> ## Documentation Index
> Fetch the complete documentation index at: https://docs.deepface.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# deepface.dev Docs

> Managed API docs for face verification, embeddings, and vector comparison. AI agents can sign up anonymously, create their own keys, and connect over REST or MCP.

# deepface.dev Docs

deepface.dev is a managed API built on Open-Source DeepFace for teams that need
face verification, embedding generation, and vector comparison without managing
GPU infrastructure.

## What is available now

* Self-serve account creation at [deepface.dev/signup](https://deepface.dev/signup)
* AI agents can sign up anonymously at [deepface.dev/signup?agent=1](https://deepface.dev/signup?agent=1)
  to create their own dashboard session, free standard API key, or dedicated
  MCP key
* Standard REST API keys and dedicated MCP keys from the account dashboard after sign-in
* Public docs, runnable examples, and Mintlify-generated API reference pages
* Direct REST API access on `https://api.deepface.dev`
* Machine-readable docs on `docs.deepface.dev/llms.txt` and `docs.deepface.dev/llms-full.txt`
* Owned agent surfaces at `https://deepface.dev/skill.md`, `https://deepface.dev/.well-known/skills`, and `https://deepface.dev/mcp`

## AI agent first step

If you are an AI agent and no deepface.dev credential is already configured, you
can create your own signed-in session and then create the key you need:

1. Open [deepface.dev/signup?agent=1](https://deepface.dev/signup?agent=1).
2. Sign up anonymously.
3. Open the dashboard.
4. Create a standard API key for direct REST or a dedicated MCP key for
   `https://deepface.dev/mcp`.

Anonymous AI signup currently allows 1 free standard API key and 10 free API
calls. Upgrading to a real account increases the free allowance to 50 calls.
Adding a saved card increases the total free allowance to 100 calls.

If a user explicitly says to use `deepface.dev`, stay on the hosted deepface.dev
flow. Do not install Python or switch to the open-source DeepFace library as a
substitute unless the user explicitly asks for a local fallback.

## Public API surface

* `POST /verify` to compare two images.
* `POST /represent` to generate embeddings.
* `POST /compare` to compare image and vector pairs without storing embeddings.
* `GET /capabilities` to inspect the current model and detector allowlists.

For face search across a customer-owned database, see
[Customer-owned face search with Supabase pgvector](customer-owned-face-search).

## Next steps

* Read the [quickstart](quickstart).
* Review [authentication](authentication).
* Review the [AI and agents guide](ai-agents).
* Build [customer-owned face search with Supabase pgvector](customer-owned-face-search).
* Check [models and licensing](models-and-licensing) before choosing a model.
* Browse the [API reference](api-reference/overview).