# OpenAI

Access GPT models including standard chat models and reasoning models (o1, o3, GPT-5).

ReqLLM also exposes a separate `openai_codex` provider for the ChatGPT Codex backend used by OAuth Codex tokens.

## Configuration

```bash
OPENAI_API_KEY=sk-...
```

## Model Specs

For the full model-spec workflow, see [Model Specs](model-specs.md).

Use exact OpenAI IDs from [LLMDB.xyz](https://llmdb.xyz) when possible. For brand-new model IDs, local OpenAI-compatible servers, or proxies, use `ReqLLM.model!/1` with `provider: :openai`, an explicit `id`, and `base_url` when needed.

### OAuth Access Token (optional)

If you use OAuth instead of API keys, pass an access token and set auth mode:

```elixir
ReqLLM.generate_text(
  "openai:gpt-5-codex",
  "Write a test",
  auth_mode: :oauth,
  access_token: System.fetch_env!("OPENAI_ACCESS_TOKEN")
)
```

You can also pass these under `provider_options`.

### ChatGPT Codex Backend (`openai_codex`)

Use `openai_codex:*` when your token comes from the ChatGPT/Codex OAuth flow and you want requests routed to `https://chatgpt.com/backend-api/codex/responses` instead of platform OpenAI `/v1/responses`.

This provider is OAuth-only and resolves `chatgpt_account_id` in this order:

- explicit `provider_options: [chatgpt_account_id: "..."]`
- `accountId` / `account_id` in the oauth/auth JSON file
- JWT claim extraction from the access token

Example:

```elixir
ReqLLM.generate_text(
  "openai_codex:gpt-5.3-codex-spark",
  "Write a test for this function",
  provider_options: [
    auth_mode: :oauth,
    oauth_file: "/path/to/auth.json"
  ]
)
```

### OAuth Files (`oauth.json` / `auth.json`)

ReqLLM can also read provider credentials from a JSON file using the same shape used by `pi-ai`:

```json
{
  "openai-codex": {
    "type": "oauth",
    "access": "eyJ...",
    "refresh": "oai_rt_...",
    "expires": 1762857415123,
    "accountId": "user_123"
  }
}
```

When `auth_mode: :oauth` is enabled and no explicit `access_token` is passed, ReqLLM will:

- load credentials from `provider_options: [oauth_file: "..."]`
- accept `auth_file` as an alias
- fall back to `oauth.json` or `auth.json` in the current working directory
- refresh expired `openai-codex` credentials automatically and persist the updated file
- reuse `accountId` from the file or derive it from the refreshed access token for Codex requests

Example:

```elixir
ReqLLM.generate_text(
  "openai:gpt-5-codex",
  "Write a test",
  provider_options: [
    auth_mode: :oauth,
    oauth_file: "/path/to/oauth.json"
  ]
)
```

If you need to customize the refresh HTTP client, pass `oauth_http_options` under `provider_options`.

For `openai_codex`, you can also override the backend headers with:

- `provider_options: [chatgpt_account_id: "..."]`
- `provider_options: [codex_originator: "pi"]`

## Attachments

OpenAI Chat Completions API only supports image attachments (JPEG, PNG, GIF, WebP).
For document support (PDFs, etc.), use Anthropic or Google providers.

## Dual API Architecture

OpenAI provider automatically routes between two APIs based on model metadata:

- **Chat Completions API**: Standard GPT models (gpt-4o, gpt-4-turbo, gpt-3.5-turbo)
- **Responses API**: Reasoning models (o1, o3, o4-mini, gpt-5) with extended thinking

## Provider Options

Passed via `:provider_options` keyword:

### `max_completion_tokens`

- **Type**: Integer
- **Purpose**: Required for reasoning models (o1, o3, gpt-5)
- **Note**: ReqLLM auto-translates `max_tokens` to `max_completion_tokens` for reasoning models
- **Example**: `provider_options: [max_completion_tokens: 4000]`

### `openai_structured_output_mode`

- **Type**: `:auto` | `:json_schema` | `:tool_strict`
- **Default**: `:auto`
- **Purpose**: Control structured output strategy
- **`:auto`**: Use json_schema when supported, else strict tools
- **`:json_schema`**: Force response_format with json_schema
- **`:tool_strict`**: Force strict: true on function tools
- **Example**: `provider_options: [openai_structured_output_mode: :json_schema]`

### `response_format`

- **Type**: Map
- **Purpose**: Custom response format configuration
- **Example**:
  ```elixir
  provider_options: [
    response_format: %{
      type: "json_schema",
      json_schema: %{
        name: "person",
        schema: %{type: "object", properties: %{name: %{type: "string"}}}
      }
    }
  ]
  ```

### `openai_parallel_tool_calls`

- **Type**: Boolean | nil
- **Default**: `nil`
- **Purpose**: Override parallel tool call behavior
- **Example**: `provider_options: [openai_parallel_tool_calls: false]`

