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Fetch the complete documentation index at: https://agno-v2-shaloo-ai-support-link.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

In this guide, you’ll build an agent that:
  • Connects to an MCP server
  • Stores and retrieves past conversations
  • Runs as a production API
All in about 20 lines of code.

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1. Define the Agent

Save the following code as agno_assist.py:
agno_assist.py
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.anthropic import Claude
from agno.os import AgentOS
from agno.tools.mcp import MCPTools

agno_assist = Agent(
    name="Agno Assist",
    model=Claude(id="claude-sonnet-4-5"),
    db=SqliteDb(db_file="agno.db"),                     # session storage
    tools=[MCPTools(url="https://docs.agno.com/mcp")],  # Agno docs via MCP
    add_datetime_to_context=True,
    add_history_to_context=True,                         # include past runs
    num_history_runs=3,                                  # last 3 conversations
    markdown=True,
)

# Serve via AgentOS → streaming, auth, session isolation, API endpoints
agent_os = AgentOS(agents=[agno_assist], tracing=True)
app = agent_os.get_app()
You now have:
  • A stateful agent
  • Streaming responses
  • Per-user session isolation
  • A production-ready API
  • Tracing enabled out of the box
No 3rd-party services required

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2. Run Your AgentOS

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Install dependencies

uv pip install -U 'agno[os]' anthropic mcp
3

Export your Anthropic API key

export ANTHROPIC_API_KEY=sk-***
4

Run your AgentOS

fastapi dev agno_assist.py
Your AgentOS is now running at:http://localhost:8000API documentation is automatically available at:http://localhost:8000/docs
You can add your own routes, middleware, or any FastAPI feature on top.

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3. Connect to the AgentOS UI

The AgentOS UI connects directly from your browser to your runtime. It lets you test, monitor, and manage your agents in real time.
  1. Open os.agno.com and sign in.
  2. Click “Add new OS” in the top navigation.
  3. Select “Local” to connect to a local AgentOS.
  4. Enter your endpoint URL (default: http://localhost:8000).
  5. Name it something like “Development OS”.
  6. Click “Connect”.
You’ll see your OS with a live status indicator once connected.

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Chat with your Agent

Open Chat, select your agent, and ask:
What is Agno?
The agent retrieves context from the Agno MCP server and responds with grounded answers.
Click Sessions in the sidebar to inspect stored conversations.All session data is stored in your local database. No third-party tracing or hosted memory service is required.

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What You Just Built

In 20 lines, you built:
  • A stateful agent
  • Tool-augmented retrieval via MCP
  • A streaming API
  • Session isolation
  • A production-ready runtime
You can use this exact same architecture for running multi-agent systems in production.

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