🎓 From prototype to production, as a method
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✨ Introduction
Agents Towards Production is your hands-on guide to every building block of a GenAI agent stack.
All knowledge is delivered through runnable tutorials covering orchestration, memory, observability, deployment, security, and more. Each tutorial lives in its own folder with ready-to-run notebooks or code files, so you can move from concept to working agent in minutes.
🏗️ AI Agent Architecture
📚 Tutorials
🔌 Tool Integration
📊 Data Processing
🔍 RAG & Knowledge Management
🧠 Memory
🚀 Deployment
👥 Multi-agent Coordination
| Tutorial | Description | View |
|---|---|---|
| Multi-Agent Communication with A2A Protocol | Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability. |
🚀 GPU Deployment
🔒 Security
👥 Multi-agent Coordination
| Tutorial | Description | View |
|---|---|---|
| Multi-Agent Communication with A2A Protocol | Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability. |
🧩 Agent Frameworks
| Tutorial | Description | View |
|---|---|---|
| Tool & API Integration via Model Context Protocol (MCP) | Integrate agents with external tools and APIs using a standardized protocol. Example: Seamless tool and API integration for advanced agent workflows. | |
| Stateful Agent Workflows with LangGraph | Design complex, stateful agent workflows using a directed graph architecture. Example: Multi-step text analysis pipeline with classification, entity extraction, and summarization. | |
| Deploying Agents as APIs with FastAPI | Create and deploy agents as performant APIs, supporting both synchronous and streaming endpoints. | |
| Building AI Agents in Kotlin with Koog | Build your first AI agent in Kotlin using JetBrains' Koog framework. Step-by-step from hello world to tool calling and structured output in under 30 minutes. |
🛠️ Model Customization
🔍 Tracing & Debugging
📊 Evaluation
🖥️ UI & Frontend
| Tutorial | Description | View |
|---|---|---|
| Building a Chatbot UI with Streamlit | Build a beginner-friendly chatbot web app with a chat interface, file upload, and session state for interactive agent demos. |
🚀 Getting Started
Transform your AI agent ideas into production-ready systems using our battle-tested patterns and implementations.
📖 Browse Online
Explore tutorials directly on GitHub to understand production-grade implementations, architectural decisions, and integration patterns. Each tutorial includes comprehensive documentation and code that you can study and adapt to your specific requirements without any local setup.
🛠️ Clone and Build
Download the repository to run tutorials locally, experiment with configurations, customize implementations, and integrate proven patterns directly into your agent development workflow.
Quick Setup
1. Get the Code
git clone https://github.com/NirDiamant/agents-towards-production.git
cd agents-towards-production2. Install Dependencies
Navigate to your target tutorial and set up the environment:
# Example: Multi-tool agent orchestration
cd tutorials/agentic-applications-by-xpander.ai
pip install -r meeting-recorder-agent/requirements.txt3. Deploy and Test
Launch tutorials through their preferred interface:
# Run interactive notebooks for experimentation
jupyter notebook tutorial.ipynb
# Execute production scripts for integration testing
python app.py📚 Recommended reading
This list contains Amazon affiliate links. As an Amazon Associate I earn from qualifying purchases. Every book below is one I've read and genuinely recommend to engineers working in this space. The companion book to this repo is featured separately at the top of this README.
- Build a Large Language Model (From Scratch) by Sebastian Raschka. Build a GPT-style model end to end in PyTorch.
- AI Engineering: Building Applications with Foundation Models by Chip Huyen. Canonical reference for productionizing foundation-model apps.
- Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst. Visual, practical LLM walkthroughs.
- Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf. From the Hugging Face team.
- Designing Machine Learning Systems by Chip Huyen. ML systems in production, still the standard reference.
📚 More from the same author
Prompt Engineering: Master the Art of AI Interaction - the prompting foundation, 22 hands-on techniques.
Run a course, newsletter, or dev community? You can earn 25% recommending RAG Made Simple to your audience.
🤝 Contributing
We welcome contributions of tools, infrastructure, and frameworks that support agent development. This includes monitoring, deployment platforms, security tools, databases, APIs, and other horizontal services that enable production agent systems.
Please see our Contributing Guidelines for more details.
⚠️ Disclaimer
Educational use only. Authors disclaim all responsibility for use, misuse, or consequences. We do not endorse, verify, or guarantee third-party companies, tools, or services referenced herein. Not liable for damages, losses, security breaches, or fraudulent activities by referenced parties.
Your responsibility: Conduct due diligence, verify legitimacy, test in isolation, ensure legal compliance. Security tools require ethical use with proper authorization.
By using this repository, you agree to this disclaimer.
📜 License
This project is licensed under a custom non-commercial license - see the LICENSE file for details.