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llms.txt Documentation

Overview

The llms.txt file is a specialized documentation format designed to help AI agents (like Claude, GPT-4, Gemini, etc.) quickly understand your project's structure, purpose, and development workflow. It serves as a concise project overview optimized for LLM consumption.

Purpose

AI agents need context to provide effective assistance. The llms.txt file provides:

  • Quick project overview and key features
  • Essential code examples
  • Project structure visualization
  • Development workflow commands
  • API reference highlights

Template Usage

📚 Use the llms.txt template: python/templates/llms.txt

Creating Your llms.txt

  1. Copy the template:
cp python/templates/llms.txt ./llms.txt
  1. Replace placeholders with your project details:
    • {{project_name}} - Your project's directory name
    • {{package_name}} - Python package name (underscores, not hyphens)
    • {{description}} - Brief project description
    • {{project_type}} - Type of project (e.g., "library", "CLI tool", "web API")
    • {{key_functionality}} - What the project does in one sentence
    • {{feature_1/2/3}} - Main features (keep concise)
    • {{main_class_or_function}} - Primary entry point
    • {{python_version}} - Minimum Python version required
    • {{license}} - License type (e.g., "MIT", "Apache 2.0")

Content Guidelines

1. Overview Section

Keep it brief but informative:

  • One-sentence description
  • 3-5 key features with emoji indicators
  • Focus on what makes the project unique

2. Quick Start

Provide the simplest working example:

from package_name import main_function

# Minimal working example
result = main_function()

3. Project Structure

Show only essential directories and files:

project/
├── src/package/     # Core code
├── tests/           # Test suite
├── docs/            # Documentation
└── pyproject.toml   # Configuration

4. Development Commands

Include only the most common commands:

  • Installation: uv sync --dev
  • Testing: uv run pytest
  • Quality checks: make qa
  • Help: make help

5. Key APIs

List 3-5 most important functions/classes:

  • Include brief descriptions
  • Focus on public APIs
  • Mention configuration options

Best Practices

DO's

✅ Keep it under 150 lines
✅ Use concrete examples
✅ Include actual command syntax
✅ Reference other documentation files
✅ Update when APIs change
✅ Use emoji sparingly for visual structure

DON'Ts

❌ Don't duplicate README content extensively
❌ Don't include implementation details
❌ Don't list every function
❌ Don't include lengthy explanations
❌ Don't forget to update placeholders

Example: Real Project

Here's an example for a data processing library:

# DataFlow

> High-performance data pipeline library for Python

## Overview

DataFlow is a Python library that simplifies building efficient data processing pipelines.

Key features:
- 🚀 Parallel processing with async support
- 📊 Built-in data validation
- 🔧 Extensible transformer system

## Quick Start for AI Agents

The simplest way to use DataFlow:

```python
from dataflow import Pipeline, Transform

# Create and run a pipeline
pipeline = Pipeline()
pipeline.add(Transform.normalize())
result = pipeline.run(data)

Project Structure

dataflow/
├── src/dataflow/     # Core pipeline code
│   ├── pipeline.py   # Main pipeline class
│   └── transforms/   # Built-in transformers
├── tests/            # Comprehensive test suite
└── examples/         # Usage examples

[Rest of content following template...]


## Integration with AI Tools

### GitHub Copilot
Place `llms.txt` in the root directory for automatic context.

### Claude Projects
Upload `llms.txt` as a project knowledge file.

### Custom AI Assistants
Include `llms.txt` in system prompts or context windows.

## Maintenance

### When to Update
- API changes
- New major features
- Project structure changes
- Development workflow updates

### Review Schedule
- Monthly for active projects
- Before major releases
- When onboarding new AI tools

## Relationship to Other Files

- **README.md**: User-facing documentation
- **CLAUDE.md**: Claude-specific project instructions  
- **llms.txt**: Generic AI agent context
- **pyproject.toml**: Technical project configuration

The `llms.txt` file complements but doesn't replace these files.

## Common Mistakes

1. **Too Much Detail**: Keep it high-level
2. **Outdated Examples**: Test code examples regularly
3. **Missing Structure**: Always include project layout
4. **No Commands**: Include actual runnable commands
5. **Wrong Audience**: Write for AI agents, not humans

## Template Variables Reference

| Variable | Description | Example |
|----------|-------------|---------|
| `{{project_name}}` | Project directory name | `my-awesome-project` |
| `{{package_name}}` | Python package name | `my_awesome_project` |
| `{{description}}` | One-line description | `Fast data processing library` |
| `{{project_type}}` | Type of project | `library`, `CLI tool`, `API` |
| `{{key_functionality}}` | Main purpose | `processes data pipelines efficiently` |
| `{{feature_1/2/3}}` | Key features | `Async support`, `Type safety` |
| `{{main_class_or_function}}` | Entry point | `Pipeline`, `process_data` |
| `{{python_version}}` | Min Python version | `3.10`, `3.11` |
| `{{license}}` | License type | `MIT`, `Apache 2.0`, `GPL-3.0` |

## Related Documentation

- [Project Templates](../templates/)
- [BOOTSTRAP.md](../BOOTSTRAP.md) - Full project setup guide
- [AI_AGENT_GUIDE.md](../AI_AGENT_GUIDE.md) - AI agent best practices
- [PACKAGE_MANAGEMENT.md](PACKAGE_MANAGEMENT.md) - Package management details

## Template Metadata

- **Repository**: [vibe-coding-templates](https://github.com/chrishayuk/vibe-coding-templates)
- **Path**: python/docs/LLMS_TXT.md
- **Version**: 1.0.0
- **Date**: 2025-01-24
- **Author**: chrishayuk