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图

LangGraph 的核心是将智能体工作流程建模为图。您使用三个关键组件定义智能体的行为：
  1. State（状态）：表示应用程序当前快照的共享数据结构。它可以是任何数据类型，但通常使用共享状态模式定义。
  2. Nodes（节点）：编码智能体逻辑的函数。它们接收当前状态作为输入，执行一些计算或副作用，并返回更新的状态。
  3. Edges（边）：基于当前状态确定下一个要执行的 Node 的函数。它们可以是条件分支或固定转换。
通过组合 Nodes 和 Edges，您可以创建随时间演变状态的复杂循环工作流程。然而，真正的力量来自 LangGraph 如何管理该状态。需要强调的是：Nodes 和 Edges 只不过是函数 - 它们可以包含 LLM 或只是普通代码。 简而言之：节点完成工作，边告诉接下来做什么。 LangGraph 的底层图算法使用消息传递来定义通用程序。当节点完成其操作时，它沿着一个或多个边将消息发送到其他节点。这些接收节点然后执行其函数，将生成的消息传递给下一组节点，过程继续进行。受 Google 的 Pregel 系统启发，程序以离散的”超级步骤”进行。 超级步骤可以被视为对图节点的单次迭代。并行运行的节点属于同一超级步骤，而顺序运行的节点属于不同的超级步骤。在图执行开始时，所有节点都以 inactive（非活动）状态开始。当节点在其任何传入边（或”通道”）上接收到新消息（状态）时，它变为 active（活动）。活动节点然后运行其函数并响应更新。在每个超级步骤结束时，没有传入消息的节点通过将自己标记为 inactive 来投票 halt（停止）。当所有节点都处于 inactive 且没有消息在传输时，图执行终止。

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StateGraph

StateGraph 类是要使用的主要图类。这由用户定义的 State 对象参数化。

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编译您的图

要构建您的图，首先定义状态，然后添加节点和边，然后编译它。编译您的图到底是什么，为什么需要它？ 编译是一个相当简单的步骤。它对图的结构提供了一些基本检查（没有孤立节点等）。这也是您可以指定运行时参数（如检查点器和断点）的地方。您只需调用 .compile 方法即可编译图： 
graph = graph_builder.compile(...)
您必须先编译图，然后才能使用它。

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状态

定义图时首先要做的是定义图的 State（状态）。State 由图的模式以及指定如何将更新应用于状态的 reducer 函数组成。State 的模式将是图中所有 Nodes 和 Edges 的输入模式，可以是 TypedDict 或 Pydantic 模型。所有 Nodes 都将向 State 发出更新，然后使用指定的 reducer 函数应用这些更新。

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模式

指定图模式的主要记录方式是使用 TypedDict。如果您想在状态中提供默认值，请使用 dataclass。如果您想要递归数据验证，我们还支持使用 Pydantic BaseModel 作为您的图状态（但请注意，pydantic 的性能低于 TypedDict 或 dataclass）。 默认情况下，图将具有相同的输入和输出模式。如果您想更改此设置，还可以直接指定显式输入和输出模式。当您有很多键，有些明确用于输入，其他用于输出时，这很有用。有关如何使用，请参阅此处的指南。

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多个模式

通常，所有图节点使用单个模式进行通信。这意味着它们将读取和写入相同的状态通道。但是，在某些情况下我们希望对此有更多控制：
  • 内部节点可以传递图的输入/输出中不需要的信息。
  • 我们可能还希望为图使用不同的输入/输出模式。例如，输出可能只包含一个相关的输出键。
可以让节点写入图内部的私有状态通道以进行内部节点通信。我们可以简单地定义一个私有模式 PrivateState。 还可以为图定义显式的输入和输出模式。在这些情况下，我们定义一个包含与图操作相关的_所有_键的”内部”模式。但是，我们还定义 input 和 output 模式，它们是”内部”模式的子集，以约束图的输入和输出。有关更多详细信息，请参阅本指南。 让我们看一个示例： 
class InputState(TypedDict):
    user_input: str

class OutputState(TypedDict):
    graph_output: str

class OverallState(TypedDict):
    foo: str
    user_input: str
    graph_output: str

class PrivateState(TypedDict):
    bar: str

def node_1(state: InputState) -> OverallState:
    # Write to OverallState
    return {"foo": state["user_input"] + " name"}

def node_2(state: OverallState) -> PrivateState:
    # Read from OverallState, write to PrivateState
    return {"bar": state["foo"] + " is"}

def node_3(state: PrivateState) -> OutputState:
    # Read from PrivateState, write to OutputState
    return {"graph_output": state["bar"] + " Lance"}

builder = StateGraph(OverallState,input_schema=InputState,output_schema=OutputState)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
builder.add_node("node_3", node_3)
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
builder.add_edge("node_2", "node_3")
builder.add_edge("node_3", END)

