Deploy and Run

fast-agent provides flexible deployment options to meet a variety of use cases, from interactive development to production server deployments.

Interactive Mode

Run fast-agent programs interactively for development, debugging, or direct user interaction.

agent.py

import asyncio
from mcp_agent.core.fastagent import FastAgent

fast = FastAgent("My Interactive Agent")

@fast.agent(instruction="You are a helpful assistant")
async def main():
    async with fast.run() as agent:
        # Start interactive prompt
        await agent()

if __name__ == "__main__":
    asyncio.run(main())

When started with uv run agent.py, this begins an interactive prompt where you can chat directly with the configured agents, apply prompts, save history and so on.

Command Line Execution

fast-agent supports command-line arguments to run agents and workflows with specific messages.

# Send a message to a specific agent
uv run agent.py --agent default --message "Analyze this dataset"

# Override the default model
uv run agent.py --model gpt-4o --agent default --message "Complex question"

# Run with minimal output
uv run agent.py --quiet --agent default --message "Background task"

This is perfect for scripting, automation, or one-off queries.

The --quiet flag switches off the Progress, Chat and Tool displays.

MCP Server Deployment

Any fast-agent application can be deployed as an MCP (Message Control Protocol) server with a simple command-line switch.

Starting an MCP Server

# Start as an SSE server (HTTP)
uv run agent.py --server --transport sse --port 8080

# Start as a stdio server (for piping to other processes)
uv run agent.py --server --transport stdio

Each agent exposes an MCP Tool for sending messages to the agent, and a Prompt that returns the conversation history.

This enables cross-agent state transfer via the MCP Prompts.

The MCP Server can also be started programatically.

Programmatic Server Startup

import asyncio
from mcp_agent.core.fastagent import FastAgent

fast = FastAgent("Server Agent")

@fast.agent(instruction="You are an API agent")
async def main():
    # Start as a server programmatically
    await fast.start_server(
        transport="sse",
        host="0.0.0.0",
        port=8080,
        server_name="API-Agent-Server",
        server_description="Provides API access to my agent"
    )

if __name__ == "__main__":
    asyncio.run(main())

Python Program Integration

Embed fast-agent into existing Python applications to add MCP agent capabilities.

import asyncio
from mcp_agent.core.fastagent import FastAgent

fast = FastAgent("Embedded Agent")

@fast.agent(instruction="You are a data analysis assistant")
async def analyze_data(data):
    async with fast.run() as agent:
        result = await agent.send(f"Analyze this data: {data}")
        return result

# Use in your application
async def main():
    user_data = get_user_data()
    analysis = await analyze_data(user_data)
    display_results(analysis)

if __name__ == "__main__":
    asyncio.run(main())