syntropylabs-evalkit 0.1.17

EvalKit Python SDK — LLM observability and tracing

EvalKit Python SDK

LLM observability and tracing for Python apps. One init() call auto-instruments your LLM clients, HTTP calls, database queries, and logging — then streams traces to Syntropy Labs.

Installation

pip install syntropylabs-evalkit

Optional provider extras:

pip install "syntropylabs-evalkit[openai]"      # OpenAI
pip install "syntropylabs-evalkit[anthropic]"   # Anthropic
pip install "syntropylabs-evalkit[all]"         # everything

The PyPI package is syntropylabs-evalkit, but you import it as evalkit.

Quickstart

import evalkit

evalkit.init(
    subscription_key="sk_...",       # your Syntropy Labs key
    service_name="my-service",
)

# That's it — your OpenAI / Anthropic / HTTP / DB calls are now traced automatically.
from openai import OpenAI

client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

init() sets up auto-instrumentation for you. Context (including trace IDs) propagates automatically across threads — no manual wiring required.

Web frameworks

# FastAPI / Starlette
from evalkit import EvalKitMiddleware
app.add_middleware(EvalKitMiddleware)

# Flask
import evalkit
evalkit.instrument_flask(app)

# Django — add to MIDDLEWARE
"evalkit.EvalKitDjangoMiddleware"

Manual spans

import evalkit

end, ctx = evalkit.start_span("my-operation", {"key": "value"})
try:
    ...  # your work
finally:
    end("ok")

# Or as a decorator
@evalkit.trace_function()
def do_work(x):
    return x * 2

SQLAlchemy

import evalkit
evalkit.patch_sqlalchemy_engine(engine)

Flushing

Traces are batched and exported in the background. Flush before exit if needed:

evalkit.flush()

Links

• Website: https://syntropylabs.ai
• Documentation: https://syntropylabs.ai/docs

License

Proprietary — © 2026 Syntropy Labs. All rights reserved. See LICENSE.