graph = builder.compile()
graph.invoke({"user_input":"My"})
# {'graph_output': 'My name is Lance'}
There are two subtle and important points to note here:
  1. We pass state: InputState as the input schema to node_1. But, we write out to foo, a channel in OverallState. How can we write out to a state channel that is not included in the input schema? This is because a node can write to any state channel in the graph state. The graph state is the union of the state channels defined at initialization, which includes OverallState and the filters InputState and OutputState.
  2. We initialize the graph with StateGraph(OverallState,input_schema=InputState,output_schema=OutputState). So, how can we write to PrivateState in node_2? How does the graph gain access to this schema if it was not passed in the StateGraph initialization? We can do this because nodes can also declare additional state channels as long as the state schema definition exists. In this case, the PrivateState schema is defined, so we can add bar as a new state channel in the graph and write to it.

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Reducers

Reducers are key to understanding how updates from nodes are applied to the State. Each key in the State has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. There are a few different types of reducers, starting with the default type of reducer:

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Default Reducer

These two examples show how to use the default reducer: Example A: 
from typing_extensions import TypedDict

class State(TypedDict):
    foo: int
    bar: list[str]
In this example, no reducer functions are specified for any key. Let’s assume the input to the graph is: {"foo": 1, "bar": ["hi"]}. Let’s then assume the first Node returns {"foo": 2}. This is treated as an update to the state. Notice that the Node does not need to return the whole State schema - just an update. After applying this update, the State would then be {"foo": 2, "bar": ["hi"]}. If the second node returns {"bar": ["bye"]} then the State would then be {"foo": 2, "bar": ["bye"]} Example B: 
from typing import Annotated
from typing_extensions import TypedDict
from operator import add

class State(TypedDict):
    foo: int
    bar: Annotated[list[str], add]
In this example, we’ve used the Annotated type to specify a reducer function (operator.add) for the second key (bar). Note that the first key remains unchanged. Let’s assume the input to the graph is {"foo": 1, "bar": ["hi"]}. Let’s then assume the first Node returns {"foo": 2}. This is treated as an update to the state. Notice that the Node does not need to return the whole State schema - just an update. After applying this update, the State would then be {"foo": 2, "bar": ["hi"]}. If the second node returns {"bar": ["bye"]} then the State would then be {"foo": 2, "bar": ["hi", "bye"]}. Notice here that the bar key is updated by adding the two lists together.

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Overwrite

In some cases, you may want to bypass a reducer and directly overwrite a state value. LangGraph provides the Overwrite type for this purpose. Learn how to use Overwrite here.

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Working with Messages in Graph State

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Why use messages?

Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain’s ChatModel in particular accepts a list of Message objects as inputs. These messages come in a variety of forms such as HumanMessage (user input) or AIMessage (LLM response). To read more about what message objects are, please refer to this conceptual guide.

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Using Messages in your Graph

In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of Message objects and annotate it with a reducer function (see messages key in the example below). The reducer function is vital to telling the graph how to update the list of Message objects in the state with each state update (for example, when a node sends an update). If you don’t specify a reducer, every state update will overwrite the list of messages with the most recently provided value. If you wanted to simply append messages to the existing list, you could use operator.add as a reducer. However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use operator.add, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt add_messages function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly.

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Serialization

In addition to keeping track of message IDs, the add_messages function will also try to deserialize messages into LangChain Message objects whenever a state update is received on the messages channel. See more information on LangChain serialization/deserialization here. This allows sending graph inputs / state updates in the following format: 
# this is supported
{"messages": [HumanMessage(content="message")]}

# and this is also supported
{"messages": [{"type": "human", "content": "message"}]}
Since the state updates are always deserialized into LangChain Messages when using add_messages, you should use dot notation to access message attributes, like state["messages"][-1].content. Below is an example of a graph that uses add_messages as its reducer function. 
from langchain.messages import AnyMessage
from langgraph.graph.message import add_messages
from typing import Annotated
from typing_extensions import TypedDict

class GraphState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

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MessagesState

Since having a list of messages in your state is so common, there exists a prebuilt state called MessagesState which makes it easy to use messages. MessagesState is defined with a single messages key which is a list of AnyMessage objects and uses the add_messages reducer. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like: 
from langgraph.graph import MessagesState

class State(MessagesState):
    documents: list[str]

