pyqual 0.1.36

Declarative quality gate loops for AI-assisted development

pyqual

AI Cost Tracking

• 🤖 LLM usage: $1.0500 (7 commits)
• 👤 Human dev: ~$500 (5.0h @ $100/h, 30min dedup)

Generated on 2026-03-29 using openrouter/qwen/qwen3-coder-next

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Declarative quality gate loops for AI-assisted development.

One YAML file. One command. Pipeline iterates until your code meets quality thresholds.

pip install pyqual
pyqual init
pyqual run

The problem

You use Copilot, Claude, GPT. They generate code. But nobody checks if that code meets your quality standards before it hits code review. And nobody automatically iterates if it doesn't.

pyqual closes that gap: define metrics → run tools → check gates → if fail, LLM fixes → re-check → repeat until pass.

How it works

pyqual.yaml defines everything:
    ┌─────────────────────────────────────────┐
    │  metrics:                               │
    │    cc_max: 15        ← quality gates    │
    │    vallm_pass_min: 90                   │
    │    coverage_min: 80                     │
    │                                         │
    │  stages:                                │
    │    - analyze  (code2llm)                │
    │    - validate (vallm)                   │
    │    - fix      (llx/aider, when: fail)   │
    │    - test     (pytest)                  │
    │                                         │
    │  loop:                                  │
    │    max_iterations: 3                    │
    │    on_fail: report                      │
    └─────────────────────────────────────────┘

pyqual run:
    Iteration 1 → analyze → validate → fix → test → check gates
                                                         │
                                              ┌── PASS ──┴── FAIL ──┐
                                              │                     │
                                           Done ✅          Iteration 2...

pyqual.yaml

pipeline:
  name: quality-loop

  metrics:
    cc_max: 15           # cyclomatic complexity per function
    vallm_pass_min: 90   # vallm validation pass rate (%)
    coverage_min: 80     # test coverage (%)

  stages:
    - name: analyze
      run: code2llm ./ -f toon,evolution

    - name: validate
      run: vallm batch ./ --recursive --errors-json > .pyqual/errors.json

    - name: fix
      run: echo "Connect your LLM fixer here"
      when: metrics_fail    # only runs if gates fail

    - name: test
      run: pytest --cov --cov-report=json:.pyqual/coverage.json

  loop:
    max_iterations: 3
    on_fail: report         # report | create_ticket | block

CLI

pyqual init              # create pyqual.yaml
pyqual run               # execute full loop
pyqual run --dry-run     # preview without executing
pyqual gates             # check gates without running stages
pyqual status            # show current metrics
pyqual doctor            # check tool availability
pyqual mcp-fix           # run the llx-backed MCP fix workflow
pyqual mcp-refactor      # run the llx-backed MCP refactor workflow
pyqual mcp-service       # start the persistent llx MCP service
pyqual tickets todo      # sync TODO.md through planfile
pyqual tickets github    # sync GitHub issues through planfile
pyqual tickets all       # sync TODO.md and GitHub tickets

# Plugin management
pyqual plugin list                   # list all plugins
pyqual plugin list --tag security     # filter by tag
pyqual plugin search <query>          # search plugins
pyqual plugin info <name>             # show plugin details
pyqual plugin add <name>              # add plugin to config
pyqual plugin remove <name>           # remove plugin from config
pyqual plugin validate                # validate configuration

Python API

from pyqual import Pipeline, PyqualConfig

config = PyqualConfig.load("pyqual.yaml")
pipeline = Pipeline(config, workdir="./my-project")
result = pipeline.run()

if result.final_passed:
    print(f"All gates passed in {result.iteration_count} iterations")
else:
    print("Gates not met — check result.iterations for details")

LLM Integration

pyqual includes built-in LLM support via liteLLM. Configure via .env:

The convenience wrapper now lives upstream in llx.llm; pyqual re-exports it so existing imports keep working.

