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CI/CD verification pipeline for AI agent outputs — fact check, schema validation, diff verification

Project description

agent-ci-verify

CI/CD verification pipeline for AI agent outputs.
Don't trust your agent's output — verify it.

CI PyPI version Python License: MIT

中文


Why agent-ci-verify?

AI agents are entering production, but no one can answer "can I trust this output?"

Existing tools are all "eval libraries" — you import them and write tests yourself. That's self-review, not independent verification.

agent-ci-verify is your agent's CI/CD pipeline — plug it in, and every agent output goes through an independent verification layer before it reaches your users.

Quick Start

pip install agent-ci-verify
agent-ci ./agent-output/
agent-ci v0.1.0
Output dir: ./agent-output/
Checkers: schema, fact, diff

                               📋 Schema Checker
┏━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ✅   │ json_valid           │                                                ┃
┃ ✅   │ yaml_valid           │                                                ┃
┃ ✅   │ security_scan        │ No secrets detected                            ┃
┗━━━━━━┻━━━━━━━━━━━━━━━━━━━━━━┻━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

                               🔍 Fact Checker
┏━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ✅   │ fact:file_count      │ 1 files for '*.json'                           ┃
┃ ✅   │ fact:content_contains│ 'success' found in result.json                 ┃
┗━━━━━━┻━━━━━━━━━━━━━━━━━━━━━━┻━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

╭────────────────────────────────── Verdict ────────────────────────────────╮
│   ✅  PASS                                                                 │
╰───────────────────────────────────────────────────────────────────────────╯

Three Verification Layers

Layer What it checks Example
Schema Format, structure, security Valid JSON? API key leaked? Required files present?
Fact File existence, API reconciliation, LLM judging Agent claimed result.json exists — does it? API returned 200?
Diff Regression detection, semantic drift Output changed vs baseline? Similarity below threshold?

Configuration

Drop .agent-ci.yaml in your agent project root:

pipeline:
  enabled_checkers: [schema, fact, diff]
  fail_fast: false

schema:
  security:
    enabled: true
  required_files:
    - "output/result.json"
  json_schemas:
    schemas/output.schema.json: "output/**/*.json"

fact:
  files:
    - pattern: "output/**/*.json"
      expected_count: 1
      min_size_bytes: 10
      content_checks:
        - type: contains
          value: "success"
        - type: not_contains
          value: "error"
  api:
    - endpoint: "https://api.example.com/health"
      expected_status: 200
  llm_judge:
    - file: "output/answer.md"
      rubric: "Is the answer factually correct?"
      model: "gpt-4o-mini"

diff:
  baseline: "./baseline-output/"
  semantic_threshold: 0.7
  max_changed_files: 5

Security Scanning

Built-in patterns detect:

  • AWS Access Keys (AKIA...)
  • GitHub Tokens (ghp_...)
  • OpenAI API Keys (sk-proj-...)
  • JWT Tokens
  • Private Keys (RSA, EC, DSA, OpenSSH)
  • Password/Secret assignments

CI Integration

# JSON output for programmatic parsing
agent-ci --json ./output/ | jq .verdict
# "PASS"

agent-ci --json ./output/ | jq .summary
# {"total_checks": 6, "passed": 5, "warnings": 1, "failed": 0}
# .github/workflows/agent-check.yml
- name: Verify agent output
  run: |
    pip install agent-ci-verify
    agent-ci --json ./output/ | tee result.json

Plugins

Write custom checkers in any .py file:

from agent_ci.checkers import BaseChecker
from agent_ci.types import CheckResult, CheckerReport, Severity

class SizeChecker(BaseChecker):
    name = "size"

    async def verify(self, output_dir):
        report = CheckerReport(checker_name=self.name)
        total = sum(f.stat().st_size for f in output_dir.rglob("*") if f.is_file())
        limit = self.config.get("size", {}).get("max_bytes", 10_000_000)
        severity = Severity.FAIL if total > limit else Severity.PASS
        report.checks.append(CheckResult(
            checker=self.name, check_name="size_limit",
            severity=severity,
            message=f"Output size: {total:,} bytes (limit: {limit:,})",
        ))
        return report

Configure in .agent-ci.yaml:

plugins:
  paths:
    - ./checks/

pipeline:
  enabled_checkers: [schema, fact, size]
  parallel: true  # Run all checkers concurrently

size:
  max_bytes: 5000000

Development

git clone https://github.com/Lewis-404/agent-ci-verify.git
cd agent-ci-verify
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v

License

MIT — see LICENSE

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