animica-ai 0.1.0

Official Python SDK for the Animica API (OpenAI-compatible inference, embeddings, usage, and webhook verification).

animica-ai

Official, dependency-free Python SDK for the Animica API.

The Animica API is OpenAI-compatible, so this client mirrors the familiar chat/completions, completions, embeddings, and models surface while adding Animica-specific helpers (usage reporting and webhook signature verification).

• Zero third-party dependencies — uses only the Python standard library.
• Python 3.9+
• Streaming support via server-sent events.
• Idempotency keys for safe retries.
• Webhook verification built in.

Install

pip install animica-ai

The import package is animica_ai:

from animica_ai import Animica, AnimicaError, verify_webhook

Authentication

Get an API key from your Animica dashboard. Production keys look like anm_live_...; test keys look like anm_test_.... The key is sent as Authorization: Bearer <api_key>.

from animica_ai import Animica

client = Animica(api_key="anm_live_xxx")
# Optional: override the base URL (defaults to https://api.animica.org/v1).
# client = Animica(api_key="anm_live_xxx", base_url="https://console.animica.org/v1")

OpenAI drop-in

Because the API is OpenAI-compatible, you can also use the official OpenAI SDK by pointing it at the Animica base URL — no other code changes required:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.animica.org/v1",
    api_key="anm_live_xxx",
)

resp = client.chat.completions.create(
    model="anm-fast-8b",
    messages=[{"role": "user", "content": "Hello from OpenAI's SDK!"}],
)
print(resp.choices[0].message.content)

Use animica-ai when you want a tiny, dependency-free client (and the webhook helper); use the OpenAI SDK if you already depend on it.

Models

Available models include:

Model	Purpose
anm-fast-8b	Fast general chat
anm-code-7b	Code generation
anm-pro-70b	High-quality reasoning
anm-bittensor-router	Routed inference
anm-embed	Embeddings
anm-worker-small	Worker tasks
anm-worker-code	Worker code tasks

for m in client.models()["data"]:
    print(m["id"])

Quickstart

Chat completions

resp = client.chat_completions(
    model="anm-fast-8b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a haiku about mining."},
    ],
    temperature=0.7,
    max_tokens=128,
)
print(resp["choices"][0]["message"]["content"])
print(resp["usage"])  # {prompt_tokens, completion_tokens, total_tokens}

Extra parameters such as top_p, stop, and n are passed straight through:

resp = client.chat_completions(
    model="anm-pro-70b",
    messages=[{"role": "user", "content": "List 3 primes."}],
    top_p=0.9,
    stop=["\n\n"],
)

Streaming

When stream=True, chat_completions returns a generator that yields parsed chat.completion.chunk dicts and stops at [DONE]:

stream = client.chat_completions(
    model="anm-fast-8b",
    messages=[{"role": "user", "content": "Stream a short story."}],
    stream=True,
)
for chunk in stream:
    delta = chunk["choices"][0]["delta"]
    if "content" in delta:
        print(delta["content"], end="", flush=True)
print()

Text completions

resp = client.completions(
    model="anm-fast-8b",
    prompt="Once upon a time",
    max_tokens=64,
)
print(resp["choices"][0]["text"])

Embeddings

resp = client.embeddings(
    model="anm-embed",
    input=["hello world", "goodbye world"],
)
for item in resp["data"]:
    print(item["index"], len(item["embedding"]))
print(resp["usage"])  # {prompt_tokens, total_tokens}

Usage

u = client.usage()
print(u["totalSpentUsd"], u["count"])

Idempotency

Pass idempotency_key to make POST requests safe to retry — the server replays the stored response for a repeated key:

resp = client.chat_completions(
    model="anm-fast-8b",
    messages=[{"role": "user", "content": "Charge me once."}],
    idempotency_key="order-12345",
)

Error handling

Non-2xx responses raise AnimicaError, which carries the OpenAI-shaped error fields plus the HTTP status and the x-request-id response header:

from animica_ai import Animica, AnimicaError

client = Animica(api_key="anm_live_xxx")
try:
    client.chat_completions(
        model="anm-fast-8b",
        messages=[{"role": "user", "content": "Hi"}],
    )
except AnimicaError as e:
    print("status:", e.status)          # e.g. 429
    print("type:", e.type)              # authentication_error | invalid_request_error |
                                        # insufficient_quota | rate_limit_error |
                                        # permission_error | api_error
    print("code:", e.code)
    print("message:", e.message)
    print("request_id:", e.request_id)  # from x-request-id, for support

Rate-limit responses (HTTP 429) include a retry-after header and the x-ratelimit-* headers are present on all responses.

Webhook verification

Animica signs webhook deliveries with the header:

X-Animica-Signature: t=<unixSeconds>,v1=<hex>

where hex = HMAC_SHA256(endpointSecret, f"{t}.{rawRequestBody}"). Verify the raw request body (not re-serialized JSON) with verify_webhook, which does a constant-time comparison and enforces a timestamp tolerance (default 300s):

from animica_ai import verify_webhook

# Flask example
from flask import Flask, request, abort
import json

app = Flask(__name__)
ENDPOINT_SECRET = "whsec_xxx"

@app.post("/animica/webhook")
def webhook():
    raw = request.get_data()  # bytes — must be the raw body
    sig = request.headers.get("X-Animica-Signature", "")
    if not verify_webhook(ENDPOINT_SECRET, raw, sig):
        abort(400)
    event = json.loads(raw)
    print(event["type"], event["id"])  # evt_...
    return "", 204

payload may be str or bytes. Set tolerance_sec=0 to disable the timestamp window (not recommended in production).

License

MIT