#1616th of 28 in Learning and cookbooks
agents-towards-production
Jupyter tutorials for taking LLM agents from prototype to production
- Stars
- 21.5k
- License
- custom license
- Last commit
- Sep 2026
- Language
- Jupyter Notebook
Missed the latest GitHub check; these facts may be older.
Overview
A collection of 28 tutorial notebooks on production concerns for GenAI agents: stateful workflows with LangGraph, memory (Redis, Mem0), web search APIs, FastAPI and Docker deployment, guardrails, GPU deployment, observability and evaluation. Many tutorials are contributed by vendors the README lists as sponsors. The README also promotes the author's paid course and newsletter.
Who it is for: Engineers learning to deploy and operate LLM agents in production
Strengths
- 28 tutorials covering memory, deployment, guardrails, GPU scaling, evaluation and UI
- Spans several stacks: LangGraph, Redis, Mem0, Arcade, Inngest, and Kotlin with JetBrains Koog
- Each topic has its own tutorials/ folder, so one can be read without the rest
Weaknesses
- Many tutorials are sponsor-contributed and built around the sponsor's hosted product
- Several tutorials need external services such as Tavily, Bright Data and RunPod
- README leads with course and newsletter promotion before the tutorial list
- License is not identified (NOASSERTION); no releases or versioning
What it needs
- GPU optional
- Needs LangGraph, Redis, Mem0, Tavily, Bright Data, Contextual AI, Arcade, RunPod, Inngest
Also in Learning and cookbooks
See all 28| Rank | Project | Score |
|---|---|---|
| 1 | ai-engineering-from-scratchHands-on AI engineering curriculum with 542 lessons across 20 phases | 86 out of 100 |
| 2 | generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript | 81 out of 100 |
| 3 | hello-agentsChinese-language course on building LLM agents from scratch | 77 out of 100 |
| #4 | ai-agents-for-beginners18-lesson course on building AI agents with Microsoft Agent Framework | 77 out of 100 |
| #5 | llms-from-scratchCode for the book on building a GPT-style LLM in PyTorch | 76 out of 100 |