Mistral

Lightweight, dependency-free, in-memory Mistral AI API fake. Mistral is OpenAI-compatible under /v1, so both the official @mistralai/mistralai SDK and the openai SDK work against this fake. All generated content is deterministic — text and embedding vectors are derived from a hash of the input.

Default port: 4755

Quick start

import { MistralServer } from "./services/mistral/src/server.js";

const server = new MistralServer(4755);
await server.start();
// ... run your app/tests ...
await server.stop();

Point the openai SDK at it via baseURL (or set the Mistral SDK's serverURL):

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "parlel",
  baseURL: "http://127.0.0.1:4755/v1",
});

const completion = await client.chat.completions.create({
  model: "mistral-large-latest",
  messages: [{ role: "user", content: "Hello Mistral" }],
});

Implemented operations

All /v1/* routes require an Authorization: Bearer <key> header (any non-empty bearer token is accepted). State is in-memory and ephemeral.

POST /v1/chat/completions — chat completion. Supports stream: true (SSE ending with data: [DONE]).
POST /v1/embeddings — deterministic 1024-dim embedding vectors. Accepts a string or array of strings.
GET /v1/models — list models.

Service & inspection operations (parlel extensions)

GET / — service metadata.
GET /health — health check ({ status: "ok" }).
POST /__parlel/reset — reset all in-memory state.
GET /__parlel/requests — list captured requests.
DELETE /__parlel/requests — clear the captured request log.

SDK usage example

from mistralai import Mistral

client = Mistral(api_key="parlel", server_url="http://127.0.0.1:4755")
resp = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Hello Mistral"}],
)
print(resp.choices[0].message.content)

Access via MCP / preview URL

HTTP services are auto-exposed at the Daytona preview URL. Send requests to the preview URL with the x-daytona-preview-token header. Set MISTRAL_BASE_URL to <preview-url>/v1.

Surface coverage

This emulator faithfully replicates the API surface most application code and agents exercise. Anything below the supported lines is either an intentional design choice for a fast, zero-cost local emulator (✓ By design) or a candidate for a future release (⟳ Roadmap) — never a silent inaccuracy.

Legend: ✅ fully supported · ◐ accepted (stored, not strictly enforced) · ✓ by design · ⟳ on the roadmap.

Feature	Status
`chat/completions` (+ streaming SSE)	✅ Supported
`embeddings` (deterministic 1024-dim)	✅ Supported
`models` list	✅ Supported
Request inspection	✅ Supported (parlel extension)
Real model inference / quality	✓ By design — Deterministic stub output — repeatable assertions, no API spend
`tools` / function calling	◐ Accepted, not executed (`tool_calls: null`)
FIM / agents / fine-tuning endpoints	⟳ Roadmap
Token counts	◐ Approximate word-based
Bearer-token validity / quota	✓ By design — Never throttles — local tests run at full speed, zero cost

Manifest

See services/mistral/manifest.json:

name: mistral, image: parlel/mistral:1
port: 4755, protocol: http, healthcheck: /health, startup ≈ 100ms
env: MISTRAL_API_KEY, MISTRAL_BASE_URL

Configuration — `test.env`

Copy these into your test.env (used by the bridge sidecar flow). Tokens are Parlel's seeded test credentials — any non-empty value is accepted by the emulator, so you rarely need to change them. Swap in real credentials only when pointing at the live service in prod.env.

MISTRAL_API_KEY=parlel
MISTRAL_BASE_URL=http://parlel-bridge:4755/v1