Content
<picture>
<img alt="" src="./static/image.jpg" width="full">
</picture>
<h1 align="center">Unified MCP Client Library </h1>
[](https://pypi.org/project/mcp_use/)
[](https://pypi.org/project/mcp_use/)
[](https://pypi.org/project/mcp_use/)
[](https://pypi.org/project/mcp_use/)
[](https://docs.mcp-use.io)
[](https://mcp-use.io)
[](https://github.com/pietrozullo/mcp-use/blob/main/LICENSE)
[](https://github.com/astral-sh/ruff)
[](https://github.com/pietrozullo/mcp-use/stargazers)
[](https://x.com/pietrozullo)
🌐 MCP-Use is the open source way to connect **any LLM to any MCP server** and build custom agents that have tool access, without using closed source or application clients.
💡 Let developers easily connect any LLM to tools like web browsing, file operations, and more.
# Features
## ✨ Key Features
| Feature | Description |
|---------|-------------|
| 🔄 [**Ease of use**](#quick-start) | Create your first MCP capable agent you need only 6 lines of code |
| 🤖 [**LLM Flexibility**](#installing-langchain-providers) | Works with any langchain supported LLM that supports tool calling (OpenAI, Anthropic, Groq, LLama etc.) |
| 🌐 [**Code Builder**](https://mcp-use.io/builder) | Explore MCP capabilities and generate starter code with the interactive [code builder](https://mcp-use.io/builder). |
| 🔗 [**HTTP Support**](#http-connection-example) | Direct connection to MCP servers running on specific HTTP ports |
| ⚙️ [**Dynamic Server Selection**](#dynamic-server-selection-server-manager) | Agents can dynamically choose the most appropriate MCP server for a given task from the available pool |
| 🧩 [**Multi-Server Support**](#multi-server-support) | Use multiple MCP servers simultaneously in a single agent |
| 🛡️ [**Tool Restrictions**](#tool-access-control) | Restrict potentially dangerous tools like file system or network access |
| 🔧 [**Custom Agents**](#build-a-custom-agent) | Build your own agents with any framework using the LangChain adapter or create new adapters |
# Quick start
With pip:
```bash
pip install mcp-use
```
Or install from source:
```bash
git clone https://github.com/pietrozullo/mcp-use.git
cd mcp-use
pip install -e .
```
### Installing LangChain Providers
mcp_use works with various LLM providers through LangChain. You'll need to install the appropriate LangChain provider package for your chosen LLM. For example:
```bash
# For OpenAI
pip install langchain-openai
# For Anthropic
pip install langchain-anthropic
# For other providers, check the [LangChain chat models documentation](https://python.langchain.com/docs/integrations/chat/)
```
and add your API keys for the provider you want to use to your `.env` file.
```bash
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
```
> **Important**: Only models with tool calling capabilities can be used with mcp_use. Make sure your chosen model supports function calling or tool use.
### Spin up your agent:
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
# Load environment variables
load_dotenv()
# Create configuration dictionary
config = {
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}
# Create MCPClient from configuration dictionary
client = MCPClient.from_dict(config)
# Create LLM
llm = ChatOpenAI(model="gpt-4o")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
# Run the query
result = await agent.run(
"Find the best restaurant in San Francisco",
)
print(f"\nResult: {result}")
if __name__ == "__main__":
asyncio.run(main())
```
You can also add the servers configuration from a config file like this:
```python
client = MCPClient.from_config_file(
os.path.join("browser_mcp.json")
)
```
Example configuration file (`browser_mcp.json`):
```json
{
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}
```
For other settings, models, and more, check out the documentation.
## Streaming Agent Output
MCP-Use supports asynchronous streaming of agent output using the `astream` method on `MCPAgent`. This allows you to receive incremental results, tool actions, and intermediate steps as they are generated by the agent, enabling real-time feedback and progress reporting.
### How to use
Call `agent.astream(query)` and iterate over the results asynchronously:
```python
async for chunk in agent.astream("Find the best restaurant in San Francisco"):
print(chunk["messages"], end="", flush=True)
```
Each chunk is a dictionary containing keys such as `actions`, `steps`, `messages`, and (on the last chunk) `output`. This enables you to build responsive UIs or log agent progress in real time.
#### Example: Streaming in Practice
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
load_dotenv()
client = MCPClient.from_config_file("browser_mcp.json")
llm = ChatOpenAI(model="gpt-4o")
agent = MCPAgent(llm=llm, client=client, max_steps=30)
async for chunk in agent.astream("Look for job at nvidia for machine learning engineer."):
print(chunk["messages"], end="", flush=True)
if __name__ == "__main__":
asyncio.run(main())
```
This streaming interface is ideal for applications that require real-time updates, such as chatbots, dashboards, or interactive notebooks.
