# 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

<!-- ZH -->


## Quick Start

```bash
pip install agno
```

Create an AI agent with tools and memory in five lines of code.

## What is Agno?

Agno (formerly Phidata) is a lightweight Python AI agent framework supporting 23+ model providers, built-in tools, knowledge bases, memory, and multi-agent teams.

**In one sentence**: Agno is a lightweight AI agent framework (formerly Phidata) — build production agents in 5 lines, with 23+ model providers, tools, RAG, and multi-agent teams — 20k+ GitHub stars.

**For**: Python developers who want a simple yet powerful agent framework. **Supports**: Claude, GPT, Gemini, Ollama, and 23+ other providers.

## Core Features

### 1. Multi-Provider Support
Switch models with one line of code.

### 2. Built-In Tools
Search, stocks, databases, files, email, and more — out of the box.

### 3. Knowledge Base (RAG)
Use PDFs, web pages, and databases as knowledge sources.

### 4. Memory and Storage
Cross-session memory with PostgreSQL persistence.

### 5. Multi-Agent Teams
Multiple agents coordinate to complete complex tasks.

### 6. Structured Output
Define output with Pydantic models.

## FAQ

**Q: What happened to Phidata?**
A: Renamed to Agno in early 2026 — the API is largely unchanged.

**Q: How does it compare to LangChain?**
A: Agno is lighter and more direct — faster to get started with fewer abstractions.

## Source & Thanks

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

---
Source: https://tokrepo.com/en/workflows/agno-lightweight-ai-agent-framework-python-2bed99e8
Author: Agno