# Mistral

> MistralLLMService streams chat completions from Mistral through its OpenAI-compatible API.

Source: https://polvallverdu.github.io/pipedog/docs/services/llm/mistral/



{/* Generated by scripts/generate-providers.ts from data/providers.ts. */}

## Overview [#overview]

Mistral AI's chat models.

* **`MistralLLMService`**: an `OpenAILLMService` subclass with Mistral's base URL and default
  model. It uses `MistralLLMAdapter`, which rewrites the history to Mistral's rules: an assistant message after every tool message, system messages only at the start, and `prefix: true` on a trailing assistant message.

<Cards>
  <Card title="Upstream page" href="https://docs.pipecat.ai/api-reference/server/services/llm/mistral">
    Pipecat's documentation for the same service.
  </Card>

  <Card title="Source" href="https://github.com/polvallverdu/pipedog/tree/main/packages/mistral/src">
    `packages/mistral/src/llm.ts` and `mistral_adapter.ts`.
  </Card>

  <Card title="Mistral docs" href="https://docs.mistral.ai/api/">
    The API reference.
  </Card>
</Cards>

## Installation [#installation]

```bash
bun add @pipedog/mistral
```

It depends on `@pipedog/openai`, which is installed with it.

```ts
import { MistralLLMService } from "@pipedog/mistral/llm";
import { MistralLLMAdapter } from "@pipedog/mistral/mistral_adapter";
```

## Prerequisites [#prerequisites]

* A Mistral account and an API key from [Mistral](https://docs.mistral.ai/api/).
* Pass the key as `apiKey`. It is required, and the service never falls back to
  `OPENAI_API_KEY`. The examples read it from `MISTRAL_API_KEY`.

## MistralLLMService [#mistralllmservice]

### Configuration [#configuration]

| Option | Type | Required | Description |
|---|---|---|---|
| `taskManager` | `BaseTaskManager \| null \| undefined` | no | Task manager for handling tasks. |
| `apiKey` | `string` | yes | The API key for accessing Mistral's API. |
| `baseUrl` | `string \| null \| undefined` | no | The base URL for Mistral API. Defaults to `"https://api.mistral.ai/v1"`. |
| `settings` | `MistralLLMSettings \| null \| undefined` | no | Runtime-updatable settings; values override the built-in defaults. |
| `name` | `string \| null \| undefined` | no | Custom name; defaults to `ClassName#count`. |
| `enableDirectMode` | `boolean \| undefined` | no | Process frames immediately instead of using internal queues. |
| `metrics` | `FrameProcessorMetrics \| null \| undefined` | no | Optional metrics collector for this processor. |
| `runInParallel` | `boolean \| undefined` | no | Whether to run function calls in parallel or sequentially. Defaults to true. |
| `groupParallelTools` | `boolean \| undefined` | no | Whether to group parallel function calls so the LLM is triggered exactly once after all calls in the batch complete. When false, each function call result triggers the LLM independently as it arrives. Defaults to true. |
| `functionCallTimeoutSecs` | `number \| null \| undefined` | no | Optional timeout in seconds for function calls. A call that runs past it is cancelled: its handler is cancelled (it gets a `CancelledError`), the call is settled as cancelled, and inference runs so the LLM can report that it didn't complete. |
| `fileResolver` | `FileResolver \| null \| undefined` | no | Resolves file URLs in the context that this provider can't fetch itself (see `FileResolver`). Pass one configured with a `fileStorage` to resolve uploaded-file URLs (`new FileResolver({ fileStorage: runnerArgs.fileStorage })` when a runner hosts the bot), and share one instance across services that may consume the same files (e.g. services switched between mid-session) so downloads are reused. Defaults to a resolver that handles `data:` and `http(s)` URLs only. |
| `organization` | `string \| null \| undefined` | no | OpenAI organization ID. |
| `project` | `string \| null \| undefined` | no | OpenAI project ID. |
| `defaultHeaders` | `Record<string, string> \| null \| undefined` | no | Additional HTTP headers. |
| `serviceTier` | `string \| null \| undefined` | no | Service tier to use (e.g., "auto", "flex", "priority"). |
| `retryTimeoutSecs` | `number \| null \| undefined` | no | How long an inference may go without producing output before it is abandoned and re-issued, when `retryOnTimeout` is set. Defaults to 5.0 seconds. |
| `retryOnTimeout` | `boolean \| null \| undefined` | no | Whether to re-issue the request once if the first attempt produces no output within `retryTimeoutSecs`. The retry is unbounded. |

