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# Minion Agent
A simple agent framework that's capable of browser use + mcp + auto instrument + plan + deep research + more
## 🎬 Demo Videos
- [Compare Price Demo](https://youtu.be/O0RhA3eeDlg)
- [Deep Research Demo](https://youtu.be/tOd56nagsT4)
- [Generating Snake Game Demo](https://youtu.be/UBquRXD9ZJc)
## Installation
```bash
pip install minion-agent-x
```
## Or from source
```bash
git clone git@github.com:femto/minion-agent.git
cd minion-agent
pip install -e .
```
## Usage
Here's a simple example of how to use Minion Agent:
```python
from minion_agent import MinionAgent, AgentConfig, AgentFramework
from dotenv import load_dotenv
import os
load_dotenv()
async def main():
# Configure the agent
agent_config = AgentConfig(
model_id=os.environ.get("AZURE_DEPLOYMENT_NAME"),
name="research_assistant",
description="A helpful research assistant",
model_args={"azure_endpoint": os.environ.get("AZURE_OPENAI_ENDPOINT"),
"api_key": os.environ.get("AZURE_OPENAI_API_KEY"),
"api_version": os.environ.get("OPENAI_API_VERSION"),
},
model_type="AzureOpenAIServerModel", # use "AzureOpenAIServerModel" for auzre, use "OpenAIServerModel" for openai, use "LiteLLMModel" for litellm
)
agent = await MinionAgent.create(AgentFramework.SMOLAGENTS, agent_config)
# Run the agent with a question
result = agent.run("What are the latest developments in AI?")
print("Agent's response:", result)
import asyncio
asyncio.run(main())
```
see example.py
see example_browser_use.py
see example_with_managed_agents.py
see example_deep_research.py
## Configuration
The `AgentConfig` class accepts the following parameters:
- `model_id`: The ID of the model to use (e.g., "gpt-4")
- `name`: Name of the agent (default: "Minion")
- `description`: Optional description of the agent
- `instructions`: Optional system instructions for the agent
- `tools`: List of tools the agent can use
- `model_args`: Optional dictionary of model-specific arguments
- `agent_args`: Optional dictionary of agent-specific arguments
## MCP Tool Support
Minion Agent supports Model Context Protocol (MCP) tools. Here's how to use them:
### Standard MCP Tool
```python
from minion_agent.config import MCPTool
agent_config = AgentConfig(
# ... other config options ...
tools=[
"minion_agent.tools.browser_tool.browser", # Regular tools
MCPTool(
command="npx",
args=["-y", "@modelcontextprotocol/server-filesystem", "/path/to/workspace"]
) # MCP tool
]
)
```
### SSE-based MCP Tool
You can also use MCP tools over Server-Sent Events (SSE). This is useful for connecting to remote MCP servers:
```python
from minion_agent.config import MCPTool
agent_config = AgentConfig(
# ... other config options ...
tools=[
MCPTool({"url": "http://localhost:8000/sse"}), # SSE-based tool
]
)
```
⚠️ **Security Warning**: When using MCP servers over SSE, be extremely cautious and only connect to trusted and verified servers. Always verify the source and security of any MCP server before connecting.
You can also use multiple MCP tools together:
```python
tools=[
MCPTool(command="npx", args=["..."]), # Standard MCP tool
MCPTool({"url": "http://localhost:8000/sse"}), # SSE-based tool
MCPTool({"url": "http://localhost:8001/sse"}) # Another SSE-based tool
]
```
## Planning Support
You can enable automatic planning by setting the `planning_interval` in `agent_args`:
```python
agent_config = AgentConfig(
# ... other config options ...
agent_args={
"planning_interval": 3, # Agent will create a plan every 3 steps
"additional_authorized_imports": "*"
}
)
```
The `planning_interval` parameter determines how often the agent should create a new plan. When set to 3, the agent will:
1. Create an initial plan for the task
2. Execute 3 steps according to the plan
3. Re-evaluate and create a new plan based on progress
4. Repeat until the task is complete
## Environment Variables
Make sure to set up your environment variables in a `.env` file:
```env
OPENAI_API_KEY=your_api_key_here
```
## Development
To set up for development:
```bash
# Clone the repository
git clone https://github.com/yourusername/minion-agent.git
cd minion-agent
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install development dependencies
pip install -e ".[dev]"
```
## Deep Research
See [Deep Research Documentation](docs/deep_research.md) for usage instructions.
## Community
Join our WeChat discussion group to connect with other users and get help:
群聊: minion-agent讨论群
## License
MIT License
