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Metadata-Version: 2.4 Name: langgraph-prebuilt Version: 0.6.2 Summary: Library with high-level APIs for creating and executing LangGraph agents and tools. Project-URL: Repository, https://www.github.com/langchain-ai/langgraph License-Expression: MIT License-File: LICENSE Requires-Python: >=3.9 Requires-Dist: langchain-core>=0.3.67 Requires-Dist: langgraph-checkpoint<3.0.0,>=2.1.0 Description-Content-Type: text/markdown # 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](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) of a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent - `create_react_agent`: ```bash pip install langchain-anthropic ``` ```python 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](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode) of a node that executes tool calls - `ToolNode`: ```python 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](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_validator.ValidationNode) of a node that validates tool calls against a pydantic schema - `ValidationNode`: ```python 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](https://github.com/langchain-ai/agent-inbox) with LangGraph agents. Learn more about how to use Agent Inbox [here](https://github.com/langchain-ai/agent-inbox#interrupts). ```python 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 ... ```