End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Agents Towards Production is a collection of 28 Jupyter Notebook tutorials teaching developers how to build and ship GenAI agents in real-world environments. The tutorials cover LangGraph stateful workflows, Redis-backed vector memory, real-time web search API integration, Docker and FastAPI deployment, security guardrails, GPU scaling, browser automation, fine-tuning, multi-agent coordination, LLM observability, evaluation pipelines, and UI development. Claude and other LLMs connect through standard APIs and MCP, with several tutorials addressing MCP-based agent tooling directly. Sponsor-contributed notebooks from LangChain, Redis, and Contextual AI provide worked examples that go beyond toy demos, including a dedicated RAG tutorial using Contextual AI's retrieval stack. The repository targets ML engineers and backend developers who need to move agents past the prototype stage into containerized, monitored, production deployments. A notable detail is that each tutorial is code-first and self-contained inside a notebook, making the progression from local experimentation to enterprise infrastructure traceable step by step.
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
- ✓Mature repo (>1y old)
- ✓Documented (README)
- !Licence file present but not machine-readable
git clone https://github.com/NirDiamant/agents-towards-production && cp agents-towards-production/*.md ~/.claude/agents/Resumen de Subagents
<div align="center">
# 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.
### ⭐ **If you find value in this project, PLEASE STAR IT to help others discover these tutorials!**
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---
<h2 align="center">🎓 From prototype to production, as a method</h2>
<div align="center">
**[Prompt to Production](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=agents-towards-production--readme&click=course-free-module-cta&target=https%3A%2F%2Fwww.diamant-ai.com%2Fcourses%3Futm_source%3Dgithub%26utm_medium%3Dreadme%26utm_campaign%3Dagents-towards-production&retarget=0&text=course-free-module-cta)** - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 20 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system.
**The course is live.** Every module is out, lecture and lab.
### 🎁 Try a full module, free
<table>
<tr>
<td align="center">🎬<br><b>7-minute<br>video lecture</b></td>
<td align="center">🛠️<br><b>Hands-on<br>tutorial</b></td>
<td align="center">🤖<br><b>AI assistant<br>inside Claude Code</b></td>
</tr>
</table>
One `npm install` adds the module's AI assistant to your Claude Code, and it guides you through the tutorial as you build.
<a href="https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=agents-towards-production--readme&click=course-free-module-cta&target=https%3A%2F%2Fwww.diamant-ai.com%2Fcourses%3Futm_source%3Dgithub%26utm_medium%3Dreadme%26utm_campaign%3Dagents-towards-production&retarget=0&text=course-free-module-cta"><img src="images/free-module-button.svg" alt="Claim your free module" width="420"></a>
### 👉 [**Get the full course**](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=agents-towards-production--readme&click=course-full-cta&target=https%3A%2F%2Fwww.diamant-ai.com%2Fcourses%3Futm_source%3Dgithub%26utm_medium%3Dreadme%26utm_campaign%3Dagents-towards-production&retarget=0&text=course-full-cta)
</div>
---
<div align="center">
> **28 production-grade tutorials** covering stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, and more.
## 💎 Tutorial Sponsors
<p align="center"><em>
Companies that have contributed step-by-step tutorials to this repository.<br>
Click a logo to open the tutorial. Use Ctrl‑/⌘‑click to keep this page open.
