout`, `idle_timeout`, `elapsed`, and `kind` (`"run"`
or `"idle"`). If the node's `retry_policy` permits `TimeoutError` it'll
retry; the timer resets per attempt.
## Why async-only
Sync Python code cannot be safely cancelled in-process, so timeouts only
apply to async nodes/tasks. Sync nodes with a `timeout` are rejected at
compile time (covers direct nodes, wrapped runnables, sequences, and
`RunnableParallel` branches); `run_with_retry` rejects them at runtime
as a safety net.
## What gets cancelled and what doesn't
When a watchdog fires:
1. The attempt scope is closed under a lock so any in-flight
`CONFIG_KEY_SEND` / stream / child-task scheduling that races with the
timeout is dropped atomically.
2. Buffered `task.writes` are cleared so pre-timeout writes from the
failed attempt don't leak into the checkpoint after a retry succeeds.
3. The background `asyncio.Task` is cancelled; its eventual exception is
drained via a done-callback so asyncio doesn't log it.
Only the watchdog's own `TimeoutError` converts to `NodeTimeoutError`,
so user-raised `asyncio.TimeoutError`, built-in `TimeoutError`, and
`NodeTimeoutError` from a child node continue to propagate unchanged.
Child tasks already scheduled before the timeout fired still complete —
they aren't part of the cancelled task's structured cancellation
surface. This is intentional and tested.
## External-watchdog hook
WARNING: THIS API IS IN ALPHA AND SUBJECT TO CHANGE.
`CONFIG_KEY_TIMED_ATTEMPT_OBSERVER` is a per-config callback that
receives lifecycle events for each timed attempt:
- `start` — fired before the proc runs, with `task_id`, `task_name`,
`attempt`, `run_id`, `thread_id`, `checkpoint_ns`, `started_at`, and the
configured `run_timeout_secs` / `idle_timeout_secs` / `refresh_on`.
- `progress` — fired on each progress signal that resets the idle clock,
rate-limited to ~4 events per `idle_timeout` window so token-rate
callbacks don't flood the observer. Carries the same context plus
`progress_at`.
- `finish` — fired with `finished_at`, `status` (`"success"`/`"error"`),
`error_type`, `error_message`. `ParentCommand` and `GraphBubbleUp` are
treated as control flow, not errors.
This lets an orchestrating process listen to `start` + rolling
`progress` to compute its own kill deadline (`progress_at +
idle_timeout_secs`) and hard-kill a worker process if the in-process
cancellation deadlocks. Observer callbacks run in whatever thread fires
them, and any exception they raise is logged and swallowed.
## Implementation Notes
`runtime.heartbeat()` updates the progress timestamp without taking the
guarded write lock. This avoids lock overhead on high-frequency
callback/token paths and accepts a small timestamp race window, which is
negligible for expected coarse idle-timeout configurations.
## Testing
Covers timeout retry behavior, user-raised timeout propagation,
stale-write suppression, stream / callback / heartbeat progress resets,
sync-node rejection, `RunnableParallel` branch validation,
`StateGraph.add_node` behavior, lower-level Pregel behavior, observer
start/progress/finish events, and functional API compatibility.
---------
Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@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
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.