Autono
A ReAct-Based Highly Robust Autonomous Agent (Harness) Framework.
What is Autono?
Autono is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to react-based highly robust autonomous agent (harness) framework.
A ReAct-Based Highly Robust Autonomous Agent (Harness) Framework.
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- A ReAct-Based Highly Robust Autonomous Agent (Harness) Frame
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx autonoConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Autono
Autono is a ReAct-based autonomous agent framework for Python that enables developers to build robust, self-directing AI agents that reason and act in iterative loops. Built on LangChain and compatible with OpenAI models, it provides decorators for converting ordinary Python functions into agent tools (@ability), enabling agent-to-agent communication (@agentic), and connecting agents to MCP servers (@mcp_session). The framework supports both cautious (PRUDENT) and exploratory (INQUISITIVE) personality modes, making it suitable for building everything from careful task automation agents to exploratory research agents that can chain multiple tools across stdio, HTTP/SSE, and WebSocket MCP connections.
Prerequisites
- Python 3.10 or higher
- An OpenAI API key (OPENAI_API_KEY) — the framework currently uses OpenAI models
- pip for package installation
- An MCP-compatible client if using Autono as an MCP server
- Familiarity with Python decorators and async programming
Install Autono
Install the latest version of Autono from PyPI. Use the -U flag to ensure you get the most recent release.
pip install -U autonoSet your OpenAI API key
Autono uses OpenAI models for agent reasoning. Export your API key as an environment variable.
export OPENAI_API_KEY=sk-your-openai-api-keyDefine your agent's tools using @ability
Decorate any Python function with @ability to make it available as a tool for your agent. The function's docstring and type hints are used to describe the tool to the agent.
from autono import ability
@ability
def search_web(query: str) -> str:
"""Search the web for information about the given query."""
# your implementation here
return resultsCreate and configure an agent
Instantiate an Agent with your tools, choose a personality (PRUDENT for cautious execution, INQUISITIVE for exploratory behavior), and assign it a task.
from autono import Agent, Personality, get_openai_model
model = get_openai_model()
agent = Agent(
abilities=[search_web],
brain=model,
personality=Personality.INQUISITIVE
)
agent.assign("Research the latest developments in quantum computing")
response = agent.just_do_it()
print(response)Connect to MCP servers using @mcp_session
Use the @mcp_session decorator and StdioMcpConfig to give your agent access to external MCP servers as additional tools.
from autono import McpAgent, StdioMcpConfig
mcp_config = StdioMcpConfig(command="npx", args=["-y", "@modelcontextprotocol/server-filesystem"])
agent = McpAgent(mcp_configs=[mcp_config], brain=model, personality=Personality.PRUDENT)
agent.assign("List files in the current directory")
response = agent.just_do_it()Autono Examples
Client configuration
To run Autono as an MCP server itself, configure your claude_desktop_config.json to launch a Python script that exposes Autono agents over the MCP protocol.
{
"mcpServers": {
"autono": {
"command": "python",
"args": ["/path/to/your/autono_mcp_server.py"],
"env": {
"OPENAI_API_KEY": "sk-your-openai-api-key"
}
}
}
}Prompts to try
Once your Autono agent is running with abilities configured, you can assign it complex multi-step tasks.
- "Research the top 5 Python web frameworks and compare their performance benchmarks"
- "Analyze the files in this directory and generate a summary report"
- "Run a sequence of database queries and compile the results into a CSV"
- "Monitor this API endpoint every 5 minutes and alert if response time exceeds 2 seconds"Troubleshooting Autono
Agent loops indefinitely without completing the task
Switch to Personality.PRUDENT mode, which makes the agent more conservative about tool usage and more likely to stop when it has a reasonable answer. You can also set a max_iterations limit on the agent to cap the number of ReAct cycles.
OPENAI_API_KEY not found or invalid
Ensure the environment variable is exported in the same shell session where you run your Python script, or set it directly in your OS environment. The key should start with 'sk-'. Verify it is valid by testing it with the OpenAI API directly.
MCP server connection fails with StdioMcpConfig
Verify the command and args in your StdioMcpConfig are correct and the target MCP server is installed. Test the MCP server command directly in a terminal first to confirm it starts without errors before adding it to the Autono agent.
Frequently Asked Questions about Autono
What is Autono?
Autono is a Model Context Protocol (MCP) server that react-based highly robust autonomous agent (harness) framework. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Autono?
Follow the installation instructions on the Autono GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Autono?
Autono works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Autono free to use?
Yes, Autono is open source and available under the GPL-3.0 license. You can use it freely in both personal and commercial projects.
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