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Nodes

In LangGraph, nodes are Python functions (either synchronous or asynchronous) that accept the following arguments:
  1. state: The state of the graph
  2. config: A RunnableConfig object that contains configuration information like thread_id and tracing information like tags
  3. runtime: A Runtime object that contains runtime context and other information like store and stream_writer
Similar to NetworkX, you add these nodes to a graph using the add_node method: 
from dataclasses import dataclass
from typing_extensions import TypedDict

from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph
from langgraph.runtime import Runtime

class State(TypedDict):
    input: str
    results: str

@dataclass
class Context:
    user_id: str

builder = StateGraph(State)

def plain_node(state: State):
    return state

def node_with_runtime(state: State, runtime: Runtime[Context]):
    print("In node: ", runtime.context.user_id)
    return {"results": f"Hello, {state['input']}!"}

def node_with_config(state: State, config: RunnableConfig):
    print("In node with thread_id: ", config["configurable"]["thread_id"])
    return {"results": f"Hello, {state['input']}!"}


builder.add_node("plain_node", plain_node)
builder.add_node("node_with_runtime", node_with_runtime)
builder.add_node("node_with_config", node_with_config)
...
Behind the scenes, functions are converted to RunnableLambdas, which add batch and async support to your function, along with native tracing and debugging. If you add a node to a graph without specifying a name, it will be given a default name equivalent to the function name. 
builder.add_node(my_node)
# You can then create edges to/from this node by referencing it as `"my_node"`

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START Node

The START Node is a special node that represents the node that sends user input to the graph. The main purpose for referencing this node is to determine which nodes should be called first. 
from langgraph.graph import START

graph.add_edge(START, "node_a")

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END Node

The END Node is a special node that represents a terminal node. This node is referenced when you want to denote which edges have no actions after they are done. 
from langgraph.graph import END

graph.add_edge("node_a", END)

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Node Caching

LangGraph supports caching of tasks/nodes based on the input to the node. To use caching:
  • Specify a cache when compiling a graph (or specifying an entrypoint)
  • Specify a cache policy for nodes. Each cache policy supports:
    • key_func used to generate a cache key based on the input to a node, which defaults to a hash of the input with pickle.
    • ttl, the time to live for the cache in seconds. If not specified, the cache will never expire.
For example: 
import time
from typing_extensions import TypedDict
from langgraph.graph import StateGraph
from langgraph.cache.memory import InMemoryCache
from langgraph.types import CachePolicy


class State(TypedDict):
    x: int
    result: int


builder = StateGraph(State)


def expensive_node(state: State) -> dict[str, int]:
    # expensive computation
    time.sleep(2)
    return {"result": state["x"] * 2}


builder.add_node("expensive_node", expensive_node, cache_policy=CachePolicy(ttl=3))
builder.set_entry_point("expensive_node")
builder.set_finish_point("expensive_node")

graph = builder.compile(cache=InMemoryCache())

print(graph.invoke({"x": 5}, stream_mode='updates'))    
# [{'expensive_node': {'result': 10}}]
print(graph.invoke({"x": 5}, stream_mode='updates'))    
# [{'expensive_node': {'result': 10}, '__metadata__': {'cached': True}}]
  1. First run takes two seconds to run (due to mocked expensive computation).
  2. Second run utilizes cache and returns quickly.

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Edges

Edges define how the logic is routed and how the graph decides to stop. This is a big part of how your agents work and how different nodes communicate with each other. There are a few key types of edges:
  • Normal Edges: Go directly from one node to the next.
  • Conditional Edges: Call a function to determine which node(s) to go to next.
  • Entry Point: Which node to call first when user input arrives.
  • Conditional Entry Point: Call a function to determine which node(s) to call first when user input arrives.
A node can have MULTIPLE outgoing edges. If a node has multiple out-going edges, all of those destination nodes will be executed in parallel as a part of the next superstep.

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Normal Edges

If you always want to go from node A to node B, you can use the add_edge method directly. 
graph.add_edge("node_a", "node_b")

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Conditional Edges

If you want to optionally route to one or more edges (or optionally terminate), you can use the add_conditional_edges method. This method accepts the name of a node and a “routing function” to call after that node is executed: 
graph.add_conditional_edges("node_a", routing_function)
Similar to nodes, the routing_function accepts the current state of the graph and returns a value. By default, the return value routing_function is used as the name of the node (or list of nodes) to send the state to next. All those nodes will be run in parallel as a part of the next superstep. You can optionally provide a dictionary that maps the routing_function’s output to the name of the next node. 
graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
Use Command instead of conditional edges if you want to combine state updates and routing in a single function.

