kalibra 0.2.2

Regression detection and CI quality gates for AI agents.

Kalibra
Regression detection and CI quality gates for AI agents.

Success rate: 80% → 80%. Duration: flat. Tokens: flat. Everything looks the same — but 2 task types that always passed started failing, and 2 that always failed started passing. The aggregate hid it. The per-task breakdown caught it.

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pip install kalibra
kalibra compare baseline.jsonl current.jsonl -v
kalibra demo    # try it with sample data

• Statistical rigor — bootstrap 95% CIs on continuous metrics, two-proportion z-test on rates
• Quality gates — regressions <= 2 fails your CI pipeline (exit 1) when thresholds are violated
• Per-task and per-span breakdown — catches regressions that cancel out in the aggregate
• Two dependencies — click + pyyaml. No ML frameworks, no API keys, no LLM calls

Quality gates for CI

# kalibra.yml
baseline:
  path: ./baselines/production.jsonl
current:
  path: ./eval-output/canary.jsonl

require:
  - success_rate_delta >= -2     # max 2pp success rate drop
  - regressions <= 5             # max 5 tasks regressed
  - cost_delta_pct <= 20         # max 20% cost increase

kalibra compare        # reads kalibra.yml, exits 1 on failure

GitHub Actions

- uses: khan5v/kalibra-action@v1
  with:
    baseline: baselines/production.jsonl
    current: current.jsonl
    config: kalibra.yml

Posts a markdown report as a PR comment. Exits 1 on gate failure.

Full workflow example

name: Agent Quality Gate
on: [pull_request]

jobs:
  kalibra:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - uses: actions/checkout@v5
      - run: python eval.py --output current.jsonl
      - uses: khan5v/kalibra-action@v1
        with:
          baseline: baselines/production.jsonl
          current: current.jsonl
          config: kalibra.yml

Integrations

Kalibra auto-detects trace formats. Each tutorial works without an API key.

Integration	Trace format	Demo scenario	Tutorial
Phoenix / OpenInference	llm.*, openinference.*	Multi-step agent with span tree aggregation
OTel GenAI	gen_ai.*	Truncation regression hidden by aggregate improvement
CrewAI	Flat JSONL	Failure redistribution and cost explosion

Filtering with where

Split a single trace file into populations using Prometheus-style matchers:

sources:
  baseline:
    path: ./traces.jsonl
    where:
      - variant == baseline
  current:
    path: ./traces.jsonl
    where:
      - variant == current

Operators: == (equal), != (not equal), =~ (regex match), !~ (regex not match). Multiple matchers are ANDed. Traces missing the field are excluded.

Field mapping

Kalibra works with any JSONL shape. Map your fields in config or on the command line:

fields:
  outcome: metadata.result
  cost: agent_cost.total_cost
  task_id: metadata.task_name

kalibra compare a.jsonl b.jsonl --outcome metadata.result --cost usage.total_cost

Override fields per source for different schemas:

baseline:
  path: ./langfuse.jsonl
  fields: { outcome: metadata.result, cost: usage.total_cost }
current:
  path: ./braintrust.jsonl
  fields: { outcome: scores.correctness, cost: metrics.cost }

Python API

from kalibra.loader import load_traces
from kalibra.engine import compare
from kalibra.renderers import render

baseline = load_traces("baseline.jsonl")
current = load_traces("current.jsonl")

result = compare(baseline, current, require=["success_rate_delta >= -5"])
print(render(result, "terminal", verbose=True))
print("passed:", result.passed)

Development

git clone https://github.com/khan5v/kalibra.git
cd kalibra
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest