NirDiamant / agents-towards-production
This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.
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Repository Summary (README)
PreviewAgents 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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💎 Tutorial Sponsors
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Agent Framework & Workflows 
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Memory & Vector Database
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RAG & Knowledge Management
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Web Data Platform
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Real‑time Web Search API
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MCP Runtime
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Kotlin AI Agent Framework
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Self-Improving AI Memory
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GPU Cloud Computing
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💎 General Sponsors
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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
This diagram shows the flow of building a production-level agent. The tutorials in this repository cover each of these components step-by-step.
📚 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. |
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📊 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. |
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| 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. |
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🔍 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. |
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🧠 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. |
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| 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. |
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| 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. |
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🚀 Deployment
| Tutorial | Description | View |
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| 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. |
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| Containerizing Agents with Docker | Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments. |
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| 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. |
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👥 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. |
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🚀 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. |
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🔒 Security
| Tutorial | Description | View |
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| Comprehensive Agent Security (LlamaFirewall) | Apply comprehensive input, output, and tool security guardrails for agents. Covers prompt injection, behavior alignment, and tool access control. |
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| Hands-On Agent Security Evaluation (Apex) | Hands-on prompt injection attacks, defenses, and automated security testing for AI agents. |
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👥 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. |
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🧩 Agent Frameworks
| Tutorial | Description | View |
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| 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. |
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| 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. |
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| Deploying Agents as APIs with FastAPI | Create and deploy agents as performant APIs, supporting both synchronous and streaming endpoints. |
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| 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. |
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🛠️ 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. |
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🔍 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. |
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📊 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. |
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🖥️ 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. |
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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: Multi-tool agent orchestration
cd tutorials/agentic-applications-by-xpander.ai
pip install -r meeting-recorder-agent/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
🤝 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.
⭐️ If you find this repository helpful, please consider giving it a star!
Keywords: AI Agents, Production Deployment, LLM, Orchestration, Multi-agent Systems, Memory Systems, Monitoring, Security, Observability, Agent Frameworks, Infrastructure, Serverless, Enterprise AI, Tool Integration