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Entry Point

The entry point is the first node(s) that are run when the graph starts. You can use the add_edge method from the virtual START node to the first node to execute to specify where to enter the graph. 
from langgraph.graph import START

graph.add_edge(START, "node_a")

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Conditional Entry Point

A conditional entry point lets you start at different nodes depending on custom logic. You can use add_conditional_edges from the virtual START node to accomplish this. 
from langgraph.graph import START

graph.add_conditional_edges(START, routing_function)
You can optionally provide a dictionary that maps the routing_function’s output to the name of the next node. 
graph.add_conditional_edges(START, routing_function, {True: "node_b", False: "node_c"})

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Send

By default, Nodes and Edges are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of State to exist at the same time. A common example of this is with map-reduce design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input State to the downstream Node should be different (one for each generated object). To support this design pattern, LangGraph supports returning Send objects from conditional edges. Send takes two arguments: first is the name of the node, and second is the state to pass to that node. 
def continue_to_jokes(state: OverallState):
    return [Send("generate_joke", {"subject": s}) for s in state['subjects']]

graph.add_conditional_edges("node_a", continue_to_jokes)

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Command

It can be useful to combine control flow (edges) and state updates (nodes). For example, you might want to BOTH perform state updates AND decide which node to go to next in the SAME node. LangGraph provides a way to do so by returning a Command object from node functions: 
def my_node(state: State) -> Command[Literal["my_other_node"]]:
    return Command(
        # state update
        update={"foo": "bar"},
        # control flow
        goto="my_other_node"
    )
With Command you can also achieve dynamic control flow behavior (identical to conditional edges): 
def my_node(state: State) -> Command[Literal["my_other_node"]]:
    if state["foo"] == "bar":
        return Command(update={"foo": "baz"}, goto="my_other_node")
When returning Command in your node functions, you must add return type annotations with the list of node names the node is routing to, e.g. Command[Literal["my_other_node"]]. This is necessary for the graph rendering and tells LangGraph that my_node can navigate to my_other_node.
Check out this how-to guide for an end-to-end example of how to use Command.

​
When should I use Command instead of conditional edges?

  • Use Command when you need to both update the graph state and route to a different node. For example, when implementing multi-agent handoffs where it’s important to route to a different agent and pass some information to that agent.
  • Use conditional edges to route between nodes conditionally without updating the state.
If you are using subgraphs, you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify graph=Command.PARENT in Command: 
def my_node(state: State) -> Command[Literal["other_subgraph"]]:
    return Command(
        update={"foo": "bar"},
        goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph
        graph=Command.PARENT
    )
Setting graph to Command.PARENT will navigate to the closest parent graph.When you send updates from a subgraph node to a parent graph node for a key that’s shared by both parent and subgraph state schemas, you must define a reducer for the key you’re updating in the parent graph state. See this example.
This is particularly useful when implementing multi-agent handoffs. Check out this guide for detail.

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Using inside tools

A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. Refer to this guide for detail.

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Human-in-the-loop

Command is an important part of human-in-the-loop workflows: when using interrupt() to collect user input, Command is then used to supply the input and resume execution via Command(resume="User input"). Check out this conceptual guide for more information.

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Graph Migrations

LangGraph can easily handle migrations of graph definitions (nodes, edges, and state) even when using a checkpointer to track state.
  • For threads at the end of the graph (i.e. not interrupted) you can change the entire topology of the graph (i.e. all nodes and edges, remove, add, rename, etc)
  • For threads currently interrupted, we support all topology changes other than renaming / removing nodes (as that thread could now be about to enter a node that no longer exists) — if this is a blocker please reach out and we can prioritize a solution.
  • For modifying state, we have full backwards and forwards compatibility for adding and removing keys
  • State keys that are renamed lose their saved state in existing threads
  • State keys whose types change in incompatible ways could currently cause issues in threads with state from before the change — if this is a blocker please reach out and we can prioritize a solution.

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Runtime Context

When creating a graph, you can specify a context_schema for runtime context passed to nodes. This is useful for passing information to nodes that is not part of the graph state. For example, you might want to pass dependencies such as model name or a database connection. 
@dataclass
class ContextSchema:
    llm_provider: str = "openai"

graph = StateGraph(State, context_schema=ContextSchema)
You can then pass this context into the graph using the context parameter of the invoke method. 
graph.invoke(inputs, context={"llm_provider": "anthropic"})
You can then access and use this context inside a node or conditional edge: 
from langgraph.runtime import Runtime

def node_a(state: State, runtime: Runtime[ContextSchema]):
    llm = get_llm(runtime.context.llm_provider)
    # ...
See this guide for a full breakdown on configuration.

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Recursion Limit

The recursion limit sets the maximum number of super-steps the graph can execute during a single execution. Once the limit is reached, LangGraph will raise GraphRecursionError. By default this value is set to 25 steps. The recursion limit can be set on any graph at runtime, and is passed to invoke/stream via the config dictionary. Importantly, recursion_limit is a standalone config key and should not be passed inside the configurable key as all other user-defined configuration. See the example below: 
graph.invoke(inputs, config={"recursion_limit": 5}, context={"llm": "anthropic"})
Read this how-to to learn more about how the recursion limit works.

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Visualization

It’s often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See this how-to guide for more info.
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