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NirDiamant/agents-towards-production
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
About NirDiamant/agents-towards-production
NirDiamant/agents-towards-production is an open-source project on GitHub, mainly written in Jupyter Notebook. End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment. It currently holds 21,525 stars and 2,852 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).
Project Overview
AI Homed tracks it on the Today's Trending board, currently at rank #86 with 3 new stars today.
GitHub Repository Details
README
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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🎓 From prototype to production, as a method
Prompt to Production - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 17 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 it on your own repo, free
Your coding agent starts every session knowing nothing about your project, so it guesses. Paste one line into the agent you already have open, and about fifteen minutes later your repository has a docs layer written from the code itself, plus a card scoring what your agent knew before and after.
We ran it on six repositories you already depend on. Each one was asked five questions about itself, cold, then again after the layer was written. The last column counts statements in that project's own documentation that its own code disproves:
| repo | before | after | own docs its code disproves | |---|---|---|---| | fastapi | 2 of 5 | 5 of 5 | 2 | | flask | 3 of 5 | 5 of 5 | 6 | | django | 3 of 5 | 4 of 5 | 1 | | express | 2 of 5 | 4 of 5 | 4 | | requests | 2 of 5 | 4 of 5 | 0 | | langchain | 5 of 5 | 4 of 5 | 5 |
Flask's six include four documentation examples that raise TypeError when you run them. Langchain scored lower afterwards, because it already ships a 380-line agent instruction file and the cold read was grading theirs; that row is in the table anyway.
Clone any of those repos, paste the same line, and check the number yourself. No signup.
👉 Get the full course
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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.
💎 Tutorial Sponsors
Companies that have contributed step-by-step tutorials to this repository.
Click a logo to open the tutorial. Use Ctrl‑/⌘‑click to keep this page open.
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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Durable Execution
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💎 General Sponsors
Companies that support this project through partnerships and resources.
Click a logo to visit their website.
AI Code Review
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💎 Become a Sponsor
Get in touch:
📫 Stay Updated!
| 🚀 Cutting-edge Updates |
💡 Expert Insights |
🎯 Top 0.1%Content |
_Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials!_ _Plus, subscribers get exclusive early access and special 33% discounts to my book and upcoming courses!_
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🎬 Prefer video?
I break these ideas down into short, one-idea-per-episode explainers on YouTube.
AI Agents Are Just While Loops. That's the Scary Part. the whole agent is a text file re-read in a loop, and it can trap itself |
Context Is the New Code the shift from writing the code to shaping what the model sees |
Stop Thinking Claude Code Is Magic. Here's How It Works what the agent loop is actually doing on every turn |
How LLMs Actually Work (and Why AI Makes Things Up) recalling a fact and inventing one are literally the same move |
💬 Join Our Community
Stay connected with the latest in GenAI and agent development:
r/EducationalAI
_Join our growing community discussing cutting-edge AI research, agent development, and production insights!_
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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.
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📚 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 |
|---|---|---|
| Agent File Conversion, Locally and Privately (hushvert) |
Give agents file-conversion abilities with a two-lane setup: an open-source WebAssembly engine converts images, HEIC, audio, and archives on the user's device, while an MCP tool call handles office documents and PDF-to-markdown. Includes an honest fidelity benchmark against popular extractors. |
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| Web Data Collection for AI Agents (Bright Data) |
Build agents that collect and process web data at scale using enterprise-grade scraping infras
GitHub Stars & Activity21,525Stars 2,852Forks 0Open issues Jupyter NotebookLanguage GitHub PopularityGitHub stars21,525 Forks2,852 Open issues0 Primary languageJupyter Notebook License- Stars gained today3 Created- Last pushed- Trending HistoryDaily boardrank #86 · ▲ 3 stars Related AI Projects→2  →3  →4  →5  →6  →7  →8  →More AI Rankings |