breakpoint-ai 0.2.0

Local-first decision engine for baseline vs candidate LLM output checks.

BreakPoint AI

BreakPoint blocks risky LLM changes before they ship.

Problem: You change a prompt or swap a model. Output looks fine. But cost jumps 38%, a phone number slips in, or the format breaks your parser. You ship it. Users and budgets get hurt.

Who it's for: Teams shipping LLM features to production, merging prompt or model changes via PR, or running cost-sensitive systems.

When to use it: Before every deploy. Gate PRs. Catch regressions before they reach users.

If you don't: Cost drift, PII leaks, and format regressions ship unnoticed. Unit tests won't catch these—LLM output isn't deterministic.

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3-Step Mental Model

Step 1: Capture baseline   →  Approved output artifact (store in repo)
Step 2: Generate candidate →  New output from your changed prompt/model
Step 3: Gate in CI         →  breakpoint evaluate baseline.json candidate.json

Baseline  ──→  Candidate  ──→  BreakPoint  ──→  ALLOW / WARN / BLOCK  ──→  CI

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Lite Mode (Zero Config)

Out of the box, no config needed:

• Cost: WARN at +20%, BLOCK at +40%
• PII: BLOCK on email, phone, credit card (Luhn), SSN
• Drift: WARN at +35% length delta, BLOCK at +70%, BLOCK on empty output

Exit codes: 0 = ALLOW, 1 = WARN, 2 = BLOCK.

Advanced (config, presets, waivers): --mode full → docs/user-guide-full-mode.md.

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60-Second Quickstart

pip install breakpoint-ai
breakpoint evaluate baseline.json candidate.json

Each JSON needs output (string). Optional: cost_usd, tokens_in, tokens_out, model, latency_ms.

Example BLOCK:

  Final Decision: BLOCK

  • Cost increased by 68.9% ($0.0450 → $0.0760), exceeding 40% block threshold.
  • PII: US phone number pattern detected.

  Exit: 2

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Baseline: Treat LLM Output Like a Code Artifact

Baselines are approved snapshots. You commit them. You diff against them. When a change is intentional and you've reviewed it, promote the candidate to baseline:

breakpoint accept baseline.json candidate.json

CI flow: Fails → Human reviews → Accept baseline (or fix) → Merge.

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CI Integration

Run the gate directly—no marketplace action required:

breakpoint evaluate baseline.json candidate.json --fail-on warn

--fail-on warn fails CI on WARN or BLOCK. Use --fail-on block to fail only on BLOCK.

Minimal GitHub Actions:

- uses: actions/checkout@v4
- name: Generate candidate
  run: # ... produce candidate.json
- name: BreakPoint Gate
  run: breakpoint evaluate baseline.json candidate.json --fail-on warn

Or use the BreakPoint Evaluate action.

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Why Not Just Unit Tests?

Unit tests assume deterministic behavior. LLM output is not. BreakPoint catches what tests miss:

• Cost drift (same output, higher token bill)
• Subtle regressions (format change, dropped keys)
• PII leaks (phone, email, credit card)

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Real Story

"We swapped GPT-4 to GPT-4.1. Output looked identical. Cost rose 38%. BreakPoint blocked it before deploy."

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Try in 60 Seconds – FastAPI Demo

git clone https://github.com/cholmess/breakpoint-ai
cd breakpoint-ai/examples/fastapi-llm-demo
make install
make good        # PASS
make bad-tokens  # BLOCK

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When To Use / When Not

Use: Production LLM features, PR merges, cost-sensitive systems.

Skip: One-off experiments, hobby scripts, non-production.

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Why Local-First?

Most tools send prompts and outputs to SaaS. BreakPoint runs on your machine. Artifacts stay in your repo. No network calls for evaluation.

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Four Examples

breakpoint evaluate examples/install_worthy/baseline.json examples/install_worthy/candidate_cost_model_swap.json
breakpoint evaluate examples/install_worthy/baseline.json examples/install_worthy/candidate_format_regression.json
breakpoint evaluate examples/install_worthy/baseline.json examples/install_worthy/candidate_pii_verbosity.json
breakpoint evaluate examples/install_worthy/baseline.json examples/install_worthy/candidate_killer_tradeoff.json

Details: docs/install-worthy-examples.md.

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CLI

breakpoint evaluate baseline.json candidate.json
breakpoint evaluate payload.json                    # combined {baseline, candidate}
breakpoint accept baseline.json candidate.json     # promote candidate to baseline
breakpoint evaluate ... --verbose                   # full policy output
breakpoint evaluate ... --json --fail-on warn       # CI-friendly

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Input Schema

output (string) required. Optional: cost_usd, tokens_in, tokens_out, model, latency_ms. Combined: {"baseline": {...}, "candidate": {...}}.

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Pytest Plugin

def test_my_agent(breakpoint):
    response = call_my_llm("Hello")
    breakpoint.assert_stable(response, candidate_metadata={"cost_usd": 0.002})

Update baselines: BREAKPOINT_UPDATE_BASELINES=1 pytest.

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Python API

from breakpoint import evaluate

decision = evaluate(
    baseline_output="hello",
    candidate_output="hello there",
    metadata={"baseline_tokens": 100, "candidate_tokens": 140},
)
print(decision.status, decision.reasons)

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Troubleshooting

• ModuleNotFoundError: breakpoint → pip install breakpoint-ai
• File not found → Check paths.
• JSON validation → Ensure output (string) in each object.

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Docs

• docs/user-guide.md
• docs/user-guide-full-mode.md
• docs/quickstart-10min.md
• docs/install-worthy-examples.md
• docs/baseline-lifecycle.md
• docs/ci-templates.md

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Maintainer

BreakPoint is maintained by Christopher Holmes Silva.

• X: https://x.com/cholmess
• LinkedIn: https://linkedin.com/in/cholmess

Feedback and real-world usage stories welcome—open an issue or [e-mail].