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## Summary Extends `ToolNode` so that a single tool invocation can return `list[Command | ToolMessage]` instead of only a single `Command` or `ToolMessage`. This brings `ToolNode`'s per-tool-call contract in line with the rest of LangGraph, where nodes can already return multiple Commands. Depends on langchain-ai/langchain#36963 which allows `list[ToolOutputMixin]` to pass through `BaseTool._format_output` unchanged. ## Changes ### `libs/prebuilt/langgraph/prebuilt/tool_node.py` **New list-return gate in `_execute_tool_sync` / `_execute_tool_async`** — After the existing `Command` and `ToolMessage` checks, a new branch accepts `list[Command | ToolMessage]` and routes it through `_validate_tool_command_list`. Lists with non-`Command`/`ToolMessage` elements raise `TypeError`. Both sync and async paths are updated symmetrically. **`_validate_tool_command_list`** — Enforces the terminating-ToolMessage rule: exactly one `ToolMessage` in the list must carry `tool_call_id == <outer_id>` (top-level or nested inside a `Command.update["messages"]`). Zero or multiple terminators raise `_MissingToolMessageError`. Individual Commands in the list are validated via the existing `_validate_tool_command`; when a Command lacks the terminator (which is allowed since the list-level check handles it), the `_MissingToolMessageError` is caught and the already-normalized command from the exception is used. **`_MissingToolMessageError`** — A `ValueError` subclass raised by `_validate_tool_command` (and `_validate_tool_command_list`) when no matching `ToolMessage` is found. Carries the already-normalized command so callers can recover without re-doing deepcopy/message-conversion work. Using a typed exception avoids brittle string-matching on error messages. **`_combine_tool_outputs`** — Flattens list entries at the top of the method so downstream combiner logic (parent-`goto` accumulation, ToolMessage wrapping) is unchanged. **Response processing moved inside try/except** — In both sync and async execute methods, the response validation (Command/ToolMessage/list checks) now runs inside the existing error-handling try block, so validation errors from the list path go through `_handle_tool_errors` like other tool errors. **Return type signatures** widened on `_execute_tool_sync`, `_execute_tool_async`, `_run_one`, `_arun_one` to include `list[Command | ToolMessage]`. ### `libs/prebuilt/tests/test_tool_node.py` New tests covering: valid list returns (top-level terminator, nested terminator, parent-goto + terminator), regression tests for single Command/ToolMessage returns, invalid cases (no terminator, multiple terminators), async parity, integration with mixed list/non-list tool calls, and `_handle_tool_errors` interaction. --------- Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.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.