Sakshi GuptaandGitHub
7bbe8d8628
docs (graphapi) : Handle Missing Context in LLM Invocation - Invoking the LLM without explicitly passing a context parameter resulted in the following error: `AttributeError: 'NoneType' object has no attribute 'model_provider'` This occurred because the context was None, and the system attempted to access model_provider. This PR ensures that when context is not provided, an empty context is passed explicitly. This allows the system to correctly fall back to the default value defined in the ContextSchema.model_provider attribute.
100 KiB
How to use the graph API
This guide demonstrates the basics of LangGraph's Graph API. It walks through state, as well as composing common graph structures such as sequences, branches, and loops. It also covers LangGraph's control features, including the Send API for map-reduce workflows and the Command API for combining state updates with "hops" across nodes.
Setup
:::python
Install langgraph:
pip install -U langgraph
:::
:::js
Install langgraph:
npm install @langchain/langgraph
:::
!!! tip "Set up LangSmith for better debugging"
Sign up for [LangSmith](https://smith.langchain.com) to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started in the [docs](https://docs.smith.langchain.com).
Define and update state
Here we show how to define and update state in LangGraph. We will demonstrate:
- How to use state to define a graph's schema
- How to use reducers to control how state updates are processed.
Define state
:::python
State in LangGraph can be a TypedDict, Pydantic model, or dataclass. Below we will use TypedDict. See this section for detail on using Pydantic.
:::
:::js State in LangGraph can be defined using Zod schemas. Below we will use Zod. See this section for detail on using alternative approaches. :::
By default, graphs will have the same input and output schema, and the state determines that schema. See this section for how to define distinct input and output schemas.
Let's consider a simple example using messages. This represents a versatile formulation of state for many LLM applications. See our concepts page for more detail.
:::python
from langchain_core.messages import AnyMessage
from typing_extensions import TypedDict
class State(TypedDict):
messages: list[AnyMessage]
extra_field: int
This state tracks a list of message objects, as well as an extra integer field. :::
:::js
import { BaseMessage } from "@langchain/core/messages";
import { z } from "zod";
const State = z.object({
messages: z.array(z.custom<BaseMessage>()),
extraField: z.number(),
});
This state tracks a list of message objects, as well as an extra integer field. :::
Update state
:::python Let's build an example graph with a single node. Our node is just a Python function that reads our graph's state and makes updates to it. The first argument to this function will always be the state:
from langchain_core.messages import AIMessage
def node(state: State):
messages = state["messages"]
new_message = AIMessage("Hello!")
return {"messages": messages + [new_message], "extra_field": 10}
This node simply appends a message to our message list, and populates an extra field. :::
:::js Let's build an example graph with a single node. Our node is just a TypeScript function that reads our graph's state and makes updates to it. The first argument to this function will always be the state:
import { AIMessage } from "@langchain/core/messages";
const node = (state: z.infer<typeof State>) => {
const messages = state.messages;
const newMessage = new AIMessage("Hello!");
return { messages: messages.concat([newMessage]), extraField: 10 };
};
This node simply appends a message to our message list, and populates an extra field. :::
!!! important
Nodes should return updates to the state directly, instead of mutating the state.
:::python Let's next define a simple graph containing this node. We use StateGraph to define a graph that operates on this state. We then use add_node populate our graph.
from langgraph.graph import StateGraph
builder = StateGraph(State)
builder.add_node(node)
builder.set_entry_point("node")
graph = builder.compile()
:::
:::js Let's next define a simple graph containing this node. We use StateGraph to define a graph that operates on this state. We then use addNode populate our graph.
import { StateGraph } from "@langchain/langgraph";
const graph = new StateGraph(State)
.addNode("node", node)
.addEdge("__start__", "node")
.compile();
:::
LangGraph provides built-in utilities for visualizing your graph. Let's inspect our graph. See this section for detail on visualization.
:::python
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
:::
:::js
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
:::
In this case, our graph just executes a single node. Let's proceed with a simple invocation:
:::python
from langchain_core.messages import HumanMessage
result = graph.invoke({"messages": [HumanMessage("Hi")]})
result
{'messages': [HumanMessage(content='Hi'), AIMessage(content='Hello!')], 'extra_field': 10}
:::
:::js
import { HumanMessage } from "@langchain/core/messages";
const result = await graph.invoke({ messages: [new HumanMessage("Hi")], extraField: 0 });
console.log(result);
{ messages: [HumanMessage { content: 'Hi' }, AIMessage { content: 'Hello!' }], extraField: 10 }
:::
Note that:
- We kicked off invocation by updating a single key of the state.
- We receive the entire state in the invocation result.
:::python For convenience, we frequently inspect the content of message objects via pretty-print:
for message in result["messages"]:
message.pretty_print()
================================ Human Message ================================
Hi
================================== Ai Message ==================================
Hello!
:::
:::js For convenience, we frequently inspect the content of message objects via logging:
for (const message of result.messages) {
console.log(`${message.getType()}: ${message.content}`);
}
human: Hi
ai: Hello!
:::
Process state updates with reducers
Each key in the state can have its own independent reducer function, which controls how updates from nodes are applied. If no reducer function is explicitly specified then it is assumed that all updates to the key should override it.
:::python
For TypedDict state schemas, we can define reducers by annotating the corresponding field of the state with a reducer function.
In the earlier example, our node updated the "messages" key in the state by appending a message to it. Below, we add a reducer to this key, such that updates are automatically appended:
from typing_extensions import Annotated
def add(left, right):
"""Can also import `add` from the `operator` built-in."""
return left + right
class State(TypedDict):
# highlight-next-line
messages: Annotated[list[AnyMessage], add]
extra_field: int
Now our node can be simplified:
def node(state: State):
new_message = AIMessage("Hello!")
# highlight-next-line
return {"messages": [new_message], "extra_field": 10}
:::
:::js
For Zod state schemas, we can define reducers by using the special .langgraph.reducer() method on the schema field.
In the earlier example, our node updated the "messages" key in the state by appending a message to it. Below, we add a reducer to this key, such that updates are automatically appended:
import "@langchain/langgraph/zod";
const State = z.object({
// highlight-next-line
messages: z.array(z.custom<BaseMessage>()).langgraph.reducer((x, y) => x.concat(y)),
extraField: z.number(),
});
Now our node can be simplified:
const node = (state: z.infer<typeof State>) => {
const newMessage = new AIMessage("Hello!");
// highlight-next-line
return { messages: [newMessage], extraField: 10 };
};
:::
:::python
from langgraph.graph import START
graph = StateGraph(State).add_node(node).add_edge(START, "node").compile()
result = graph.invoke({"messages": [HumanMessage("Hi")]})
for message in result["messages"]:
message.pretty_print()
================================ Human Message ================================
Hi
================================== Ai Message ==================================
Hello!
:::
:::js
import { START } from "@langchain/langgraph";
const graph = new StateGraph(State)
.addNode("node", node)
.addEdge(START, "node")
.compile();
const result = await graph.invoke({ messages: [new HumanMessage("Hi")] });
for (const message of result.messages) {
console.log(`${message.getType()}: ${message.content}`);
}
human: Hi
ai: Hello!
:::
MessagesState
In practice, there are additional considerations for updating lists of messages:
- We may wish to update an existing message in the state.
- We may want to accept short-hands for message formats, such as OpenAI format.
:::python
LangGraph includes a built-in reducer add_messages that handles these considerations:
from langgraph.graph.message import add_messages
class State(TypedDict):
# highlight-next-line
messages: Annotated[list[AnyMessage], add_messages]
extra_field: int
def node(state: State):
new_message = AIMessage("Hello!")
return {"messages": [new_message], "extra_field": 10}
graph = StateGraph(State).add_node(node).set_entry_point("node").compile()
# highlight-next-line
input_message = {"role": "user", "content": "Hi"}
result = graph.invoke({"messages": [input_message]})
for message in result["messages"]:
message.pretty_print()
================================ Human Message ================================
Hi
================================== Ai Message ==================================
Hello!
This is a versatile representation of state for applications involving chat models. LangGraph includes a pre-built MessagesState for convenience, so that we can have:
from langgraph.graph import MessagesState
class State(MessagesState):
extra_field: int
:::
:::js
LangGraph includes a built-in MessagesZodState that handles these considerations:
import { MessagesZodState } from "@langchain/langgraph";
const State = z.object({
// highlight-next-line
messages: MessagesZodState.shape.messages,
extraField: z.number(),
});
const graph = new StateGraph(State)
.addNode("node", (state) => {
const newMessage = new AIMessage("Hello!");
return { messages: [newMessage], extraField: 10 };
})
.addEdge(START, "node")
.compile();
// highlight-next-line
const inputMessage = { role: "user", content: "Hi" };
const result = await graph.invoke({ messages: [inputMessage] });
for (const message of result.messages) {
console.log(`${message.getType()}: ${message.content}`);
}
human: Hi
ai: Hello!
This is a versatile representation of state for applications involving chat models. LangGraph includes this pre-built MessagesZodState for convenience, so that we can have:
import { MessagesZodState } from "@langchain/langgraph";
const State = MessagesZodState.extend({
extraField: z.number(),
});
:::
Define input and output schemas
By default, StateGraph operates with a single schema, and all nodes are expected to communicate using that schema. However, it's also possible to define distinct input and output schemas for a graph.
When distinct schemas are specified, an internal schema will still be used for communication between nodes. The input schema ensures that the provided input matches the expected structure, while the output schema filters the internal data to return only the relevant information according to the defined output schema.
Below, we'll see how to define distinct input and output schema.
