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Lesson 14: Agno - Production Agent OS

Topics Covered
  • Why Agno: Performance benchmarks and the case for a unified platform.
  • The Three Layers: Framework, AgentOS runtime, and control plane.
  • Building Agents: Instructions, memory, knowledge, and tools.
  • Teams and Workflows: Multi-agent orchestration in Agno.
  • Deployment: From local development to production runtime.
  • Control Plane: Monitoring, evaluation, and management at scale.

You've learned LangChain, Pydantic AI, and LangGraph for building agents, and CrewAI/Autogen for multi-agent systems. Agno takes a different approach: a vertically integrated platform where the framework, runtime, and monitoring are designed together. The result is 529x faster agent instantiation than LangGraph and a 24x smaller memory footprint. In this lesson, you'll build and deploy production agents with Agno's Agent OS.

Synopsis​

1. The Case for Agno​

  • The fragmentation problem: framework + runtime + observability as separate concerns
  • Agno's unified approach: built together, optimized together
  • Performance benchmarks vs LangGraph, CrewAI, Pydantic AI
  • When Agno makes sense (and when it doesn't)
  • Privacy-first: your cloud, your data

2. Agno Architecture Overview​

  • Layer 1: Open-source Python framework
  • Layer 2: AgentOS high-performance runtime
  • Layer 3: Control plane for monitoring and management
  • How the layers communicate
  • Local development vs production deployment

3. Your First Agno Agent​

  • Installing the Agno framework
  • The Agent class: instructions, model, tools
  • Defining agent behavior with natural language instructions
  • Running agents locally
  • Inspecting agent responses and reasoning

4. Instructions and Prompting​

  • Writing effective agent instructions
  • Structured instruction patterns
  • Dynamic instructions based on context
  • Instruction versioning and A/B testing
  • Common instruction anti-patterns

5. Memory in Agno​

  • Built-in memory management
  • Chat history and conversation context
  • Long-term memory persistence
  • Memory across sessions
  • Memory configuration and limits

6. Knowledge: Connecting to Your Data​

  • What is knowledge in Agno (RAG integration)
  • Connecting vector databases
  • Document ingestion and chunking
  • Knowledge retrieval configuration
  • Combining knowledge with agent reasoning

7. Tools in Agno​

  • Defining tools as Python functions
  • Tool schemas and validation
  • Async tools for performance
  • Built-in tool integrations
  • Tool error handling

8. Teams: Multi-Agent in Agno​

  • Defining agent teams
  • Role assignment and specialization
  • Team coordination patterns
  • Handoffs between team members
  • Team-level memory and context

9. Workflows: Orchestrating Complex Tasks​

  • Agno workflows vs LangGraph graphs
  • Defining workflow steps
  • Conditional branching
  • Parallel execution
  • Workflow persistence and resumption

10. Deploying to AgentOS​

  • From local to production runtime
  • AgentOS deployment options
  • Scaling agents horizontally
  • Load balancing and failover
  • Security and authentication

11. The Control Plane​

  • Accessing the control plane UI
  • Real-time agent monitoring
  • Memory inspection and management
  • Knowledge base organization
  • Performance metrics and evaluation
  • Cost tracking and optimization

12. Agno vs The Ecosystem​

  • Agno vs LangChain/LangGraph: unified vs modular
  • Agno vs CrewAI/Autogen: built-in teams vs framework patterns
  • Migration paths from other frameworks
  • Using Agno alongside existing tools
  • Future roadmap and community

Additional Resources​