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

# OpenAI chat completions

OpenClaw's Gateway can serve a small OpenAI-compatible Chat Completions endpoint.

This endpoint is **disabled by default**. Enable it in config first.

* `POST /v1/chat/completions`
* Same port as the Gateway (WS + HTTP multiplex): `http://<gateway-host>:<port>/v1/chat/completions`

When the Gateway's OpenAI-compatible HTTP surface is enabled, it also serves:

* `GET /v1/models`
* `GET /v1/models/{id}`
* `POST /v1/embeddings`
* `POST /v1/responses`

Under the hood, requests are executed as a normal Gateway agent run (same codepath as `openclaw agent`), so routing/permissions/config match your Gateway.

## Authentication

Uses the Gateway auth configuration.

Common HTTP auth paths:

* shared-secret auth (`gateway.auth.mode="token"` or `"password"`):
  `Authorization: Bearer <token-or-password>`
* trusted identity-bearing HTTP auth (`gateway.auth.mode="trusted-proxy"`):
  route through the configured identity-aware proxy and let it inject the
  required identity headers
* private-ingress open auth (`gateway.auth.mode="none"`):
  no auth header required

Notes:

* When `gateway.auth.mode="token"`, use `gateway.auth.token` (or `OPENCLAW_GATEWAY_TOKEN`).
* When `gateway.auth.mode="password"`, use `gateway.auth.password` (or `OPENCLAW_GATEWAY_PASSWORD`).
* When `gateway.auth.mode="trusted-proxy"`, the HTTP request must come from a
  configured trusted proxy source; same-host loopback proxies require explicit
  `gateway.auth.trustedProxy.allowLoopback = true`.
* If `gateway.auth.rateLimit` is configured and too many auth failures occur, the endpoint returns `429` with `Retry-After`.

## Security boundary (important)

Treat this endpoint as a **full operator-access** surface for the gateway instance.

* HTTP bearer auth here is not a narrow per-user scope model.
* A valid Gateway token/password for this endpoint should be treated like an owner/operator credential.
* Requests run through the same control-plane agent path as trusted operator actions.
* There is no separate non-owner/per-user tool boundary on this endpoint; once a caller passes Gateway auth here, OpenClaw treats that caller as a trusted operator for this gateway.
* For shared-secret auth modes (`token` and `password`), the endpoint restores the normal full operator defaults even if the caller sends a narrower `x-openclaw-scopes` header.
* Trusted identity-bearing HTTP modes (for example trusted proxy auth or `gateway.auth.mode="none"`) honor `x-openclaw-scopes` when present and otherwise fall back to the normal operator default scope set.
* If the target agent policy allows sensitive tools, this endpoint can use them.
* Keep this endpoint on loopback/tailnet/private ingress only; do not expose it directly to the public internet.

Auth matrix:

* `gateway.auth.mode="token"` or `"password"` + `Authorization: Bearer ...`
  * proves possession of the shared gateway operator secret
  * ignores narrower `x-openclaw-scopes`
  * restores the full default operator scope set:
    `operator.admin`, `operator.approvals`, `operator.pairing`,
    `operator.read`, `operator.talk.secrets`, `operator.write`
  * treats chat turns on this endpoint as owner-sender turns
* trusted identity-bearing HTTP modes (for example trusted proxy auth, or `gateway.auth.mode="none"` on private ingress)
  * authenticate some outer trusted identity or deployment boundary
  * honor `x-openclaw-scopes` when the header is present
  * fall back to the normal operator default scope set when the header is absent
  * only lose owner semantics when the caller explicitly narrows scopes and omits `operator.admin`

See [Security](/gateway/security) and [Remote access](/gateway/remote).

## Agent-first model contract

OpenClaw treats the OpenAI `model` field as an **agent target**, not a raw provider model id.

* `model: "openclaw"` routes to the configured default agent.
* `model: "openclaw/default"` also routes to the configured default agent.
* `model: "openclaw/<agentId>"` routes to a specific agent.

Optional request headers:

* `x-openclaw-model: <provider/model-or-bare-id>` overrides the backend model for the selected agent.
* `x-openclaw-agent-id: <agentId>` remains supported as a compatibility override.
* `x-openclaw-session-key: <sessionKey>` fully controls session routing.
* `x-openclaw-message-channel: <channel>` sets the synthetic ingress channel context for channel-aware prompts and policies.

