Observed since 21 Oct, 10:07 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
CI for LLM JSON outputs - Validate, test, and measure LLM outputs
Reliability tooling for structured LLM outputs.
Measure, validate, and enforce JSON stability in CI.
LLMs are probabilistic systems.
If your application depends on structured JSON generated by an LLM, that output becomes an implicit contract within your system. Model updates, prompt changes, or configuration tweaks can cause that contract to drift — often without immediate visibility.
Traditional tests typically validate a single run. They do not measure variability across repeated executions.
Aicert treats LLM output like any other production dependency:
Instead of assuming structured outputs remain stable, you can verify that they do.
Aicert helps you:
It converts non-deterministic behavior into measurable signals.
Aicert is designed for teams shipping structured LLM outputs into production systems:
If structured LLM output is part of your system contract, Aicert provides guardrails.
pip install aicert
For development:
pip install -e .
aicert.yamlproject: my-project
providers:
- id: openai-gpt4
provider: openai
model: gpt-4
temperature: 0.1
prompt_file: prompt.txt
cases_file: cases.jsonl
schema_file: schema.json
runs: 50
concurrency: 10
timeout_s: 30
validation:
extract_json: true
allow_extra_keys: false
thresholds:
min_stability: 85
min_compliance: 95
p95_latency_ms: 5000
ci:
runs: 10
save_on_fail: true
aicert stability aicert.yaml
aicert ci aicert.yaml
Non-zero exit codes indicate threshold failures.
aicert init
aicert doctor aicert.yaml
aicert run aicert.yaml
aicert stability aicert.yaml
aicert compare aicert.yaml
aicert ci aicert.yaml
aicert diff <run_a> <run_b>
aicert report <run_dir>
Aicert Pro turns measurement into enforcement.
The core package tells you how your LLM behaves. Pro defines what behavior is acceptable — and blocks regressions automatically.
When structured outputs drive production systems, “informational metrics” are not enough. You need a reliability boundary.
Aicert Pro adds:
Pro is designed for teams that treat LLM output as production infrastructure.
Without Pro:
You measure stability. You review results manually. Regressions can slip through if thresholds are ignored.
With Pro:
Known-good behavior is locked. Deviations are compared automatically. CI fails when output contracts drift. Reliability becomes enforceable policy. Pro moves you from observation to control.
After purchasing a subscription:
pip install aicert-pro
Set your license key:
export AICERT_LICENSE="your_signed_license_key"
Verify:
aicert-pro license verify
Use baseline enforcement:
aicert-pro baseline save aicert.yaml
aicert-pro baseline check aicert.yaml
Aicert Pro uses signed offline license keys.
Purchase: https://mfifth.github.io/aicert/
Questions: mfifth@gmail.com
- run: pip install aicert
- run: aicert ci aicert.yaml
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
Pro enforcement in CI:
- run: pip install aicert-pro
- run: aicert-pro baseline check aicert.yaml
env:
AICERT_LICENSE: ${{ secrets.AICERT_LICENSE }}
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Threshold failure |
| 3 | Config/schema error |
| 4 | Provider/auth error |
| 5 | License error (Pro only) |
Core package (aicert) is MIT licensed.
Aicert Pro is commercial software.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.0 | 5 Feb 2026 | 11 |
This describes our collected data; it is not a security assessment.
Observed since 21 Oct, 10:07 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:add592889375f7f86d8f929fb4d32db62a5f945f0a6a4b6a9bb044326559bd38