### `reasoning_effort`

- **Type**: `:low` | `:medium` | `:high`
- **Purpose**: Control reasoning effort (Responses API only)
- **Example**: `reasoning_effort: :high`

### `service_tier`

- **Type**: `:auto` | `:default` | `:flex` | `:priority` | String
- **Purpose**: Service tier for request prioritization
- **Example**: `service_tier: :auto`

### `seed`

- **Type**: Integer
- **Purpose**: Set seed for reproducible outputs
- **Example**: `provider_options: [seed: 42]`

### `logprobs`

- **Type**: Boolean
- **Purpose**: Request log probabilities
- **Example**: `provider_options: [logprobs: true, top_logprobs: 3]`

### `top_logprobs`

- **Type**: Integer (1-20)
- **Purpose**: Number of log probabilities to return
- **Requires**: `logprobs: true`
- **Example**: `provider_options: [logprobs: true, top_logprobs: 5]`

### `user`

- **Type**: String
- **Purpose**: Track usage by user identifier
- **Example**: `provider_options: [user: "user_123"]`

### `verbosity`

- **Type**: `"low"` | `"medium"` | `"high"`
- **Default**: `"medium"`
- **Purpose**: Control output detail level
- **Example**: `provider_options: [verbosity: "high"]`

### `openai_stream_transport`

- **Type**: `:sse` | `:websocket`
- **Default**: `:sse`
- **Purpose**: Select the streaming transport for Responses models
- **Note**: `:websocket` currently applies to OpenAI Responses models only
- **Example**: `provider_options: [openai_stream_transport: :websocket]`

### Embedding Options

#### `dimensions`

- **Type**: Positive integer
- **Purpose**: Control embedding dimensions (model-specific ranges)
- **Example**: `provider_options: [dimensions: 512]`

#### `encoding_format`

- **Type**: `"float"` | `"base64"`
- **Purpose**: Format for embedding output
- **Example**: `provider_options: [encoding_format: "base64"]`

### Responses API Resume Flow

#### `previous_response_id`

- **Type**: String
- **Purpose**: Resume tool calling flow from previous response
- **Example**: `provider_options: [previous_response_id: "resp_abc123"]`

#### `tool_outputs`

- **Type**: List of `%{call_id, output}` maps
- **Purpose**: Provide tool execution results for resume flow
- **Example**: `provider_options: [tool_outputs: [%{call_id: "call_1", output: "result"}]]`

## WebSocket Mode

ReqLLM keeps SSE as the default transport for OpenAI streaming, but Responses models can opt into OpenAI WebSocket mode per request:

```elixir
{:ok, stream_response} =
  ReqLLM.stream_text(
    "openai:gpt-5",
    "Write a short summary",
    provider_options: [openai_stream_transport: :websocket]
  )

text = ReqLLM.StreamResponse.text(stream_response)
usage = ReqLLM.StreamResponse.usage(stream_response)
```

Use this when you want a call-scoped WebSocket transport while keeping the existing `StreamResponse` API. SSE remains the safer default for broad provider parity and existing fixture coverage.

## Realtime API

ReqLLM also exposes an experimental low-level Realtime WebSocket client for session-oriented workflows that do not fit `stream_text/3`:

```elixir
{:ok, session} = ReqLLM.OpenAI.Realtime.connect("gpt-realtime")

:ok =
  ReqLLM.OpenAI.Realtime.session_update(session, %{
    "type" => "realtime",
    "instructions" => "Be concise and friendly."
  })

{:ok, event} = ReqLLM.OpenAI.Realtime.next_event(session)

:ok = ReqLLM.OpenAI.Realtime.close(session)
```

This API is intentionally low-level. You send JSON events, receive JSON events, and manage the session lifecycle explicitly.

## Usage Metrics

OpenAI provides comprehensive usage data including:

- `reasoning_tokens` - For reasoning models (o1, o3, gpt-5)
- `cached_tokens` - Cached input tokens
- Standard input/output/total tokens and costs

### Web Search (Responses API)

Models using the Responses API (o1, o3, gpt-5) support web search tools:

```elixir
{:ok, response} = ReqLLM.generate_text(
  "openai:gpt-5-mini",
  "What are the latest AI announcements?",
  tools: [%{"type" => "web_search"}]
)

# Access web search usage
response.usage.tool_usage.web_search
#=> %{count: 2, unit: "call"}

# Access cost breakdown
response.usage.cost
#=> %{tokens: 0.002, tools: 0.02, images: 0.0, total: 0.022}
```

### Image Generation

Image generation costs are tracked separately:

```elixir
{:ok, response} = ReqLLM.generate_image("openai:gpt-image-1", prompt)

response.usage.image_usage
#=> %{generated: %{count: 1, size_class: "1024x1024"}}

response.usage.cost
#=> %{tokens: 0.0, tools: 0.0, images: 0.04, total: 0.04}
```

See the [Image Generation Guide](image-generation.md) for more details.

## Resources

- [OpenAI API Documentation](https://platform.openai.com/docs/api-reference)
- [Model Overview](https://platform.openai.com/docs/models)