Compatibility aliases still accepted:

* `model: "openclaw:<agentId>"`
* `model: "agent:<agentId>"`

## Enabling the endpoint

Set `gateway.http.endpoints.chatCompletions.enabled` to `true`:

```json5 theme={"theme":{"light":"min-light","dark":"min-dark"}}
{
  gateway: {
    http: {
      endpoints: {
        chatCompletions: { enabled: true },
      },
    },
  },
}
```

## Disabling the endpoint

Set `gateway.http.endpoints.chatCompletions.enabled` to `false`:

```json5 theme={"theme":{"light":"min-light","dark":"min-dark"}}
{
  gateway: {
    http: {
      endpoints: {
        chatCompletions: { enabled: false },
      },
    },
  },
}
```

## Session behavior

By default the endpoint is **stateless per request** (a new session key is generated each call).

If the request includes an OpenAI `user` string, the Gateway derives a stable session key from it, so repeated calls can share an agent session.

## Why this surface matters

This is the highest-leverage compatibility set for self-hosted frontends and tooling:

* Most Open WebUI, LobeChat, and LibreChat setups expect `/v1/models`.
* Many RAG systems expect `/v1/embeddings`.
* Existing OpenAI chat clients can usually start with `/v1/chat/completions`.
* More agent-native clients increasingly prefer `/v1/responses`.

## Model list and agent routing

<AccordionGroup>
  <Accordion title="What does `/v1/models` return?">
    An OpenClaw agent-target list.

    The returned ids are `openclaw`, `openclaw/default`, and `openclaw/<agentId>` entries.
    Use them directly as OpenAI `model` values.
  </Accordion>

  <Accordion title="Does `/v1/models` list agents or sub-agents?">
    It lists top-level agent targets, not backend provider models and not sub-agents.

    Sub-agents remain internal execution topology. They do not appear as pseudo-models.
  </Accordion>

  <Accordion title="Why is `openclaw/default` included?">
    `openclaw/default` is the stable alias for the configured default agent.

    That means clients can keep using one predictable id even if the real default agent id changes between environments.
  </Accordion>

  <Accordion title="How do I override the backend model?">
    Use `x-openclaw-model`.

    Examples:
    `x-openclaw-model: openai/gpt-5.4`
    `x-openclaw-model: gpt-5.5`

    If you omit it, the selected agent runs with its normal configured model choice.
  </Accordion>

  <Accordion title="How do embeddings fit this contract?">
    `/v1/embeddings` uses the same agent-target `model` ids.

    Use `model: "openclaw/default"` or `model: "openclaw/<agentId>"`.
    When you need a specific embedding model, send it in `x-openclaw-model`.
    Without that header, the request passes through to the selected agent's normal embedding setup.
  </Accordion>
</AccordionGroup>

## Streaming (SSE)

Set `stream: true` to receive Server-Sent Events (SSE):

* `Content-Type: text/event-stream`
* Each event line is `data: <json>`
* Stream ends with `data: [DONE]`

## Chat tool contract

`/v1/chat/completions` supports a function-tool subset compatible with common OpenAI Chat clients.

### Supported request fields

* `tools`: array of `{ "type": "function", "function": { ... } }`
* `tool_choice`: `"auto"`, `"none"`
* `messages[*].role: "tool"` follow-up turns
* `messages[*].tool_call_id` for binding tool results back to a prior tool call
* `max_completion_tokens`: number; per-call cap for total completion tokens (reasoning tokens included). Current OpenAI Chat Completions field name; preferred when both `max_completion_tokens` and `max_tokens` are sent.
* `max_tokens`: number; legacy alias accepted for backwards compatibility. Ignored when `max_completion_tokens` is also present.
* `temperature`: number; best-effort sampling temperature forwarded to the upstream provider via the agent stream-param channel.
* `top_p`: number; best-effort nucleus sampling forwarded to the upstream provider via the agent stream-param channel.

When either token-cap field is set, the value is forwarded to the upstream provider via the agent stream-param channel. The actual wire field name sent to the upstream provider is chosen by the provider transport: `max_completion_tokens` for OpenAI-family endpoints, and `max_tokens` for providers that only accept the legacy name (such as Mistral and Chutes). Sampling fields (`temperature`, `top_p`) follow the same stream-param channel; the ChatGPT-based Codex Responses backend strips them server-side since it uses fixed sampling.

