argus-sdk 0.1.2

Detect when your LLM's behavior has statistically shifted — one line of code to instrument, one Docker container to run.

argus-sdk

Detect when your LLM's behavior has statistically shifted — one line of code to instrument, one Docker container to run.

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What it does

LLMs change. Model providers silently update weights, swap infrastructure, or adjust safety filters. Your evals pass, but production quietly drifts. Argus catches this.

argus-sdk wraps your existing Anthropic or OpenAI client and captures derived signals from every LLM call — output tokens, latency, finish reason. It ships those signals in the background to a self-hosted Argus server, which runs statistical tests (Mann-Whitney U + Bonferroni correction) to detect distribution shifts and alerts you via Slack when drift is confirmed.

No prompt text. No completion text. Derived signals only.

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Install

pip install argus-sdk

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Quick start

1. Run the Argus server

docker run -p 4000:4000 -p 3000:3000 -v argus-data:/data ghcr.io/whozpj/argus:latest

Dashboard: http://localhost:3000
Ingest API: http://localhost:4000

2. Instrument your client

Auto-mode — instruments all clients created after patch():

from argus_sdk import patch
patch(endpoint="http://localhost:4000")

import anthropic
client = anthropic.Anthropic()  # automatically instrumented

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=256,
    messages=[{"role": "user", "content": "Hello"}],
)

Explicit mode — instrument a specific instance:

import anthropic
from argus_sdk import patch

client = anthropic.Anthropic()
patch(endpoint="http://localhost:4000", client=client)

Both OpenAI and Anthropic sync clients are supported.

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Flush on exit (short scripts & CLIs)

By default, signals are sent in a background worker thread and flushed automatically when your process exits. For short-lived scripts where you want to guarantee delivery before exit:

from argus_sdk import patch, flush

patch(endpoint="http://localhost:4000")

# ... your LLM calls ...

flush()  # blocks until all queued events are sent

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What gets captured

Field	Example
model	claude-sonnet-4-6
provider	anthropic
input_tokens	312
output_tokens	87
latency_ms	843
finish_reason	stop
timestamp_utc	2026-04-07T14:22:01Z

No prompt text. No completion text.

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How drift detection works

The Argus server builds a baseline per model using Welford's online algorithm (ready after 200 events). Every 60 seconds it runs a Mann-Whitney U test on output tokens and latency against the current window. Bonferroni correction controls false positives across multiple signals. A hysteresis state machine fires alerts when drift score exceeds 0.7 and clears when it drops below 0.4 for three consecutive windows.

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Environment

Variable	Default	Description
ARGUS_ADDR	:4000	Server listen address
ARGUS_DB_PATH	argus.db	SQLite file path
ARGUS_SLACK_WEBHOOK	(empty)	Slack webhook URL for alerts

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Self-hosted

Argus is fully self-hosted. No data leaves your infrastructure. The server is a single Go binary backed by SQLite, bundled with the Next.js dashboard into one Docker image.

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License

MIT