# Agno — Lightweight AI Agent Framework for Python

> Build AI agents in 5 lines of Python. Agno provides model-agnostic agents with tools, memory, knowledge bases, and team coordination at 10x less overhead.

## Install

Paste the prompt below into your AI tool:

## Quick Use

```bash
pip install agno
```

```python
from agno.agent import Agent
from agno.models.anthropic import Claude

agent = Agent(model=Claude(id="claude-sonnet-4-20250514"), markdown=True)
agent.print_response("What is the capital of France?")
```

## What is Agno?

Agno (formerly Phidata) is a lightweight Python framework for building AI agents with minimal boilerplate. It supports 23+ model providers, built-in tools (web search, file ops, SQL), knowledge bases, memory, and multi-agent teams — all in a clean, composable API.

**Answer-Ready**: Agno is a lightweight AI agent framework for Python (formerly Phidata) supporting 23+ model providers, built-in tools, knowledge bases, memory, and multi-agent teams. Build production agents in 5 lines of code. 20k+ GitHub stars.

**Best for**: Python developers who want a simple yet powerful agent framework. **Works with**: Claude, GPT, Gemini, Groq, Ollama, and 18+ more providers. **Setup time**: Under 1 minute.

## Core Features

### 1. Multi-Provider Support

```python
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.models.google import Gemini
from agno.models.ollama import Ollama

agent = Agent(model=Claude(id="claude-sonnet-4-20250514"))
agent = Agent(model=OpenAIChat(id="gpt-4o"))
agent = Agent(model=Gemini(id="gemini-2.5-pro"))
agent = Agent(model=Ollama(id="llama3"))
```

### 2. Built-In Tools

```python
from agno.agent import Agent
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    tools=[DuckDuckGoTools(), YFinanceTools()],
    show_tool_calls=True,
)
agent.print_response("What is AAPL stock price and latest news?")
```

Available tools: DuckDuckGo, YFinance, Newspaper, Shell, Python, SQL, File, Email, Slack, and more.

### 3. Knowledge Bases (RAG)

```python
from agno.agent import Agent
from agno.knowledge.pdf import PDFKnowledgeBase
from agno.vectordb.pgvector import PgVector

knowledge = PDFKnowledgeBase(
    path="docs/",
    vector_db=PgVector(table_name="pdf_docs", db_url="postgresql://..."),
)

agent = Agent(knowledge=knowledge, search_knowledge=True)
agent.print_response("What does the contract say about termination?")
```

### 4. Memory & Storage

```python
from agno.agent import Agent
from agno.memory.db.postgres import PgMemory

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    memory=PgMemory(db_url="postgresql://..."),
    enable_memory=True,
)
# Agent remembers past conversations
```

### 5. Multi-Agent Teams

```python
from agno.agent import Agent
from agno.team import Team

researcher = Agent(name="Researcher", role="Research topics", tools=[DuckDuckGoTools()])
writer = Agent(name="Writer", role="Write articles based on research")

team = Team(
    agents=[researcher, writer],
    mode="coordinate",
)
team.print_response("Write an article about AI agents in 2026")
```

### 6. Structured Outputs

```python
from pydantic import BaseModel

class MovieReview(BaseModel):
    title: str
    rating: float
    summary: str

agent = Agent(model=Claude(id="claude-sonnet-4-20250514"), response_model=MovieReview)
review = agent.run("Review the movie Inception")
print(review.rating)  # 9.2
```

## FAQ

**Q: What happened to Phidata?**
A: Phidata was renamed to Agno in early 2026. The API is largely the same with improvements.

**Q: How does it compare to LangChain?**
A: Agno is more lightweight and opinionated. Less abstraction, faster to get started, but fewer integrations than LangChain.

**Q: Is it production ready?**
A: Yes, used in production by many teams. The framework is mature (originally Phidata, 2+ years old).

## Source & Thanks

> Created by [Agno Team](https://github.com/agno-agi). Licensed under MPL-2.0.
>
> [agno-agi/agno](https://github.com/agno-agi/agno) — 20k+ stars

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## 快速使用

```bash
pip install agno
```

5 行代码创建带工具和记忆的 AI 代理。

## 什么是 Agno？

Agno（原 Phidata）是轻量级 Python AI 代理框架，支持 23+ 模型供应商、内置工具、知识库、记忆和多代理团队。

**一句话总结**：Agno 是轻量 AI 代理框架（原 Phidata），5 行代码构建生产级代理，支持 23+ 模型、工具、RAG 和多代理团队，20k+ GitHub stars。

**适合人群**：想要简单强大代理框架的 Python 开发者。**支持**：Claude、GPT、Gemini、Ollama 等 23+ 供应商。

## 核心功能

### 1. 多供应商支持
一行代码切换模型。

### 2. 内置工具
搜索、股票、数据库、文件、邮件等开箱即用。

### 3. 知识库（RAG）
PDF、网页、数据库作为知识源。

### 4. 记忆与存储
跨会话记忆，PostgreSQL 持久化。

### 5. 多代理团队
多个代理协调完成复杂任务。

### 6. 结构化输出
Pydantic 模型定义输出。

## 常见问题

**Q: Phidata 怎么了？**
A: 2026 年初更名为 Agno，API 基本不变。

**Q: 和 LangChain 比较？**
A: Agno 更轻量直接，上手更快，抽象更少。

## 来源与致谢

> [agno-agi/agno](https://github.com/agno-agi/agno) — 20k+ stars, MPL-2.0

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Source: https://tokrepo.com/en/workflows/2bed99e8-77af-4998-b5cd-756b0a588d00
Author: Prompt Lab