Install & Compatibility
Where this runs
tested against v1.1.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
installs and imports cleanly · install 0.0s · import 0.000s · 101.1MB
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 10.2s · import 0.000s · 109MB
104MB installed
● package 104MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
create_react_agent
✓ from langgraph.prebuilt import create_react_agent
ToolNode
✓ from langgraph.prebuilt import ToolNode
tools_condition
✓ from langgraph.prebuilt import tools_condition
MessagesState
✓ from langgraph.graph import MessagesState
✗ from langgraph.prebuilt import MessagesState
MessagesState is part of the core langgraph graph module, not prebuilt.
This quickstart demonstrates how to set up and use `create_react_agent` from `langgraph-prebuilt`. It initializes an OpenAI LLM, a Tavily search tool, and then creates an agent capable of using these tools. The agent is then invoked with a query, showcasing its ability to use tools for information retrieval and simple calculations. Ensure `OPENAI_API_KEY` and `TAVILY_API_KEY` are set in your environment for the example to run. [8, 13]
import os
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langgraph.prebuilt import create_react_agent
from typing import Annotated, Sequence, TypedDict
from langchain_core.messages import BaseMessage
# Set API keys (replace with actual keys or set as environment variables)
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")
os.environ["TAVILY_API_KEY"] = os.environ.get("TAVILY_API_KEY", "YOUR_TAVILY_API_KEY")
# Define the agent state (LangGraph requires a defined state)
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], lambda x, y: x + y]
# Initialize LLM and tools
llm = ChatOpenAI(model="gpt-4o-mini") # or any other tool-calling capable LLM
search_tool = TavilySearchResults(max_results=3)
tools = [search_tool]
# Create the prebuilt ReAct agent
agent_executor = create_react_agent(llm, tools=tools)
# Example usage
# Ensure OPENAI_API_KEY and TAVILY_API_KEY are set
if os.environ["OPENAI_API_KEY"] == "YOUR_OPENAI_API_KEY" or os.environ["TAVILY_API_KEY"] == "YOUR_TAVILY_API_KEY":
print("Please set OPENAI_API_KEY and TAVILY_API_KEY environment variables or replace placeholders.")
else:
response = agent_executor.invoke(
{"messages": [("human", "What is the weather in London and what is 123 + 456?")]}
)
print(response["messages"][-1].content)
Debug
Known issues
gotchaLangGraph is a low-level framework, while `langgraph-prebuilt` offers higher-level abstractions. Mixing low-level graph construction (e.g., `StateGraph`, `add_node`) with prebuilt agents (like `create_react_agent`) without understanding LangGraph's state management can lead to unexpected behavior or difficult-to-debug issues. Stick to one paradigm or thoroughly understand how state is managed across both. [3, 5, 6]fixFor complex customizations, consider extending or modifying the prebuilt agent's internal graph if the documentation provides a clear path, or build a custom graph from scratch using `langgraph.graph.StateGraph` for full control. For simpler cases, leverage the provided prebuilt agent parameters and hooks. Consult the official LangGraph documentation for advanced patterns. [5, 8, 9]
affects: All versions
gotchaPrebuilt agents often rely on external services (LLMs, search tools). Failing to set required API keys as environment variables (e.g., `OPENAI_API_KEY`, `TAVILY_API_KEY`) or passing incorrect values will cause agents to fail silently or with authentication errors. [1, 6, 13]fixAlways verify that all necessary API keys are correctly set in your environment or explicitly passed to the respective LLM/tool constructors before running the agent. Test credentials independently if an agent is failing. Use a `.env` file and `python-dotenv` for local development. [1, 6]
affects: All versions
deprecatedThe LangGraph ecosystem, including `langgraph-prebuilt`, is actively developed. Older patterns for agents or tool integration, while functional, might be superseded by more efficient, flexible, or recommended approaches in newer releases. [7]fixRegularly consult the official LangChain/LangGraph documentation and quickstarts for the latest recommended practices and agent architectures. Pay attention to release notes for potential API changes or new high-level abstractions. [1, 7, 10]
affects: Versions <= 1.0.8, potentially future versions
gotchaThe `langchain_community` package, which provides various tools and utilities (e.g., `TavilySearchResults`), is a common dependency for agents utilizing external services. If it's not explicitly installed, importing modules from it will result in a `ModuleNotFoundError`.fixEnsure `langchain_community` is listed in your project's `requirements.txt` or explicitly installed via `pip install langchain-community` in your environment before running agents that rely on it. Verify your environment setup, especially in containerized or CI/CD contexts, to ensure all necessary dependencies are installed.
affects: All versions
breakingA `ModuleNotFoundError` for packages like `langchain_community` indicates that required dependencies are not installed in the execution environment. Prebuilt agents and tools often rely on specific packages that must be explicitly added.fixEnsure all necessary Python packages are installed in the environment. This typically involves running `pip install <package_name>` for each required package, or using `pip install -r requirements.txt` if a `requirements.txt` file is present. Verify that the correct virtual environment is activated if applicable.
affects: All versions
Upgrade
Version history
1.1.0latest on PyPI · released May 12, 2026
Audit
Dependencies
langgraphrequiredCore orchestration framework upon which prebuilt agents are built.
langchain-openaioptionalCommon LLM integration for agent models.
tavily-pythonoptionalCommon tool integration for search functionality.
pydanticoptionalUsed for defining state and output schemas.