AI Agents - An Introduction
Hey, Bala again. In this session, Vivek Pathania teaches us how to use Agno to build powerful AI agents easily. This is the first of two sessions.
Key Takeaways
- Agno is a lightweight, easy-to-use framework for building AI agents with a gentle learning curve
- Key components: agents, knowledge bases, memories, tools, MCPs (Model Context Protocols)
- Demonstrated simple agent creation, tool usage, and MCP concepts
- More advanced topics like workflows, scraping, and production-ready agents require further exploration
Topics
- Introduction to Agno Framework
- Lightweight alternative to more complex frameworks like Langchain
- Good for prototyping and smaller applications
- Clean documentation and active development (e.g. recent hackathon)
- Uses Python/TypeScript, integrates with various LLMs
Key Components of Agno
- Agents: Core building blocks, can use LLMs, tools, instructions
- Knowledge Bases: RAG capabilities, vector DBs, session memories
- Tools: Extend agent capabilities (e.g. web search, calculations)
- MCPs (Model Context Protocols): Standardized way to expose tool capabilities
Demonstration: Building a Simple Agent
- Used UV for Python environment setup (faster than pip)
- Created basic agent with Grok LLM integration
- Added custom calculator tool and DuckDuckGo search tool
- Showed how to provide instructions to guide tool usage
MCP Concept and Usage
- Separates tool logic from agent, exposes standardized interface
- Can provide better security and control over tool capabilities
- Brief demo of SQL-focused MCP server for database interactions
Advanced Topics (briefly covered)
- Workflows for complex multi-step agent processes
- Web scraping capabilities (requires additional tools)
- Building production-ready e-commerce assistant (multi-agent system)
Here's the entire recording of the session.