libs/prebuilt/README.md
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uv add langgraph
This library defines high-level APIs for creating and executing LangGraph agents and tools. It includes prebuilt components such as create_react_agent, ToolNode, validation helpers, and Agent Inbox schemas.
For full documentation, see the API reference. For conceptual guides and tutorials, see the LangGraph Docs.
[!IMPORTANT] This library is bundled with
langgraph; most users should installlanggraphinstead of installinglanggraph-prebuiltdirectly.
langgraph-prebuilt provides an implementation of a tool-calling ReAct-style agent - create_react_agent:
uv add 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"}]},
)
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]})
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"}])]
})
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
...
See our Releases and Versioning policies.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.