:::python
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
# Define the schema for the input
class InputState(TypedDict):
question: str
# Define the schema for the output
class OutputState(TypedDict):
answer: str
# Define the overall schema, combining both input and output
class OverallState(InputState, OutputState):
pass
# Define the node that processes the input and generates an answer
def answer_node(state: InputState):
# Example answer and an extra key
return {"answer": "bye", "question": state["question"]}
# Build the graph with input and output schemas specified
builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)
builder.add_node(answer_node) # Add the answer node
builder.add_edge(START, "answer_node") # Define the starting edge
builder.add_edge("answer_node", END) # Define the ending edge
graph = builder.compile() # Compile the graph
# Invoke the graph with an input and print the result
print(graph.invoke({"question": "hi"}))
{'answer': 'bye'}
:::
:::js
import { StateGraph, START, END } from "@langchain/langgraph";
import { z } from "zod";
// Define the schema for the input
const InputState = z.object({
question: z.string(),
});
// Define the schema for the output
const OutputState = z.object({
answer: z.string(),
});
// Define the overall schema, combining both input and output
const OverallState = InputState.merge(OutputState);
// Build the graph with input and output schemas specified
const graph = new StateGraph({
input: InputState,
output: OutputState,
state: OverallState,
})
.addNode("answerNode", (state) => {
// Example answer and an extra key
return { answer: "bye", question: state.question };
})
.addEdge(START, "answerNode")
.addEdge("answerNode", END)
.compile();
// Invoke the graph with an input and print the result
console.log(await graph.invoke({ question: "hi" }));
{ answer: 'bye' }
:::
Notice that the output of invoke only includes the output schema.
Pass private state between nodes
In some cases, you may want nodes to exchange information that is crucial for intermediate logic but doesn't need to be part of the main schema of the graph. This private data is not relevant to the overall input/output of the graph and should only be shared between certain nodes.
Below, we'll create an example sequential graph consisting of three nodes (node_1, node_2 and node_3), where private data is passed between the first two steps (node_1 and node_2), while the third step (node_3) only has access to the public overall state.
:::python
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
# The overall state of the graph (this is the public state shared across nodes)
class OverallState(TypedDict):
a: str
# Output from node_1 contains private data that is not part of the overall state
class Node1Output(TypedDict):
private_data: str
# The private data is only shared between node_1 and node_2
def node_1(state: OverallState) -> Node1Output:
output = {"private_data": "set by node_1"}
print(f"Entered node `node_1`:\n\tInput: {state}.\n\tReturned: {output}")
return output
# Node 2 input only requests the private data available after node_1
class Node2Input(TypedDict):
private_data: str
def node_2(state: Node2Input) -> OverallState:
output = {"a": "set by node_2"}
print(f"Entered node `node_2`:\n\tInput: {state}.\n\tReturned: {output}")
return output
# Node 3 only has access to the overall state (no access to private data from node_1)
def node_3(state: OverallState) -> OverallState:
output = {"a": "set by node_3"}
print(f"Entered node `node_3`:\n\tInput: {state}.\n\tReturned: {output}")
return output
# Connect nodes in a sequence
# node_2 accepts private data from node_1, whereas
# node_3 does not see the private data.
builder = StateGraph(OverallState).add_sequence([node_1, node_2, node_3])
builder.add_edge(START, "node_1")
graph = builder.compile()
# Invoke the graph with the initial state
response = graph.invoke(
{
"a": "set at start",
}
)
print()
print(f"Output of graph invocation: {response}")
Entered node `node_1`:
Input: {'a': 'set at start'}.
Returned: {'private_data': 'set by node_1'}
Entered node `node_2`:
Input: {'private_data': 'set by node_1'}.
Returned: {'a': 'set by node_2'}
Entered node `node_3`:
Input: {'a': 'set by node_2'}.
Returned: {'a': 'set by node_3'}
Output of graph invocation: {'a': 'set by node_3'}
:::
:::js
import { StateGraph, START, END } from "@langchain/langgraph";
import { z } from "zod";
// The overall state of the graph (this is the public state shared across nodes)
const OverallState = z.object({
a: z.string(),
});
// Output from node1 contains private data that is not part of the overall state
const Node1Output = z.object({
privateData: z.string(),
});
// The private data is only shared between node1 and node2
const node1 = (state: z.infer<typeof OverallState>): z.infer<typeof Node1Output> => {
const output = { privateData: "set by node1" };
console.log(`Entered node 'node1':\n\tInput: ${JSON.stringify(state)}.\n\tReturned: ${JSON.stringify(output)}`);
return output;
};
// Node 2 input only requests the private data available after node1
const Node2Input = z.object({
privateData: z.string(),
});
const node2 = (state: z.infer<typeof Node2Input>): z.infer<typeof OverallState> => {
const output = { a: "set by node2" };
console.log(`Entered node 'node2':\n\tInput: ${JSON.stringify(state)}.\n\tReturned: ${JSON.stringify(output)}`);
return output;
};
// Node 3 only has access to the overall state (no access to private data from node1)
const node3 = (state: z.infer<typeof OverallState>): z.infer<typeof OverallState> => {
const output = { a: "set by node3" };
console.log(`Entered node 'node3':\n\tInput: ${JSON.stringify(state)}.\n\tReturned: ${JSON.stringify(output)}`);
return output;
};
// Connect nodes in a sequence
// node2 accepts private data from node1, whereas
// node3 does not see the private data.
const graph = new StateGraph({
state: OverallState,
nodes: {
node1: { action: node1, output: Node1Output },
node2: { action: node2, input: Node2Input },
node3: { action: node3 },
}
})
.addEdge(START, "node1")
.addEdge("node1", "node2")
.addEdge("node2", "node3")
.addEdge("node3", END)
.compile();
// Invoke the graph with the initial state
const response = await graph.invoke({ a: "set at start" });
console.log(`\nOutput of graph invocation: ${JSON.stringify(response)}`);
Entered node 'node1':
Input: {"a":"set at start"}.
Returned: {"privateData":"set by node1"}
Entered node 'node2':
Input: {"privateData":"set by node1"}.
Returned: {"a":"set by node2"}
Entered node 'node3':
Input: {"a":"set by node2"}.
Returned: {"a":"set by node3"}
Output of graph invocation: {"a":"set by node3"}
:::
:::python
Use Pydantic models for graph state
A StateGraph accepts a state_schema argument on initialization that specifies the "shape" of the state that the nodes in the graph can access and update.
In our examples, we typically use a python-native TypedDict or dataclass for state_schema, but state_schema can be any type.
Here, we'll see how a Pydantic BaseModel can be used for state_schema to add run-time validation on inputs.
!!! note "Known Limitations"
- Currently, the output of the graph will **NOT** be an instance of a pydantic model.
- Run-time validation only occurs on inputs into nodes, not on the outputs.
- The validation error trace from pydantic does not show which node the error arises in.
- Pydantic's recursive validation can be slow. For performance-sensitive applications, you may want to consider using a `dataclass` instead.
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from pydantic import BaseModel
# The overall state of the graph (this is the public state shared across nodes)
class OverallState(BaseModel):
a: str
def node(state: OverallState):
return {"a": "goodbye"}
# Build the state graph
builder = StateGraph(OverallState)
builder.add_node(node) # node_1 is the first node
builder.add_edge(START, "node") # Start the graph with node_1
builder.add_edge("node", END) # End the graph after node_1
graph = builder.compile()
# Test the graph with a valid input
graph.invoke({"a": "hello"})
Invoke the graph with an invalid input
try:
graph.invoke({"a": 123}) # Should be a string
except Exception as e:
print("An exception was raised because `a` is an integer rather than a string.")
print(e)
An exception was raised because `a` is an integer rather than a string.
1 validation error for OverallState
a
Input should be a valid string [type=string_type, input_value=123, input_type=int]
For further information visit https://errors.pydantic.dev/2.9/v/string_type
See below for additional features of Pydantic model state:
??? example "Serialization Behavior"
When using Pydantic models as state schemas, it's important to understand how serialization works, especially when:
- Passing Pydantic objects as inputs
- Receiving outputs from the graph
- Working with nested Pydantic models
Let's see these behaviors in action.
```python
from langgraph.graph import StateGraph, START, END
from pydantic import BaseModel
class NestedModel(BaseModel):
value: str
class ComplexState(BaseModel):
text: str
count: int
nested: NestedModel
def process_node(state: ComplexState):
# Node receives a validated Pydantic object
print(f"Input state type: {type(state)}")
print(f"Nested type: {type(state.nested)}")
# Return a dictionary update
return {"text": state.text + " processed", "count": state.count + 1}
# Build the graph
builder = StateGraph(ComplexState)
builder.add_node("process", process_node)
builder.add_edge(START, "process")
builder.add_edge("process", END)
graph = builder.compile()
# Create a Pydantic instance for input
input_state = ComplexState(text="hello", count=0, nested=NestedModel(value="test"))
print(f"Input object type: {type(input_state)}")
# Invoke graph with a Pydantic instance
result = graph.invoke(input_state)
print(f"Output type: {type(result)}")
print(f"Output content: {result}")
# Convert back to Pydantic model if needed
output_model = ComplexState(**result)
print(f"Converted back to Pydantic: {type(output_model)}")
```
??? example "Runtime Type Coercion"
Pydantic performs runtime type coercion for certain data types. This can be helpful but also lead to unexpected behavior if you're not aware of it.
```python
from langgraph.graph import StateGraph, START, END
from pydantic import BaseModel
class CoercionExample(BaseModel):
# Pydantic will coerce string numbers to integers
number: int
# Pydantic will parse string booleans to bool
flag: bool
def inspect_node(state: CoercionExample):
print(f"number: {state.number} (type: {type(state.number)})")
print(f"flag: {state.flag} (type: {type(state.flag)})")
return {}
builder = StateGraph(CoercionExample)
builder.add_node("inspect", inspect_node)
builder.add_edge(START, "inspect")
builder.add_edge("inspect", END)
graph = builder.compile()
# Demonstrate coercion with string inputs that will be converted
result = graph.invoke({"number": "42", "flag": "true"})
# This would fail with a validation error
try:
graph.invoke({"number": "not-a-number", "flag": "true"})
except Exception as e:
print(f"\nExpected validation error: {e}")
```
??? example "Working with Message Models"
When working with LangChain message types in your state schema, there are important considerations for serialization. You should use `AnyMessage` (rather than `BaseMessage`) for proper serialization/deserialization when using message objects over the wire.
