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Closes #8038 ## Description The `ensure_config` merge introduced in #7926 caused a child graph invoked inside a parent node to inherit the parent task's `checkpoint_ns` from the ambient run context (`var_child_runnable_config`), so the child's checkpoints were written under an unreadable namespace and re-ran from scratch each turn (#8038). The first explicitly passed `configurable` that carries a checkpoint coordinate (a `thread_id`, or any `checkpoint_ns`/`checkpoint_id`/`checkpoint_map`) now replaces the ambient one, while subsequent explicit configs still shallow-merge — preserving #7926's `with_config(...)` semantics. ## Contract `ensure_config` merges an explicit `configurable` over the ambient run context (`var_child_runnable_config`) with one rule: **an explicit `configurable` that supplies its own checkpoint coordinate addresses its own checkpoint lineage, so the ambient `configurable` is dropped rather than merged over.** Coordinate keys are `thread_id`, `checkpoint_ns`, `checkpoint_id`, and `checkpoint_map` (grouped as `_CHECKPOINT_COORDINATE_KEYS`). A non-coordinate `configurable` keeps the ambient and shallow-merges over it. Below, each case shows the parent/child graph wiring that triggers it and the resulting namespacing. The child is always a compiled subgraph invoked from inside a parent node. ### 1. Subgraph invoked with no new config → ambient inherited ```python child = StateGraph(ChildState).add_node("n", child_node).add_edge(START, "n").compile(checkpointer=checkpointer) def parent_node(state): child.invoke({}, config=None) # no explicit configurable return {"result": "ok"} parent = StateGraph(ParentState).add_node("p", parent_node).add_edge(START, "p").compile(checkpointer=checkpointer) parent.invoke({"result": ""}, config={"configurable": {"thread_id": "parent"}}) ``` Child inherits the parent task's `checkpoint_ns` (`p:<parent-task>`); its checkpoints are written as a discoverable child of the parent run. Pre-#7926 behavior, unchanged. ### 2. Subgraph invoked with a new thread_id → ambient dropped ```python child = StateGraph(ChildState).add_node("n", child_node).add_edge(START, "n").compile(checkpointer=checkpointer) child_config = {"configurable": {"thread_id": str(uuid4())}} def parent_node(state): child.invoke({}, config=child_config) # explicit new thread_id return {"result": "ok"} parent = StateGraph(ParentState).add_node("p", parent_node).add_edge(START, "p").compile(checkpointer=checkpointer) parent.invoke({"result": ""}, config={"configurable": {"thread_id": "parent"}}) ``` Child starts its own lineage on `child_config`'s thread; `checkpoint_ns == ""`; `child.get_state(child_config)` reads back state across repeated parent turns. Fixes #8038. ### 3. Subgraph invoked with the same thread_id as parent → ambient still dropped ```python child = StateGraph(ChildState).add_node("n", child_node).add_edge(START, "n").compile(checkpointer=checkpointer) def parent_node(state): # reuses the parent's thread_id as the child's own child.invoke({}, config={"configurable": {"thread_id": state["parent_thread"]}}) return {"result": "ok"} parent = StateGraph(ParentState).add_node("p", parent_node).add_edge(START, "p").compile(checkpointer=checkpointer) parent_thread = str(uuid4()) parent.invoke({"parent_thread": parent_thread, "result": ""}, config={"configurable": {"thread_id": parent_thread}}) ``` Child addresses its own root namespace on the shared thread; `checkpoint_ns == ""`. The parent task's `checkpoint_ns` must not leak in, or `child.get_state({"configurable": {"thread_id": parent_thread}})` returns empty state and the child re-runs from scratch each turn. ### 4. Subagent invoked with a non-coordinate key only → ambient inherited ```python child = StateGraph(ChildState).add_node("n", child_node).add_edge(START, "n").compile(checkpointer=checkpointer) def parent_node(state): # ls_agent_type is not a checkpoint coordinate, so ambient is kept child.invoke({}, config={"configurable": {"ls_agent_type": "subagent"}}) return {"result": "ok"} parent = StateGraph(ParentState).add_node("p", parent_node).add_edge(START, "p").compile(checkpointer=checkpointer) parent.invoke({"result": ""}, config={"configurable": {"thread_id": "parent"}}) ``` Child remains a discoverable child of the parent run; ambient `thread_id` and `checkpoint_ns` preserved (deepagents `task` tool pattern). ### 5. with_config(...) + invoke-time thread_id → ambient dropped, then merged ```python # child compiled with a non-coordinate configurable via with_config child = ( StateGraph(ChildState) .add_node("n", child_node) .add_edge(START, "n") .compile(checkpointer=checkpointer) .with_config({"configurable": {"ls_agent_type": "root"}}) ) def parent_node(state): # invoke-time config supplies the thread_id; with_config's ls_agent_type survives child.invoke({}, config={"configurable": {"thread_id": "child"}}) return {"result": "ok"} parent = StateGraph(ParentState).add_node("p", parent_node).add_edge(START, "p").compile(checkpointer=checkpointer) parent.invoke({"result": ""}, config={"configurable": {"thread_id": "parent"}}) ``` First coordinate-bearing config (`thread_id`) drops the ambient; subsequent explicit configs still shallow-merge, so `ls_agent_type` survives alongside `thread_id`. Preserves #7926 `with_config(...)` semantics. ### Regression note Cases 1, 4, and 5 are the pre-#7926 behavior and are preserved unchanged. Cases 2 and 3 fix the regression introduced by #7926: an explicit `thread_id` resets the ambient even when it equals the ambient thread id, because a child reusing the parent's thread id still addresses its own root namespace on that thread, not the parent task's. ## Self-Hosted Release Note Fix regression where a nested subgraph with its own `thread_id` invoked inside a parent node lost its persisted state across turns. ## Test Plan - [x] `pytest tests/test_subgraph_persistence.py -k test_child_with_own_thread_id_keeps_namespace` (case 2) - [x] `pytest tests/test_utils.py -k ensure_config_explicit_configurable_replaces_ambient` (case 2) - [x] `pytest tests/test_utils.py -k ensure_config_ambient_inherited_when_no_explicit_configurable` (case 1) - [x] `pytest tests/test_utils.py -k ensure_config_non_coordinate_config_keeps_ambient_checkpoint_ns` (case 4) - [x] `pytest tests/test_utils.py -k ensure_config_explicit_configurables_still_merge_over_ambient` (case 5) - [x] `pytest tests/test_utils.py -k ensure_config_same_thread_id_still_clears_ambient` (case 3) Made by [Open SWE](https://openswe.vercel.app) --------- Co-authored-by: Nick Hollon <274035459+nick-hollon-lc@users.noreply.github.com> Co-authored-by: open-swe[bot] <open-swe@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
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.