# Example Use Cases
## Web Browsing with Playwright
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
# Load environment variables
load_dotenv()
# Create MCPClient from config file
client = MCPClient.from_config_file(
os.path.join(os.path.dirname(__file__), "browser_mcp.json")
)
# Create LLM
llm = ChatOpenAI(model="gpt-4o")
# Alternative models:
# llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# llm = ChatGroq(model="llama3-8b-8192")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
# Run the query
result = await agent.run(
"Find the best restaurant in San Francisco USING GOOGLE SEARCH",
max_steps=30,
)
print(f"\nResult: {result}")
if __name__ == "__main__":
asyncio.run(main())
```
## Airbnb Search
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient
async def run_airbnb_example():
# Load environment variables
load_dotenv()
# Create MCPClient with Airbnb configuration
client = MCPClient.from_config_file(
os.path.join(os.path.dirname(__file__), "airbnb_mcp.json")
)
# Create LLM - you can choose between different models
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
try:
# Run a query to search for accommodations
result = await agent.run(
"Find me a nice place to stay in Barcelona for 2 adults "
"for a week in August. I prefer places with a pool and "
"good reviews. Show me the top 3 options.",
max_steps=30,
)
print(f"\nResult: {result}")
finally:
# Ensure we clean up resources properly
if client.sessions:
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(run_airbnb_example())
```
Example configuration file (`airbnb_mcp.json`):
```json
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": ["-y", "@openbnb/mcp-server-airbnb"]
}
}
}
```
## Blender 3D Creation
```python
import asyncio
from dotenv import load_dotenv
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient
async def run_blender_example():
# Load environment variables
load_dotenv()
# Create MCPClient with Blender MCP configuration
config = {"mcpServers": {"blender": {"command": "uvx", "args": ["blender-mcp"]}}}
client = MCPClient.from_dict(config)
# Create LLM
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
try:
# Run the query
result = await agent.run(
"Create an inflatable cube with soft material and a plane as ground.",
max_steps=30,
)
print(f"\nResult: {result}")
finally:
# Ensure we clean up resources properly
if client.sessions:
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(run_blender_example())
```
# Configuration File Support
MCP-Use supports initialization from configuration files, making it easy to manage and switch between different MCP server setups:
```python
import asyncio
from mcp_use import create_session_from_config
async def main():
# Create an MCP session from a config file
session = create_session_from_config("mcp-config.json")
# Initialize the session
await session.initialize()
# Use the session...
# Disconnect when done
await session.disconnect()
if __name__ == "__main__":
asyncio.run(main())
```
## HTTP Connection Example
MCP-Use supports HTTP connections, allowing you to connect to MCP servers running on specific HTTP ports. This feature is particularly useful for integrating with web-based MCP servers.
Here's an example of how to use the HTTP connection feature:
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
"""Run the example using a configuration file."""
# Load environment variables
load_dotenv()
config = {
"mcpServers": {
"http": {
"url": "http://localhost:8931/sse"
}
}
}
# Create MCPClient from config file
client = MCPClient.from_dict(config)
# Create LLM
llm = ChatOpenAI(model="gpt-4o")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
# Run the query
result = await agent.run(
"Find the best restaurant in San Francisco USING GOOGLE SEARCH",
max_steps=30,
)
print(f"\nResult: {result}")
if __name__ == "__main__":
# Run the appropriate example
asyncio.run(main())
```
This example demonstrates how to connect to an MCP server running on a specific HTTP port. Make sure to start your MCP server before running this example.
# Multi-Server Support
MCP-Use allows configuring and connecting to multiple MCP servers simultaneously using the `MCPClient`. This enables complex workflows that require tools from different servers, such as web browsing combined with file operations or 3D modeling.
## Configuration
You can configure multiple servers in your configuration file:
```json
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"]
},
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}
```
## Usage
The `MCPClient` class provides methods for managing connections to multiple servers. When creating an `MCPAgent`, you can provide an `MCPClient` configured with multiple servers.
By default, the agent will have access to tools from all configured servers. If you need to target a specific server for a particular task, you can specify the `server_name` when calling the `agent.run()` method.
```python
# Example: Manually selecting a server for a specific task
result = await agent.run(
"Search for Airbnb listings in Barcelona",
server_name="airbnb" # Explicitly use the airbnb server
)
result_google = await agent.run(
"Find restaurants near the first result using Google Search",
server_name="playwright" # Explicitly use the playwright server
)
```
## Dynamic Server Selection (Server Manager)
For enhanced efficiency and to reduce potential agent confusion when dealing with many tools from different servers, you can enable the Server Manager by setting `use_server_manager=True` during `MCPAgent` initialization.
