https://github.com/composiohq/ai-agents-workshop-07-25
Repository for the hands-on workshop on building AI agents hosted by Composio and the Fifth Elephant
https://github.com/composiohq/ai-agents-workshop-07-25
Last synced: 2 months ago
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Repository for the hands-on workshop on building AI agents hosted by Composio and the Fifth Elephant
- Host: GitHub
- URL: https://github.com/composiohq/ai-agents-workshop-07-25
- Owner: ComposioHQ
- Created: 2025-07-15T14:51:20.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2025-07-16T15:59:56.000Z (3 months ago)
- Last Synced: 2025-07-17T11:27:28.430Z (3 months ago)
- Language: Python
- Size: 103 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Graduating your AI Agents Workshop 🤖
Welcome to the **Graduating your AI Agents Workshop**! This repository contains a comprehensive, hands-on tutorial for building AI agents that are ready for production use.
## 🎯 Workshop Overview
This workshop takes you from a simple toy example to a production-ready AI agent system. You'll learn the essential patterns, tools, and practices needed to build, deploy, and maintain AI agents in real-world scenarios.
### 🚀 What You'll Build
By the end of this workshop, you'll have built:
- A simple coding agent (toy example)
- A multi-agent system with specialized roles
- An agent with state management and memory
- A fully instrumented system with tracing and observability
- A production-ready agent with evaluation and monitoring
### 📋 Prerequisites
- **Python 3.8+** installed on your system
- **OpenAI API Key** (get one from [OpenAI Platform](https://platform.openai.com/api-keys))
- **Basic Python knowledge** (functions, classes, async/await)
- **Git** for version control
- **Terminal/Command Line** familiarity
## 🗺️ Workshop Structure
### 🎬 Introduction & Motivation (30 min)
**What we'll cover:**
- Setting up the development environment
- Understanding what makes an AI agent "production-ready"
- Analyzing existing production systems (e.g., GitHub Copilot)
- Key challenges in agent development
**Learning outcomes:**
- Understand the difference between toy examples and production systems
- Identify the key components of a production AI agent
- Set up your development environment
---
### 🧸 [Module 1: Toy Example](./1-toy-example/) (30 min)
**Build a simple coding agent from scratch**
**What you'll learn:**
- Core agent architecture patterns
- Tool calling and integration
- Basic error handling
- Agent-tool interaction loops
**What you'll build:**
- A coding agent that can write, execute, and test Python code
- Integration with file system operations using Composio's FileTool app
- A simple REPL (Read-Eval-Print Loop) tool
**Key files:**
- `coding_agent.py` - Main agent implementation
- `examples.py` - Demonstration scripts
- `requirements.txt` - Dependencies
[**📚 Start with Module 1 →**](./1-toy-example/README.md)
---
### 👥 [Module 2: Multi-Agent Systems](./2-multi-agent/) (30 min)
**When and how to use multiple agents**
**What you'll learn:**
- Multi-agent design patterns
- Agent coordination and communication
- Task decomposition and specialization
- When to use multiple agents vs. a single agent
**What you'll build:**
- A software development team with specialized agents
- Agent-to-agent communication protocols
- Task orchestration and workflow management
**Key concepts:**
- Agent roles and responsibilities
- Inter-agent communication
- Conflict resolution
- Performance optimization
---
### 🔍 [Module 3: Tracing & Observability](./3-tracing-observability/) (20 min)
**Monitoring, debugging, and performance optimization**
**What you'll learn:**
- Tracing for agent systems
- Performance monitoring and alerting
- Debugging complex agent behaviors
- Integration with observability platforms
- Lifecycle hooks (pre/post execution)
**What you'll build:**
- Comprehensive logging system
- Trace correlation across agent calls
- Error tracking and alerting
- Lifecycle event handlers
**Key concepts:**
- Structured logging
- Performance bottleneck identification
- Real-time monitoring
- Hook-based instrumentation
---
### 🧠 [Module 4: State Management](./4-state-management/) (30 min)
**Memory and context management**
**What you'll learn:**
- State persistence patterns
- Memory management for long-running agents
**What you'll build:**
- Persistent agent memory system
- Context-aware agents
**Key concepts:**
- Short-term vs. long-term memory
- Context retrieval and relevance
---
### 🧪 [Module 5: Evaluation & Testing](./5-evaluation/) (15 min)
**Quality assurance and continuous improvement**
**What you'll learn:**
- Agent evaluation frameworks
- LLM-based evaluation techniques
**What you'll build:**
- Automated evaluation pipeline
- Performance benchmarking system
**Key concepts:**
- Evaluation metrics design
---
## 🚀 Quick Start
### 1. Clone the Repository
```bash
git clone https://github.com/yourusername/ai-agents-workshop.git
cd ai-agents-workshop
```
### 2. Set Up Environment
```bash
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies (we'll do this per module)
```
### 3. Get API Keys
You'll need an OpenAI API key for all modules:
1. Go to [OpenAI Platform](https://platform.openai.com/api-keys)
2. Create a new API key
3. Keep it secure - you'll add it to each module's `.env` file
### 4. Start with Module 1
```bash
cd 1-toy-example
pip install -r requirements.txt
# Follow the README instructions to set up your .env file
python coding_agent.py
```
## 📚 Learning Path
### For Beginners
Start with Module 1 and work through each module sequentially. Each module builds on the previous one.
### For Intermediate Developers
You can jump to specific modules based on your interests:
- **Multi-agent patterns** → Module 2
- **Monitoring** → Module 3
- **State management** → Module 4
- **Testing** → Module 5
### For Advanced Users
Focus on Modules 3-5 for production-ready patterns, or use the repository as a reference implementation.
## 🛠️ Development Environment
### Recommended Setup
- **IDE**: VS Code with Python extension
- **Python**: 3.8+ (3.10+ recommended)
- **Terminal**: Built-in terminal or iTerm2/Windows Terminal
- **Git**: Latest version
### Optional Tools
- **Docker**: For containerized development
- **Poetry**: For dependency management
- **Pre-commit**: For code quality checks
## 📖 Additional Resources
### Documentation
- [OpenAI API Documentation](https://platform.openai.com/docs)
- [Composio Documentation](https://docs.composio.dev/)
- [LangChain Documentation](https://docs.langchain.com/)
### Community & Support
- [Workshop Discussions](https://github.com/yourusername/ai-agents-workshop/discussions)
- [Report Issues](https://github.com/yourusername/ai-agents-workshop/issues)
- [Join Our Discord](https://discord.gg/your-discord-link)
### Further Learning
- [Production AI Systems](https://www.productionforcasting.com/)
- [Agent Design Patterns](https://github.com/microsoft/autogen)
- [AI Agent Papers](https://arxiv.org/list/cs.AI/recent)
## 🤝 Contributing
We welcome contributions! Here's how you can help:
1. **Report bugs** or suggest improvements
2. **Add new examples** or exercises
3. **Improve documentation**
4. **Share your agent implementations**
See [CONTRIBUTING.md](./CONTRIBUTING.md) for detailed guidelines.
## 📄 License
This workshop is licensed under the MIT License. See [LICENSE](./LICENSE) for details.
## 🙏 Acknowledgments
- Thanks to the OpenAI team for the excellent APIs and tools
- Thanks to Composio for the comprehensive tool integrations
- Thanks to the open-source community for inspiration and tools
---
**Ready to start building production-ready AI agents?**
[**🚀 Begin with Module 1: Toy Example →**](./1-toy-example/README.md)
---
*This workshop is designed for educational purposes. Please follow OpenAI's usage policies and best practices when building production systems.*