veritas-sdk 0.1.1

AI cost attribution and change detection for developers

Veritas

AI cost observability for developers. Track token usage, costs, and latency across every LLM call — automatically. Spot cost regressions before they hit production.

pip install veritas-sdk[anthropic]
# or
pip install veritas-sdk[openai]
# or
pip install veritas-sdk[all]

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What it does

Veritas wraps your existing Anthropic or OpenAI client with a transparent proxy. Every API call is tracked silently in the background — your application code stays identical.

For every call, Veritas captures:

Field	Description
feature	The feature name you assign
model	Model used (e.g. claude-3-haiku, gpt-4o-mini)
tokens_in / tokens_out	Input and output token counts
cost_usd	Computed cost based on current pricing
latency_ms	End-to-end request time
code_version	Current git commit hash (auto-detected)

Events are sent to a Veritas dashboard where you can track spend over time, break costs down by feature, and compare costs between code versions.

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Quickstart

1. Configure

import veritas

veritas.init(
    api_key="sk-vrt-your-key-here",
    endpoint="https://your-veritas-server.com/api/v1/events",
)

Alternatively, use environment variables — Veritas auto-configures on import:

VERITAS_API_KEY=sk-vrt-your-key-here
VERITAS_API_URL=https://your-veritas-server.com/api/v1/events

2. Wrap your client

Anthropic:

import anthropic
import veritas

veritas.init(api_key="sk-vrt-...", endpoint="https://your-server.com/api/v1/events")

client = veritas.Anthropic(
    anthropic.Anthropic(),
    feature_name="chat_search",   # group calls by feature in the dashboard
)

response = client.messages.create(
    model="claude-3-haiku-20240307",
    max_tokens=256,
    messages=[{"role": "user", "content": "Hello!"}],
)
# ^ tracked automatically — response is unchanged

OpenAI:

import openai
import veritas

veritas.init(api_key="sk-vrt-...", endpoint="https://your-server.com/api/v1/events")

client = veritas.OpenAI(
    openai.OpenAI(),
    feature_name="summarizer",
)

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

Streaming works too — just pass stream=True as normal.

3. Use the @track decorator (alternative)

from veritas import track

@track(feature="document_summary")
def summarize(text: str):
    return anthropic_client.messages.create(...)   # any call that returns usage data

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Safety guarantees

• Never crashes your app — all tracking is fire-and-forget; exceptions are swallowed silently
• No prompt data transmitted — only metadata (tokens, cost, latency, model, commit hash)
• Async-safe — uses asyncio.to_thread in async contexts so the event loop is never blocked
• Zero-config git integration — commit hash is auto-detected via git rev-parse

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Requirements

• Python 3.9+
• requests (for HTTP sink)
• anthropic>=0.39 (if using veritas-sdk[anthropic])
• openai>=1.0.0 (if using veritas-sdk[openai])

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Links

• GitHub
• Dashboard — request access at [e-mail]