aikraft

Privacy-first monitoring for LLM applications. Wrap your Anthropic or OpenAI client and Aikraft tracks latency, token usage, refusals, truncations and errors over time, alerts you when they drift, and keeps your EU AI Act post-market monitoring record up to date.

No prompts, responses or personal data ever leave your infrastructure. Only per-minute aggregates do.

pip install aikraft

import aikraft
from anthropic import Anthropic  # or: from openai import OpenAI

aikraft.init(workspace="<workspace-id>", system="<system-id>")  # API key from AIKRAFT_API_KEY
client = aikraft.wrap(Anthropic())

client.messages.create(model="claude-sonnet-5-5", max_tokens=1024, messages=[...])  # unchanged

Your workspace and system IDs, and a ready-to-paste snippet, are in Aikraft under Monitoring → Set up telemetry. Create an API key with read/write scope in Settings → API keys.

What leaves your infrastructure

For each minute, per model and optional tag, the SDK sends counts only:

Never sent: prompts, system prompts, responses, tool inputs or outputs, user or request IDs, or anything else from a single request. Response text is only measured (its length) and immediately discarded. Unknown stop reasons are reported as other, never as raw strings.

A full payload looks like this:

{
  "schema": "aikraft.llm.v1",
  "batch_id": "3f0c…",
  "sdk": {"name": "aikraft-python", "version": "0.2.0"},
  "windows": [{
    "start": "2026-10-08T14:00:00+00:00", "end": "2026-10-08T14:01:00+00:00",
    "model": "claude-sonnet-5-5", "tag": "support-bot", "calls": 128,
    "histograms": {"latency_ms": {"counts": [...], "sum": 91234.5}, "output_tokens": {"counts": [...], "sum": 18003}},
    "categories": {"stop_reason": {"end_turn": 120, "max_tokens": 5, "refusal": 3}, "status": {"ok": 126, "rate_limited": 2}}
  }]
}

Supported clients

aikraft.wrap() works with the sync and async clients of:

• Anthropic: messages.create() (including stream=True) and messages.stream()
• OpenAI: chat.completions.create(), responses.create() (including stream=True) and their .stream() helpers

It patches the client instance you pass, so other clients are untouched, and returns the same client. Return values and exceptions are exactly what the underlying SDK gives you. Failed calls are counted by status and the original exception is re-raised.

For OpenAI chat streaming, token counts are only available if you request them with stream_options={"include_usage": True}. Without it, latency, length and stop reasons are still tracked.

Tags

Group calls by feature or endpoint:

with aikraft.tag("support-bot"):
    client.messages.create(...)

Tags are context-local, so they're safe across threads and asyncio tasks, and must match [A-Za-z0-9_.-]{1,64}.

Feedback

aikraft.feedback(1.0, model="claude-sonnet-5-5", tag="support-bot")  # e.g. 1.0 thumbs up, 0.0 down

Only the count and sum per minute are sent, never which response they belong to.

Configuration

Without an API key, or when disabled, init() logs a warning and everything becomes a no-op. Your app keeps working. Logs go to the aikraft logger.

Seeing exactly what is sent

debug prints every payload before it is sent, and the server's reply, to stderr (or to wherever your app already routes the aikraft logger). dry_run does the same without sending anything and without needing an API key, which is handy to check the data locally before connecting:

$ AIKRAFT_DRY_RUN=1 python app.py
2026-10-17 22:09:24 aikraft: dry run, not sending batch b03e… (1 windows) to https://api.aikraft.eu/…
{
  "schema": "aikraft.llm.v1",
  …
      "categories": {"status": {"connection_error": 1}}
}

The API key is never logged. In the Aikraft app, the Telemetry tab also lists what was received.

Behaviour you can rely on

• Never breaks your app. Monitoring errors are logged, not raised. If Aikraft is unreachable, data is retried and, if it can't be delivered, dropped.
• No double counting. Every payload carries a batch_id; retries reuse it and the server ignores repeats.
• Bounded memory. At most 1000 open windows; beyond that, calls are counted as dropped and logged.
• Pre-fork servers. gunicorn and celery are fine: each worker process starts its own background thread and buffer.
• Serverless. Background threads freeze between invocations, so call aikraft.flush() at the end of each handler (AWS Lambda, Cloud Functions…).
• Shutdown. Buffered data is flushed at interpreter exit. Call aikraft.shutdown() to do it explicitly.

Requirements

Python 3.10+. The only dependency is httpx. anthropic and openai aren't dependencies; the SDK works with whichever version you have installed.

License

Apache-2.0