```python
from langgraph.graph import StateGraph, START, END
from pydantic import BaseModel
from langchain_core.messages import HumanMessage, AIMessage, AnyMessage
from typing import List
class ChatState(BaseModel):
messages: List[AnyMessage]
context: str
def add_message(state: ChatState):
return {"messages": state.messages + [AIMessage(content="Hello there!")]}
builder = StateGraph(ChatState)
builder.add_node("add_message", add_message)
builder.add_edge(START, "add_message")
builder.add_edge("add_message", END)
graph = builder.compile()
# Create input with a message
initial_state = ChatState(
messages=[HumanMessage(content="Hi")], context="Customer support chat"
)
result = graph.invoke(initial_state)
print(f"Output: {result}")
# Convert back to Pydantic model to see message types
output_model = ChatState(**result)
for i, msg in enumerate(output_model.messages):
print(f"Message {i}: {type(msg).__name__} - {msg.content}")
```
:::
:::js
Alternative state definitions
While Zod schemas are the recommended approach, LangGraph also supports other ways to define state schemas:
import { BaseMessage } from "@langchain/core/messages";
import { StateGraph } from "@langchain/langgraph";
interface WorkflowChannelsState {
messages: BaseMessage[];
question: string;
answer: string;
}
const workflowWithChannels = new StateGraph<WorkflowChannelsState>({
channels: {
messages: {
reducer: (currentState, updateValue) => currentState.concat(updateValue),
default: () => [],
},
question: null,
answer: null,
},
});
:::
Add runtime configuration
Sometimes you want to be able to configure your graph when calling it. For example, you might want to be able to specify what LLM or system prompt to use at runtime, without polluting the graph state with these parameters.
To add runtime configuration:
- Specify a schema for your configuration
- Add the configuration to the function signature for nodes or conditional edges
- Pass the configuration into the graph.
See below for a simple example:
:::python
from langgraph.graph import END, StateGraph, START
from langgraph.runtime import Runtime
from typing_extensions import TypedDict
# 1. Specify config schema
class ContextSchema(TypedDict):
my_runtime_value: str
# 2. Define a graph that accesses the config in a node
class State(TypedDict):
my_state_value: str
# highlight-next-line
def node(state: State, runtime: Runtime[ContextSchema]):
# highlight-next-line
if runtime.context["my_runtime_value"] == "a":
return {"my_state_value": 1}
# highlight-next-line
elif runtime.context["my_runtime_value"] == "b":
return {"my_state_value": 2}
else:
raise ValueError("Unknown values.")
# highlight-next-line
builder = StateGraph(State, context_schema=ContextSchema)
builder.add_node(node)
builder.add_edge(START, "node")
builder.add_edge("node", END)
graph = builder.compile()
# 3. Pass in configuration at runtime:
# highlight-next-line
print(graph.invoke({}, context={"my_runtime_value": "a"}))
# highlight-next-line
print(graph.invoke({}, context={"my_runtime_value": "b"}))
{'my_state_value': 1}
{'my_state_value': 2}
:::
:::js
import { StateGraph, END, START } from "@langchain/langgraph";
import { RunnableConfig } from "@langchain/core/runnables";
import { z } from "zod";
// 1. Specify config schema
const ConfigurableSchema = z.object({
myRuntimeValue: z.string(),
});
// 2. Define a graph that accesses the config in a node
const State = z.object({
myStateValue: z.number(),
});
const graph = new StateGraph(State)
.addNode("node", (state, config) => {
// highlight-next-line
if (config?.configurable?.myRuntimeValue === "a") {
return { myStateValue: 1 };
// highlight-next-line
} else if (config?.configurable?.myRuntimeValue === "b") {
return { myStateValue: 2 };
} else {
throw new Error("Unknown values.");
}
})
.addEdge(START, "node")
.addEdge("node", END)
.compile();
// 3. Pass in configuration at runtime:
// highlight-next-line
console.log(await graph.invoke({}, { configurable: { myRuntimeValue: "a" } }));
// highlight-next-line
console.log(await graph.invoke({}, { configurable: { myRuntimeValue: "b" } }));
{ myStateValue: 1 }
{ myStateValue: 2 }
:::
??? example "Extended example: specifying LLM at runtime"
:::python
Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models.
```python
from dataclasses import dataclass
from langchain.chat_models import init_chat_model
from langgraph.graph import MessagesState, END, StateGraph, START
from langgraph.runtime import Runtime
from typing_extensions import TypedDict
@dataclass
class ContextSchema:
model_provider: str = "anthropic"
MODELS = {
"anthropic": init_chat_model("anthropic:claude-3-5-haiku-latest"),
"openai": init_chat_model("openai:gpt-4.1-mini"),
}
def call_model(state: MessagesState, runtime: Runtime[ContextSchema]):
model = MODELS[runtime.context.model_provider]
response = model.invoke(state["messages"])
return {"messages": [response]}
builder = StateGraph(MessagesState, context_schema=ContextSchema)
builder.add_node("model", call_model)
builder.add_edge(START, "model")
builder.add_edge("model", END)
graph = builder.compile()
# Usage
input_message = {"role": "user", "content": "hi"}
# With no configuration, uses default (Anthropic)
response_1 = graph.invoke({"messages": [input_message]}, context=ContextSchema())["messages"][-1]
# Or, can set OpenAI
response_2 = graph.invoke({"messages": [input_message]}, context={"model_provider": "openai"})["messages"][-1]
print(response_1.response_metadata["model_name"])
print(response_2.response_metadata["model_name"])
```
```
claude-3-5-haiku-20241022
gpt-4.1-mini-2025-04-14
```
:::
:::js
Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models.
```typescript
import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";
import { MessagesZodState, StateGraph, START, END } from "@langchain/langgraph";
import { RunnableConfig } from "@langchain/core/runnables";
import { z } from "zod";
const ConfigSchema = z.object({
modelProvider: z.string().default("anthropic"),
});
const MODELS = {
anthropic: new ChatAnthropic({ model: "claude-3-5-haiku-latest" }),
openai: new ChatOpenAI({ model: "gpt-4o-mini" }),
};
const graph = new StateGraph(MessagesZodState)
.addNode("model", async (state, config) => {
const modelProvider = config?.configurable?.modelProvider || "anthropic";
const model = MODELS[modelProvider as keyof typeof MODELS];
const response = await model.invoke(state.messages);
return { messages: [response] };
})
.addEdge(START, "model")
.addEdge("model", END)
.compile();
// Usage
const inputMessage = { role: "user", content: "hi" };
// With no configuration, uses default (Anthropic)
const response1 = await graph.invoke({ messages: [inputMessage] });
// Or, can set OpenAI
const response2 = await graph.invoke(
{ messages: [inputMessage] },
{ configurable: { modelProvider: "openai" } }
);
console.log(response1.messages.at(-1)?.response_metadata?.model);
console.log(response2.messages.at(-1)?.response_metadata?.model);
```
```
claude-3-5-haiku-20241022
gpt-4o-mini-2024-07-18
```
:::
??? example "Extended example: specifying model and system message at runtime"
:::python
Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime.
```python
from dataclasses import dataclass
from typing import Optional
from langchain.chat_models import init_chat_model
from langchain_core.messages import SystemMessage
from langgraph.graph import END, MessagesState, StateGraph, START
from langgraph.runtime import Runtime
from typing_extensions import TypedDict
@dataclass
class ContextSchema:
model_provider: str = "anthropic"
system_message: str | None = None
MODELS = {
"anthropic": init_chat_model("anthropic:claude-3-5-haiku-latest"),
"openai": init_chat_model("openai:gpt-4.1-mini"),
}
def call_model(state: MessagesState, runtime: Runtime[ContextSchema]):
model = MODELS[runtime.context.model_provider]
messages = state["messages"]
if (system_message := runtime.context.system_message):
messages = [SystemMessage(system_message)] + messages
response = model.invoke(messages)
return {"messages": [response]}
builder = StateGraph(MessagesState, context_schema=ContextSchema)
builder.add_node("model", call_model)
builder.add_edge(START, "model")
builder.add_edge("model", END)
graph = builder.compile()
# Usage
input_message = {"role": "user", "content": "hi"}
response = graph.invoke({"messages": [input_message]}, context={"model_provider": "openai", "system_message": "Respond in Italian."})
for message in response["messages"]:
message.pretty_print()
```
```
================================ Human Message ================================
hi
================================== Ai Message ==================================
Ciao! Come posso aiutarti oggi?
```
:::
:::js
Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime.
```typescript
import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";
import { SystemMessage } from "@langchain/core/messages";
import { MessagesZodState, StateGraph, START, END } from "@langchain/langgraph";
import { z } from "zod";
const ConfigSchema = z.object({
modelProvider: z.string().default("anthropic"),
systemMessage: z.string().optional(),
});
const MODELS = {
anthropic: new ChatAnthropic({ model: "claude-3-5-haiku-latest" }),
openai: new ChatOpenAI({ model: "gpt-4o-mini" }),
};
const graph = new StateGraph(MessagesZodState)
.addNode("model", async (state, config) => {
const modelProvider = config?.configurable?.modelProvider || "anthropic";
const systemMessage = config?.configurable?.systemMessage;
const model = MODELS[modelProvider as keyof typeof MODELS];
let messages = state.messages;
if (systemMessage) {
messages = [new SystemMessage(systemMessage), ...messages];
}
const response = await model.invoke(messages);
return { messages: [response] };
})
.addEdge(START, "model")
.addEdge("model", END)
.compile();
// Usage
const inputMessage = { role: "user", content: "hi" };
const response = await graph.invoke(
{ messages: [inputMessage] },
{
configurable: {
modelProvider: "openai",
systemMessage: "Respond in Italian."
}
}
);
for (const message of response.messages) {
console.log(`${message.getType()}: ${message.content}`);
}
```
```
human: hi
ai: Ciao! Come posso aiutarti oggi?
