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  • 1.1.0 3330ccdea4
    比较

    release(langgraph): 1.1 (#7102)

    frostbyte_neo 发布于 2026-03-10 12:41:30 +00:00 | 438 次提交 在此版本后已推送到 main

    exciting!

    relnotes preview:

    LangGraph 1.1.0 Release Notes

    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.
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