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Master the art of building AI agents that can interact with the real world through tools, APIs, and external systems.
This repository is a comprehensive educational resource for developers, AI enthusiasts, and engineers who want to learn how to build AI agents with tool-calling capabilities. We cover both emerging protocols (UTCP & MCP) and general best practices for creating agents that can interact with external systems, APIs, databases, and command-line tools.
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Large Language Models (LLMs) are powerful but limited—they can't:
- Access real-time information
- Execute actions in the real world
- Query databases or call APIs
- Run calculations or system commands
Tool-calling solves this.[1] It enables AI agents to extend their capabilities by invoking external tools, turning static models into dynamic, interactive agents that can truly help users accomplish tasks.
This repository takes you from zero to building production-ready AI agents:
- ✅ Fundamentals: What tool-calling is and why it's essential for modern AI agents
- ✅ Protocols: Deep dives into UTCP (Universal Tool Calling Protocol) and MCP (Model Context Protocol)
- ✅ Architecture Patterns: Reactive agents, Planner-Executor, Multi-Agent systems
- ✅ Hands-on Examples: Working code in Python, TypeScript, and more
- ✅ Real-World Projects: Data analyst bot, DevOps assistant, customer support agent
- ✅ Security & Reliability: Best practices for safe, production-ready agents
- ✅ Design Patterns: Proven patterns and anti-patterns from real implementations
- ✅ Interview Prep: Questions, scenarios, and design challenges
- Python 3.10+ (recommended)
- Basic understanding of APIs and LLMs
- OpenAI API key (for running examples) or local LLM setup
- Start with Introduction to Tool-Calling
- Run the Basic Python Example
- Learn Agent Architectures
- Build your first Simple Project
- Deep dive into MCP Protocol
- Deep dive into UTCP Protocol
- Explore Design Patterns
- Build the Data Analyst Bot
- Study Security & Reliability
- Learn Multi-Agent Systems
- Review Anti-Patterns
- Build the DevOps Copilot
- Tackle Interview Challenges
| Architecture | Direct, stateless | Client-server, stateful |
| Setup Complexity | Low (JSON manual) | Medium (server process) |
| Latency | Lower (direct calls) | Higher (proxy hop) |
| Security Model | Native API security (reduced attack surface) | Centralized control (increased attack surface) |
| Attack Surface | Minimal (no intermediary) | Higher (additional infrastructure) |
| Best For | Quick integrations, performance, most use cases | Specific compliance requirements, tools without existing security |
Security Note: Recent security analyses show UTCP generally offers better security due to reduced attack surface and use of battle-tested native security mechanisms. See our Security Comparison for details.
- 🐍 Python: LangChain, bare-metal, AutoGen
- 📘 TypeScript: Node.js agents, browser-based
- 🦀 Rust: (coming soon)
- 🎯 Go: (coming soon)
We welcome contributions! Whether it's:
- 📝 Improving documentation
- 💻 Adding new examples
- 🐛 Fixing bugs
- 🌟 Sharing your own tool integrations
Please see CONTRIBUTING.md for guidelines.
- What is Tool-Calling? - Start here if you're new
- UTCP vs MCP: When to Use Which - Understand the differences
- Security Comparison: MCP vs UTCP - Deep dive into security models
- Security Best Practices - Build safe agents
- Design Patterns - Learn from proven approaches
- Interview Questions - Prepare for AI agent roles
We believe in learning through visuals. This repository includes:
- 📊 Architecture diagrams for every major concept
- 🎨 Flowcharts for agent decision-making processes
- 📈 Comparison charts for protocols and patterns
- 🖼️ Code visualization and execution traces
- 💬 Discussions: Use GitHub Discussions for questions and ideas
- 🐛 Issues: Report bugs or request features
- 📧 Contact: [Your contact info]
- 🐦 Updates: Follow development updates [link]
This repository is designed for:
- Software Engineers building AI-powered applications
- AI/ML Engineers integrating LLMs with existing systems
- Students learning about agentic AI systems
- Researchers exploring agent architectures
- Technical Leaders evaluating tool-calling standards
This project is licensed under the MIT License - see the LICENSE file for details.
This educational resource is informed by:
- The UTCP open-source community
- Anthropic's MCP specification and reference implementations
- Research from leading AI labs
- Contributions from the open-source community
Not Official: This is not an official specification repository for UTCP or MCP. For official specs, visit:
- Core documentation and examples
- Python implementations
- TypeScript/JavaScript examples
- Advanced multi-agent tutorials
- Video tutorials and walkthroughs
- GUI demo applications
- More language bindings (Rust, Go, Java)
- Performance benchmarking suite
- Integration with popular frameworks
If this project helped you, please consider giving it a star! It motivates us to create more educational content.
Built with ❤️ by the community, for the community.
Let's build the future of AI agents together! 🚀
