keld

An OpenAI-compatible Python client that reports its own usage telemetry to Keld Atlas. keld.Keld subclasses openai.OpenAI, so chat.completions, responses, streaming, retries, every response type and every type hint are the openai SDK's own.

from keld import Keld                                # replaces: from openai import OpenAI

client = Keld(                                       # replaces: client = OpenAI(...)
    base_url="https://api-gateway.keld.co/v1",       # where inference goes
    api_key=os.environ["KELD_TOKEN"],                # that endpoint's key
    atlas_token=os.environ["ATLAS_INGEST_TOKEN"],    # turns telemetry on
)

client.chat.completions.create(...)                  # unchanged from here on

atlas_token is the only thing that turns telemetry on — omit it and this is a plain inference client that exports nothing. base_url points at anything speaking the OpenAI API: OpenAI, https://api.anthropic.com/v1/, https://bedrock-runtime.{region}.amazonaws.com/openai/v1, or your own gateway.

Install

pip install keld

Python 3.10 or newer. There are no extras: the client and the instrumentation are both core dependencies, so no install can import but silently under-report.

Options

Everything is a keyword argument to Keld / AsyncKeld. Anything this SDK does not name is forwarded to openai.OpenAI untouched, so the request on the wire is unchanged.

atlas_metadata is attached to every call the process reports:

client = Keld(
    base_url=..., api_key=..., atlas_token=...,
    atlas_metadata={
        "environment": "production",         # feeds Atlas's CapEx/OpEx classification
        "repo": "acme/checkout-service",     # the calling application
        "team": "checkout",                  # anything else Atlas slices by
    },
)

Keys reach Atlas bare, under exactly the names you give, so a new dimension needs a change at the Atlas end and no release here. An entry with a non-string or empty key, a non-string value, or a key the SDK writes itself (service.name, tool, sdk.language, sdk.version) is dropped with a warning naming it; the rest of the mapping still reports, and nothing is raised.

Sync or async

Two classes, because openai has two and they are not interchangeable. Pick the one that matches the code you already have.

from keld import Keld, AsyncKeld

# Sync — scripts, notebooks, batch jobs, Celery workers
client = Keld(base_url=..., api_key=..., atlas_token=...)
resp = client.chat.completions.create(model=..., messages=[...])

# Async — FastAPI, aiohttp, anything running an event loop
client = AsyncKeld(base_url=..., api_key=..., atlas_token=...)
resp = await client.chat.completions.create(model=..., messages=[...])

AsyncKeld subclasses openai.AsyncOpenAI and takes exactly the same arguments, telemetry included. Neither is the default: if your handler is async def, the sync client blocks the event loop and stalls every other request in the process while it waits on the model — so the framework you already chose decides this, not us.

What the SDK does on its own

• /v1 is appended to a base_url that names only a host, so https://api-gateway.keld.co and https://api-gateway.keld.co/v1 both work. One that already carries a path is left alone.
• Streamed chat.completions calls get stream_options={"include_usage": True} — the one place the SDK modifies an outgoing request. Without it an OpenAI-compatible endpoint omits token usage from a streamed response, and the call bills as zero tokens. Your own stream_options are merged with, never replaced; every chunk, including the usage-only chunk with empty choices, reaches your code unchanged; non-streaming requests are untouched.
• provider is attributed from the endpoint's host. OpenAI, Anthropic, Bedrock's per-region runtime hosts, OpenRouter, Together, Fireworks, Groq, DeepSeek, xAI, Mistral and any host under .keld.co are recognised by name; Ollama by its registered port 11434; Azure by what the instrumentation reports. Anything else reports hosted_vllm with a one-time warning, and there is no override.
• Telemetry never breaks inference. Constructing the client never fails because of it, and a bug in our extraction or export is caught and logged. Provider errors reach you unchanged, and are also recorded as a zero-token event with an error attribute so error rates stay visible. Export runs on a daemon thread over a bounded queue that drops the oldest entries under backpressure, so it never blocks your request thread or on network I/O. Telemetry starts once per process: several clients still report each call once.
• The queue drains when the interpreter exits. Telemetry registers an atexit hook when it starts, so a script, a job, a CLI or a notebook cell that makes its calls and ends reports them. Without it the flush interval — a couple of seconds, on a daemon thread — would take the last batch with the process, silently. The hook waits at most 2 seconds, never raises, and is registered only once telemetry actually starts: importing keld in a process that passes no atlas_token leaves nothing behind.
• An existing global TracerProvider is reused, not replaced, so your own OTel exporters keep seeing openai spans. Without one, the SDK runs a private provider.

keld.shutdown() is what remains for the exits the hook cannot see: os._exit(), os.execv(), a crash, or a signal your app does not handle — the SDK installs no signal handler of its own, because attaching one would change how your process shuts down. It is also how to get the data out of a process that stops making calls but keeps running, such as a worker between jobs or an open notebook kernel. It takes a timeout (5 seconds by default, as a total budget), and pairs safely with the hook: whichever drains first clears the queue and the other returns. That function is the package's only public name besides the two clients.

What gets reported

Every call is one OTLP log record carrying provider, model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens and duration_ms, plus a session.id, event.sequence and prompt.id the SDK mints itself; request_id when the provider returns one, and error when the call failed. Token counts always come from the provider's own response — the SDK never estimates them.

cost_usd is sent only when the provider itself reports what the call cost — a Keld gateway returns it as usage.cost, streamed or not. The SDK never prices a call: with no reported cost there is no cost_usd attribute, and Atlas prices the call from its price table.

Never sent: prompt text, completion text, message bodies, or your API keys — the telemetry path only reads the response of calls your own client makes.

Reported surfaces are chat.completions.create, chat.completions.parse and the Responses API (create / stream / retrieve). Embeddings calls are deliberately not reported.

Known limits

• Native anthropic.Anthropic() and boto3 bedrock-runtime clients are not instrumented. This SDK instruments the OpenAI-compatible surface and nothing else. Reach those providers through their OpenAI-compatible endpoints and they report like any other host; an app calling the native SDKs gets no telemetry at all.
• Bedrock's OpenAI-compatible endpoint does not serve Claude. It serves the OpenAI-format models (verified against openai.gpt-oss-120b-1:0). Claude on Bedrock is reachable only through APIs this SDK does not instrument.
• A private or self-hosted host reports hosted_vllm and prices at $0. There is no way to override the label; open an issue to have a host added to the table.
• An endpoint that rejects stream_options will reject every streamed chat call. The SDK always injects it and there is no flag to turn that off; a flag would trade a broken call for silent zero-token billing. Every endpoint we test against accepts it.

Development

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[test]"
pytest -v

[test] is dev-only and the package's only extra. tests/test_contract.py pins the OTLP export payload to tests/fixtures/atlas-sdk-otlp-export.golden.json, a copy of the normative fixture published by the keld-atlas backend; if you touch the wire format, keep both copies in sync rather than fixing the diff locally.

To cut a release: bump __version__ in src/keld/_version.py (the single source of truth — pyproject.toml reads it), move the Unreleased notes into a new CHANGELOG.md section, merge to main, then git tag v1.0.0 && git push origin v1.0.0.

.github/workflows/release.yml verifies the tag matches __version__, builds an sdist and a wheel, runs the suite against the built wheel on Python 3.10 through 3.13, and only then publishes — via PyPI Trusted Publishing, so no API token lives in this repo. Running the workflow manually publishes to TestPyPI instead.

MIT licensed — see LICENSE.