Working with llms.txt
OpenBox publishes its documentation in machine-readable formats following the llms.txt specification. If you're building AI agents, coding assistants, or tooling that needs to understand OpenBox — these files give you structured access to everything without scraping HTML.
The Problem with HTML Docs
When an LLM reads a rendered documentation page, it has to wade through navigation chrome, JavaScript-rendered tabs, hidden content panels, and HTML formatting artifacts. This wastes context window tokens and often misses content that's collapsed or behind tab switches.
The llms.txt standard solves this by providing clean, pre-processed markdown at well-known URLs. The LLM gets the full content in a format it can reason over directly.
What We Publish
OpenBox generates three resources at build time. They update automatically with every documentation change — there's no manual step to keep them in sync.
| Resource | URL | When to use |
|---|---|---|
llms.txt | /llms.txt | Discover what's available, find the right page |
llms-full.txt | /llms-full.txt | Ingest the entire corpus at once |
*.md files | Append .md to any doc URL | Fetch a single page's content |
llms.txt — The Index
The llms.txt file is a structured table of contents for the entire documentation site. It mirrors the sidebar hierarchy and includes a short description after each link so an LLM can decide whether to fetch the full page:
## Core Concepts
- [Trust Scores](https://docs.openbox.ai/core-concepts/trust-scores.md): How OpenBox quantifies agent trustworthiness
- [Trust Tiers](https://docs.openbox.ai/core-concepts/trust-tiers.md): Tiered classification of agent trust levels
The file opens with a platform summary that gives an LLM enough context to answer basic questions about OpenBox without fetching any additional pages.
llms-full.txt — The Full Corpus
The llms-full.txt file concatenates every documentation page into a single markdown file. Each section includes a source URL for attribution. All HTML, JSX, and frontmatter is stripped — what remains is clean, parseable markdown.
This is the right choice when you want to load everything at once: populating a vector store, building a RAG pipeline, or giving an agent complete context about the platform.
Individual .md Files
Every documentation page is available as standalone markdown by appending .md to its URL:
| HTML page | Plain text |
|---|---|
/core-concepts/trust-scores | /core-concepts/trust-scores.md |
/developer-guide/sdk-reference | /developer-guide/sdk-reference.md |
These are the files linked from llms.txt. They're useful when an LLM only needs one or two pages rather than the full corpus. Compared to the rendered HTML:
- Fewer tokens — No navigation, no script tags, no styling markup
- Complete content — Tabbed panels and collapsed sections are fully expanded
- Preserved structure — Headings, lists, and tables remain intact as markdown
Integrating with AI Tools
IDE Assistants
Add https://docs.openbox.ai/llms.txt as a documentation source in Cursor, Windsurf, or any IDE tool that supports the llms.txt standard. The tool will use the index to pull relevant pages into context as you work.
Custom Agents
For agents that need to answer questions about OpenBox:
- Fetch
/llms.txtto get the index - Match the user's question against the link descriptions
- Fetch the individual
.mdfiles for the most relevant pages
This two-step approach keeps token usage low while still giving the agent access to the full documentation when it needs it.
RAG and Vector Stores
Use /llms-full.txt as an ingestion source. The markdown sections are already chunked by page with clear heading boundaries, making them straightforward to split for embedding.
Learn More
- llms.txt specification — The community standard behind the format