Agno — Production AI Agent Runtime

What it is

Agno is a runtime for building and managing agentic software at scale. It provides stateful agents with memory, FastAPI-based serving for production deployment, 100+ tool integrations, and built-in tracing for observability. Agno agents maintain state across conversations and can coordinate with other agents.

Agno targets engineering teams building production AI agent systems who need a framework that handles state management, deployment, and monitoring.

How it saves time or tokens

Agno's built-in state management eliminates the need to build custom session stores. Agents remember previous interactions, tool results, and user preferences without external databases. The 100+ pre-built tool integrations (search, email, calendar, databases) mean you connect capabilities instead of writing API wrappers.

FastAPI serving provides production-grade deployment with automatic API documentation, authentication, and health checks.

How to use

1. Install Agno: pip install agno
2. Create an agent with tools and memory configuration
3. Serve the agent with FastAPI for production access
4. Monitor with the built-in tracing dashboard

Example

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    model=OpenAIChat(id='gpt-4o'),
    tools=[DuckDuckGoTools()],
    instructions=['Always cite sources', 'Be concise'],
    show_tool_calls=True,
    markdown=True,
)

# Run interactively
agent.print_response('What are the latest developments in AI agents?', stream=True)

# Or serve via FastAPI
from agno.playground import Playground
app = Playground(agents=[agent]).get_app()
# Run: uvicorn app:app --reload

Related on TokRepo

• AI agent tools -- Agent frameworks and platforms
• Multi-agent frameworks -- Agno multi-agent deep-dive

Common pitfalls

• Stateful agents consume memory proportional to conversation length; configure max history limits for long-running agents
• The 100+ tool integrations each require their own API keys; missing keys cause runtime errors
• FastAPI serving exposes agent capabilities via HTTP; add authentication middleware before deploying publicly