GenAI Agents to Production

GenAI Agents to Production is an open-source repository created by Nir Diamant that serves as a comprehensive playbook for building production-ready generative AI agents. It contains 28 production-grade tutorials covering everything from stateful workflows and vector memory to Docker deployment, security guardrails, GPU scaling, and multi-agent coordination.

The project is designed to bridge the gap between prototype and production, providing developers with practical, tested approaches for deploying GenAI agents in real-world applications. It is hosted on GitHub at github.com/NirDiamant/agents-towards-production and has become one of the most popular resources for production AI agent development.

The repository contains 28 hands-on tutorials organized by topic, each with complete code and documentation:

Category	Topics Covered
Agent Foundations	Stateful workflows, prompt engineering, structured outputs, tool calling
RAG & Memory	Vector databases, agent memory with Redis, contextual RAG, Mem0
Multi-Agent	LangGraph agent orchestration, multi-agent coordination, agent teams
Security	Guardrails, secure tool calling with Arcade, input validation
Infrastructure	Docker deployment, GPU scaling with RunPod, FastAPI endpoints
Observability	Agent monitoring, evaluation frameworks, tracing, logging

A significant portion of the playbook focuses on Retrieval-Augmented Generation (RAG) and memory systems, which are essential for production agents that need to access and retain information. Tutorials cover vector databases, hybrid search, agent memory with Redis, contextual RAG with Contextual AI, and self-improving memory with Mem0.

The RAG tutorials progress from basic vector search implementations to advanced techniques like metadata filtering, hybrid search, re-ranking, and query transformation. The memory tutorials cover both short-term conversation memory and long-term persistent memory for agent state management.

The playbook includes comprehensive coverage of multi-agent orchestration patterns using LangGraph. Tutorials demonstrate how to build agent teams with specialized roles, implement supervisor agents for task delegation, create agent communication protocols, and handle complex workflows that require multiple agents collaborating.

• LangGraph Agent — Build stateful, graph-based agent workflows with conditional branching and human-in-the-loop
• Agent Teams — Coordinate multiple specialized agents that work together on complex tasks
• Supervisor Pattern — Implement hierarchical agent control with task planning and delegation
• Tool Sharing — Design shared tool ecosystems that multiple agents can access securely

Moving from prototype to production requires robust deployment infrastructure. The playbook covers Docker containerization, FastAPI endpoint creation, GPU-accelerated inference with RunPod, and horizontal scaling strategies for agent workloads.

Each deployment tutorial includes complete Dockerfiles, Kubernetes configurations, and infrastructure-as-code templates that can be adapted for cloud providers including AWS, GCP, and Azure. The scaling tutorials cover both model inference scaling and agent worker scaling patterns.

Production agents require comprehensive monitoring and evaluation. The playbook covers logging, tracing, performance metrics, and agent behavior evaluation. Tutorials demonstrate how to set up dashboards, track agent decisions, measure response quality, and detect regressions in agent performance over time.

• Evaluation Frameworks — Systematic testing of agent outputs against expected results
• Tracing — End-to-end traceability of agent calls through LLM, tools, and memory
• Performance Monitoring — Track latency, token usage, error rates, and cost metrics
• Regression Detection — Automated testing pipelines that catch agent behavior changes

The project has a growing community and sponsorship from leading AI infrastructure companies:

• GitHub — Repository and discussion at github.com/NirDiamant/agents-towards-production
• Sponsors — Including LangChain, Redis, Contextual AI, Bright Data, Tavily, Arcade, and JetBrains
• Discord Community — Active discussions and support for production AI agent development
• LinkedIn — Follow Nir Diamant for updates and AI agent best practices