GigaFlow CLI

Command-line client for GigaFlow — connect your LLM/agent traces to a GigaFlow backend and run Flow analysis on them: atomize each step, attribute information flow between atoms, score groundedness / relevance / fulfilment, and diagnose failures.

The CLI is a thin, zero-dependency client (Python standard library only). Your traces live in an observability platform (Arize Phoenix, Logfire, Braintrust, MLflow, W&B Weave) or are sent via OTLP; the backend does the compute; this CLI drives ingest → compute → inspection.

Install

pip install gigaflow

Sign in

gigaflow login

gigaflow login signs you in with your waitlist email and stores your credentials in ~/.gigaflow/config.json, so you only do it once. gigaflow setup also signs you in automatically — no API key or backend URL needed, the hosted service is the default.

For repeatable or CI setups, see gigaflow.env.

End-to-end in three commands

gigaflow setup                                   # pick your tracing tool, connect it, sync
gigaflow compute "SELECT trace_id FROM trace_metrics WHERE run_id IS NULL"
gigaflow inspect <trace_id>                      # open the browser Flow viewer

Where to next

• Connect a trace source — Arize Phoenix, Logfire, Braintrust, MLflow, W&B Weave, or direct OTLP.
• Querying — explore the trace_metrics view with SQL.
• Transform configs — map raw vendor spans to GigaFlow primitives.
• Changelog — release history.

The full command reference lives in the project README.