annapurna-meter 0.2.0

Annapurna metering hook — emit per-call LLM usage events for per-feature cost attribution.

annapurna-meter

The optional metering hook for Annapurna — a thin, fail-safe wrapper that reports per-call LLM usage so spend can be attributed per feature. Stdlib-only, no dependencies. Cost is computed server-side from Annapurna's pricing tables — the SDK never sees prices, and it never sends prompt or response content, only token counts and a feature_id.

It is fail-safe: reporting is fire-and-forget on a background thread and can never raise into your request path. With no ingest URL/token configured, every call is a no-op.

Install

pip install annapurna-meter

Use

Recommended — wrap the client once (no per-call code):

from annapurna_meter import wrap

client = wrap(anthropic_client, feature_id="feature-threat-triage")  # reads ENV

resp = client.messages.create(model="claude-sonnet-4-6", ...)   # metered automatically

Provider is auto-detected; each call is recorded with its latency. Pass an optional metadata={…} (e.g. environment, customer_id) for extra attribution. Streaming/async calls use the explicit form below.

Explicit — one line per call:

from annapurna_meter import Meter

meter = Meter(feature_id="feature-threat-triage")
resp = anthropic_client.messages.create(model="claude-sonnet-4-6", ...)
meter.record_anthropic(resp)   # <- the whole hook

Helpers: record_anthropic, record_openai, record_gemini, record_openai_compatible, or the generic record(provider=…, model=…, tokens_in=…, tokens_out=…, feature_id=…).

Config

Env var	What it is
ANNAPURNA_INGEST_URL	e.g. https://app.example.com/api/hook/events
ANNAPURNA_INGEST_TOKEN	the per-workspace ingest token from the dashboard

License

Apache-2.0. (The Annapurna server is AGPL-3.0; this client SDK is permissive so you can embed it in a proprietary app.)