快速上手

简单的对话

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    tools=[YFinanceTools(stock_price=True)],
    instructions="Use tables to display data. Don't include any other text.",
    markdown=True,
)
agent.print_response("What is the stock price of Apple?", stream=True)

使用自定义模型

import os
from dotenv import load_dotenv

load_dotenv()

BASE_URL = os.getenv("OPENAI_API_BASE")
API_KEY = os.getenv("OPENAI_API_KEY")
from agno.models.openai.like import OpenAILike

model = OpenAILike(id="gpt-4o-mini", api_key=API_KEY, base_url=BASE_URL)

def new_model(id: str):
    return OpenAILike(id=id, api_key=API_KEY, base_url=BASE_URL)

根据网页搭建知识库

from agno.agent import Agent
from agno.embedder.openai import OpenAIEmbedder
from agno.knowledge.url import UrlKnowledge
from agno.models.anthropic import Claude
from agno.storage.sqlite import SqliteStorage
from agno.vectordb.lancedb import LanceDb, SearchType

# 只获取单页面, 不会递归检索里面的a标签
knowledge = UrlKnowledge(
    urls=["https://docs.agno.com/introduction.md"],
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small", dimensions=1536),
    ),
)

storage = SqliteStorage(table_name="agent_sessions", db_file="tmp/agent.db")

agent = Agent(
    name="Agno Assist",
    model=new_model(id="gpt-4o-mini"),
    instructions=[
        "Search your knowledge before answering the question.",
        "Only include the output in your response. No other text.",
    ],
    knowledge=knowledge,
    storage=storage,
    add_datetime_to_instructions=True,
    # 加到聊天记录里
    add_history_to_messages=True,
    num_history_runs=3,
    markdown=True,
)

if __name__ == "__main__":
    agent.knowledge.load(recreate=False)
    agent.print_response("What is Agno?", stream=True)

自动管理的记忆

from agno.agent import Agent
from agno.memory.v2.db.sqlite import SqliteMemoryDb
from agno.memory.v2.memory import Memory
from agno.models.anthropic import Claude
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools

memory = Memory(
    # Use any model for creating and managing memories
    model=new_model(id="gpt-4o-mini"),
    # Store memories in a SQLite database
    db=SqliteMemoryDb(table_name="user_memories", db_file="tmp/agent.db"),
    # We disable deletion by default, enable it if needed
    delete_memories=True,
    clear_memories=True,
)

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    tools=[
        ReasoningTools(add_instructions=True),
        YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True),
    ],
    # User ID for storing memories, `default` if not provided
    user_id="ava",
    instructions=[
        "Use tables to display data.",
        "Include sources in your response.",
        "Only include the report in your response. No other text.",
    ],
    memory=memory,
    # Let the Agent manage its memories
    enable_agentic_memory=True,
    markdown=True,
)

if __name__ == "__main__":
    # This will create a memory that "ava's" favorite stocks are NVIDIA and TSLA
    agent.print_response(
        "My favorite stocks are NVIDIA and TSLA",
        stream=True,
        show_full_reasoning=True,
        stream_intermediate_steps=True,
    )
    # This will use the memory to answer the question
    agent.print_response(
        "Can you compare my favorite stocks?",
        stream=True,
        show_full_reasoning=True,
        stream_intermediate_steps=True,
    )

这个Memory跟聊天记录不一样, Memory会由llm提取出看上去重要的东西, 然后存在sqlite里. 之后的对话会加载.

多Agent(team)

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.reasoning import ReasoningTools

web_agent = Agent(
    name="Web Search Agent",
    role="Handle web search requests and general research",
    model=new_model("gpt-4o-mini"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    add_datetime_to_instructions=True,
)

reasoning_team = Team(
    name="Reasoning Team",
    mode="coordinate",
    model=new_model("gpt-4o-mini"),
    members=[web_agent],
    tools=[ReasoningTools(add_instructions=True)],
    instructions=[
        "Collaborate with the web search agent to answer the question.",
    ],
    markdown=True,
    show_members_responses=True,
    enable_agentic_context=True,
    add_datetime_to_instructions=True,
)

if __name__ == "__main__":
    reasoning_team.print_response("What is the stock price of Apple?", stream=True,show_full_reasoning=True,
        stream_intermediate_steps=True,)

workflow

这太奇怪了, 为什么文档里的过不了我的静态检查, 但是跑得起来?