agents-towards-production

AI Agents Made Simple reached #1 in Software Development, Software Engineering and Software Design Tools on Amazon.com's Kindle Store. A decision guide to building AI agents that work: 28 chapters, 148 illustrations, no code. Get the book on Amazon →

Agents Towards Production

The open-source playbook for turning AI agents into real-world products.

Agents Towards Production is your go‑to resource for building production‑ready GenAI agents that scale from prototype to enterprise. Tutorials cover stateful workflows, vector memory, real‑time web search APIs, Docker deployment, FastAPI endpoints, security guardrails, GPU scaling, browser automation, fine‑tuning, multi‑agent coordination, observability, evaluation, and UI development.

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Related repos: RAG Techniques · GenAI Agents · Prompt Engineering · Agent Memory Techniques

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AI Agents Made Simple - the decision guide to building agents that work: 28 chapters on what each pattern does, what breaks, what it costs, and when it is overkill. No code.

Get it on Amazon →

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28 production-grade tutorials covering stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, and more.

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I break these ideas down into short, one-idea-per-episode explainers on YouTube.

  Browse every episode →

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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.

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🏗️ AI Agent Architecture

This diagram shows the flow of building a production-level agent. The tutorials in this repository cover each of these components step-by-step.

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📚 Tutorials

🔌 Tool Integration

📊 Data Processing

🔍 RAG & Knowledge Management

🧠 Memory

🚀 Deployment

👥 Multi-agent Coordination

🚀 GPU Deployment

🔒 Security

🧩 Agent Frameworks

🛠️ Model Customization

🔍 Tracing & Debugging

📊 Evaluation

🖥️ UI & Frontend

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🚀 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-production

2. Install Dependencies Navigate to your target tutorial and set up the environment:

# Example: a chatbot UI with Streamlit
cd tutorials/agent-with-streamlit-ui
pip install -r requirements.txt

3. 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

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📚 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.

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📚 More from DiamantAI

AI Agents Made Simple - the companion book to this repo: when each agent pattern is worth it, what breaks, and what it costs.

Agent Memory Made Simple - the visual guide to memory for AI agents: 30 techniques, zero code required.

RAG Made Simple - go deeper on RAG (Amazon bestseller, ⭐ 4.6) · read Chapter 1 free.

Prompt Engineering: Master the Art of AI Interaction - the prompting foundation, 22 hands-on techniques.

Also from DiamantAI: RAG Techniques · GenAI Agents · Prompt Engineering · Agent Memory Techniques · Newsletter · YouTube · Discord · diamant-ai.com · Courses

To hear when the next book comes out, follow the author on Amazon.

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.

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⚠️ 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.

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📜 License

This project is licensed under a custom non-commercial license - see the LICENSE file for details.

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