OPENROUTER_API_KEY=sk-or-v1-...
LLM_MODEL=openrouter/qwen/qwen3-coder-next

Use in your code:

from pyqual import get_llm

llm = get_llm()  # Auto-loads config from .env

# Simple completion
response = llm.complete("Explain Python decorators")
print(response.content)

# Fix code issues
response = llm.fix_code(
    code="def foo(x): return x + 1",  # missing type hints
    error="Function lacks type annotations"
)
print(response.content)

# Access cost info
print(f"Cost: ${response.cost:.4f}")

Docker-backed MCP fixer/refactor

If you want pyqual to delegate automatic fixes or refactors to a Dockerized llx MCP service:

The MCP client, service, workflow orchestration (LlxMcpRunResult, run_llx_fix_workflow, run_llx_refactor_workflow), issue parsing and prompt building all live in the upstream llx package (≥ 0.1.47). pyqual re-exports them for backward compatibility. Install pyqual[llx] or pyqual[mcp] so those shared helpers are available.

docker compose -f examples/llm_fix/docker-compose.yml up --build -d
pyqual plugin add llx-mcp-fixer
pyqual run

The plugin writes results to .pyqual/llx_mcp.json, which is also collected by pyqual status and can be gated with llx_fix_* metrics.

If you only want to run the workflows directly, use:

pyqual mcp-fix --workdir . --project-path /workspace/project
pyqual mcp-refactor --workdir . --project-path /workspace/project

If you want to run the service standalone in development, use:

pyqual mcp-service --host 0.0.0.0 --port 8000

See examples/llm_fix/ for complete examples.

Metric sources

pyqual automatically collects metrics from:

Source	File	Metrics
Analysis	analysis_toon.yaml	cc (CC̄), critical
Validation	validation_toon.yaml	vallm_pass
	.pyqual/errors.json	error_count
Coverage	.pyqual/coverage.json	coverage
Performance	.pyqual/asv.json	bench_regression, bench_time
	.pyqual/mem.json	mem_usage, cpu_time
Security	.pyqual/secrets.json	secrets_severity, secrets_count
	.pyqual/vulns.json	vuln_critical, vuln_count
	.pyqual/sbom.json	sbom_compliance, license_blacklist
Project Health	.pyqual/vulture.json	unused_count
	.pyqual/pyroma.json	pyroma_score
	.pyqual/git_metrics.json	git_branch_age, todo_count
LLM/AI	.pyqual/humaneval.json	llm_pass_rate
	.pyqual/llm_analysis.json	llm_cc, hallucination_rate, prompt_bias_score, agent_efficiency
	.pyqual/llx_mcp.json	llx_fix_success, llx_fix_returncode, llx_tool_calls, llx_fix_tier_rank
	.pyqual/costs.json	ai_cost
Linting	.pyqual/ruff.json	ruff_errors, ruff_fatal, ruff_warnings
	.pyqual/pylint.json	pylint_errors, pylint_fatal, pylint_error, pylint_warnings, pylint_score
	.pyqual/flake8.json	flake8_violations, flake8_errors, flake8_warnings, flake8_conventions
Documentation	.pyqual/interrogate.json	docstring_coverage, docstring_total, docstring_missing

Custom metrics: extend GateSet._collect_metrics() or add your own collector.

Gate operators

metrics:
  cc_max: 15           # cc ≤ 15
  coverage_min: 80     # coverage ≥ 80
  critical_max: 0      # critical ≤ 0
  error_count_max: 5   # error_count ≤ 5
  vallm_pass_min: 90   # vallm_pass ≥ 90

Suffixes: _max → ≤, _min → ≥, _lt → <, _gt → >, _eq → =

Integration with ecosystem

pyqual is intentionally small (~800 lines). It orchestrates, not implements:

• code2llm does analysis → pyqual reads the .toon output
• vallm does validation → pyqual reads pass rates
• llx does LLM routing, MCP workflows, issue parsing → pyqual calls it as a stage (requires Python ≥ 3.10)
• planfile manages tickets → pyqual syncs TODO.md and GitHub tickets through planfile
• costs tracks spending → pyqual can gate on budget
• algitex can import pyqual as a dependency for its go command

Examples

See examples/ directory for real-world configurations:

Project setups:

• python-package — Standard Python package (src-layout)
• python-flat — Simple project without src/
• monorepo — Multiple packages in one repository

CI/CD:

• github-actions — CI/CD with GitHub Actions
• gitlab-ci — CI/CD with GitLab CI

Python API usage:

• basic — Using Pipeline and GateSet from Python
• llm_fix — LLM integration for auto-fixing code
• custom_gates — Custom quality gates and metrics

Why not add this to algitex?

algitex has 29,448 lines, CC̄=3.6, 64 critical issues, vallm pass 42.8%. Adding more features makes it worse. pyqual does one thing well: declarative quality gate loops. algitex imports pyqual. Both improve.

License

Licensed under Apache-2.0.