WRONG - Recreates agent every time (significant overhead)

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):
```python
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:
```python
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
```python
# 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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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()
```

Common Mistakes
- Creating agents in loops (massive performance hit)
- Using Team when single agent would work
- Forgetting search_knowledge=True with knowledge
- Using SQLite in production
- Not adding history when context matters
- Missing output_schema validation

Production
- Use PostgresDb not SqliteDb
- Set show_tool_calls=False, debug_mode=False
- Wrap agent.run() in try-except

Docs: https://docs.agno.com