```
:::
Add retry policies
There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. LangGraph lets you add retry policies to nodes.
:::python
To configure a retry policy, pass the retry_policy parameter to the add_node. The retry_policy parameter takes in a RetryPolicy named tuple object. Below we instantiate a RetryPolicy object with the default parameters and associate it with a node:
from langgraph.types import RetryPolicy
builder.add_node(
"node_name",
node_function,
retry_policy=RetryPolicy(),
)
By default, the retry_on parameter uses the default_retry_on function, which retries on any exception except for the following:
ValueErrorTypeErrorArithmeticErrorImportErrorLookupErrorNameErrorSyntaxErrorRuntimeErrorReferenceErrorStopIterationStopAsyncIterationOSError
In addition, for exceptions from popular http request libraries such as requests and httpx it only retries on 5xx status codes.
:::
:::js
To configure a retry policy, pass the retryPolicy parameter to the addNode. The retryPolicy parameter takes in a RetryPolicy object. Below we instantiate a RetryPolicy object with the default parameters and associate it with a node:
import { RetryPolicy } from "@langchain/langgraph";
const graph = new StateGraph(State)
.addNode("nodeName", nodeFunction, { retryPolicy: {} })
.compile();
By default, the retry policy retries on any exception except for the following:
TypeErrorSyntaxErrorReferenceError:::
??? example "Extended example: customizing retry policies"
:::python
Consider an example in which we are reading from a SQL database. Below we pass two different retry policies to nodes:
```python
import sqlite3
from typing_extensions import TypedDict
from langchain.chat_models import init_chat_model
from langgraph.graph import END, MessagesState, StateGraph, START
from langgraph.types import RetryPolicy
from langchain_community.utilities import SQLDatabase
from langchain_core.messages import AIMessage
db = SQLDatabase.from_uri("sqlite:///:memory:")
model = init_chat_model("anthropic:claude-3-5-haiku-latest")
def query_database(state: MessagesState):
query_result = db.run("SELECT * FROM Artist LIMIT 10;")
return {"messages": [AIMessage(content=query_result)]}
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": [response]}
# Define a new graph
builder = StateGraph(MessagesState)
builder.add_node(
"query_database",
query_database,
retry_policy=RetryPolicy(retry_on=sqlite3.OperationalError),
)
builder.add_node("model", call_model, retry_policy=RetryPolicy(max_attempts=5))
builder.add_edge(START, "model")
builder.add_edge("model", "query_database")
builder.add_edge("query_database", END)
graph = builder.compile()
```
:::
:::js
Consider an example in which we are reading from a SQL database. Below we pass two different retry policies to nodes:
```typescript
import Database from "better-sqlite3";
import { ChatAnthropic } from "@langchain/anthropic";
import { StateGraph, START, END, MessagesZodState } from "@langchain/langgraph";
import { AIMessage } from "@langchain/core/messages";
import { z } from "zod";
// Create an in-memory database
const db: typeof Database.prototype = new Database(":memory:");
const model = new ChatAnthropic({ model: "claude-3-5-sonnet-20240620" });
const callModel = async (state: z.infer<typeof MessagesZodState>) => {
const response = await model.invoke(state.messages);
return { messages: [response] };
};
const queryDatabase = async (state: z.infer<typeof MessagesZodState>) => {
const queryResult: string = JSON.stringify(
db.prepare("SELECT * FROM Artist LIMIT 10;").all(),
);
return { messages: [new AIMessage({ content: "queryResult" })] };
};
const workflow = new StateGraph(MessagesZodState)
// Define the two nodes we will cycle between
.addNode("call_model", callModel, { retryPolicy: { maxAttempts: 5 } })
.addNode("query_database", queryDatabase, {
retryPolicy: {
retryOn: (e: any): boolean => {
if (e instanceof Database.SqliteError) {
// Retry on "SQLITE_BUSY" error
return e.code === "SQLITE_BUSY";
}
return false; // Don't retry on other errors
},
},
})
.addEdge(START, "call_model")
.addEdge("call_model", "query_database")
.addEdge("query_database", END);
const graph = workflow.compile();
```
:::
:::python
Add node caching
Node caching is useful in cases where you want to avoid repeating operations, like when doing something expensive (either in terms of time or cost). LangGraph lets you add individualized caching policies to nodes in a graph.
To configure a cache policy, pass the cache_policy parameter to the add_node function. In the following example, a CachePolicy object is instantiated with a time to live of 120 seconds and the default key_func generator. Then it is associated with a node:
from langgraph.types import CachePolicy
builder.add_node(
"node_name",
node_function,
cache_policy=CachePolicy(ttl=120),
)
Then, to enable node-level caching for a graph, set the cache argument when compiling the graph. The example below uses InMemoryCache to set up a graph with in-memory cache, but SqliteCache is also available.
from langgraph.cache.memory import InMemoryCache
graph = builder.compile(cache=InMemoryCache())
:::
Create a sequence of steps
!!! info "Prerequisites"
This guide assumes familiarity with the above section on [state](#define-and-update-state).
Here we demonstrate how to construct a simple sequence of steps. We will show:
- How to build a sequential graph
- Built-in short-hand for constructing similar graphs.
:::python
To add a sequence of nodes, we use the .add_node and .add_edge methods of our graph:
from langgraph.graph import START, StateGraph
builder = StateGraph(State)
# Add nodes
builder.add_node(step_1)
builder.add_node(step_2)
builder.add_node(step_3)
# Add edges
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")
We can also use the built-in shorthand .add_sequence:
builder = StateGraph(State).add_sequence([step_1, step_2, step_3])
builder.add_edge(START, "step_1")
:::
:::js
To add a sequence of nodes, we use the .addNode and .addEdge methods of our graph:
import { START, StateGraph } from "@langchain/langgraph";
const builder = new StateGraph(State)
.addNode("step1", step1)
.addNode("step2", step2)
.addNode("step3", step3)
.addEdge(START, "step1")
.addEdge("step1", "step2")
.addEdge("step2", "step3");
:::
??? info "Why split application steps into a sequence with LangGraph?" LangGraph makes it easy to add an underlying persistence layer to your application. This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:
- How state updates are checkpointed
- How interruptions are resumed in human-in-the-loop workflows
- How we can "rewind" and branch-off executions using LangGraph's time travel features
They also determine how execution steps are streamed, and how your application is visualized and debugged using LangGraph Studio.
Let's demonstrate an end-to-end example. We will create a sequence of three steps:
- Populate a value in a key of the state
- Update the same value
- Populate a different value
Let's first define our state. This governs the schema of the graph, and can also specify how to apply updates. See this section for more detail.
In our case, we will just keep track of two values:
:::python
from typing_extensions import TypedDict
class State(TypedDict):
value_1: str
value_2: int
:::
:::js
import { z } from "zod";
const State = z.object({
value1: z.string(),
value2: z.number(),
});
:::
:::python Our nodes are just Python functions that read our graph's state and make updates to it. The first argument to this function will always be the state:
def step_1(state: State):
return {"value_1": "a"}
def step_2(state: State):
current_value_1 = state["value_1"]
return {"value_1": f"{current_value_1} b"}
def step_3(state: State):
return {"value_2": 10}
:::
:::js Our nodes are just TypeScript functions that read our graph's state and make updates to it. The first argument to this function will always be the state:
const step1 = (state: z.infer<typeof State>) => {
return { value1: "a" };
};
const step2 = (state: z.infer<typeof State>) => {
const currentValue1 = state.value1;
return { value1: `${currentValue1} b` };
};
const step3 = (state: z.infer<typeof State>) => {
return { value2: 10 };
};
:::
!!! note
Note that when issuing updates to the state, each node can just specify the value of the key it wishes to update.
By default, this will **overwrite** the value of the corresponding key. You can also use [reducers](../concepts/low_level.md#reducers) to control how updates are processed— for example, you can append successive updates to a key instead. See [this section](#process-state-updates-with-reducers) for more detail.
Finally, we define the graph. We use StateGraph to define a graph that operates on this state.
:::python We will then use add_node and add_edge to populate our graph and define its control flow.
from langgraph.graph import START, StateGraph
builder = StateGraph(State)
# Add nodes
builder.add_node(step_1)
builder.add_node(step_2)
builder.add_node(step_3)
# Add edges
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")
:::
:::js We will then use addNode and addEdge to populate our graph and define its control flow.
import { START, StateGraph } from "@langchain/langgraph";
const graph = new StateGraph(State)
.addNode("step1", step1)
.addNode("step2", step2)
.addNode("step3", step3)
.addEdge(START, "step1")
.addEdge("step1", "step2")
.addEdge("step2", "step3")
.compile();
:::
:::python !!! tip "Specifying custom names"
You can specify custom names for nodes using `.add_node`:
```python
builder.add_node("my_node", step_1)
```
:::
:::js !!! tip "Specifying custom names"
You can specify custom names for nodes using `.addNode`:
```typescript
const graph = new StateGraph(State)
.addNode("myNode", step1)
.compile();
```
:::
Note that:
:::python
.add_edgetakes the names of nodes, which for functions defaults tonode.__name__.- We must specify the entry point of the graph. For this we add an edge with the START node.
- The graph halts when there are no more nodes to execute.
We next compile our graph. This provides a few basic checks on the structure of the graph (e.g., identifying orphaned nodes). If we were adding persistence to our application via a checkpointer, it would also be passed in here.
graph = builder.compile()
:::
:::js
.addEdgetakes the names of nodes, which for functions defaults tonode.name.- We must specify the entry point of the graph. For this we add an edge with the START node.
- The graph halts when there are no more nodes to execute.
We next compile our graph. This provides a few basic checks on the structure of the graph (e.g., identifying orphaned nodes). If we were adding persistence to our application via a checkpointer, it would also be passed in here. :::
LangGraph provides built-in utilities for visualizing your graph. Let's inspect our sequence. See this guide for detail on visualization.
