Awesome LLM Apps
100+ runnable AI agent and RAG apps you can clone, customize, and ship
About Awesome LLM Apps
Awesome LLM Apps is a curated collection of more than 100 AI agent and RAG applications you can actually run — not just links, but working code you clone, customize, and ship. Each app demonstrates a real pattern, from multi-agent teams to retrieval pipelines.
Why it is worth bookmarking
If you learn best by reading real implementations, this is one of the fastest ways to see how production-shaped agent and RAG apps are wired together. It spans models, frameworks, and use cases, so you can find a starting point close to whatever you are building.
- 100+ runnable agent and RAG apps with source code
- Covers multi-agent systems, RAG, and tool use
- A practical reference library for builders
Browse the collection on GitHub.
More Guides
An honest side-by-side of two coding agents from someone who actually used both
Pi Agent vs OpenCode after 100+ Hours of Real Use
A comparison written after real sustained use rather than a weekend trial, covering where each agent holds up and where each one falls over. The useful kind of tool comparison: specific about the failure modes, and willing to say when the answer is "it depends on what you are building".
Turn Codex from an interactive assistant into something you can script
Running Codex as a Headless Agent
A practical walkthrough of driving Codex headlessly, so it becomes a programmable step in a pipeline rather than a chat window you sit in front of. The shift from interactive to scripted is where coding agents stop being a productivity toy and start being infrastructure. This covers the mechanics of getting there.
Time to First Token
Time to First Token is a structured 10 week curriculum covering LLM inference serving and optimization, designed around 30 minutes of study a day. It walks through batching, KV caching, quantization, throughput versus latency tradeoffs and the serving stack choices that decide your infrastructure bill. Worth the hour a week the moment self-hosting or inference cost stops being a rounding error.
Standing Rules Instead of Permission Prompts
Ashley Childress spent four and a half months rebuilding how she works with AI coding agents, and the core move is a good one: stop treating permission prompts as your safety system. Clicking "approve" forty times per task is not oversight — it is fatigue theater. She replaced it with 134 standing rules that define what the agent may do up front. What the guide covers Why click-to-approve stops being a real guardrail once volume goes up. How to write standing rules that constrain behavior instead of interrupting it. Treating the agent like a direct report: context, expectations, and review at the right altitude. Nine concrete practice changes, each with the reasoning and evidence behind it. Why it is worth your time This is the rare "here is how I do things differently" post that shows its work. It is especially relevant now that agents are being handed more autonomy by default — the guardrails have to move from per-action prompts to written policy, and this is a working example of what that looks like. Read it on dev.to .
awesome-mcp-servers
awesome-mcp-servers is one of the most complete curated lists of Model Context Protocol servers — the growing catalog of tools you can plug into Claude, Cursor, and any MCP-capable agent to give it real capabilities. Browsing it is the fastest way to discover what your agent could be connected to: databases, browsers, file systems, APIs, and hundreds of niche integrations. A must-bookmark reference as MCP becomes the default way agents touch the outside world.