Plain-text context
llms.txt, llms-full.txt, and Markdown for every page.
Every page here is available as Markdown, and the whole corpus as a single file, so you can put the documentation into a model's context without parsing HTML.
The files
| URL | Contents |
|---|---|
/llms.txt | An index: every page, with its title, description and URL. |
/llms-full.txt | The entire corpus of guides in one file. |
/openapi.yaml | The complete API contract. |
/openapi.json | The same contract, as JSON. |
api.flambe.dev/openapi.json | The same document again, served by the API itself. |
The API's copy is byte-identical and verified on every build, so either source works. Prefer the API's if you want the contract that shipped with the running service.
Start with llms.txt to decide what you need, then fetch the specific pages.
Reach for llms-full.txt when you would rather hold everything at once — it is
small enough that this is usually the right call.
One page as Markdown
Two ways, both returning the same thing:
# Append .md to any docs URL
curl https://docs.flambe.dev/docs/guides/imports.md
# Or negotiate on the canonical URL
curl -H 'Accept: text/markdown' https://docs.flambe.dev/docs/guides/importsThe negotiated response carries Vary: Accept, so a cache in front of it will
not serve HTML to a client that asked for Markdown.
Every page in the web UI also has a Copy Markdown button and a view menu for opening the page directly in an assistant.
Sizing
At the time of writing the corpus is roughly 30k tokens including the OpenAPI spec — comfortably inside any current model's context window. You do not need to chunk it or stand up a vector store. Our own Ask AI does exactly this: the whole corpus in the system prompt, behind prompt caching.
Keeping current
There is no version in these URLs; they always describe production. If you cache the corpus, re-fetch on deploy rather than pinning — the API reference in particular is regenerated from the server's routes on every build and is verified to match them.