langgraph-prebuilt 1.1.0a1

Library with high-level APIs for creating and executing LangGraph agents and tools.

LangGraph Prebuilt

This library defines high-level APIs for creating and executing LangGraph agents and tools.

[!IMPORTANT] This library is meant to be bundled with langgraph, don't install it directly

Agents

langgraph-prebuilt provides an implementation of a tool-calling ReAct-style agent - create_react_agent:

pip install langchain-anthropic

from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent

# Define the tools for the agent to use
def search(query: str):
    """Call to surf the web."""
    # This is a placeholder, but don't tell the LLM that...
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."

tools = [search]
model = ChatAnthropic(model="claude-3-7-sonnet-latest")

app = create_react_agent(model, tools)
# run the agent
app.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
)

Tools

ToolNode

langgraph-prebuilt provides an implementation of a node that executes tool calls - ToolNode:

from langgraph.prebuilt import ToolNode
from langchain_core.messages import AIMessage

def search(query: str):
    """Call to surf the web."""
    # This is a placeholder, but don't tell the LLM that...
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."

tool_node = ToolNode([search])
tool_calls = [{"name": "search", "args": {"query": "what is the weather in sf"}, "id": "1"}]
ai_message = AIMessage(content="", tool_calls=tool_calls)
# execute tool call
tool_node.invoke({"messages": [ai_message]})

ValidationNode

langgraph-prebuilt provides an implementation of a node that validates tool calls against a pydantic schema - ValidationNode:

from pydantic import BaseModel, field_validator
from langgraph.prebuilt import ValidationNode
from langchain_core.messages import AIMessage


class SelectNumber(BaseModel):
    a: int

    @field_validator("a")
    def a_must_be_meaningful(cls, v):
        if v != 37:
            raise ValueError("Only 37 is allowed")
        return v

validation_node = ValidationNode([SelectNumber])
validation_node.invoke({
    "messages": [AIMessage("", tool_calls=[{"name": "SelectNumber", "args": {"a": 42}, "id": "1"}])]
})

Agent Inbox

The library contains schemas for using the Agent Inbox with LangGraph agents. Learn more about how to use Agent Inbox here.

from langgraph.types import interrupt
from langgraph.prebuilt.interrupt import HumanInterrupt, HumanResponse

def my_graph_function():
    # Extract the last tool call from the `messages` field in the state
    tool_call = state["messages"][-1].tool_calls[0]
    # Create an interrupt
    request: HumanInterrupt = {
        "action_request": {
            "action": tool_call['name'],
            "args": tool_call['args']
        },
        "config": {
            "allow_ignore": True,
            "allow_respond": True,
            "allow_edit": False,
            "allow_accept": False
        },
        "description": _generate_email_markdown(state) # Generate a detailed markdown description.
    }
    # Send the interrupt request inside a list, and extract the first response
    response = interrupt([request])[0]
    if response['type'] == "response":
        # Do something with the response
    ...