gigaflow 0.2.1

GigaFlow CLI — connect Arize Phoenix traces to GigaFlow and analyze them

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, diagnose failures).

The CLI is a thin client: 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

# From source (current):
git clone https://github.com/GigaFlow-AI-Incorporated/gigaflow-sdk
cd gigaflow-sdk && pip install -e .

# From PyPI (once published):
pip install gigaflow

The CLI is standard-library only — nothing else to pull in.

Configure the backend + key

Two independent credentials:

Credential	Env var	Where it goes	Purpose
GigaFlow API key	GIGAFLOW_API_KEY	Authorization: Bearer header	Authenticates you to the GigaFlow backend. Required on any hosted backend.
OpenAI API key	OPENAI_API_KEY	compute request body	Required by the CLI's compute command. On the hosted service, Flow LLM calls currently run on GigaFlow's platform key; per-customer key billing is on the roadmap.

export GIGAFLOW_BACKEND_URL=https://api.gigaflow.io/api/v1
export GIGAFLOW_API_KEY=<your GigaFlow API key>
export OPENAI_API_KEY=sk-...

Resolution order (first set wins):

• Backend URL: --backend <url> > $GIGAFLOW_BACKEND_URL > saved config > http://localhost:8000/api/v1
• API key: --api-key <key> > $GIGAFLOW_API_KEY > saved config > none

gigaflow setup also prompts for these and persists them to ~/.gigaflow/config.json, so the exports are optional on later runs.

---

The end-to-end flow

# 1. Connect a trace source (creates a project + registers a datasource).
#    Arize Phoenix is wizard-driven; other vendors are a one-time API call.
#    → see docs/sources/<vendor>.md
gigaflow setup                  # Arize Phoenix wizard
# (or register another source via the API — see the per-vendor docs)

# 2. Pull traces into GigaFlow.
gigaflow sync

# 3. Compute Flow for every trace that doesn't have results yet.
gigaflow compute "SELECT trace_id FROM trace_metrics WHERE run_id IS NULL"

# 4. Open a trace in the browser viewer (Trace / Orchestration / Atomic / Metrics).
gigaflow inspect <trace_id>

# 5. Query results as data.
gigaflow query "SELECT trace_id, groundedness, total_cost_usd FROM trace_metrics ORDER BY total_cost_usd DESC LIMIT 20"

Connect your trace source

Pick your platform — each guide covers the exact datasource config + whether a custom transform is needed:

Source	Setup	Bundled transform?	Guide
Arize Phoenix	gigaflow setup wizard	✅ yes	docs/sources/arize-phoenix.md
Logfire	API datasource	✅ yes	docs/sources/logfire.md
Braintrust	API datasource	⚠️ custom transform	docs/sources/braintrust.md
MLflow	API datasource	⚠️ custom transform	docs/sources/mlflow.md
W&B Weave	API datasource	⚠️ custom transform	docs/sources/wb-weave.md
Direct OTLP	project token + exporter	n/a (per-project transform)	docs/sources/otlp.md

Only Arize Phoenix has a wizard today; the others register a datasource with a single POST /api/v1/datasources/ call (shown in each guide), then gigaflow sync.

Commands

Command	What it does
gigaflow setup	First-run wizard (Arize Phoenix): backend, project, transform, datasource, sync
gigaflow sync	Pull traces from the configured datasource (append-only)
gigaflow traces	List traces (auto-syncs first)
gigaflow spans <trace_id>	List spans for a trace
gigaflow compute "<SQL>"	Batch-compute Flow for matching traces
gigaflow inspect <trace_id>	Open the browser viewer
gigaflow query "<SQL>"	Run SQL against the trace_metrics view (read-only) — see QUERYING.md
gigaflow config show / clear	Show / reset saved config

Transform configs

A transform config (YAML) maps a source's raw spans to GigaFlow primitives (llm_call, tool_invocation, user_input, transform) via filter (classify) and mapping (extract fields). Bundled configs ship for Arize Phoenix and Logfire (gigaflow/transforms/); other sources need a custom one (each vendor guide explains the shape). Re-upload to an existing project without re-running setup:

curl -X PUT "$GIGAFLOW_BACKEND_URL/projects/<project_id>/transform" \
  -H "Authorization: Bearer $GIGAFLOW_API_KEY" \
  -H "Content-Type: text/plain" --data-binary @my_transform.yml

Re-sync after changing a transform to reclassify spans.

Full grammar + the per-project upload flow: docs/transforms.md.

Publish to PyPI

See the release steps in docs/publishing.md (token reserved in the company vault; CI publish is wired in the infra repo).