flameiq-core 1.0.2

Why FlameIQ?

Performance regressions are rarely caught in code review. They accumulate silently across hundreds of commits — a 3 ms latency increase here, a 2% throughput drop there — until they become expensive production incidents.

FlameIQ brings the same engineering discipline to performance that type checkers bring to correctness: automated, deterministic, CI-enforced.

Design Principles

Installation

pip install flameiq-core

Requires Python 3.10+. No system dependencies.

Quick Start

Step 1: Initialise

cd my-project
flameiq init

Step 2: Write a FlameIQ v1 metrics file

{
  "schema_version": 1,
  "metadata": {
    "commit": "abc123",
    "branch": "main",
    "environment": "ci"
  },
  "metrics": {
    "latency": {
      "mean": 120.5,
      "p95": 180.0,
      "p99": 240.0
    },
    "throughput": 950.2,
    "memory_mb": 512.0
  }
}

Step 3: Set a baseline

flameiq baseline set --metrics benchmark.json

Step 4: Compare on every PR

flameiq compare --metrics current.json --fail-on-regression

Step 5: Generate an HTML report

flameiq report --metrics current.json --output report.html

GitHub Actions Integration

- name: Install FlameIQ
  run: pip install flameiq-core

- name: Restore baseline cache
  uses: actions/cache@v4
  with:
    path: .flameiq/
    key: flameiq-${{ github.base_ref }}

- name: Run benchmarks
  run: python run_benchmarks.py > metrics.json

- name: Check for regressions
  run: flameiq compare --metrics metrics.json --fail-on-regression

Exit Codes

Python API

from flameiq.schema.v1.models import PerformanceSnapshot
from flameiq.core.comparator import compare_snapshots
from flameiq.core.models import RegressionStatus
from flameiq.storage.baseline_store import BaselineStore

store = BaselineStore()
baseline = store.load_baseline()

current = PerformanceSnapshot.from_dict({
    "schema_version": 1,
    "metadata": {"commit": "def456"},
    "metrics": {"latency": {"p95": 185.0}, "throughput": 940.0},
})

result = compare_snapshots(baseline, current)

if result.status == RegressionStatus.REGRESSION:
    for reg in result.regressions:
        print(f"  REGRESSION: {reg.metric_key} {reg.change_percent:+.2f}%")
else:
    print("All metrics within threshold.")

# Machine-readable JSON
import json
print(json.dumps(result.to_dict(), indent=2))

Configuration

flameiq.yaml (created by flameiq init):

thresholds:
  latency.p95:   10%
  latency.p99:   15%
  throughput:    -5%
  memory_mb:      8%

baseline:
  strategy: rolling_median
  rolling_window: 5

statistics:
  enabled: false
  confidence: 0.95

provider: json

Documentation

Full documentation: https://flameiq-core.readthedocs.io

• Installation Guide

• Quick Start

• CLI Reference

• Configuration Reference

• Statistical Methodology

• Schema v1 Specification

• Custom Provider Guide

Contributing

Contributions are welcome. Please read CONTRIBUTING.rst and open an issue before starting significant work.

License

Apache License 2.0. See LICENSE.

“FlameIQ” is a trademark of the FlameIQ project. Use of the name or logo to endorse derived products requires written permission. Contact: Angufibo Lincoln <angufibolinc@gmail.com>