Agno — framework d’Agents rapide et prêt pour la production (successeur de Phidata)

Quick Start — Single Agent with Memory + Knowledge

memory=Memory(...) persiste entre exécutions ; knowledge=PDFUrlKnowledgeBase(...) + vector_db=... donne à l’Agent du RAG contre vos docs. enable_agentic_memory=True laisse l’Agent décider lui-même de ce qu’il mémorise (à la Letta). add_references=True injecte les attributions de source dans les réponses.

# pip install -U agno openai lancedb
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.memory.v2.memory import Memory
from agno.memory.v2.db.sqlite import SqliteMemoryDb
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb, SearchType

# Persistent memory across sessions
memory = Memory(
    db=SqliteMemoryDb(table_name="user_memory", db_file="tmp/agent.db"),
    model=OpenAIChat(id="gpt-4o-mini"),
)

# Knowledge base from a public PDF
kb = PDFUrlKnowledgeBase(
    urls=["https://arxiv.org/pdf/2310.08560"],         # the MemGPT paper
    vector_db=LanceDb(table_name="agent_kb", uri="tmp/lancedb", search_type=SearchType.hybrid),
)
kb.load(recreate=False)   # index once

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    memory=memory,
    knowledge=kb,
    user_id="william",
    enable_agentic_memory=True,   # agent can write/read its own memory
    add_references=True,          # cite knowledge passages in replies
    markdown=True,
)

agent.print_response("What does the MemGPT paper say about paged memory? Remember that I care about Python examples.",
                     stream=True)

# Next session the same agent remembers the "Python examples" preference,
# and answers about MemGPT using retrieved passages with citations.

Fonctionnalités clés

Comparaison

Cas d'usage

Tarification et licence

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