Awesome LLM Apps: 90+ Ready-to-Run AI Agent Projects You Can Learn From

TL;DR

awesome-llm-apps is a GitHub repo with 92,000+ stars containing 90+ working LLM applications you can clone and run. It covers everything from simple chatbots to complex multi-agent teams, voice agents, RAG implementations, and MCP integrations. Each project comes with code, requirements, and instructions. If you want to build AI agents, this is your cookbook.

Why it matters: Instead of piecing together tutorials from blog posts, you get battle-tested implementations across every major LLM pattern. Want to build a research agent? There’s code for that. Multi-agent finance team? Done. Voice-enabled RAG? Covered.

Who it’s for: Developers who learn by doing. If you prefer reading working code over documentation, this repo is gold.

The Goldmine You’ve Been Missing

I’ve been building AI agents for a while now, and the hardest part isn’t understanding the concepts—it’s seeing how all the pieces fit together in real applications. Documentation tells you what an API does. Tutorials show you one narrow path. But working code? That shows you how experienced developers actually structure these systems.

That’s what makes awesome-llm-apps different. Created by Shubham Saboo (who runs Unwind AI), this isn’t just a list of links. It’s 90+ complete, runnable projects covering virtually every pattern you’d want to implement with LLMs.

The numbers speak for themselves:

92,000+ stars (and growing fast)
13,300+ forks
90+ complete projects
Actively maintained with new additions regularly

Let me break down what’s actually in here and why it matters.

How It’s Organized

The repo divides projects into logical categories. This isn’t random—it mirrors how you’d actually progress in building AI applications:

🌱 Starter AI Agents

These are your “Hello World” equivalents for different agent types. Simple enough to understand in one sitting, but complete enough to actually do something useful:

AI Blog to Podcast Agent — Converts written content to audio
AI Data Analysis Agent — Analyzes datasets and generates insights
AI Travel Agent — Plans trips with both local and cloud options
AI Medical Imaging Agent — Analyzes medical images (with appropriate disclaimers)
Web Scraping AI Agent — Automated data extraction
xAI Finance Agent — Financial analysis using Grok

What I like about these: they’re not toy examples. The travel agent actually considers real constraints. The data analysis agent produces charts. They’re minimal but functional.

🚀 Advanced AI Agents

This is where it gets interesting. These aren’t tutorials—they’re closer to production-ready templates:

AI Deep Research Agent — Multi-step research with source verification
AI System Architect Agent — Designs system architectures using reasoning models
AI VC Due Diligence Agent Team — Analyzes startups across multiple dimensions
AI Journalist Agent — Researches and writes articles with citations
AI Product Launch Intelligence Agent — Monitors and analyzes product launches
AI Self-Evolving Agent — An agent that improves its own capabilities

The AI Deep Research Agent is particularly valuable if you’re building anything that needs to gather and synthesize information. It shows how to handle the messiness of real-world research—conflicting sources, incomplete data, verification steps.

🤝 Multi-Agent Teams

This is the frontier right now. Single agents are limited; teams of specialized agents can tackle complex problems:

AI Finance Agent Team — Multiple agents handling different financial tasks
AI Legal Agent Team — Cloud and local options for legal document analysis
AI Recruitment Agent Team — Screening, evaluation, and matching
AI Real Estate Agent Team — Property analysis, market research, client matching
Multimodal Coding Agent Team — Agents that can see and write code
AI Services Agency (CrewAI) — A template for building agent-based services

The Multimodal Coding Agent Team caught my attention. It combines vision models (for understanding UI mockups or diagrams) with code generation. That’s a pattern I expect to see much more of.

🗣️ Voice AI Agents

Voice is underrated for AI applications. These show how to build conversational agents:

AI Audio Tour Agent — Location-aware audio guides
Customer Support Voice Agent — Phone-style support automation
Voice RAG Agent — Combines voice with retrieval-augmented generation
Open Source Voice Dictation Agent — Wispr Flow-style dictation

The Voice RAG Agent is clever—it lets you have a conversation with your documents, literally speaking questions and hearing answers. Useful for hands-free scenarios or accessibility.

MCP AI Agents

Model Context Protocol (MCP) is Anthropic’s standard for connecting AI to external tools. These projects show practical implementations:

Browser MCP Agent — Web browsing through MCP
GitHub MCP Agent — Repository management
Notion MCP Agent — Document and database integration
AI Travel Planner MCP Agent — Trip planning with tool access

If you’re building agents that need to interact with external services, these MCP examples are invaluable. The protocol is still relatively new, and good implementation examples are scarce.

