Awesome AI Apps is a collection of 132 projects, tutorials, and recipes for building applications powered by large language models. Developers use it to explore text and voice agents, retrieval-augmented generation, workflows, MCP tools, memory, and fine-tuning.
Borrowing it
Nothing to install: this file belongs to Arindam200/awesome-ai-apps. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Arindam200/awesome-ai-apps/main/CLAUDE.mdgit clone --depth 1 https://github.com/Arindam200/awesome-ai-appsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/arindam200/awesome-ai-apps/claude-md)<a href="https://agentmods.dev/instructions/arindam200/awesome-ai-apps/claude-md"><img src="https://agentmods.dev/badge/instructions/arindam200/awesome-ai-apps/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/arindam200/awesome-ai-apps/claude-md"><img src="https://agentmods.dev/badge/instructions/arindam200/awesome-ai-apps/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01687 | $0.01687 |
| Opus 5 | $0.00843 | $0.00843 |
| Sonnet 5 | $0.00337 | $0.00337 |
| Haiku 4.5 | $0.00169 | $0.00169 |
Grade A, and why
awesome-ai-apps CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to awesome-ai-apps AGENTS.md — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Repository Overview
This is a comprehensive collection of practical LLM-powered application examples, tutorials, and recipes organized by complexity and use case. The repository contains 70+ example projects demonstrating various AI frameworks and patterns.
Project Categories
Projects are organized into six main categories:
- starter_ai_agents/ - Quick-start boilerplate examples for learning different AI frameworks (Agno, OpenAI SDK, LlamaIndex, CrewAI, PydanticAI, LangChain, AWS Strands, Camel AI, DSPy, Google ADK)
- simple_ai_agents/ - Straightforward, single-purpose agents (finance tracking, web automation, newsletter generation, calendar scheduling, etc.)
- mcp_ai_agents/ - Projects using Model Context Protocol for semantic RAG, database interactions, and external tool integrations
- memory_agents/ - Agents with persistent memory capabilities using frameworks like GibsonAI Memori
- rag_apps/ - Retrieval-Augmented Generation examples with vector databases and document processing
- advance_ai_agents/ - Complex multi-agent workflows and production-ready applications (research agents, job finders, meeting assistants, etc.)
- course/ - Structured learning materials, including the complete AWS Strands course (8 lessons)
Common Development Commands
Running Individual Projects
Each project is self-contained with its own dependencies. Navigate to the specific project directory first:
cd <category>/<project_name>
Installing Dependencies
Projects use either requirements.txt or pyproject.toml:
# For requirements.txt projects
pip install -r requirements.txt
# For pyproject.toml projects (newer projects)
pip install -e .
# or with uv (preferred for faster installs)
uv pip install -e .
Running Projects
Most projects use simple Python execution:
python main.py
# or
python app.py
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 187 lines · 1,687 tokens per session scan A 912cee1c7719
awesome-ai-apps CLAUDE.md is an instructions file published in the GitHub repository Arindam200/awesome-ai-apps (14,056 stars, last pushed yesterday), licensed MIT. It adds 1,687 tokens to every session, about $0.0084 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to awesome-ai-apps AGENTS.md, differing in 4 lines, and is treated as a copy.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.