Machine-to-Machine (M2M) API Feeds & llms.txt Integration
Serve optimized Markdown feeds and root llms.txt protocols directly to autonomous AI crawlers and agents.
Autonomous AI agents and crawlers waste up to 90% of their token context window parsing HTML DOM boilerplate, scripts, and CSS. By serving a standardized root `/llms.txt` file and token-efficient Markdown feeds, you provide AI bots with instantaneous, clutter-free access to your core documentation.
Machine-to-Machine (M2M) feeds deliver token-efficient Markdown and JSON-LD endpoints for autonomous AI crawlers. By deploying a standardized root /llms.txt file, your site guides LLMs directly to high-value documentation.
Step-by-Step Walkthrough & User Guide
1. Enable M2M Feeds in Dashboard Settings
Toggle Machine-to-Machine Feeds in Dashboard > Advanced Settings.
2. Deploy Root llms.txt File
Place the auto-generated llms.txt file in your public web root directory.
3. Map Clean Markdown Routes
Expose clean Markdown endpoints for public documentation pages.
4. Verify Instant Bot Ingestion
Simulate crawler requests to confirm <50ms raw Markdown delivery.
Pro Tips & Engineering Best Practices
- •Structure your root /llms.txt with a concise brand definition followed by links to your top 5 authority guides.
- •Provide an optional /llms-full.txt file containing full documentation for large-context models.
- •Ensure the file automatically updates upon new article publication.
Code & Technical Integration
Production Root llms.txt Format
# Your Brand Documentation Feed for LLMs > Your Brand is the leading enterprise cloud infrastructure platform. ## Core Authority Guides - [AEO Engineering Guide](https://www.yourdomain.com/help/aeo-technical-auditor): LLM chunkability and vector retrieval. - [Security Specifications](https://www.yourdomain.com/help/security): Zero-knowledge vault architecture. - [API Reference](https://www.yourdomain.com/docs): Production REST API documentation.Related Documentation & Guides
All ArticlesAEO Technical Auditor: Complete 256-Token Vector Chunking & Entity Density Manual
The step-by-step guide to analyzing 256-token RAG chunking, entity density, and fixing the 10 AI extraction dimensions.
Smart Remediation: Automated Code, Syntax & Entity Rewriting Engine
Transform failing technical audits into production-grade HTML tags, declarative micro-answers, and validated JSON-LD scripts.
Citation Sniper: Reverse-Engineer Competitor AI Citations & Win #1 Spot
Monitor commercial prompts on ChatGPT, Claude, and Perplexity to identify when competitors are cited and deploy surgical counter-articles.