agno

<div align="center" id="top"> <a href="https://agno.com"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://agno-public.s3.us-east-1.amazonaws.com/assets/logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://agno-public.s3.us-east-1.amazonaws.com/assets/logo-light.svg"> <img src="https://agno-public.s3.us-east-1.amazonaws.com/assets/logo-light.svg" alt="Agno"> </picture> </a> </div> <p align="center"> Build multi-agent systems that learn. </p> <div align="center"> <a href="https://docs.agno.com">Docs</a> <span>&nbsp;•&nbsp;</span> <a href="https://github.com/agno-agi/agno/tree/main/cookbook">Cookbook</a> <span>&nbsp;•&nbsp;</span> <a href="https://community.agno.com/">Community</a> <span>&nbsp;•&nbsp;</span> <a href="https://www.agno.com/discord">Discord</a> </div>

What is Agno?

A framework for building multi-agent systems that learn and improve with every interaction.

Most agents are stateless. They reason, respond, forget. Session history helps, but they're exactly as capable on day 1000 as they were on day 1.

Agno agents are different. They remember users across sessions, accumulate knowledge across conversations, and learn from decisions. Insights from one user benefit everyone. The system gets smarter over time.

Everything runs in your cloud. Your data never leaves your environment.

Quick Example

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="tmp/agents.db"),
    learning=True,
)

One line. Your agent now remembers users, accumulates knowledge, and improves over time.

Production Stack

Agno provides the complete infrastructure for building multi-agent systems that learn:

Layer	What it does
Framework	Build agents with learning, tools, knowledge, and guardrails
Runtime	Run in production using AgentOS
Control Plane	Monitor and manage via the AgentOS UI

Get Started

1. Build your first agent
2. Build your first multi-agent system
3. Deploy to production

More: Docs · Cookbook

Features

Category	What you get
Learning	User profiles that persist across sessions. User memories that accumulate over time. Learned knowledge that transfers across users. Always or agentic learning modes.
Core	Model-agnostic: OpenAI, Anthropic, Google, local models. Type-safe I/O with input_schema and output_schema. Async-first, built for long-running tasks. Natively multimodal (text, images, audio, video, files).
Knowledge	Agentic RAG with 20+ vector stores, hybrid search, reranking. Persistent storage for session history and state.
Orchestration	Human-in-the-loop (confirmations, approvals, overrides). Guardrails for validation and security. First-class MCP and A2A support. 100+ built-in toolkits.
Production	Ready-to-use FastAPI runtime. Integrated control plane UI. Evals for accuracy, performance, latency.

IDE Integration

Add our docs to your AI-enabled editor:

Cursor: Settings → Indexing & Docs → Add https://docs.agno.com/llms-full.txt

Also works with VSCode, Windsurf, and similar tools.

Contributing

See the contributing guide.

Telemetry

Agno logs which model providers are used to prioritize updates. Disable with AGNO_TELEMETRY=false.

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