verica-observability 0.1.5

Two-line LLM tracing for Verica: init(token) and your OpenAI/Anthropic calls land as evaluable traces.

verica-observability

Two-line LLM tracing for Verica: your OpenAI/Anthropic calls land as evaluable traces.

Install

pip install verica-observability

Use

import verica

verica.init(token=os.environ["VERICA_TOKEN"])

# Import the clients AFTER init so they are patched. OpenAI, Anthropic, and
# Gemini (the google-genai SDK) are all auto-traced, no extra code.
from openai import OpenAI
from anthropic import Anthropic
from google import genai

Sessions (multi-turn)

Traces sharing a conversation id reassemble into a Verica session. In a server handling many conversations, scope the id per request:

with verica.conversation(f"chat-{chat_id}"):
    response = client.chat.completions.create(...)

Context-local (async- and thread-safe); it overrides the global conversation_id from init(), and verica.conversation(None) suppresses the attribute for the block. For a single-conversation script, passing conversation_id="..." to init() is enough.

Serverless

Call verica.flush() (or verica.shutdown()) before the runtime freezes so the span batch is exported.

Options

Option / env var	Default	Notes
token / VERICA_TOKEN	(required)	ingest-scoped API token
capture_content / VERICA_CAPTURE_CONTENT	true	send prompt/response content
conversation_id	(none)	global gen_ai.conversation.id; per-request: verica.conversation(id)
service_name / OTEL_SERVICE_NAME	app	resource service.name
debug / VERICA_DEBUG	false	log export errors

Fail-open by design: if Verica is unreachable or the token is invalid, spans are dropped and your app is never affected. Export errors are silent unless debug is on.