Vantage LiteLLM Callback

Install a released version with pip install vantage-litellm-callback. To work from a source checkout, install this directory with pip install .. Configure LiteLLM with:

litellm_settings:
  callbacks:
    - vantage_callback.callback_instance

See the repository README for collector setup and lifecycle billing semantics.

The callback is fail-open: collector outages, acknowledgement failures, malformed responses, queue pressure, and projection errors never block or fail a provider request. Usage events are placed into a bounded in-process queue and delivered by a background worker. When delivery is unhealthy, the worker opens a circuit, aggregates bounded loss information, and periodically probes the collector with a versioned delivery_gap control frame. Normal delivery resumes only after that frame receives a durable acknowledgement.

Configuration:

• VANTAGE_COLLECTOR_DELIVERY_QUEUE_SIZE (default 1024)
• VANTAGE_COLLECTOR_RECOVERY_PROBE_SECONDS (default 5)
• VANTAGE_COLLECTOR_GAP_EVENT_ID_SAMPLE_SIZE (default 32)
• VANTAGE_COLLECTOR_GAP_SUMMARY_INTERVAL_SECONDS (default 60)
• VANTAGE_COLLECTOR_SOCKET_PATH (default /var/run/vantage-collector/collector.sock)
• VANTAGE_COLLECTOR_ACK_TIMEOUT_SECONDS (default 5)
• VANTAGE_COLLECTOR_CONNECTION_POOL_SIZE (default 16)

The queue and gap aggregate are intentionally process-local and bounded. A process exit may therefore lose queued events; the gap frame reports losses observed while the process remains alive.

Running the provider matrix locally

tests/test_provider_matrix.py is a regression matrix over the provider and mode combinations whose usage shapes disagree with one another. Every cell is a recorded fixture, so the matrix needs no API keys and runs on every pull request:

pip install --editable . pytest pytest-asyncio
PYTHONPATH=. pytest tests/test_provider_matrix.py -v

Run a single cell while iterating on a provider:

PYTHONPATH=. pytest tests/test_provider_matrix.py -k anthropic

Fixtures live in tests/fixtures/, one JSON file per cell:

Each file carries its usage payload, the expected_usage projection, the expected_billable_total, and a note citing the LiteLLM transform the shape was taken from. Adding a provider means adding a JSON file — the tests parametrize over the directory, so a new file becomes a new cell with no test changes.

The matrix asserts three things per cell: the four projected Usage fields, that those fields are mutually exclusive (they must sum to the billable total, so no token is counted twice), and that the callback emits exactly one started and one terminal event. Two further tests assert that nothing the callback attaches to a request reaches the outbound provider body.

Two token-semantics rules are what the matrix exists to protect, both of which have regressed before:

• The input count is gross wherever the cache is reported nested. LiteLLM's transforms fold cache reads and writes into prompt_tokens, and the Responses API counts input_tokens_details.cached_tokens inside input_tokens. Those buckets have to be subtracted or the cached tokens bill twice. Raw Anthropic usage is the exception: it puts cache_*_input_tokens beside a net input_tokens, so nothing is subtracted there.
• text_tokens is not "uncached input". It means the text modality for OpenAI but the net raw input for Anthropic. Reading it verbatim silently drops image, video, and audio input tokens, so uncached input is derived from the gross count instead.

For an end-to-end check against a live proxy on :4000, demo/send_requests.sh sends tagged requests through LiteLLM; see demo/README.md. That path needs a running proxy and collector and is a manual smoke test, not part of CI.