ai-engineering-hub vs awesome-llm-apps
36k-star hub of runnable AI engineering tutorials — LLMs, RAG and agent apps as self-contained projects, including build-code-harness: a Claude-Code-style coding harness rebuilt on CrewAI + E2B. — versus — 141k-star collection of 100+ runnable AI agent, agent-skill and RAG app templates — clone one, pip install, run — across Claude, Gemini, GPT and open models. Apache-2.0.
Both are giant hubs of self-contained, runnable LLM/RAG/agent projects to clone and learn from; similar scope, different curation and stacks.
| ai-engineering-hub | awesome-llm-apps | |
|---|---|---|
| Stars | 38k | 141k |
| Forks | 6.3k | 21k |
| Language | Jupyter Notebook | Python |
| License | MIT | Apache-2.0 |
| Last activity | 1 months ago | 11 days ago |
| Topics | coding | agents, rag |
| Curated connections | 4 | 2 |
ai-engineering-hub — the curator's take
A tutorial hub, not a tool — each folder is a complete, runnable project with real dependencies. The standout is build-code-harness: it rebuilds a coding-agent harness (planning, memory, checkpoints, sandbox, human gate) one layer at a time on CrewAI + E2B, the best way to understand what harnesses like Claude Code actually do. Content repo caveat: quality varies by folder, and there's a newsletter funnel attached.
awesome-llm-apps — the curator's take
The fastest way to see a working agent or RAG pattern end to end and fork it: travel agents, research teams, voice agents, MCP apps, installable skills. Treat each folder as a starting point, not a dependency — most are single-file Streamlit demos with no shared library, no versioning and no production hardening. Want the patterns themselves as a reusable library with tests? all-agentic-architectures is the better shelf.