The other options (`organization`, `project`, `defaultHeaders`, `serviceTier`,
`retryOnTimeout`, `retryTimeoutSecs`, function calling) are `OpenAILLMService`'s. See
[OpenAI](https://polvallverdu.github.io/pipedog/docs/services/llm/openai/#configuration).

|               |                                                                                          |
| ------------- | ---------------------------------------------------------------------------------------- |
| Base URL      | `https://api.mistral.ai/v1`                                                              |
| Default model | `mistral-small-latest`                                                                   |
| Models to try | `mistral-small-latest`, `mistral-large-latest`. See the provider's catalog for the rest. |

### Settings [#settings]

Runtime-updatable settings, passed as `settings: new MistralLLMService.Settings({...})`
(`MistralLLMSettings`). They are [OpenAI's settings](https://polvallverdu.github.io/pipedog/docs/services/llm/openai/#settings), with these
defaults:

| Setting | Type     | Default                  | Description |
| ------- | -------- | ------------------------ | ----------- |
| `model` | `string` | `"mistral-small-latest"` | Model name. |

Every other setting keeps OpenAI's default. Unset sampling settings are left out of the request, so
the provider's own defaults apply. `extra` carries provider-specific request fields.

### Usage [#usage]

```ts title="llm.ts"
import { LLMUpdateSettingsFrame } from "@pipedog/core/frames/frames";
import { MistralLLMService } from "@pipedog/mistral/llm";

const llm = new MistralLLMService({
  apiKey: process.env.MISTRAL_API_KEY ?? "",
  settings: new MistralLLMService.Settings({
    model: "mistral-small-latest",
    systemInstruction: "You are a helpful assistant in a voice conversation.",
    temperature: 0.7,
  }),
});
```

Change settings mid-call with an `LLMUpdateSettingsFrame`. The next completion uses them:

```ts
// Queue it on the worker, or push it from a processor upstream of the service.
const update = new LLMUpdateSettingsFrame({
  delta: new MistralLLMService.Settings({ temperature: 0.2, maxTokens: 300 }),
});
```

### Notes [#notes]

* Runs a completion for each `LLMContextFrame`, between an `LLMFullResponseStartFrame` and an
  `LLMFullResponseEndFrame`, and streams the reply as `LLMTextFrame`s, as
  [`OpenAILLMService`](https://polvallverdu.github.io/pipedog/docs/services/llm/openai.md) does. Function calls, token usage metrics and
  `runInference` work the same way.
* "developer" messages are sent as "user" messages, since the API doesn't support that role (`supportsDeveloperRole` is `false`).
* Messages go through `MistralLLMAdapter`, which rewrites the history to Mistral's rules: an assistant message after every tool message, system messages only at the start, and `prefix: true` on a trailing assistant message.
* `seed` is sent as Mistral's `random_seed`. `stream_options`, `max_completion_tokens` and `service_tier` are not sent.
* Mistral detects function calls from the whole history, so calls that already have a tool result in the context are skipped instead of run twice. `on_function_calls_started` may still fire twice.
* The same package has Mistral's [speech-to-text](https://polvallverdu.github.io/pipedog/docs/services/stt/mistral.md) and [text-to-speech](https://polvallverdu.github.io/pipedog/docs/services/tts/mistral.md) services.

## Runtime notes [#runtime-notes]

| Runtime            | Works | What it needs |
| ------------------ | ----- | ------------- |
| Bun                | ✅ yes | Nothing.      |
| Node.js            | ✅ yes | Nothing.      |
| Deno               | ✅ yes | Nothing.      |
| Cloudflare Workers | ✅ yes | Nothing.      |

Generated from `pipecat.runtimes` and `pipecat.compat` in `packages/mistral/package.json`: ✅ means the compat smoke test passes on that runtime.

* The client is the official `openai` JS SDK (through `@pipedog/openai`), which talks through
  `fetch`. It works on Bun, Node, Deno and Cloudflare Workers.
* The deprecated `model` constructor option is not ported. Use `settings`.
* Tested against a local OpenAI-compatible mock server, not against Mistral itself.