Agno

• Key points are in the visual diagram above.

Agno reduces boilerplate for common patterns (RAG agent, SQL agent, team of agents) while staying lightweight enough to understand in an afternoon.

Agno is a prefab agent kit — snap together memory, knowledge, and tools like modules instead of wiring everything from scratch.

Scenario

Finance agent with Agno: knowledge base over 10-K filings, SQL tool over warehouse, memory remembers user's portfolio preferences.

Solution

In Agent Framework Landscape, apply Agno to this scenario: Finance agent with Agno: knowledge base over 10-K filings, SQL tool over warehouse, memory remembers user's portfolio preferences. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Agno (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Agno happens in the code.

1from agno.agent import Agent  # import dependencies2from agno.models.openai import OpenAIChat  # import dependencies3
4agent = Agent(5    model=OpenAIChat(id="gpt-4o"),6    instructions="Answer questions about SEC filings.",7    knowledge=pdf_knowledge,8    tools=[sql_tool],9)10agent.print_response("What's Apple's R&D spend trend?")

• Treating Agno as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for agno

Quick recap — the most important points from this module.