:::python
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
:::
:::js
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
:::
Let's proceed with a simple invocation:
:::python
graph.invoke({"value_1": "c"})
{'value_1': 'a b', 'value_2': 10}
:::
:::js
const result = await graph.invoke({ value1: "c" });
console.log(result);
{ value1: 'a b', value2: 10 }
:::
Note that:
- We kicked off invocation by providing a value for a single state key. We must always provide a value for at least one key.
- The value we passed in was overwritten by the first node.
- The second node updated the value.
- The third node populated a different value.
:::python !!! tip "Built-in shorthand"
`langgraph>=0.2.46` includes a built-in short-hand `add_sequence` for adding node sequences. You can compile the same graph as follows:
```python
# highlight-next-line
builder = StateGraph(State).add_sequence([step_1, step_2, step_3])
builder.add_edge(START, "step_1")
graph = builder.compile()
graph.invoke({"value_1": "c"})
```
:::
Create branches
Parallel execution of nodes is essential to speed up overall graph operation. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out and fan-in mechanisms, utilizing both standard edges and conditional_edges. Below are some examples showing how to add create branching dataflows that work for you.
Run graph nodes in parallel
In this example, we fan out from Node A to B and C and then fan in to D. With our state, we specify the reducer add operation. This will combine or accumulate values for the specific key in the State, rather than simply overwriting the existing value. For lists, this means concatenating the new list with the existing list. See the above section on state reducers for more detail on updating state with reducers.
:::python
import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
def a(state: State):
print(f'Adding "A" to {state["aggregate"]}')
return {"aggregate": ["A"]}
def b(state: State):
print(f'Adding "B" to {state["aggregate"]}')
return {"aggregate": ["B"]}
def c(state: State):
print(f'Adding "C" to {state["aggregate"]}')
return {"aggregate": ["C"]}
def d(state: State):
print(f'Adding "D" to {state["aggregate"]}')
return {"aggregate": ["D"]}
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(c)
builder.add_node(d)
builder.add_edge(START, "a")
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()
:::
:::js
import "@langchain/langgraph/zod";
import { StateGraph, START, END } from "@langchain/langgraph";
import { z } from "zod";
const State = z.object({
// The reducer makes this append-only
aggregate: z.array(z.string()).langgraph.reducer((x, y) => x.concat(y)),
});
const nodeA = (state: z.infer<typeof State>) => {
console.log(`Adding "A" to ${state.aggregate}`);
return { aggregate: ["A"] };
};
const nodeB = (state: z.infer<typeof State>) => {
console.log(`Adding "B" to ${state.aggregate}`);
return { aggregate: ["B"] };
};
const nodeC = (state: z.infer<typeof State>) => {
console.log(`Adding "C" to ${state.aggregate}`);
return { aggregate: ["C"] };
};
const nodeD = (state: z.infer<typeof State>) => {
console.log(`Adding "D" to ${state.aggregate}`);
return { aggregate: ["D"] };
};
const graph = new StateGraph(State)
.addNode("a", nodeA)
.addNode("b", nodeB)
.addNode("c", nodeC)
.addNode("d", nodeD)
.addEdge(START, "a")
.addEdge("a", "b")
.addEdge("a", "c")
.addEdge("b", "d")
.addEdge("c", "d")
.addEdge("d", END)
.compile();
:::
:::python
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
:::
:::js
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
:::
With the reducer, you can see that the values added in each node are accumulated.
:::python
graph.invoke({"aggregate": []}, {"configurable": {"thread_id": "foo"}})
Adding "A" to []
Adding "B" to ['A']
Adding "C" to ['A']
Adding "D" to ['A', 'B', 'C']
:::
:::js
const result = await graph.invoke({
aggregate: [],
});
console.log(result);
Adding "A" to []
Adding "B" to ['A']
Adding "C" to ['A']
Adding "D" to ['A', 'B', 'C']
{ aggregate: ['A', 'B', 'C', 'D'] }
:::
!!! note
In the above example, nodes `"b"` and `"c"` are executed concurrently in the same [superstep](../concepts/low_level.md#graphs). Because they are in the same step, node `"d"` executes after both `"b"` and `"c"` are finished.
Importantly, updates from a parallel superstep may not be ordered consistently. If you need a consistent, predetermined ordering of updates from a parallel superstep, you should write the outputs to a separate field in the state together with a value with which to order them.
??? note "Exception handling?"
LangGraph executes nodes within [supersteps](../concepts/low_level.md#graphs), meaning that while parallel branches are executed in parallel, the entire superstep is **transactional**. If any of these branches raises an exception, **none** of the updates are applied to the state (the entire superstep errors).
Importantly, when using a [checkpointer](../concepts/persistence.md), results from successful nodes within a superstep are saved, and don't repeat when resumed.
If you have error-prone (perhaps want to handle flakey API calls), LangGraph provides two ways to address this:
1. You can write regular python code within your node to catch and handle exceptions.
2. You can set a **[retry_policy](../reference/types.md#langgraph.types.RetryPolicy)** to direct the graph to retry nodes that raise certain types of exceptions. Only failing branches are retried, so you needn't worry about performing redundant work.
Together, these let you perform parallel execution and fully control exception handling.
:::python
Defer node execution
Deferring node execution is useful when you want to delay the execution of a node until all other pending tasks are completed. This is particularly relevant when branches have different lengths, which is common in workflows like map-reduce flows.
The above example showed how to fan-out and fan-in when each path was only one step. But what if one branch had more than one step? Let's add a node "b_2" in the "b" branch:
import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
def a(state: State):
print(f'Adding "A" to {state["aggregate"]}')
return {"aggregate": ["A"]}
def b(state: State):
print(f'Adding "B" to {state["aggregate"]}')
return {"aggregate": ["B"]}
def b_2(state: State):
print(f'Adding "B_2" to {state["aggregate"]}')
return {"aggregate": ["B_2"]}
def c(state: State):
print(f'Adding "C" to {state["aggregate"]}')
return {"aggregate": ["C"]}
def d(state: State):
print(f'Adding "D" to {state["aggregate"]}')
return {"aggregate": ["D"]}
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(b_2)
builder.add_node(c)
# highlight-next-line
builder.add_node(d, defer=True)
builder.add_edge(START, "a")
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "b_2")
builder.add_edge("b_2", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
graph.invoke({"aggregate": []})
Adding "A" to []
Adding "B" to ['A']
Adding "C" to ['A']
Adding "B_2" to ['A', 'B', 'C']
Adding "D" to ['A', 'B', 'C', 'B_2']
In the above example, nodes "b" and "c" are executed concurrently in the same superstep. We set defer=True on node d so it will not execute until all pending tasks are finished. In this case, this means that "d" waits to execute until the entire "b" branch is finished.
:::
Conditional branching
:::python
If your fan-out should vary at runtime based on the state, you can use add_conditional_edges to select one or more paths using the graph state. See example below, where node a generates a state update that determines the following node.
import operator
from typing import Annotated, Literal, Sequence
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
aggregate: Annotated[list, operator.add]
# Add a key to the state. We will set this key to determine
# how we branch.
which: str
def a(state: State):
print(f'Adding "A" to {state["aggregate"]}')
# highlight-next-line
return {"aggregate": ["A"], "which": "c"}
def b(state: State):
print(f'Adding "B" to {state["aggregate"]}')
return {"aggregate": ["B"]}
def c(state: State):
print(f'Adding "C" to {state["aggregate"]}')
return {"aggregate": ["C"]}
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(c)
builder.add_edge(START, "a")
builder.add_edge("b", END)
builder.add_edge("c", END)
def conditional_edge(state: State) -> Literal["b", "c"]:
# Fill in arbitrary logic here that uses the state
# to determine the next node
return state["which"]
# highlight-next-line
builder.add_conditional_edges("a", conditional_edge)
graph = builder.compile()
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
result = graph.invoke({"aggregate": []})
print(result)
Adding "A" to []
Adding "C" to ['A']
{'aggregate': ['A', 'C'], 'which': 'c'}
:::
:::js
If your fan-out should vary at runtime based on the state, you can use addConditionalEdges to select one or more paths using the graph state. See example below, where node a generates a state update that determines the following node.
import "@langchain/langgraph/zod";
import { StateGraph, START, END } from "@langchain/langgraph";
import { z } from "zod";
const State = z.object({
aggregate: z.array(z.string()).langgraph.reducer((x, y) => x.concat(y)),
// Add a key to the state. We will set this key to determine
// how we branch.
which: z.string().langgraph.reducer((x, y) => y ?? x),
});
const nodeA = (state: z.infer<typeof State>) => {
console.log(`Adding "A" to ${state.aggregate}`);
// highlight-next-line
return { aggregate: ["A"], which: "c" };
};
const nodeB = (state: z.infer<typeof State>) => {
console.log(`Adding "B" to ${state.aggregate}`);
return { aggregate: ["B"] };
};
const nodeC = (state: z.infer<typeof State>) => {
console.log(`Adding "C" to ${state.aggregate}`);
return { aggregate: ["C"] };
};
const conditionalEdge = (state: z.infer<typeof State>): "b" | "c" => {
// Fill in arbitrary logic here that uses the state
// to determine the next node
return state.which as "b" | "c";
};
// highlight-next-line
const graph = new StateGraph(State)
.addNode("a", nodeA)
.addNode("b", nodeB)
.addNode("c", nodeC)
.addEdge(START, "a")
.addEdge("b", END)
.addEdge("c", END)
.addConditionalEdges("a", conditionalEdge)
.compile();
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
const result = await graph.invoke({ aggregate: [] });
console.log(result);
Adding "A" to []
Adding "C" to ['A']
{ aggregate: ['A', 'C'], which: 'c' }
:::
!!! tip
Your conditional edges can route to multiple destination nodes. For example:
:::python
```python
def route_bc_or_cd(state: State) -> Sequence[str]:
if state["which"] == "cd":
return ["c", "d"]
return ["b", "c"]
```
:::
:::js
```typescript
const routeBcOrCd = (state: z.infer<typeof State>): string[] => {
if (state.which === "cd") {
return ["c", "d"];
}
return ["b", "c"];
};
```
:::
Map-Reduce and the Send API
LangGraph supports map-reduce and other advanced branching patterns using the Send API. Here is an example of how to use it:
:::python
from langgraph.graph import StateGraph, START, END
from langgraph.types import Send
from typing_extensions import TypedDict, Annotated
import operator
class OverallState(TypedDict):
topic: str
subjects: list[str]
jokes: Annotated[list[str], operator.add]
best_selected_joke: str
def generate_topics(state: OverallState):
return {"subjects": ["lions", "elephants", "penguins"]}
def generate_joke(state: OverallState):
joke_map = {
"lions": "Why don't lions like fast food? Because they can't catch it!",
"elephants": "Why don't elephants use computers? They're afraid of the mouse!",
"penguins": "Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice."
