1.1.0

Changes since 1.0.10

• release(langgraph): 1.1 (#7102)
• fix: replay behavior for parent + subgraphs! (#7038)
• feat: type safe stream/invoke w/ proper output type coercion (#6961)

Type-Safe Streaming & Invoke

LangGraph 1.1 introduces version="v2" — a new opt-in streaming format that brings full type safety to stream(), astream(), invoke(), and ainvoke().

What's changing

v1 (default, unchanged): stream() yields bare tuples like (stream_mode, data) or just data. invoke() returns a plain dict. Interrupts are mixed into the output dict under "__interrupt__".

v2 (opt-in): stream() yields strongly-typed StreamPart dicts with type, ns, data, and (for values) interrupts fields. invoke() returns a GraphOutput object with .value and .interrupts attributes. When your state schema is a Pydantic model or dataclass, outputs are automatically coerced to the correct type.

invoke() / ainvoke() with version="v2"

from langgraph.types import GraphOutput

result = graph.invoke({"input": "hello"}, version="v2")

# result is a GraphOutput, not a dict
assert isinstance(result, GraphOutput)
result.value       # your output — dict, Pydantic model, or dataclass
result.interrupts  # tuple[Interrupt, ...], empty if none occurred

With a non-"values" stream mode, invoke(..., stream_mode="updates", version="v2") returns list[StreamPart] instead of list[tuple].

stream() / astream() with version="v2"

for part in graph.stream({"input": "hello"}, version="v2"):
    if part["type"] == "values":
        part["data"]        # OutputT — full state
        part["interrupts"]  # tuple[Interrupt, ...]
    elif part["type"] == "updates":
        part["data"]        # dict[str, Any]
    elif part["type"] == "messages":
        part["data"]        # tuple[BaseMessage, dict]
    elif part["type"] == "custom":
        part["data"]        # Any
    elif part["type"] == "tasks":
        part["data"]        # TaskPayload | TaskResultPayload
    elif part["type"] == "debug":
        part["data"]        # DebugPayload

Each stream mode has its own TypedDict — ValuesStreamPart, UpdatesStreamPart, MessagesStreamPart, CustomStreamPart, CheckpointStreamPart, TasksStreamPart, DebugStreamPart — all importable from langgraph.types. The union type StreamPart is a discriminated union on part["type"], enabling full type narrowing in editors and type checkers.

Pydantic & dataclass output coercion

When your graph's state schema is a Pydantic model or dataclass, version="v2" automatically coerces outputs to the declared type:

from pydantic import BaseModel

class MyState(BaseModel):
    answer: str
    count: int

graph = StateGraph(MyState)
# ... build graph ...
compiled = graph.compile()

result = compiled.invoke({"answer": "", "count": 0}, version="v2")
assert isinstance(result.value, MyState)  # not a dict!

Backward compatibility

• Default is still version="v1" — existing code works without changes.
• To make migration easier, GraphOutput supports old-style best-effort access to graph values and interrupts. Dict-style access (result["key"], "key" in result, result["__interrupt__"]) still works and delegates to result.value / result.interrupts under the hood. However, this is deprecated and emits a LangGraphDeprecatedSinceV11 warning. It will be removed in v3.0 — migrate to result.value and result.interrupts at your convenience.

result = graph.invoke({"input": "hello"}, version="v2")

# Old style — still works, but deprecated
result["input"]          # delegates to result.value["input"]
result["__interrupt__"]  # delegates to result.interrupts
"input" in result        # delegates to "input" in result.value

# New style — preferred
result.value["input"]
result.interrupts

Migration Guide

1. No action required — version="v1" remains the default. All existing code continues to work.
2. Adopt v2 incrementally — Add version="v2" to individual invoke()/stream() calls to get typed outputs.
3. Use typed imports — Import GraphOutput, StreamPart, and individual part types from langgraph.types for type-safe code.