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## More robust Pydantic support for v2 streaming
When using `stream_version="v2"`, stream data and invoke results now
respect the graph's output/state schema types (Pydantic models,
dataclasses, etc.) instead of always returning raw dicts. This makes
working with typed state much more natural — no more manual
`Model(**chunk)` calls scattered through your code.
### Values stream coercion
`values` stream parts coerce data through the graph's output schema
mapper, so you get Pydantic models (or dataclasses) back directly:
```python
class MyState(BaseModel):
value: str
items: Annotated[list[str], operator.add]
graph = StateGraph(MyState).compile()
# v1: you get raw dicts back, have to reconstruct manually
for chunk in graph.stream(inputs, stream_mode="values"):
state = MyState(**chunk) # manual, error-prone
# v2: data is already a MyState instance
for part in graph.stream(inputs, stream_mode="values", stream_version="v2"):
assert isinstance(part["data"], MyState) # just works
print(part["data"].value) # attribute access, IDE autocomplete
```
This also works for dataclass-based state schemas. TypedDict state stays
as plain dicts (no change needed).
### Interrupts on stream parts
`values` stream parts now carry an `interrupts` field directly, removing
the need to cross-reference the `updates` stream:
```python
for part in graph.stream(inputs, config, stream_mode="values", stream_version="v2"):
if part["interrupts"]:
# handle interrupts inline — no need to check updates stream
for intr in part["interrupts"]:
print(intr.value)
```
### Checkpoint/debug coercion
Checkpoint and debug stream payloads also coerce their `values` through
the state schema mapper, so `stream_mode="checkpoints"` and
`stream_mode="debug"` return typed state too.
### Generic stream types
`StreamPart`, `ValuesStreamPart`, `CheckpointPayload`, etc. are now
generic over `StateT`/`OutputT`, enabling better static type checking
across the board.
### `GraphOutput` wrapper
This adds a new return type to `invoke()` which is a meaningful API
surface change.
`invoke(stream_version="v2")` returns a `GraphOutput[OutputT]` dataclass
with `.value` and `.interrupts` fields:
```python
result = graph.invoke({"value": "x", "items": []}, stream_version="v2")
# typed access
assert isinstance(result, GraphOutput)
assert isinstance(result.value, MyState) # coerced to schema type
assert result.interrupts == () # always available
# backward compat dict access still works
assert result["value"] == "x_a"
```
The concern: this changes the return type of `invoke()` in a way that
existing code patterns like `result["key"]` still work (via
`__getitem__`), but `isinstance(result, dict)` checks would break. Worth
discussing whether the ergonomic benefit justifies the migration cost.
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.
Get started
Install LangGraph:
pip install -U langgraph
Create a simple workflow:
from langgraph.graph import START, StateGraph
from typing_extensions import TypedDict
class State(TypedDict):
text: str
def node_a(state: State) -> dict:
return {"text": state["text"] + "a"}
def node_b(state: State) -> dict:
return {"text": state["text"] + "b"}
graph = StateGraph(State)
graph.add_node("node_a", node_a)
graph.add_node("node_b", node_b)
graph.add_edge(START, "node_a")
graph.add_edge("node_a", "node_b")
print(graph.compile().invoke({"text": ""}))
# {'text': 'ab'}
Get started with the LangGraph Quickstart.
To quickly build agents with LangChain's create_agent (built on LangGraph), see the LangChain Agents documentation.
Core benefits
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
- 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.
LangGraph’s 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:
- 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 LangGraph Studio.
- LangChain – Provides integrations and composable components to streamline LLM application development.
Note
Looking for the JS version of LangGraph? See the JS repo and the JS docs.
Additional resources
- Guides: Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- Reference: Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- Examples: Guided examples on getting started with LangGraph.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- 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.
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.