}
return {"jokes": [joke_map[state["subject"]]]}
def continue_to_jokes(state: OverallState):
return [Send("generate_joke", {"subject": s}) for s in state["subjects"]]
def best_joke(state: OverallState):
return {"best_selected_joke": "penguins"}
builder = StateGraph(OverallState)
builder.add_node("generate_topics", generate_topics)
builder.add_node("generate_joke", generate_joke)
builder.add_node("best_joke", best_joke)
builder.add_edge(START, "generate_topics")
builder.add_conditional_edges("generate_topics", continue_to_jokes, ["generate_joke"])
builder.add_edge("generate_joke", "best_joke")
builder.add_edge("best_joke", END)
graph = builder.compile()
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
# Call the graph: here we call it to generate a list of jokes
for step in graph.stream({"topic": "animals"}):
print(step)
{'generate_topics': {'subjects': ['lions', 'elephants', 'penguins']}}
{'generate_joke': {'jokes': ["Why don't lions like fast food? Because they can't catch it!"]}}
{'generate_joke': {'jokes': ["Why don't elephants use computers? They're afraid of the mouse!"]}}
{'generate_joke': {'jokes': ['Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice.']}}
{'best_joke': {'best_selected_joke': 'penguins'}}
:::
:::js
import "@langchain/langgraph/zod";
import { StateGraph, START, END, Send } from "@langchain/langgraph";
import { z } from "zod";
const OverallState = z.object({
topic: z.string(),
subjects: z.array(z.string()),
jokes: z.array(z.string()).langgraph.reducer((x, y) => x.concat(y)),
bestSelectedJoke: z.string(),
});
const generateTopics = (state: z.infer<typeof OverallState>) => {
return { subjects: ["lions", "elephants", "penguins"] };
};
const generateJoke = (state: { subject: string }) => {
const jokeMap: Record<string, string> = {
lions: "Why don't lions like fast food? Because they can't catch it!",
elephants: "Why don't elephants use computers? They're afraid of the mouse!",
penguins: "Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice."
};
return { jokes: [jokeMap[state.subject]] };
};
const continueToJokes = (state: z.infer<typeof OverallState>) => {
return state.subjects.map((subject) => new Send("generateJoke", { subject }));
};
const bestJoke = (state: z.infer<typeof OverallState>) => {
return { bestSelectedJoke: "penguins" };
};
const graph = new StateGraph(OverallState)
.addNode("generateTopics", generateTopics)
.addNode("generateJoke", generateJoke)
.addNode("bestJoke", bestJoke)
.addEdge(START, "generateTopics")
.addConditionalEdges("generateTopics", continueToJokes)
.addEdge("generateJoke", "bestJoke")
.addEdge("bestJoke", END)
.compile();
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
// Call the graph: here we call it to generate a list of jokes
for await (const step of await graph.stream({ topic: "animals" })) {
console.log(step);
}
{ generateTopics: { subjects: [ 'lions', 'elephants', 'penguins' ] } }
{ generateJoke: { jokes: [ "Why don't lions like fast food? Because they can't catch it!" ] } }
{ generateJoke: { jokes: [ "Why don't elephants use computers? They're afraid of the mouse!" ] } }
{ generateJoke: { jokes: [ "Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice." ] } }
{ bestJoke: { bestSelectedJoke: 'penguins' } }
:::
Create and control loops
When creating a graph with a loop, we require a mechanism for terminating execution. This is most commonly done by adding a conditional edge that routes to the END node once we reach some termination condition.
You can also set the graph recursion limit when invoking or streaming the graph. The recursion limit sets the number of supersteps that the graph is allowed to execute before it raises an error. Read more about the concept of recursion limits here.
Let's consider a simple graph with a loop to better understand how these mechanisms work.
!!! tip
To return the last value of your state instead of receiving a recursion limit error, see the [next section](#impose-a-recursion-limit).
When creating a loop, you can include a conditional edge that specifies a termination condition:
:::python
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
def route(state: State) -> Literal["b", END]:
if termination_condition(state):
return END
else:
return "b"
builder.add_edge(START, "a")
builder.add_conditional_edges("a", route)
builder.add_edge("b", "a")
graph = builder.compile()
:::
:::js
const graph = new StateGraph(State)
.addNode("a", nodeA)
.addNode("b", nodeB)
.addEdge(START, "a")
.addConditionalEdges("a", route)
.addEdge("b", "a")
.compile();
const route = (state: z.infer<typeof State>): "b" | typeof END => {
if (terminationCondition(state)) {
return END;
} else {
return "b";
}
};
:::
To control the recursion limit, specify "recursionLimit" in the config. This will raise a GraphRecursionError, which you can catch and handle:
:::python
from langgraph.errors import GraphRecursionError
try:
graph.invoke(inputs, {"recursion_limit": 3})
except GraphRecursionError:
print("Recursion Error")
:::
:::js
import { GraphRecursionError } from "@langchain/langgraph";
try {
await graph.invoke(inputs, { recursionLimit: 3 });
} catch (error) {
if (error instanceof GraphRecursionError) {
console.log("Recursion Error");
}
}
:::
Let's define a graph with a simple loop. Note that we use a conditional edge to implement a termination condition.
:::python
import operator
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
# The operator.add reducer fn makes this append-only
aggregate: Annotated[list, operator.add]
def a(state: State):
print(f'Node A sees {state["aggregate"]}')
return {"aggregate": ["A"]}
def b(state: State):
print(f'Node B sees {state["aggregate"]}')
return {"aggregate": ["B"]}
# Define nodes
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
# Define edges
def route(state: State) -> Literal["b", END]:
if len(state["aggregate"]) < 7:
return "b"
else:
return END
builder.add_edge(START, "a")
builder.add_conditional_edges("a", route)
builder.add_edge("b", "a")
graph = builder.compile()
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
:::
:::js
import "@langchain/langgraph/zod";
import { StateGraph, START, END } from "@langchain/langgraph";
import { z } from "zod";
const State = z.object({
// The reducer makes this append-only
aggregate: z.array(z.string()).langgraph.reducer((x, y) => x.concat(y)),
});
const nodeA = (state: z.infer<typeof State>) => {
console.log(`Node A sees ${state.aggregate}`);
return { aggregate: ["A"] };
};
const nodeB = (state: z.infer<typeof State>) => {
console.log(`Node B sees ${state.aggregate}`);
return { aggregate: ["B"] };
};
// Define edges
const route = (state: z.infer<typeof State>): "b" | typeof END => {
if (state.aggregate.length < 7) {
return "b";
} else {
return END;
}
};
const graph = new StateGraph(State)
.addNode("a", nodeA)
.addNode("b", nodeB)
.addEdge(START, "a")
.addConditionalEdges("a", route)
.addEdge("b", "a")
.compile();
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
:::
This architecture is similar to a React agent in which node "a" is a tool-calling model, and node "b" represents the tools.
In our route conditional edge, we specify that we should end after the "aggregate" list in the state passes a threshold length.
Invoking the graph, we see that we alternate between nodes "a" and "b" before terminating once we reach the termination condition.
:::python
graph.invoke({"aggregate": []})
Node A sees []
Node B sees ['A']
Node A sees ['A', 'B']
Node B sees ['A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B']
Node B sees ['A', 'B', 'A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B', 'A', 'B']
:::
:::js
const result = await graph.invoke({ aggregate: [] });
console.log(result);
Node A sees []
Node B sees ['A']
Node A sees ['A', 'B']
Node B sees ['A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B']
Node B sees ['A', 'B', 'A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B', 'A', 'B']
{ aggregate: ['A', 'B', 'A', 'B', 'A', 'B', 'A'] }
:::
Impose a recursion limit
In some applications, we may not have a guarantee that we will reach a given termination condition. In these cases, we can set the graph's recursion limit. This will raise a GraphRecursionError after a given number of supersteps. We can then catch and handle this exception:
:::python
from langgraph.errors import GraphRecursionError
try:
graph.invoke({"aggregate": []}, {"recursion_limit": 4})
except GraphRecursionError:
print("Recursion Error")
Node A sees []
Node B sees ['A']
Node C sees ['A', 'B']
Node D sees ['A', 'B']
Node A sees ['A', 'B', 'C', 'D']
Recursion Error
:::
:::js
import { GraphRecursionError } from "@langchain/langgraph";
try {
await graph.invoke({ aggregate: [] }, { recursionLimit: 4 });
} catch (error) {
if (error instanceof GraphRecursionError) {
console.log("Recursion Error");
}
}
Node A sees []
Node B sees ['A']
Node A sees ['A', 'B']
Node B sees ['A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B']
Recursion Error
:::
:::python ??? example "Extended example: return state on hitting recursion limit"
Instead of raising `GraphRecursionError`, we can introduce a new key to the state that keeps track of the number of steps remaining until reaching the recursion limit. We can then use this key to determine if we should end the run.
LangGraph implements a special `RemainingSteps` annotation. Under the hood, it creates a `ManagedValue` channel -- a state channel that will exist for the duration of our graph run and no longer.
