Track C — Full-Code
Before This Session — Pre-Session Requirements
01
Agentic AI Tutorial using Agno — Krish Naik (~45 min)
YouTube — search 'Krish Naik Agno agentic AI tutorial'. Most comprehensive single resource. Essential.
YouTube — search 'Krish Naik Agno agentic AI tutorial'. Most comprehensive single resource. Essential.
02
Agno AI Agent Tutorial Playlist (official) (~60 min)
youtube.com/playlist?list=PL3JVwFmb_BnTItOu5wk67Vb2lrp2zMb2o — Official playlist. Dip into specific videos relevant to your project.
youtube.com/playlist?list=PL3JVwFmb_BnTItOu5wk67Vb2lrp2zMb2o — Official playlist. Dip into specific videos relevant to your project.
03
VSCode Python Setup for Beginners (~15 min)
YouTube — search 'VSCode Python setup 2025'. Essential if new to VSCode — covers Python interpreter, terminal, virtual environments.
YouTube — search 'VSCode Python setup 2025'. Essential if new to VSCode — covers Python interpreter, terminal, virtual environments.
Watch: How Agno connects with OpenClaw — recommended by bioERGOtech
Watch: Build your first Agno agent in 15 minutes (15 min)
What Is Agno?
Agno is a Python framework for building, running, and managing agentic software — from a single agent to multi-agent teams. It is lightweight, model-agnostic, and built around four core concepts.
| Concept | What It Is |
|---|---|
| Agent | The core unit. An Agent has a model, a set of tools, optional memory, and instructions. You instantiate it in Python and call agent.print_response() to run it. |
| Tool | A Python function the agent can call. The @tool decorator turns any function into something the agent can use — web search, database query, API call. |
| Memory | Agents can store and retrieve information across sessions. SQLite for local development, PostgreSQL for production. |
| AgentOS | The runtime that manages agents, tools, and sessions. Makes agents composable — you can build multi-agent pipelines where agents call each other. |
Your First Agno Agent — 10 Lines
This is the complete code for a working agent with web search capability. Every line is annotated.
# Install first: pip install agno anthropic
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(
model=Claude(id='claude-sonnet-4-6'),
tools=[DuckDuckGoTools()],
instructions=['Search the web and answer accurately'],
show_tool_calls=True,
)
agent.print_response('What is Agno?', stream=True)Session Deliverable
Run this agent from your terminal. Modify the instructions to match your project domain. Screenshot the output. Paste in your project log as environment setup proof.
Watch: Build a real AI agent with RAG and Memory in Agno (25 min)
✏️ Student Reflection
After running your first Agno agent: What did it return? What would you need to change about the model, tools, or instructions to make it useful for your specific project? Write 3–4 sentences in your project log.📝 Your Lesson Submission
Share your reflection, ask a question, or leave a comment. Your mentor will respond.
🔐
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