Use Praxa documentation with LLMs and AI search

Praxa publishes a site-wide LLM index, a combined full-text export, and a Markdown representation of every navigable documentation page. AI tools should start with llms.txt, select the smallest relevant page, and verify the page’s availability and API plane before generating code.

Machine-readable documentation endpoints

Resource	URL	Use it for
Documentation index	`https://docs.praxa.io/llms.txt`	Titles, descriptions, page discovery, and OpenAPI links
Well-known index	`https://docs.praxa.io/.well-known/llms.txt`	Clients that discover LLM indexes through `.well-known`
Complete corpus	`https://docs.praxa.io/llms-full.txt`	Offline search, broad retrieval, and corpus analysis
Well-known complete corpus	`https://docs.praxa.io/.well-known/llms-full.txt`	Well-known full-corpus discovery
Individual page	`https://docs.praxa.io/<route>.md`	Focused Markdown for one guide or reference page
Search sitemap	`https://docs.praxa.io/sitemap.xml`	Search-engine URL discovery and freshness
Crawler policy	`https://docs.praxa.io/robots.txt`	Search and AI crawler permissions

For example, retrieve the SDK quickstart without page chrome:

curl --fail-with-body https://docs.praxa.io/llms.txt
curl --fail-with-body https://docs.praxa.io/sdk/quickstart.md

Retrieval workflow for an AI agent

Discover pages

Fetch /llms.txt and match the user intent against page titles and descriptions. Do not guess routes.

Fetch focused Markdown

Retrieve only the relevant /<route>.md pages first. Use /llms-full.txt only when the question truly spans the corpus.

Identify the API plane

Distinguish Execution Fabric /v1, deployment-specific Integration Gateway /v8, remote MCP, and read-only memory federation before choosing authentication or code examples.

Respect availability

Preserve labels such as live package, partner preview, deployment-specific, qualification preview, and pending. Source examples do not activate a route.

Prefer exact contracts

Use the linked OpenAPI documents and published package versions for fields, scopes, identifiers, and schemas. Never infer an undocumented endpoint.

Return citations and proof limits

Link the exact pages used and separate package tests, mocked tests, authenticated runtime checks, deployment, and user verification.

Guidance for retrieval systems

Chunk by heading while retaining the page title, route, description, and nearest parent headings.
Store the source URL and last-modified value with every chunk.
Keep code fences, tables, warnings, and availability notes attached to their explanatory text.
Rank exact product terms and error codes above generic semantic similarity.
Do not combine credentials or instructions across API planes.
Refresh when package versions, OpenAPI versions, or page timestamps change.
Delete stale chunks rather than serving two conflicting revisions.

Test LLM access

Run npm run docs:live:llms from the documentation repository after a release. The checker requires both site-wide files, the well-known aliases, crawler and sitemap discovery, inclusion of every navigable page in llms.txt, and a successful Markdown response for every individual page.

Frequently asked questions

Does each page need its own `llms.txt` file?

No. The standard site-wide llms.txt links to a Markdown endpoint for each page. Fetch /<route>.md for the individual page and /llms-full.txt for the combined corpus.

Should an AI tool use `/llms-full.txt` for every question?

No. Focused page retrieval reduces stale or irrelevant context. Use the full corpus for cross-cutting audits, indexing, or offline search.

Are Markdown routes intended for search indexing?

The canonical HTML page remains the search result. Markdown routes are a machine-readable representation and advertise the site-wide documentation index in their response content.

Does an example prove the service is live?

No. Read service status and proof boundaries and verify the intended authenticated environment before using customer data.

Get started

Core Concepts

Releases

Use Praxa documentation with LLMs and AI search

Machine-readable documentation endpoints

Retrieval workflow for an AI agent

Guidance for retrieval systems

Test LLM access

Frequently asked questions

Does each page need its own `llms.txt` file?

Should an AI tool use `/llms-full.txt` for every question?

Are Markdown routes intended for search indexing?

Does an example prove the service is live?

​Machine-readable documentation endpoints

​Retrieval workflow for an AI agent

​Guidance for retrieval systems

​Test LLM access

​Frequently asked questions

​Does each page need its own llms.txt file?

​Should an AI tool use /llms-full.txt for every question?

​Are Markdown routes intended for search indexing?

​Does an example prove the service is live?

Machine-readable documentation endpoints

Retrieval workflow for an AI agent

Guidance for retrieval systems

Test LLM access

Frequently asked questions

Does each page need its own `llms.txt` file?

Should an AI tool use `/llms-full.txt` for every question?

Are Markdown routes intended for search indexing?

Does an example prove the service is live?