pareta 0.1.0

Python client for Pareta — deploy open-weights endpoints, run metered inference, and eval models on your own data.

pareta

Python client for Pareta — deploy open-weights endpoints, run metered inference, browse the benchmark catalog, and eval models on your own data.

pip install pareta        # or: uv add pareta / poetry add pareta

from pareta import Pareta

pa = Pareta.from_env()                       # reads PARETA_API_KEY
# or: Pareta(api_key="pareta_sk_…", base_url="https://api.pareta.ai")

# OpenAI-compatible inference against a deployed endpoint
resp = pa.chat.completions.create(
    model="ep_…",                            # an endpoint id (see pa.models.list())
    messages=[{"role": "user", "content": "Extract the total from this invoice: …"}],
)
print(resp.choices[0].message.content)

# Streaming
for chunk in pa.chat.completions.create(model="ep_…", messages=[...], stream=True):
    print(chunk.choices[0].delta.content or "", end="")

# List the models (endpoints) your org can call
for m in pa.models.list():
    print(m.id)

Async mirrors the sync client:

from pareta import AsyncPareta

async with AsyncPareta.from_env() as pa:
    resp = await pa.chat.completions.create(model="ep_…", messages=[...])

Auth

Mint a pareta_sk_ key in the dashboard (key management is browser-only) and pass it as api_key= or via PARETA_API_KEY. The SDK only ever consumes a key; it never creates, lists, or revokes them.

Inference is OpenAI-compatible

You don't even need this SDK to call a deployed endpoint — point the openai client at base_url + your key:

from openai import OpenAI
client = OpenAI(api_key="pareta_sk_…", base_url="https://api.pareta.ai/v1")

This SDK's unique value is the control plane: deploy, operate, and eval models from code. Those land slice by slice — see SDK_PLAN.md.

Errors

All errors subclass pareta.ParetaError:

Exception	When
AuthenticationError (401)	bad/missing key
InsufficientCreditsError (402)	org out of credit — top up in the dashboard
NotFoundError (404)	unknown endpoint
EndpointNotReadyError (503)	endpoint stopped / cold / provider down
RateLimitError (429)	throttled (auto-retried)
BadRequestError (400/422)	malformed request
APIConnectionError / APITimeoutError	transport failure (auto-retried)

Idempotent GETs and 429/5xx/timeouts are retried with exponential backoff (max_retries, default 2).

Status

Slice 1 (this release): core client, auth, retries, typed errors, chat.completions, models. Endpoints, tasks, and evals land next (SDK_PLAN.md §11).