## What is llms-full.txt?
llms-full.txt is the companion file to `/llms.txt` — same [llmstxt.org](https://llmstxt.org/) convention, but instead of *listing* your important pages, it contains the **entire concatenated text content** of your site in a single file. One fetch, the whole corpus.
## When does it help?
llms.txt is a directory. An agent reads it, picks a relevant URL, fetches that page, repeats. That works well for agents that can browse.
llms-full.txt is the full text. An agent reads it once and has everything. That matters for:
- **No-browse retrieval pipelines** — RAG systems that ingest text but cannot make follow-up HTTP requests
- **Embedded agents** — running in environments without arbitrary network access
- **One-shot context** — when an LLM needs the whole knowledge base in its context window up front
- **Training and fine-tuning ingestion** — when content goes into a model build pipeline rather than a runtime fetch
For browsing agents, llms-full.txt is harmless overhead (they can choose not to fetch it). For non-browsing agents, it is the difference between "the agent has your docs" and "the agent has nothing about you."
## Format
Plain text or markdown. Standard convention is to start with the same header as llms.txt, then concatenate every page in your llms.txt directory, separated by clear headings or `---` separators.
Example skeleton:
```markdown
# Your Service Name
> Brief one-line description.
## Overview
Your Service does X, Y, Z.
---
### /docs/getting-started
(full markdown content of the getting-started page)
---
### /docs/api-reference
(full markdown content of the API reference)
(...continues for every page)
```
## How agents discover it
Two ways:
**1. `` in your HTML head**
```html
```
**2. Reference it from `/llms.txt`**
Most llms.txt files end with an "Optional" section. Add the companion there so agents reading the directory know the full-content version exists.
```markdown
## Optional
- [Full content](/llms-full.txt) — entire site text in one file
```
## How to generate it
Most modern docs platforms (Mintlify, Docusaurus with plugins, Hugo with templates) can auto-generate llms-full.txt from your source. If you maintain docs manually, a simple build step that concatenates your markdown source files works fine.
**Regenerate when content changes.** A stale llms-full.txt is worse than none — it gives agents outdated information about your service. Wire generation into the same CI step that builds your docs.
## Tradeoffs
- **Pro** — A single fetch gives consumers everything; no crawling needed
- **Pro** — Strong AEO signal: ChatGPT, Perplexity, and Claude all weight machine-readable corpus availability
- **Pro** — Works for agents without browsing capability (RAG pipelines, embedded agents)
- **Con** — Larger response: tens of KB to several MB depending on docs size
- **Con** — Must be regenerated when content changes
- **Con** — Concatenates everything, including content you might prefer agents fetch piecemeal (paid docs, gated content)
## What agentgrade checks
- **llms-full.txt found** — `/llms-full.txt` responds 200 with at least 20 characters of non-HTML content that does not look like a soft-404
- **llms-full.txt linked from HTML** (optional) — `` is present in your homepage ``
The check is scored at weight 1 — lower than llms.txt itself at weight 2, reflecting that llms-full is recommended companion content rather than a primary directory file.
## Real-world examples
- [docs.anthropic.com/llms-full.txt](https://docs.anthropic.com/llms-full.txt) — Anthropic's full Claude docs in one file
- [agentgrade.com/llms-full.txt](https://agentgrade.com/llms-full.txt) — our own dogfood version, generated from this knowledge base
- The original llmstxt.org spec describes both files as the canonical pair
## Spec maturity
**Emerging standard, growing fast.** Defined alongside llms.txt at llmstxt.org. Adoption is rising, driven by AI-first docs platforms (Mintlify auto-generates it) and the spread of no-browse retrieval workflows. Sites that ship both files now position themselves as authoritative sources for AI answer engines.
## Learn more
- [llmstxt.org](https://llmstxt.org/) — Specification
- [llms.txt](/kb/llms-txt) — The companion directory file
## Related
- [SKILL.md](/kb/skills)
- [OpenAPI](/kb/openapi)