```python
import operator
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.managed.is_last_step import RemainingSteps
class State(TypedDict):
aggregate: Annotated[list, operator.add]
remaining_steps: RemainingSteps
def a(state: State):
print(f'Node A sees {state["aggregate"]}')
return {"aggregate": ["A"]}
def b(state: State):
print(f'Node B sees {state["aggregate"]}')
return {"aggregate": ["B"]}
# Define nodes
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
# Define edges
def route(state: State) -> Literal["b", END]:
if state["remaining_steps"] <= 2:
return END
else:
return "b"
builder.add_edge(START, "a")
builder.add_conditional_edges("a", route)
builder.add_edge("b", "a")
graph = builder.compile()
# Test it out
result = graph.invoke({"aggregate": []}, {"recursion_limit": 4})
print(result)
```
```
Node A sees []
Node B sees ['A']
Node A sees ['A', 'B']
{'aggregate': ['A', 'B', 'A']}
```
:::
:::python ??? example "Extended example: loops with branches"
To better understand how the recursion limit works, let's consider a more complex example. Below we implement a loop, but one step fans out into two nodes:
```python
import operator
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
aggregate: Annotated[list, operator.add]
def a(state: State):
print(f'Node A sees {state["aggregate"]}')
return {"aggregate": ["A"]}
def b(state: State):
print(f'Node B sees {state["aggregate"]}')
return {"aggregate": ["B"]}
def c(state: State):
print(f'Node C sees {state["aggregate"]}')
return {"aggregate": ["C"]}
def d(state: State):
print(f'Node D sees {state["aggregate"]}')
return {"aggregate": ["D"]}
# Define nodes
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(c)
builder.add_node(d)
# Define edges
def route(state: State) -> Literal["b", END]:
if len(state["aggregate"]) < 7:
return "b"
else:
return END
builder.add_edge(START, "a")
builder.add_conditional_edges("a", route)
builder.add_edge("b", "c")
builder.add_edge("b", "d")
builder.add_edge(["c", "d"], "a")
graph = builder.compile()
```
```python
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
```
This graph looks complex, but can be conceptualized as loop of [supersteps](../concepts/low_level.md#graphs):
1. Node A
2. Node B
3. Nodes C and D
4. Node A
5. ...
We have a loop of four supersteps, where nodes C and D are executed concurrently.
Invoking the graph as before, we see that we complete two full "laps" before hitting the termination condition:
```python
result = graph.invoke({"aggregate": []})
```
```
Node A sees []
Node B sees ['A']
Node D sees ['A', 'B']
Node C sees ['A', 'B']
Node A sees ['A', 'B', 'C', 'D']
Node B sees ['A', 'B', 'C', 'D', 'A']
Node D sees ['A', 'B', 'C', 'D', 'A', 'B']
Node C sees ['A', 'B', 'C', 'D', 'A', 'B']
Node A sees ['A', 'B', 'C', 'D', 'A', 'B', 'C', 'D']
```
However, if we set the recursion limit to four, we only complete one lap because each lap is four supersteps:
```python
from langgraph.errors import GraphRecursionError
try:
result = graph.invoke({"aggregate": []}, {"recursion_limit": 4})
except GraphRecursionError:
print("Recursion Error")
```
```
Node A sees []
Node B sees ['A']
Node C sees ['A', 'B']
Node D sees ['A', 'B']
Node A sees ['A', 'B', 'C', 'D']
Recursion Error
```
:::
:::python
Async
Using the async programming paradigm can produce significant performance improvements when running IO-bound code concurrently (e.g., making concurrent API requests to a chat model provider).
To convert a sync implementation of the graph to an async implementation, you will need to:
- Update
nodesuseasync definstead ofdef. - Update the code inside to use
awaitappropriately. - Invoke the graph with
.ainvokeor.astreamas desired.
Because many LangChain objects implement the Runnable Protocol which has async variants of all the sync methods it's typically fairly quick to upgrade a sync graph to an async graph.
See example below. To demonstrate async invocations of underlying LLMs, we will include a chat model:
{% include-markdown "../../snippets/chat_model_tabs.md" %}
from langchain.chat_models import init_chat_model
from langgraph.graph import MessagesState, StateGraph
# highlight-next-line
async def node(state: MessagesState): # (1)!
# highlight-next-line
new_message = await llm.ainvoke(state["messages"]) # (2)!
return {"messages": [new_message]}
builder = StateGraph(MessagesState).add_node(node).set_entry_point("node")
graph = builder.compile()
input_message = {"role": "user", "content": "Hello"}
# highlight-next-line
result = await graph.ainvoke({"messages": [input_message]}) # (3)!
- Declare nodes to be async functions.
- Use async invocations when available within the node.
- Use async invocations on the graph object itself.
!!! tip "Async streaming"
See the [streaming guide](./streaming.md) for examples of streaming with async.
:::
Combine control flow and state updates with 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:
:::python
def my_node(state: State) -> Command[Literal["my_other_node"]]:
return Command(
# state update
update={"foo": "bar"},
# control flow
goto="my_other_node"
)
:::
:::js
import { Command } from "@langchain/langgraph";
const myNode = (state: State): Command => {
return new Command({
// state update
update: { foo: "bar" },
// control flow
goto: "myOtherNode"
});
};
:::
We show an end-to-end example below. Let's create a simple graph with 3 nodes: A, B and C. We will first execute node A, and then decide whether to go to Node B or Node C next based on the output of node A.
:::python
import random
from typing_extensions import TypedDict, Literal
from langgraph.graph import StateGraph, START
from langgraph.types import Command
# Define graph state
class State(TypedDict):
foo: str
# Define the nodes
def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
print("Called A")
value = random.choice(["b", "c"])
# this is a replacement for a conditional edge function
if value == "b":
goto = "node_b"
else:
goto = "node_c"
# note how Command allows you to BOTH update the graph state AND route to the next node
return Command(
# this is the state update
update={"foo": value},
# this is a replacement for an edge
goto=goto,
)
def node_b(state: State):
print("Called B")
return {"foo": state["foo"] + "b"}
def node_c(state: State):
print("Called C")
return {"foo": state["foo"] + "c"}
We can now create the StateGraph with the above nodes. Notice that the graph doesn't have conditional edges for routing! This is because control flow is defined with Command inside node_a.
builder = StateGraph(State)
builder.add_edge(START, "node_a")
builder.add_node(node_a)
builder.add_node(node_b)
builder.add_node(node_c)
# NOTE: there are no edges between nodes A, B and C!
graph = builder.compile()
!!! important
You might have noticed that we used `Command` as a return type annotation, e.g. `Command[Literal["node_b", "node_c"]]`. This is necessary for the graph rendering and tells LangGraph that `node_a` can navigate to `node_b` and `node_c`.
from IPython.display import display, Image
display(Image(graph.get_graph().draw_mermaid_png()))
If we run the graph multiple times, we'd see it take different paths (A -> B or A -> C) based on the random choice in node A.
graph.invoke({"foo": ""})
Called A
Called C
:::
:::js
import { StateGraph, START, Command } from "@langchain/langgraph";
import { z } from "zod";
// Define graph state
const State = z.object({
foo: z.string(),
});
// Define the nodes
const nodeA = (state: z.infer<typeof State>): Command => {
console.log("Called A");
const value = Math.random() > 0.5 ? "b" : "c";
// this is a replacement for a conditional edge function
const goto = value === "b" ? "nodeB" : "nodeC";
// note how Command allows you to BOTH update the graph state AND route to the next node
return new Command({
// this is the state update
update: { foo: value },
// this is a replacement for an edge
goto,
});
};
const nodeB = (state: z.infer<typeof State>) => {
console.log("Called B");
return { foo: state.foo + "b" };
};
const nodeC = (state: z.infer<typeof State>) => {
console.log("Called C");
return { foo: state.foo + "c" };
};
We can now create the StateGraph with the above nodes. Notice that the graph doesn't have conditional edges for routing! This is because control flow is defined with Command inside nodeA.
const graph = new StateGraph(State)
.addNode("nodeA", nodeA, {
ends: ["nodeB", "nodeC"],
})
.addNode("nodeB", nodeB)
.addNode("nodeC", nodeC)
.addEdge(START, "nodeA")
.compile();
!!! important
You might have noticed that we used `ends` to specify which nodes `nodeA` can navigate to. This is necessary for the graph rendering and tells LangGraph that `nodeA` can navigate to `nodeB` and `nodeC`.
import * as fs from "node:fs/promises";
const drawableGraph = await graph.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
If we run the graph multiple times, we'd see it take different paths (A -> B or A -> C) based on the random choice in node A.
const result = await graph.invoke({ foo: "" });
console.log(result);
Called A
Called C
{ foo: 'cc' }
:::
Navigate to a node in a parent graph
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:
:::python
def my_node(state: State) -> Command[Literal["my_other_node"]]:
return Command(
update={"foo": "bar"},
goto="other_subgraph", # where `other_subgraph` is a node in the parent graph
graph=Command.PARENT
)
:::
:::js
const myNode = (state: State): Command => {
return new Command({
update: { foo: "bar" },
goto: "otherSubgraph", // where `otherSubgraph` is a node in the parent graph
graph: Command.PARENT
});
};
:::
Let's demonstrate this using the above example. We'll do so by changing nodeA in the above example into a single-node graph that we'll add as a subgraph to our parent graph.
!!! important "State updates with Command.PARENT"
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](../concepts/low_level.md#schema), you **must** define a [reducer](../concepts/low_level.md#reducers) for the key you're updating in the parent graph state. See the example below.
