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Welcome to Shannon Tutorials

Learn how to build powerful AI agent applications with Shannon through practical, hands-on tutorials. Each tutorial includes complete working code, step-by-step instructions, and best practices.
This section is actively being developed. New tutorials are added regularly based on community feedback and common use cases.

Available Tutorials

Building Agents

Extension & Integration

What You’ll Learn

Across these tutorials, you’ll learn:
  • Agent Design: How to structure agent tasks and workflows
  • Tool Integration: Connecting agents to external APIs and services
  • Session Management: Building stateful, multi-turn conversations
  • Error Handling: Robust patterns for production agents
  • Cost Optimization: Budget management and token efficiency
  • Monitoring: Tracking agent performance and behavior

Prerequisites

Before starting these tutorials, make sure you have:
  1. Shannon Installed: Follow the Installation Guide
  2. Basic Python Knowledge: Familiarity with Python 3.9+
  3. API Keys: At least one LLM provider API key (OpenAI, Anthropic, etc.)
  4. Docker: For running Shannon services locally

Tutorial Structure

Each tutorial follows a consistent structure:
  1. Overview: What you’ll build and why
  2. Prerequisites: Required setup and dependencies
  3. Step-by-Step: Detailed implementation guide
  4. Code Examples: Complete working code you can run
  5. Testing: How to verify your implementation
  6. Next Steps: Ideas for extending the tutorial

Contributing Tutorials

Have an idea for a tutorial? We welcome community contributions!
  1. Check existing GitHub issues
  2. Propose new tutorials in GitHub Discussions
  3. Submit tutorial PRs following our contribution guidelines

Get Help

If you get stuck on any tutorial:

What’s Next?