agno

name	agno
description	Build production AI agents with Agno (formerly Phidata) — define Agent with model/tools/instructions/memory/knowledge, compose Agent Teams with coordinator routing, add Storage for persistence, and integrate RAG via built-in KnowledgeBase with PDF/URL/text sources.
triggers	["agno","phidata","agno agent","agno team","agno knowledge","agno storage","agno memory","phi agent","agent team coordinator","agent with knowledge base","agno tools","agno model"]
do_not_use_for	["State graph agents — use langgraph instead","Multi-agent role crews — use crewai instead","LLM fine-tuning — use llamafactory instead"]
see_also	["crewai","langgraph","mem0"]

Agno — Production AI Agent Framework

Source: agno-agi/agno (Mozilla PL) — formerly Phidata; full-stack agent framework

Why Agno

• One unified API for agents, teams, memory, knowledge, storage, tools
• Agent Teams: coordinator routes tasks to specialized sub-agents
• Built-in RAG: KnowledgeBase with PDF, URL, text, database sources
• Persistent storage: PostgreSQL, SQLite, MongoDB for long-term memory
• Playground UI: agent.serve() launches a ready-made web UI

Install

pip install agno
pip install agno[anthropic]   # Anthropic Claude
pip install agno[openai]      # OpenAI
pip install agno[all]         # everything

Minimal Agent

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

agent = Agent(
    model=Claude(id="claude-sonnet-4-5"),
    instructions="You are a helpful assistant.",
    markdown=True,
)

agent.print_response("What is quantum computing?")

# Or get structured response
response = agent.run("Explain RAG in 3 bullets")
print(response.content)

Agent with Tools

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools
from agno.tools.python import PythonTools

agent = Agent(
    model=Claude(id="claude-sonnet-4-5"),
    tools=[
        DuckDuckGoTools(),
        YFinanceTools(stock_price=True, analyst_recommendations=True),
        PythonTools(),
    ],
    instructions=[
        "Use DuckDuckGo to search for recent news",
        "Use YFinance for stock data",
        "Always cite your sources",
    ],
    show_tool_calls=True,
    markdown=True,
)

agent.print_response("What is Apple's current stock price and recent news?")

Memory & Storage

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.memory.db.sqlite import SqliteMemoryDb
from agno.storage.sqlite import SqliteStorage

# Memory: stores facts about users across sessions
# Storage: stores the full conversation history

agent = Agent(
    model=Claude(id="claude-sonnet-4-5"),
    # Persistent memory (facts extracted from conversations)
    memory=SqliteMemoryDb(table_name="agent_memory", db_file="agent.db"),
    enable_user_memories=True,
    # Persistent storage (full chat history)
    storage=SqliteStorage(table_name="agent_sessions", db_file="agent.db"),
    add_history_to_messages=True,
    num_history_runs=3,  # last 3 runs included in context
)

# First session
agent.print_response("My name is Alice and I love hiking.", user_id="alice")

# New session — memory persists
agent.print_response("What do you know about me?", user_id="alice")

Knowledge Base (RAG)

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.knowledge.pdf import PDFKnowledgeBase
from agno.knowledge.url import URLKnowledgeBase
from agno.knowledge.text import TextKnowledgeBase
from agno.vectordb.pgvector import PgVector
from agno.embedder.openai import OpenAIEmbedder

# Vector DB for knowledge
vector_db = PgVector(
    table_name="agent_knowledge",
    db_url="postgresql://user:pass@localhost/db",
    embedder=OpenAIEmbedder(id="text-embedding-3-small"),
)

# Load PDFs into knowledge base
knowledge = PDFKnowledgeBase(
    path="docs/",                   # directory of PDFs
    vector_db=vector_db,
)
knowledge.load(recreate=False)      # recreate=True to reindex

agent = Agent(
    model=Claude(id="claude-sonnet-4-5"),
    knowledge=knowledge,
    search_knowledge=True,          # auto-search on every query
    instructions="Answer from the knowledge base. Cite sources.",
)

agent.print_response("What does the document say about authentication?")

Agent Teams

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

web_agent = Agent(
    name="Web Researcher",
    role="Search the web for information",
    model=Claude(id="claude-sonnet-4-5"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
)

finance_agent = Agent(
    name="Finance Analyst",
    role="Analyze financial data and markets",
    model=Claude(id="claude-sonnet-4-5"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True)],
    instructions="Provide data-backed analysis",
)

# Team with coordinator routing
team = Team(
    name="Research Team",
    mode="coordinate",              # coordinator decides which agent to use
    model=Claude(id="claude-opus-4-5"),  # coordinator model
    members=[web_agent, finance_agent],
    instructions="Coordinate agents to provide comprehensive research",
)

team.print_response("What are the latest AI trends and their market impact?")

Structured Output

from pydantic import BaseModel
from agno.agent import Agent
from agno.models.anthropic import Claude

class MovieSummary(BaseModel):
    title: str
    director: str
    year: int
    genre: list[str]
    summary: str

agent = Agent(
    model=Claude(id="claude-sonnet-4-5"),
    response_model=MovieSummary,
)

movie: MovieSummary = agent.run("Tell me about Inception").content
print(movie.title, movie.director, movie.year)

Async Agent

import asyncio
from agno.agent import Agent
from agno.models.anthropic import Claude

agent = Agent(model=Claude(id="claude-sonnet-4-5"))

async def main():
    response = await agent.arun("Explain neural networks")
    print(response.content)

    async for chunk in await agent.astream("Write a poem"):
        print(chunk.content, end="", flush=True)

asyncio.run(main())

Playground (Web UI)

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.playground import Playground, serve_playground_app

agent = Agent(
    name="My Assistant",
    model=Claude(id="claude-sonnet-4-5"),
    markdown=True,
)

# Launch web UI at http://localhost:7777
app = Playground(agents=[agent]).get_app()
serve_playground_app("main:app", reload=True)

Built-in Tools

from agno.tools.duckduckgo import DuckDuckGoTools       # web search
from agno.tools.yfinance import YFinanceTools           # stock data
from agno.tools.python import PythonTools               # run Python code
from agno.tools.shell import ShellTools                 # shell commands
from agno.tools.file import FileTools                   # file operations
from agno.tools.github import GitHubTools               # GitHub API
from agno.tools.slack import SlackTools                 # Slack messages
from agno.tools.newspaper import NewspaperTools         # article extraction
from agno.tools.arxiv import ArxivTools                 # arXiv papers
from agno.tools.calculator import CalculatorTools       # math
from agno.tools.email import EmailTools                 # send email
from agno.tools.sql import SQLTools                     # SQL queries
from agno.tools.docker import DockerTools               # Docker ops

Anti-Fake-Pass Checks

• agent.run() returns RunResponse — content at .content, not .text or .data
• enable_user_memories=True requires both memory AND user_id at run time
• knowledge.load() must be called before first query — not lazy-loaded automatically
• Team mode="coordinate" uses a coordinator LLM — requires model on Team, not just members
• search_knowledge=True makes agent automatically search; knowledge.search() is manual
• add_history_to_messages=True requires storage to be set