📀 RAG (Retrieval Augmented Generation)

RAG is the workhorse of practical LLM applications. This section alone is worth the repo:

Agentic RAG with Reasoning — Combines RAG with chain-of-thought
Autonomous RAG — Self-correcting retrieval
Corrective RAG (CRAG) — Validates and refines retrieved context
Deepseek Local RAG Agent — Fully local implementation
Hybrid Search RAG — Combines semantic and keyword search
Vision RAG — Retrieval from images and documents
RAG with Database Routing — Routes queries to appropriate data sources

The Corrective RAG implementation is particularly interesting. It doesn’t just retrieve—it checks whether what it retrieved actually answers the question, and tries again if not. That’s a significant improvement over naive RAG.

💾 LLM Apps with Memory

Stateful conversations are tricky. These show different approaches:

AI ArXiv Agent with Memory — Remembers research context
Multi-LLM Application with Shared Memory — Multiple models, one memory
LLM App with Personalized Memory — User-specific context

💬 Chat with X Tutorials

Classic patterns, well-implemented:

Chat with GitHub — Ask questions about repositories
Chat with Gmail — Query your inbox
Chat with PDF — Document Q&A
Chat with YouTube Videos — Video content understanding

🎯 LLM Optimization Tools

Cost matters at scale. These focus on efficiency:

Toonify Token Optimization — 30-60% cost reduction using TOON format
Headroom Context Optimization — 50-90% savings through intelligent compression

🔧 Fine-tuning Tutorials

When you need to customize models:

Gemma 3 Fine-tuning
Llama 3.2 Fine-tuning

🧑‍🏫 Framework Crash Courses

Complete courses on building with specific frameworks:

Google ADK Crash Course — Model-agnostic agents, structured outputs, MCP tools, multi-agent patterns
OpenAI Agents SDK Crash Course — Function calling, swarm orchestration, handoffs

What Makes This Different

I’ve seen a lot of “awesome” lists. Most are just collections of links. This repo is different because:

1. Everything Actually Runs

Each project has a requirements.txt and clear setup instructions. Clone, install, configure your API keys, run. No hunting for missing dependencies or outdated code.

2. Real Patterns, Not Hello Worlds

The advanced projects show how to handle real problems: error recovery, rate limiting, multi-step workflows, state management. This is the stuff that separates a demo from a usable application.

3. Multiple Model Support

Projects work with OpenAI, Anthropic, Google, xAI, and open-source models (Llama, Qwen). You’re not locked into one provider.

4. Local Options

Many projects have “local” variants that run entirely on your machine with Ollama. Great for development, privacy-sensitive applications, or just saving on API costs.

5. Active Maintenance

The repo gets regular updates. As new patterns emerge (like MCP), new projects appear. This isn’t a dead archive.

Projects Worth Building With

If I were starting an AI project today, here’s what I’d look at first:

For a research assistant: Start with the AI Deep Research Agent. It handles source gathering, fact-checking, and synthesis. Adapt it to your domain.

For customer-facing automation: The Customer Support Voice Agent combined with RAG patterns. Add your documentation as the knowledge base.

For internal tools: Chat with GitHub + Chat with Gmail patterns. Combine them for a unified interface to your work context.

For complex workflows: The Multi-Agent Team templates. Start with the Finance or Legal teams as structural examples, then swap in your domain logic.

For cost optimization: Don’t skip the LLM Optimization Tools. The Headroom context compression can dramatically reduce costs for agents that need large contexts.

Getting Started

# Clone the repo
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git

# Pick a project (e.g., the research agent)
cd awesome-llm-apps/advanced_ai_agents/single_agent_apps/ai_deep_research_agent/

# Install dependencies
pip install -r requirements.txt

# Set your API keys (check the project README)
export OPENAI_API_KEY="your-key-here"

# Run it
python main.py

Each project’s README has specific instructions for configuration and usage.

The Bigger Picture

What I appreciate about this repo is that it’s not trying to sell you a framework or a service. It’s just working code you can learn from and adapt.

The AI agent space is moving fast. New frameworks appear monthly. Model capabilities expand constantly. Having a reference library of implementations—patterns that actually work—is invaluable for keeping up.

Whether you’re building your first agent or your fiftieth, there’s something here worth studying. The code is clean, the patterns are proven, and the breadth of coverage is unmatched.