sentrinode-llm 0.1.0

Drop-in observability for LLM calls — cost, tokens, latency, errors via OpenTelemetry GenAI conventions.

sentrinode-llm

Drop-in observability for your LLM calls. Two lines, and every Anthropic / OpenAI request is traced with model, token usage, latency, finish reason, and computed USD cost — using the OpenTelemetry GenAI semantic conventions.

Install

pip install sentrinode-llm        # add [anthropic] and/or [openai] as needed

Use

import sentrinode_llm
sentrinode_llm.instrument()   # reads SENTRINODE_API_KEY + SENTRINODE_TENANT

import anthropic
client = anthropic.Anthropic()
client.messages.create(            # ← automatically traced
    model="claude-haiku-4-5-20251001",
    max_tokens=256,
    messages=[{"role": "user", "content": "hello"}],
)

That's it — no wrapping your calls, no decorators. The same works for OpenAI:

from openai import OpenAI
OpenAI().chat.completions.create(model="gpt-4o-mini", messages=[...])  # traced

Configure

Env var	Meaning	Default
SENTRINODE_API_KEY	your tenant API key	— (required)
SENTRINODE_TENANT	your tenant slug	— (required)
SENTRINODE_LLM_ENDPOINT	ingest base URL	https://api.sentrinode.com
SENTRINODE_SERVICE_NAME	label for this app	llm-app

Or pass them directly: sentrinode_llm.instrument(api_key=..., tenant=...).

What gets recorded

Each call becomes one OpenTelemetry span:

Attribute	Example
gen_ai.system	anthropic
gen_ai.request.model	claude-haiku-4-5-20251001
gen_ai.usage.input_tokens	1432
gen_ai.usage.output_tokens	211
gen_ai.usage.cost_usd	0.002486
gen_ai.response.finish_reasons	["end_turn"]

Cost is computed from pricing.py. Override any model:

sentrinode_llm.set_price("my-model", input_per_1m=2.0, output_per_1m=8.0)

Not yet

• Streaming responses pass through untraced (usage isn't known until the stream ends — coming next).
• Async clients and embeddings — next iteration.