When enabled, the agent intelligently selects the correct MCP server based on the tool chosen by the LLM for a specific step. This minimizes unnecessary connections and ensures the agent uses the appropriate tools for the task.
```python
import asyncio
from mcp_use import MCPClient, MCPAgent
from langchain_anthropic import ChatAnthropic
async def main():
# Create client with multiple servers
client = MCPClient.from_config_file("multi_server_config.json")
# Create agent with the client
agent = MCPAgent(
llm=ChatAnthropic(model="claude-3-5-sonnet-20240620"),
client=client,
use_server_manager=True # Enable the Server Manager
)
try:
# Run a query that uses tools from multiple servers
result = await agent.run(
"Search for a nice place to stay in Barcelona on Airbnb, "
"then use Google to find nearby restaurants and attractions."
)
print(result)
finally:
# Clean up all sessions
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(main())
```
# Tool Access Control
MCP-Use allows you to restrict which tools are available to the agent, providing better security and control over agent capabilities:
```python
import asyncio
from mcp_use import MCPAgent, MCPClient
from langchain_openai import ChatOpenAI
async def main():
# Create client
client = MCPClient.from_config_file("config.json")
# Create agent with restricted tools
agent = MCPAgent(
llm=ChatOpenAI(model="gpt-4"),
client=client,
disallowed_tools=["file_system", "network"] # Restrict potentially dangerous tools
)
# Run a query with restricted tool access
result = await agent.run(
"Find the best restaurant in San Francisco"
)
print(result)
# Clean up
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(main())
```
# Build a Custom Agent:
You can also build your own custom agent using the LangChain adapter:
```python
import asyncio
from langchain_openai import ChatOpenAI
from mcp_use.client import MCPClient
from mcp_use.adapters.langchain_adapter import LangChainAdapter
from dotenv import load_dotenv
load_dotenv()
async def main():
# Initialize MCP client
client = MCPClient.from_config_file("examples/browser_mcp.json")
llm = ChatOpenAI(model="gpt-4o")
# Create adapter instance
adapter = LangChainAdapter()
# Get LangChain tools with a single line
tools = await adapter.create_tools(client)
# Create a custom LangChain agent
llm_with_tools = llm.bind_tools(tools)
result = await llm_with_tools.ainvoke("What tools do you have avilable ? ")
print(result)
if __name__ == "__main__":
asyncio.run(main())
```
# Debugging
MCP-Use provides a built-in debug mode that increases log verbosity and helps diagnose issues in your agent implementation.
## Enabling Debug Mode
There are two primary ways to enable debug mode:
### 1. Environment Variable (Recommended for One-off Runs)
Run your script with the `DEBUG` environment variable set to the desired level:
```bash
# Level 1: Show INFO level messages
DEBUG=1 python3.11 examples/browser_use.py
# Level 2: Show DEBUG level messages (full verbose output)
DEBUG=2 python3.11 examples/browser_use.py
```
This sets the debug level only for the duration of that specific Python process.
Alternatively you can set the following environment variable to the desired logging level:
```bash
export MCP_USE_DEBUG=1 # or 2
```
### 2. Setting the Debug Flag Programmatically
You can set the global debug flag directly in your code:
```python
import mcp_use
mcp_use.set_debug(1) # INFO level
# or
mcp_use.set_debug(2) # DEBUG level (full verbose output)
```
### 3. Agent-Specific Verbosity
If you only want to see debug information from the agent without enabling full debug logging, you can set the `verbose` parameter when creating an MCPAgent:
```python
# Create agent with increased verbosity
agent = MCPAgent(
llm=your_llm,
client=your_client,
verbose=True # Only shows debug messages from the agent
)
```
This is useful when you only need to see the agent's steps and decision-making process without all the low-level debug information from other components.
# Roadmap
<ul>
<li>[x] Multiple Servers at once </li>
<li>[x] Test remote connectors (http, ws)</li>
<li>[ ] ... </li>
</ul>
## Star History
[](https://www.star-history.com/#pietrozullo/mcp-use&Date)
# Contributing
We love contributions! Feel free to open issues for bugs or feature requests. Look at [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
# Requirements
- Python 3.11+
- MCP implementation (like Playwright MCP)
- LangChain and appropriate model libraries (OpenAI, Anthropic, etc.)
# Citation
If you use MCP-Use in your research or project, please cite:
```bibtex
@software{mcp_use2025,
author = {Zullo, Pietro},
title = {MCP-Use: MCP Library for Python},
year = {2025},
publisher = {GitHub},
url = {https://github.com/pietrozullo/mcp-use}
}
```
# License
MIT
