A simple agent framework that's capable of browser use + mcp + auto instrument + plan + deep research + more
pip install minion-agent-xgit clone git@github.com:femto/minion-agent.git
cd minion-agent
pip install -e .Here's a simple example of how to use Minion Agent:
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 see example_reason.py
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 agentinstructions: Optional system instructions for the agenttools: List of tools the agent can usemodel_args: Optional dictionary of model-specific argumentsagent_args: Optional dictionary of agent-specific arguments
Minion Agent supports Model Context Protocol (MCP) tools. Here's how to use them:
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
]
)You can also use MCP tools over Server-Sent Events (SSE). This is useful for connecting to remote MCP servers:
from minion_agent.config import MCPTool
agent_config = AgentConfig(
# ... other config options ...
tools=[
MCPTool({"url": "http://localhost:8000/sse"}), # SSE-based tool
]
)You can also use multiple MCP tools together:
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
]You can enable automatic planning by setting the planning_interval in agent_args:
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:
- Create an initial plan for the task
- Execute 3 steps according to the plan
- Re-evaluate and create a new plan based on progress
- Repeat until the task is complete
Make sure to set up your environment variables in a .env file:
OPENAI_API_KEY=your_api_key_hereTo set up for development:
# 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]"See Deep Research Documentation for usage instructions.
Join our WeChat discussion group to connect with other users and get help:
群聊: minion-agent讨论群
MIT License