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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Agent Framework & Workflows

Memory & Vector Database

RAG & Knowledge Management

Web Data Platform

Real‑time Web Search API

MCP Runtime

Kotlin AI Agent Framework

Self-Improving AI Memory

GPU Cloud Computing

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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.

📑 Related deep-dive write-ups

Free long-form guides on DiamantAI that complement these production tutorials:

• Why AI agents need to check their own work
• This simple trick makes AI agents far more reliable
• Memory optimization strategies in AI agents
• Agent Memory Techniques — 30 runnable notebooks on giving production agents memory: buffers, vector stores, knowledge graphs, Mem0, MemGPT/Letta, Zep, and Graphiti
• Browse all 130+ tutorials → · Read the blog →

🏗️ AI Agent Architecture

📚 Tutorials

🔌 Tool Integration

Tutorial	Description	View
Secure Tool Calling (Arcade)	Enable agents to securely call external tools (Gmail, Slack, Notion) with OAuth2 authentication and human-in-the-loop safety controls. Learn production-ready tool integration with user isolation and approval workflows.

📊 Data Processing

Tutorial	Description	View
Web Data Collection for AI Agents (Bright Data)	Build agents that collect and process web data at scale using enterprise-grade scraping infrastructure. Learn to integrate proxy networks, handle CAPTCHAs, and extract structured data from complex websites.
Real-Time Web Data Integration for Agents (Tavily)	Enable agents to access, search, and extract real-time web data. Build workflows that combine live web information with private knowledge for research, monitoring, and up-to-date recommendations.

🔍 RAG & Knowledge Management

Tutorial	Description	View
Production-Ready RAG Agents with Contextual AI (Contextual AI)	Build enterprise-grade RAG systems in 15 minutes using Contextual AI's managed platform. Learn document processing, intelligent indexing, agent deployment, and automated evaluation with LMUnit testing framework for financial document analysis.

🧠 Memory

Tutorial	Description	View
Agent Memory: Dual-Memory & Semantic Search (Redis)	Implement dual-memory (short-term and long-term), semantic search, and persistent state for agents that remember user preferences and learn from conversations.
Self-Improving Memory with Mem0: Hybrid Vector & Graph Storage	Build intelligent agents with self-improving memory that automatically extracts insights, resolves conflicts, and evolves with each interaction. Learn hybrid memory architecture combining vector search for semantic recall and graph databases for relationship mapping.
AI Memory with Cognee	Build intelligent AI memory systems that learn from Python's creator and improve your development workflow. Transform scattered development data into unified knowledge graphs with contextual insights.

🚀 Deployment

Tutorial	Description	View
AWS Bedrock AgentCore: Managed Agent Deployment	Deploy and manage AI agents on AWS Bedrock AgentCore Runtime. Learn to transform local agents into production-ready managed services with automatic infrastructure, request tracking, and standardized communication patterns.
Containerizing Agents with Docker	Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments.
On-Prem LLM Deployment with Ollama	Run and interact with large language models locally. Replace cloud APIs with on-prem models for privacy, cost control, and low-latency agent workflows.

👥 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

Tutorial	Description	View
Scalable GPU Deployment for AI Agents (Runpod)	Deploy AI agents on scalable GPU infrastructure. Learn to set up cost-effective, high-performance environments for demanding agent workloads.

🔒 Security

Tutorial	Description	View
Comprehensive Agent Security (LlamaFirewall)	Apply comprehensive input, output, and tool security guardrails for agents. Covers prompt injection, behavior alignment, and tool access control.
Hands-On Agent Security Evaluation (Apex)	Hands-on prompt injection attacks, defenses, and automated security testing for AI agents.

👥 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

Tutorial	Description	View
Fine-Tuning AI Agents for Domain Expertise & Efficiency	Learn how to fine-tune language models for specialized agent behavior, domain expertise, and efficient, cost-effective responses. Covers data preparation, training, evaluation, and integration into agent workflows.

🔍 Tracing & Debugging

Tutorial	Description	View
Agent Tracing & Debugging with LangSmith	Add comprehensive observability to AI systems. Capture detailed traces, decision points, and timing data to debug, monitor, and systematically improve agent performance.

📊 Evaluation

Tutorial	Description	View
Automated Agent Evaluation & Behavioral Analysis (IntellAgent)	Automate agent evaluation with behavioral analysis, performance metrics, and actionable insights for improving agent quality.

🖥️ 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.

git clone https://github.com/NirDiamant/agents-towards-production.git
cd agents-towards-production

# Example: Multi-tool agent orchestration
cd tutorials/agentic-applications-by-xpander.ai
pip install -r meeting-recorder-agent/requirements.txt

# 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.

🤝 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.