agno: Cursor rule for Cursor

You are an expert in Python, Agno framework, and AI agent development.

Core Rules

• NEVER create agents in loops - reuse them for performance
• Always use output_schema for structured responses
• PostgreSQL in production, SQLite for dev only
• Start with single agent, scale up only when needed

Documentation:

• Don't use f-strings for print lines where there are no variables to format.
• Don't use emojis in examples and print lines

Basic Agent (start here):

from agno.agent import Agent
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions="You are a helpful assistant",
    markdown=True,
)
agent.print_response("Your query", stream=True)

Agent with Tools:

from agno.tools.websearch import WebSearchTools

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    tools=[WebSearchTools()],
    instructions="Search the web for information",
)

CRITICAL: Agent Reuse Performance

# WRONG - Recreates agent every time (significant overhead)
for query in queries:
    agent = Agent(...)  # DON'T DO THIS
    
# CORRECT - Create once, reuse
agent = Agent(...)
for query in queries:
    agent.run(query)

When to Use Each Pattern

Single Agent (90% of use cases):

• One clear task or domain
• Can be solved with tools + instructions
• Example: Search, analyze, generate content

Team (autonomous coordination):

• Multiple specialized agents with different expertise
• Agents decide who does what via LLM
• Complex tasks requiring multiple perspectives
• Example: Research + Analysis + Writing

Workflow (programmatic control):

• Sequential steps with clear flow
• Need conditional logic or branching
• Full control over execution order
• Example: Extract → Transform → Load pipelines

Team Pattern:

from agno.team.team import Team

web_agent = Agent(
    name="Researcher",
    model=OpenAIResponses(id="gpt-5.5"),
    tools=[WebSearchTools()],
)

writer_agent = Agent(
    name="Writer",
    model=OpenAIResponses(id="gpt-5.5"),
)

team = Team(
    members=[web_agent, writer_agent],
    model=OpenAIResponses(id="gpt-5.5"),
    instructions="Research and write articles",
)

Workflow Pattern:

from agno.workflow.workflow import Workflow
from agno.db.sqlite import SqliteDb

# Define agents first (researcher, writer)
async def blog_workflow(session_state, topic: str):
    # Step 1: Research
    research = await researcher.arun(topic)
    
    # Step 2: Write
    article = await writer.arun(research.content)
    
    return article

workflow = Workflow(
    name="Blog Generator",
    steps=blog_workflow,
    db=SqliteDb(db_file="tmp/workflow.db"),
)

Knowledge/RAG:

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb, SearchType
from agno.knowledge.embedder.openai import OpenAIEmbedder

knowledge = Knowledge(
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="knowledge_base",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    knowledge=knowledge,
    search_knowledge=True,  # Critical: enables agentic RAG
    instructions="Use knowledge base, cite sources"
)

Chat History:

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=SqliteDb(db_file="tmp/agents.db"),
    user_id="user-123",
    add_history_to_context=True,  # Adds previous messages
    num_history_runs=3,
)

Structured Output:

from pydantic import BaseModel

class Result(BaseModel):
    summary: str
    findings: list[str]

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    output_schema=Result,
)
result: Result = agent.run(query).content

AgentOS Production:

from agno.os import AgentOS
from agno.db.postgres import PostgresDb

agent_os = AgentOS(
    agents=[agent],
    db=PostgresDb(db_url=os.getenv("DATABASE_URL")),
)
app = agent_os.get_app()