Development · GitHub repo
awesome-llm-apps
Open-source collection of runnable AI agent, skill, and RAG templates
awesome-llm-apps is a curated repository of tested, end-to-end Python applications covering AI agents, agent skills, and retrieval-augmented generation architectures. It provides practical boilerplate and step-by-step implementations across multimodal agents, multi-agent swarms, voice assistants, and local LLMs. It is designed for developers who want working reference code to inspect, adapt, and run locally or in production.
What it's for
- Spin up reference architectures for multi-agent workflows and task routing
- Implement agentic RAG or multimodal search using local and hosted models
- Install modular capabilities into coding assistants like Cursor and Claude Code
- Build voice-driven or generative UI prototypes from tested boilerplate
Features
- Over 100 runnable Python templates for agents, skills, and RAG systems
- Support for hosted models (Claude, Gemini, GPT) and local models (Llama, DeepSeek)
- Modular agent skills installable via CLI command (`npx skills add`)
- Examples of voice agents, generative UI frontends, and Model Context Protocol (MCP) integrations
- Autonomous game-playing, memory layers, and context-optimization scripts
- Permissive Apache-2.0 license allowing commercial reuse and modification
Pricing
Open source. Free and open source under Apache-2.0
Details
- Type
- GitHub repo
- Category
- Development
- Topic
- AI Boilerplates
- Open source
- Yes
- Platforms
- Linux, macOS, Windows, Command line
- For
- Developers and AI engineers looking for runnable templates to build agentic and RAG applications.
- Tags
- ai agents, rag, llm, python, open source, templates
Why it was saved
- @Saboo_Shubham_: The repo where the tweet's code lives.
- @Saboo_Shubham_: Broader resource of runnable LLM app examples mentioned in the same thread.
- @Saboo_Shubham_: Free, maintained collection of runnable agent examples — customer support, investment, legal, recruitment, finance, teaching, travel agents, multi-agent teams, MCP agents, and RAG patterns — useful as reference implementations for building and selling AI products.
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