Mistral

Auto-instrument the Mistral SDK (mistralai on PyPI, @mistralai/mistralai on npm). Every chat completion inside an active run emits an llm_call event with model, token usage, latency, finish reason, and tool calls. Cost is computed server-side from the rate card.

Install / enable

Python:

pip install "agentping-io[mistral]"

import agentping
from mistralai import Mistral

agentping.init()
agentping.instrument_mistral()

client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

with agentping.run("support-triage", customer_id="acme-corp"):
    reply = client.chat.complete(
        model="mistral-small-latest",
        messages=[{"role": "user", "content": "classify this ticket"}],
    )

instrument_mistral() patches chat.complete, chat.complete_async, chat.stream and chat.stream_async on the Chat resource class. Idempotent and global; call it once at module load. Supported mistralai versions: 2.x. The 0.x and 1.x clients have a different layout and are skipped with a single warning.

TypeScript:

npm install @agentping/sdk @mistralai/mistralai

import { Mistral } from "@mistralai/mistralai";
import * as agentping from "@agentping/sdk";

agentping.init({ apiKey: process.env.AGENTPING_API_KEY });

const run = agentping.run("answer-bot");
const mistral = agentping.instrumentMistral(
  new Mistral({ apiKey: process.env.MISTRAL_API_KEY }),
  { run },
);

const reply = await mistral.chat.complete({
  model: "mistral-small-latest",
  messages: [{ role: "user", content: "hi" }],
});

await run.finish({ status: "success" });

instrumentMistral returns a wrapped client covering chat.complete and chat.stream.

What's captured

Field Source
provider mistral
model The requested model, falling back to response.model
input_tokens usage.prompt_tokens
output_tokens usage.completion_tokens
finish_reason choices[0].finish_reason
tool_calls Number of message.tool_calls
latency_ms Wall-clock time of the call
stream true on chat.stream

Mistral's usage block carries no cache or reasoning breakdown, so cached_input_tokens, cache_creation_input_tokens and reasoning_tokens are never set. Embeddings (client.embeddings) and the Agents API are not instrumented; report those yourself with run.event("llm_call", {...}).

A call that raises is recorded as an llm_call with status: "error", error and exception, then re-raised.

Streaming

Both SDKs capture chat.stream (Python also chat.stream_async). Mistral yields CompletionEvent objects; the wrapper reads usage from the final event's data and emits one llm_call with stream: true once the stream is fully consumed or closed.

with agentping.run("answer-bot"):
    for event in client.chat.stream(
        model="mistral-small-latest",
        messages=[{"role": "user", "content": "explain briefly"}],
    ):
        print(event.data.choices[0].delta.content or "", end="", flush=True)
    # llm_call is emitted here

Source / notes

Default rates ship for Anthropic and OpenAI models only. Mistral calls land with their token counts and appear in the unpriced-models list until you add the model's per-million rates under Spend, Rate cards; from then on every new call is priced. See Spend.