Use Awesome LLM Apps Templates (2026)

Awesome LLM Apps provides 100+ runnable application templates for AI agents, RAG systems, and chatbots. Here is how to pick the right template, clone it, and customize it using Claude Code.

Prerequisites

  • Python 3.10+ installed
  • Claude Code installed
  • Git installed
  • An API key for your chosen LLM provider (many templates support Claude)

Step 1: Browse the Template Library

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git ~/awesome-llm-apps
ls ~/awesome-llm-apps/

Templates are organized by category:

  • agents/ — Autonomous AI agents
  • rag/ — Retrieval-augmented generation systems
  • chatbots/ — Conversational interfaces
  • pipelines/ — Data processing workflows

Browse the README at github.com/Shubhamsaboo/awesome-llm-apps for descriptions and screenshots.

Step 2: Pick a Template

Choose based on your use case. Popular starting points:

  • Code review agent — For automated PR review
  • RAG with ChromaDB — For document Q&A
  • Multi-agent system — For complex task orchestration
  • Document analysis — For PDF/document processing

Each template has its own directory with a README, requirements file, and source code.

Step 3: Set Up the Template

Navigate to your chosen template and install dependencies:

cd ~/awesome-llm-apps/agents/code-review-agent  # example
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Copy the environment template and add your API keys:

cp .env.example .env

Edit .env with your actual API keys. Many templates support ANTHROPIC_API_KEY for Claude.

Step 4: Customize With Claude Code

Now open the project in Claude Code:

claude

Ask Claude to adapt the template to your needs:

Read the source code in this project. I want to modify this code review agent to:
1. Focus on Python code only
2. Check for security vulnerabilities
3. Output results as JSON instead of markdown

Claude reads the template code, understands the architecture, and makes targeted modifications. The Apache-2.0 license means you can modify and deploy commercially.

Step 5: Run and Verify

python main.py

Test the application against sample data to verify your customizations work. Iterate with Claude Code as needed.

RAG Document Q&A: The most practical starting point for most developers. Takes a folder of documents, indexes them with a vector store, and creates a chat interface for asking questions. Modify it to use your own document corpus — internal wikis, product docs, research papers.

Multi-Agent Workflow: A template where multiple AI agents collaborate on a task, each with a specific role. Useful for building systems where one agent researches, another writes, and a third reviews. Customize the agent roles for your domain.

Code Review Bot: Automated pull request review. Reads diffs, identifies issues, and posts comments. Modify it to enforce your team’s specific coding standards and security requirements.

Data Analysis Pipeline: Takes CSV or database data, performs statistical analysis, and generates visualizations. Modify for your specific data sources and metrics.

Adapting Templates for Production

Community templates are starting points, not production-ready code. Before deploying, have Claude help you:

  1. Add error handling — Most templates assume happy paths. Ask Claude to add try-catch blocks, retry logic, and graceful degradation.

  2. Add logging — Templates rarely include logging. Ask Claude to add structured logging for debugging and monitoring.

  3. Add rate limiting — If your template calls external APIs, add rate limiting to avoid hitting quotas.

  4. Add tests — Templates rarely include tests. Ask Claude to generate a test suite before you deploy.

  5. Review security — Check for hardcoded credentials, SQL injection, and other vulnerabilities that templates may include for simplicity.

Troubleshooting

Dependency conflicts: Use a virtual environment (Step 3) to isolate template dependencies from your system Python. Never install template dependencies in your system Python.

API key errors: Verify your .env file has the correct key format. Some templates expect OPENAI_API_KEY even for Claude-compatible endpoints. Check the template’s README for the exact variable names required.

Template uses OpenAI: Many templates default to OpenAI. Ask Claude to swap the LLM provider: “Replace the OpenAI client with the Anthropic client throughout this project.” Most templates using LangChain or LiteLLM support provider switching with minimal changes.

Template is outdated: Check the last commit date. If a template has not been updated in months, some APIs may have changed. Claude Code can help update deprecated calls. Run pip install --upgrade -r requirements.txt and ask Claude to fix any compatibility issues.

Memory issues: RAG templates that load large document sets can exhaust memory. Process documents in batches or use a persistent vector store instead of in-memory storage.

Next Steps

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See Also

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