:::python
import operator
from typing_extensions import Annotated
class State(TypedDict):
# NOTE: we define a reducer here
# highlight-next-line
foo: Annotated[str, operator.add]
def node_a(state: State):
print("Called A")
value = random.choice(["a", "b"])
# this is a replacement for a conditional edge function
if value == "a":
goto = "node_b"
else:
goto = "node_c"
# note how Command allows you to BOTH update the graph state AND route to the next node
return Command(
update={"foo": value},
goto=goto,
# this tells LangGraph to navigate to node_b or node_c in the parent graph
# NOTE: this will navigate to the closest parent graph relative to the subgraph
# highlight-next-line
graph=Command.PARENT,
)
subgraph = StateGraph(State).add_node(node_a).add_edge(START, "node_a").compile()
def node_b(state: State):
print("Called B")
# NOTE: since we've defined a reducer, we don't need to manually append
# new characters to existing 'foo' value. instead, reducer will append these
# automatically (via operator.add)
# highlight-next-line
return {"foo": "b"}
def node_c(state: State):
print("Called C")
# highlight-next-line
return {"foo": "c"}
builder = StateGraph(State)
builder.add_edge(START, "subgraph")
builder.add_node("subgraph", subgraph)
builder.add_node(node_b)
builder.add_node(node_c)
graph = builder.compile()
graph.invoke({"foo": ""})
Called A
Called C
:::
:::js
import "@langchain/langgraph/zod";
import { StateGraph, START, Command } from "@langchain/langgraph";
import { z } from "zod";
const State = z.object({
// NOTE: we define a reducer here
// highlight-next-line
foo: z.string().langgraph.reducer((x, y) => x + y),
});
const nodeA = (state: z.infer<typeof State>) => {
console.log("Called A");
const value = Math.random() > 0.5 ? "nodeB" : "nodeC";
// note how Command allows you to BOTH update the graph state AND route to the next node
return new Command({
update: { foo: "a" },
goto: value,
// this tells LangGraph to navigate to nodeB or nodeC in the parent graph
// NOTE: this will navigate to the closest parent graph relative to the subgraph
// highlight-next-line
graph: Command.PARENT,
});
};
const subgraph = new StateGraph(State)
.addNode("nodeA", nodeA, { ends: ["nodeB", "nodeC"] })
.addEdge(START, "nodeA")
.compile();
const nodeB = (state: z.infer<typeof State>) => {
console.log("Called B");
// NOTE: since we've defined a reducer, we don't need to manually append
// new characters to existing 'foo' value. instead, reducer will append these
// automatically
// highlight-next-line
return { foo: "b" };
};
const nodeC = (state: z.infer<typeof State>) => {
console.log("Called C");
// highlight-next-line
return { foo: "c" };
};
const graph = new StateGraph(State)
.addNode("subgraph", subgraph, { ends: ["nodeB", "nodeC"] })
.addNode("nodeB", nodeB)
.addNode("nodeC", nodeC)
.addEdge(START, "subgraph")
.compile();
const result = await graph.invoke({ foo: "" });
console.log(result);
Called A
Called C
{ foo: 'ac' }
:::
Use 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. To update the graph state from the tool, you can return Command(update={"my_custom_key": "foo", "messages": [...]}) from the tool:
:::python
@tool
def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: RunnableConfig):
"""Use this to look up user information to better assist them with their questions."""
user_info = get_user_info(config.get("configurable", {}).get("user_id"))
return Command(
update={
# update the state keys
"user_info": user_info,
# update the message history
"messages": [ToolMessage("Successfully looked up user information", tool_call_id=tool_call_id)]
}
)
:::
:::js
import { tool } from "@langchain/core/tools";
import { Command } from "@langchain/langgraph";
import { RunnableConfig } from "@langchain/core/runnables";
import { z } from "zod";
const lookupUserInfo = tool(
async (input, config: RunnableConfig) => {
const userId = config.configurable?.userId;
const userInfo = getUserInfo(userId);
return new Command({
update: {
// update the state keys
userInfo: userInfo,
// update the message history
messages: [{
role: "tool",
content: "Successfully looked up user information",
tool_call_id: config.toolCall.id
}]
}
});
},
{
name: "lookupUserInfo",
description: "Use this to look up user information to better assist them with their questions.",
schema: z.object({}),
}
);
:::
!!! important
You MUST include `messages` (or any state key used for the message history) in `Command.update` when returning `Command` from a tool and the list of messages in `messages` MUST contain a `ToolMessage`. This is necessary for the resulting message history to be valid (LLM providers require AI messages with tool calls to be followed by the tool result messages).
If you are using tools that update state via Command, we recommend using prebuilt ToolNode which automatically handles tools returning Command objects and propagates them to the graph state. If you're writing a custom node that calls tools, you would need to manually propagate Command objects returned by the tools as the update from the node.
Visualize your graph
Here we demonstrate how to visualize the graphs you create.
You can visualize any arbitrary Graph, including StateGraph.
:::python Let's have some fun by drawing fractals :).
import random
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, add_messages]
class MyNode:
def __init__(self, name: str):
self.name = name
def __call__(self, state: State):
return {"messages": [("assistant", f"Called node {self.name}")]}
def route(state) -> Literal["entry_node", "__end__"]:
if len(state["messages"]) > 10:
return "__end__"
return "entry_node"
def add_fractal_nodes(builder, current_node, level, max_level):
if level > max_level:
return
# Number of nodes to create at this level
num_nodes = random.randint(1, 3) # Adjust randomness as needed
for i in range(num_nodes):
nm = ["A", "B", "C"][i]
node_name = f"node_{current_node}_{nm}"
builder.add_node(node_name, MyNode(node_name))
builder.add_edge(current_node, node_name)
# Recursively add more nodes
r = random.random()
if r > 0.2 and level + 1 < max_level:
add_fractal_nodes(builder, node_name, level + 1, max_level)
elif r > 0.05:
builder.add_conditional_edges(node_name, route, node_name)
else:
# End
builder.add_edge(node_name, "__end__")
def build_fractal_graph(max_level: int):
builder = StateGraph(State)
entry_point = "entry_node"
builder.add_node(entry_point, MyNode(entry_point))
builder.add_edge(START, entry_point)
add_fractal_nodes(builder, entry_point, 1, max_level)
# Optional: set a finish point if required
builder.add_edge(entry_point, END) # or any specific node
return builder.compile()
app = build_fractal_graph(3)
:::
:::js Let's create a simple example graph to demonstrate visualization.
import { StateGraph, START, END } from "@langchain/langgraph";
import { MessagesZodState } from "@langchain/langgraph";
import { z } from "zod";
const State = MessagesZodState.extend({
value: z.number(),
});
const app = new StateGraph(State)
.addNode("node1", (state) => {
return { value: state.value + 1 };
})
.addNode("node2", (state) => {
return { value: state.value * 2 };
})
.addEdge(START, "node1")
.addConditionalEdges("node1", (state) => {
if (state.value < 10) {
return "node2";
}
return END;
})
.addEdge("node2", "node1")
.compile();
:::
Mermaid
We can also convert a graph class into Mermaid syntax.
:::python
print(app.get_graph().draw_mermaid())
%%{init: {'flowchart': {'curve': 'linear'}}}%%
graph TD;
__start__([<p>__start__</p>]):::first
entry_node(entry_node)
node_entry_node_A(node_entry_node_A)
node_entry_node_B(node_entry_node_B)
node_node_entry_node_B_A(node_node_entry_node_B_A)
node_node_entry_node_B_B(node_node_entry_node_B_B)
node_node_entry_node_B_C(node_node_entry_node_B_C)
__end__([<p>__end__</p>]):::last
__start__ --> entry_node;
entry_node --> __end__;
entry_node --> node_entry_node_A;
entry_node --> node_entry_node_B;
node_entry_node_B --> node_node_entry_node_B_A;
node_entry_node_B --> node_node_entry_node_B_B;
node_entry_node_B --> node_node_entry_node_B_C;
node_entry_node_A -.-> entry_node;
node_entry_node_A -.-> __end__;
node_node_entry_node_B_A -.-> entry_node;
node_node_entry_node_B_A -.-> __end__;
node_node_entry_node_B_B -.-> entry_node;
node_node_entry_node_B_B -.-> __end__;
node_node_entry_node_B_C -.-> entry_node;
node_node_entry_node_B_C -.-> __end__;
classDef default fill:#f2f0ff,line-height:1.2
classDef first fill-opacity:0
classDef last fill:#bfb6fc
:::
:::js
const drawableGraph = await app.getGraphAsync();
console.log(drawableGraph.drawMermaid());
%%{init: {'flowchart': {'curve': 'linear'}}}%%
graph TD;
__start__([<p>__start__</p>]):::first
node1(node1)
node2(node2)
__end__([<p>__end__</p>]):::last
__start__ --> node1;
node1 -.-> node2;
node1 -.-> __end__;
node2 --> node1;
classDef default fill:#f2f0ff,line-height:1.2
classDef first fill-opacity:0
classDef last fill:#bfb6fc
:::
PNG
:::python
If preferred, we could render the Graph into a .png. Here we could use three options:
- Using Mermaid.ink API (does not require additional packages)
- Using Mermaid + Pyppeteer (requires
pip install pyppeteer) - Using graphviz (which requires
pip install graphviz)
Using Mermaid.Ink
By default, draw_mermaid_png() uses Mermaid.Ink's API to generate the diagram.
from IPython.display import Image, display
from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles
display(Image(app.get_graph().draw_mermaid_png()))
Using Mermaid + Pyppeteer
import nest_asyncio
nest_asyncio.apply() # Required for Jupyter Notebook to run async functions
display(
Image(
app.get_graph().draw_mermaid_png(
curve_style=CurveStyle.LINEAR,
node_colors=NodeStyles(first="#ffdfba", last="#baffc9", default="#fad7de"),
wrap_label_n_words=9,
output_file_path=None,
draw_method=MermaidDrawMethod.PYPPETEER,
background_color="white",
padding=10,
)
)
)
Using Graphviz
try:
display(Image(app.get_graph().draw_png()))
except ImportError:
print(
"You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt"
)
:::
:::js
If preferred, we could render the Graph into a .png. This uses the Mermaid.ink API to generate the diagram.
import * as fs from "node:fs/promises";
const drawableGraph = await app.getGraphAsync();
const image = await drawableGraph.drawMermaidPng();
const imageBuffer = new Uint8Array(await image.arrayBuffer());
await fs.writeFile("graph.png", imageBuffer);
:::