### Unsupported variants

The endpoint returns `400 invalid_request_error` for unsupported tool variants, including:

* non-array `tools`
* non-function tool entries
* missing `tool.function.name`
* `tool_choice` variants such as `allowed_tools` and `custom`
* `tool_choice: "required"` (not yet enforced at runtime; will be supported once hard enforcement is implemented)
* `tool_choice: { "type": "function", "function": { "name": "..." } }` (same rationale as `required`)
* `tool_choice.function.name` values that do not match provided `tools`

### Non-streaming tool response shape

When the agent decides to call tools, the response uses:

* `choices[0].finish_reason = "tool_calls"`
* `choices[0].message.tool_calls[]` entries with:
  * `id`
  * `type: "function"`
  * `function.name`
  * `function.arguments` (JSON string)

Assistant commentary before the tool call is returned in `choices[0].message.content` (possibly empty).

### Streaming tool response shape

When `stream: true`, tool calls are emitted as incremental SSE chunks:

* initial assistant role delta
* optional assistant commentary deltas
* one or more `delta.tool_calls` chunks carrying tool identity and argument fragments
* final chunk with `finish_reason: "tool_calls"`
* `data: [DONE]`

If `stream_options.include_usage=true`, a trailing usage chunk is emitted before `[DONE]`.

### Tool follow-up loop

After receiving `tool_calls`, the client should execute the requested function(s) and send a follow-up request that includes:

* prior assistant tool-call message
* one or more `role: "tool"` messages with matching `tool_call_id`

This allows the gateway agent run to continue the same reasoning loop and produce the final assistant answer.

## Open WebUI quick setup

For a basic Open WebUI connection:

* Base URL: `http://127.0.0.1:18789/v1`
* Docker on macOS base URL: `http://host.docker.internal:18789/v1`
* API key: your Gateway bearer token
* Model: `openclaw/default`

Expected behavior:

* `GET /v1/models` should list `openclaw/default`
* Open WebUI should use `openclaw/default` as the chat model id
* If you want a specific backend provider/model for that agent, set the agent's normal default model or send `x-openclaw-model`

Quick smoke:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
curl -sS http://127.0.0.1:18789/v1/models \
  -H 'Authorization: Bearer YOUR_TOKEN'
```

If that returns `openclaw/default`, most Open WebUI setups can connect with the same base URL and token.

## Examples

Non-streaming:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
curl -sS http://127.0.0.1:18789/v1/chat/completions \
  -H 'Authorization: Bearer YOUR_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "openclaw/default",
    "messages": [{"role":"user","content":"hi"}]
  }'
```

Streaming:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
curl -N http://127.0.0.1:18789/v1/chat/completions \
  -H 'Authorization: Bearer YOUR_TOKEN' \
  -H 'Content-Type: application/json' \
  -H 'x-openclaw-model: openai/gpt-5.4' \
  -d '{
    "model": "openclaw/research",
    "stream": true,
    "messages": [{"role":"user","content":"hi"}]
  }'
```

List models:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
curl -sS http://127.0.0.1:18789/v1/models \
  -H 'Authorization: Bearer YOUR_TOKEN'
```

Fetch one model:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
curl -sS http://127.0.0.1:18789/v1/models/openclaw%2Fdefault \
  -H 'Authorization: Bearer YOUR_TOKEN'
```

Create embeddings:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
curl -sS http://127.0.0.1:18789/v1/embeddings \
  -H 'Authorization: Bearer YOUR_TOKEN' \
  -H 'Content-Type: application/json' \
  -H 'x-openclaw-model: openai/text-embedding-3-small' \
  -d '{
    "model": "openclaw/default",
    "input": ["alpha", "beta"]
  }'
```

Notes:

* `/v1/models` returns OpenClaw agent targets, not raw provider catalogs.
* `openclaw/default` is always present so one stable id works across environments.
* Backend provider/model overrides belong in `x-openclaw-model`, not the OpenAI `model` field.
* `/v1/embeddings` supports `input` as a string or array of strings.

## Related

* [Configuration reference](/gateway/configuration-reference)
* [OpenAI](/providers/openai)