LLMTEXT (llmtext.com) is an open-source toolkit developed by Jan_Wilmake and launched under Parallel_AI's banner in October 2025. It provides three tools to help websites become accessible to AI agents through the llms.txt standard — a format proposed by Jeremy Howard to give LLMs structured, token-efficient access to website documentation.
By late 2025, AI bots had overtaken human visitors as the primary users of the web. Wikipedia reported an 8% decline in human traffic as AI summaries absorbed queries. Yet most websites continued to serve HTML built for human eyes, not structured text for AI agents.
The llms.txt standard addressed this: a simple file at /.well-known/llms.txt (or /llms.txt) listing the key documentation pages for a site, with a short description of each. LLMTEXT built the tooling to make this standard practical.
Turns any valid llms.txt file into a dedicated MCP server. Uses LLM reasoning (not vector search) to decide which docs to load — similar in spirit to how uithub lets agents selectively fetch repo contents.
Validates an existing llms.txt against the spec, surfacing common errors:
A framework-agnostic generator powered by Parallel's Extract API:
llms.txtLLMTEXT also introduced SLOP — a human-readable, llms.txt-compatible format distilled from OpenAPI specs, developed via openapisearch. It reduces massive API specs (e.g. GitHub's 2M-token spec) to ~30k tokens, making them practical for AI agent use.
LLMTEXT was Jan's most prominent Parallel_AI project, earning 2.5M X impressions and adding GitHub stars across the 2025 developer community.