cognocient 0.1.1

Drop-in async-reporting wrapper for the OpenAI and Anthropic Python SDKs — live cost attribution without changing your base_url.

cognocient

A drop-in wrapper around the OpenAI and Anthropic Python SDKs that reports usage to Cognocient asynchronously, so you get live cost attribution without changing your base_url or routing traffic through a proxy.

pip install cognocient[openai]      # or cognocient[anthropic], or both

# Before
from openai import OpenAI
client = OpenAI(api_key="sk-...")

# After
from cognocient import CognocientOpenAI as OpenAI
client = OpenAI(
    api_key="sk-...",              # your own real OpenAI key, used exactly as before
    cognocient_key="sk-cog-...",   # the same proxy key you'd use with the Cognocient proxy
)

client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "hello"}],
    cognocient_feature="support-bot",   # optional attribution — same field names the proxy accepts as X-Cost-* headers
)

Every method the real SDK exposes still works unchanged. This wrapper only intercepts chat.completions.create() (messages.create() for Anthropic) to time the call and report its usage after the fact; everything else is forwarded to the real client untouched.

This is one of three ways to see your Cognocient dashboard

	Live attribution	Pre-call enforcement (block/degrade)	Code change
Proxy (base_url swap)	Yes	Yes	One line
This wrapper	Yes	No — see below	Swap the import, add a key
CSV/OTel import	No (historical only)	No	None

Security — read this before you decide

This wrapper is not more secure than the proxy. It is a different tradeoff, not a strictly better one.

With the proxy, your real provider API key lives server-side, under Cognocient's control, in one place. With this wrapper, your real provider key stays in your own application process, exactly as it does today without Cognocient at all — the wrapper calls the provider directly, using your key, inside your runtime. Some security teams prefer that (no third-party network hop in the request path); others are less comfortable with third-party code executing inside their process with key access. Both are reasonable positions. We're not going to tell you this "removes a security roadblock" — it trades one shape of exposure for a different one.

What this wrapper honestly gives you over the proxy:

• Zero added request latency. Reporting happens after your real call already returned, on a background thread, off the critical path.
• Zero risk of a Cognocient outage affecting your production call. If Cognocient's ingestion API is down or unreachable, your call to OpenAI/Anthropic still completes normally — see "Reliability" below.

What you give up versus the proxy: pre-call enforcement. Because Cognocient only hears about a call after it already happened, budgets configured in Cognocient cannot block or degrade a call made through this wrapper before it fires. The dashboard will say so explicitly for any account using this path.

Reliability

Reporting is fire-and-forget on a background thread with a bounded local queue, flushed every few seconds or every 50 calls, whichever comes first. If the ingestion API is slow, down, or unreachable:

• Your real provider call is completely unaffected — it already happened before reporting was attempted.
• No exception is ever raised into your code from a reporting failure.
• No retry loop that could pile up work in your process — a failed batch is dropped and logged locally at DEBUG level via the cognocient logger, not retried.

See tests/test_reporter_failure_isolation.py for a test that simulates an unreachable ingestion endpoint and asserts the real call still completes normally.

Known limitation: streaming isn't reported yet

stream=True calls are passed through to the real SDK completely unmodified — your application behaves identically — but are not currently reported to Cognocient. Usage totals aren't available until a stream completes, and reliably capturing them requires wrapping the stream iterator itself, which this version doesn't do. If most of your traffic streams, this wrapper will under-report your usage today. Use the proxy or the CSV/OTel importer if that matters for your evaluation.

Attribution fields

Same field names the proxy accepts as X-Cost-* headers, passed as keyword arguments instead:

Wrapper kwarg	Proxy header
cognocient_feature	X-Cost-Feature
cognocient_department	X-Cost-Department
cognocient_user	X-Cost-User
cognocient_session	X-Cost-Session
cognocient_tier	X-Cost-Tier
cognocient_project	X-Cost-Project
cognocient_gl_account	X-Cost-GL-Account
cognocient_workload	X-Cost-Workload
cognocient_outcome	X-Cost-Outcome
cognocient_run_id	X-Cost-Run-ID

Development

pip install -e ".[dev]"
pytest
python benchmark/benchmark_wrapper_overhead.py