# Agno

> Python SDK for building, running, and managing AI agent platforms

- Category: Agents & Automation / Agent Frameworks & Builders
- Language: Python
- Licence: Apache-2.0
- Repository: https://github.com/agno-agi/agno
- Website: https://docs.agno.com
- Canonical page: https://ai-tldr.dev/tools/agno/

## In short

Agno (formerly Phidata) is an open-source Python SDK for building AI agents and running them in production. You define an agent with a model, a set of tools, and instructions in plain Python, then run it as a service with tracing, scheduling, and access control built in. It sits in the Agent Frameworks & Builders category of the Agents & Automation tools and is open-source, written in Python and licensed Apache-2.0.

## Overview

Agno (formerly Phidata) is an open-source Python SDK for building AI agents and running them in production. You define an agent with a model, a set of tools, and instructions in plain Python, then run it as a service with tracing, scheduling, and access control built in.

It is aimed at Python developers and teams who want to own their agent stack instead of relying on a hosted black box. You keep control of your data, context, tools, memory, and human-review loops, store sessions and traces in your own database, and deploy on any platform that runs containers.

As a general agent framework, Agno covers both the build step and the runtime. Beyond the core agent loop, it ships a production API, 100+ tool integrations, context providers for sources like Slack and Drive, human-approval gates, and OpenTelemetry-based observability.

## What it does

- Define agents in plain Python with a model, tools, instructions, and memory
- Run agents as a production API with 50+ endpoints, SSE, and websockets
- 100+ pre-built tool integrations plus context providers (Slack, Drive, MCP, custom)
- Human-approval gates to pause runs for confirmation and gate sensitive tools
- Built-in observability via OpenTelemetry tracing, run history, and audit logs
- Cron scheduling, background jobs, and container deploys to Docker, Railway, AWS, or GCP

## Getting started

Install Agno with a model provider, then define and run an agent in a short Python script.

1. Install Agno

   Install the agno package alongside a model provider client such as openai. The README's first-agent guide uses uv; plain pip also works.

```bash
uv pip install -U agno openai
```

2. Set your model provider key

   Export the API key for the provider you chose so the agent can call the model.

```bash
export OPENAI_API_KEY=sk-...
```

3. Build and run an agent

   Create an Agent with a model and instructions, then stream a response with print_response.

```python
from agno.agent import Agent

agent = Agent(
    model="openai:gpt-5.5",
    instructions="You are a helpful assistant.",
    markdown=True,
)

agent.print_response("Say hello and tell me what you can do", stream=True)
```

## When to use it

- Build a coding or data assistant that lives in Slack and answers from your own context sources
- Stand up an internal agent platform with tracing, RBAC, and human approval that runs in your own cloud
- Expose an agent to users over Slack, Telegram, WhatsApp, Discord, or A2A interfaces
- Schedule recurring agent jobs (cron and background tasks) without extra infrastructure