Fetch for LLMs is a multi-agent system designed to make any webpage LLM-ready, enabling large language models to interact with, analyze, and replicate dynamic web content intelligently. It serves as a critical bridge between the web and AI agents by transforming complex webpages into structured, machine-readable formats.

The platform uses distributed multi-agent orchestration, where specialized agents handle webpage cloning, DOM tree interpretation, section classification, layout analysis, and dynamic content rendering. This allows downstream AI models or agent systems to reason about page structure, style, and behavior as if they were developers.

Fetch for LLMs is deployed on Google Cloud Platform, leveraging Cloud Tasks for agent distribution and Cloud Run (Functions and Jobs) for scalability. This architecture ensures that large batches of pages can be processed concurrently, supporting real-time analysis pipelines for use cases like automated UX audits, competitive analysis, and content transformation.

Multi-agent system for transforming any webpage into LLM-consumable data

Advanced DOM and visual structure interpretation for layout and section analysis

Deployed on GCP using Cloud Tasks and Cloud Run for horizontal scalability

Modular agent framework designed for extensibility and parallel workloads

Enables downstream LLMs to understand and act on real web content autonomously

Fetch for LLMs demonstrates advanced capabilities in agentic AI, autonomous web analysis, distributed systems, and large-scale orchestration, making it a core building block for intelligent AI-driven tools.