</em></p>
<!-- ─────────── 1st row – 4 sponsors ─────────── -->
<table align="center" cellpadding="20"
style="table-layout:fixed; width:100%; border-collapse:collapse;">
<tr align="center" valign="top">
<!-- LangChain -->
<td width="200" valign="bottom">
<a href="tutorials/LangGraph-agent" title="Open LangChain tutorial">
<picture>
<source media="(prefers-color-scheme: dark)"
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_langchain_white.png">
<img src="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_langchain.png"
height="44" style="max-width:180px;" alt="LangChain - AI agent framework and workflow orchestration platform for building production-ready language model applications">
</picture>
</a><br>
<sub><span style="white-space:nowrap;">Agent Framework & Workflows</span><br>
<a href="https://langchain.com">
<img src="assets/repos_images/visit-site-badge.svg" width="56" height="16" alt="Visit LangChain AI agent framework website">
</a>
</sub>
</td>
<!-- Redis -->
<td width="200" valign="bottom">
<a href="tutorials/agent-memory-with-redis" title="Open Redis tutorial">
<picture>
<source media="(prefers-color-scheme: dark)"
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_Redis_white.svg">
<img src="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_Redis.png"
height="44" style="max-width:180px;" alt="Redis - In-memory database and vector storage for AI agent memory, caching, and real-time data processing">
</picture>
</a><br>
<sub><span style="white-space:nowrap;">Memory & Vector Database</span><br>
<a href="https://redis.io/try-free/?utm_source=nir&utm_medium=cpa&utm_campaign=2025-05-ai_in_production-influencer-nir&utm_content=sd-software_download-7013z000001WaRY">
<img src="assets/repos_images/visit-site-badge.svg" width="56" height="16" alt="Visit Redis in-memory database and vector storage website">
</a>
</sub>
</td>
<!-- Contextual AI -->
<td width="200" valign="bottom">
<a href="tutorials/agent-RAG-with-Contextual" title="Open Contextual AI tutorial">
<picture>
<source media="(prefers-color-scheme: dark)"
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_contextual_white.png">
<img src="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_contextual_black.png"
height="44" style="max-width:180px;" alt="Contextual AI - Production-ready RAG platform for building enterprise-grade retrieval augmented generation systems">
</picture>
</a><br>
<sub><span style="white-space:nowrap;">RAG & Knowledge Management</span><br>
<a href="https://app.contextual.ai/?utm_campaign=agents-towards-production&utm_source=diamantai&utm_medium=github&utm_content=notebook">
<img src="assets/repos_images/visit-site-badge.svg" width="56" height="16" alt="Visit Contextual AI RAG platform website">
</a>
</sub>
</td>
<!-- Bright Data -->
<td width="200" valign="bottom">
<a href="tutorials/agent-with-brightdata" title="Open Bright Data tutorial">
<picture>
<source media="(prefers-color-scheme: dark)"
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_brightdata_white.svg">
<img src="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_brightdata.png"
height="44" style="max-width:180px;" alt="Bright Data - Web scraping and data collection platform for AI training and agent data gathering">
</picture>
</a><br>
<sub><span style="white-space:nowrap;">Web Data Platform</span><br>
<a href="https://brightdata.com/ai?utm_source=brand&utm_campaign=brnd-mkt_github_nirdiamant_logo">
<img src="assets/repos_images/visit-site-badge.svg" width="56" height="16" alt="Visit Bright Data web scraping platform website">
</a>
</sub>
</td>
</tr>
</table>
<!-- ─────────── 2nd row – 3 sponsors ─────────── -->
<table align="center" cellpadding="20"
style="table-layout:fixed; width:100%; margin-top:16px; border-collapse:collapse;">
<tr align="center" valign="top">
<!-- Tavily -->
<td width="200" valign="bottom">
<a href="tutorials/agent-with-tavily-web-access" title="Open Tavily tutorial">
<picture>
<source media="(prefers-color-scheme: dark)"
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_tavily_white.png">
<img src="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_tavily.png"
height="44" style="max-width:180px;" alt="Tavily - Real-time web search API for AI agents with intelligent content extraction and summarization">
</picture>
</a><br>
<sub><span style="white-space:nowrap;">Real‑time Web Search API</span><br>
<a href="https://app.tavily.com/home/?utm_source=github&utm_medium=referral&utm_campaign=nir_diamant">
<img src="assets/repos_images/visit-site-badge.svg" width="56" height="16" alt="Visit Tavily real-time web search API website">
</a>
</sub>
</td>
<!-- Arcade -->
<td width="200" valign="bottom">
<a href="tutorials/arcade-secure-tool-calling" title="Open Arcade tutorial">
<picture>
<source media="(prefers-color-scheme: dark)"
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_arcade_white_tight.png">
<img src="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_arcade_black.png"
height="44" style="max-width:180px;" alt="Arcade - Multi-user tool calling platform for secure OAuth2 authentication and human-in-the-loop safety controls">
</picture>
</a><br>
<sub><span style="white-space:nowrap;">MCP Runtime</span><br>
<a href="https://docs.arcade.dev/en/home?utm_source=github&utm_medium=paid_sponsorship&utm_campaign=agents_towardLo que la gente pregunta sobre agents-towards-production
¿Qué es NirDiamant/agents-towards-production?
+
NirDiamant/agents-towards-production es subagents para el ecosistema de Claude AI. End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment. Tiene 21.4k estrellas en GitHub y su última actualización registrada es del 2026-09-06.
¿Cómo se instala agents-towards-production?
+
Puedes instalar agents-towards-production clonando el repositorio (https://github.com/NirDiamant/agents-towards-production) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
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Nuestro agente de seguridad ha analizado NirDiamant/agents-towards-production y le ha asignado un Trust Score de 95/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene NirDiamant/agents-towards-production?
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NirDiamant/agents-towards-production es mantenido por NirDiamant. La última actividad registrada en GitHub es del 2026-09-06, con 4 issues abiertos.
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Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
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