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vallm

A complete toolkit for validating LLM-generated code.

PyPIPyPI - DownloadsCILicense: Apache-2.0PythonCode style: ruffCoverageType checking: mypySecurity: banditPre-commitCodeQLDOIGitHub starsGitHub forksGitHub issuesGitHub pull requestsReleaseLast commitMaintainedPRs Welcome

AI Cost Tracking

PyPIVersionPythonLicenseAI CostHuman TimeModel

  • 🤖 LLM usage: $2.1286 (159 commits)
  • 👤 Human dev: ~$5263 (52.6h @ $100/h, 30min dedup)

Generated on 2026-07-29 using openrouter/deep/deep-v4-pro


vallm validates code proposals through a four-tier pipeline — from millisecond syntax checks to LLM-as-judge semantic review — before a single line ships.

Features

  • Multi-language AST parsing via tree-sitter (165+ languages)
  • Syntax validation with ast.parse (Python) and tree-sitter error detection
  • Import resolution checking for Python, JavaScript/TypeScript, Go, Rust, Java, C/C++
  • Complexity metrics via radon (Python) and lizard (16 languages)
  • Security scanning with language-specific patterns and optional bandit integration
  • LLM-as-judge semantic review via Ollama, litellm, or direct HTTP
  • Code graph analysis — import/call graph diffing for structural regression detection
  • AST similarity scoring with normalized fingerprinting
  • Pluggy-based plugin system for custom validators
  • Rich CLI with JSON/text output formats
  • MCP integration — Model Context Protocol server for LLM tool calling

Supported Languages

LanguageSyntaxImportsComplexitySecurity
Python✅ AST + tree-sitter✅ Full resolution (22 methods)✅ radon + lizard✅ bandit + patterns
JavaScript✅ tree-sitter✅ Node.js builtins✅ lizard✅ XSS, eval patterns
TypeScript✅ tree-sitter✅ Node.js builtins✅ lizard✅ XSS, eval patterns
Go✅ tree-sitter✅ stdlib + modules✅ lizard✅ SQL injection, exec
Rust✅ tree-sitter✅ crates✅ lizard✅ unsafe, unwrap
Java✅ tree-sitter✅ stdlib packages✅ lizard✅ Runtime.exec, SQL
C/C++✅ tree-sitter✅ std headers✅ lizard✅ buffer overflow, system
Ruby✅ tree-sitter⚠️ Limited✅ lizard⚠️ Limited
PHP✅ tree-sitter⚠️ Limited✅ lizard⚠️ Limited
Swift✅ tree-sitter⚠️ Limited✅ lizard⚠️ Limited
Kotlin✅ tree-sitter⚠️ Limited✅ lizard⚠️ Limited
Scala✅ tree-sitter⚠️ Limited✅ lizard⚠️ Limited

Installation

pip install vallm

With optional dependencies:

pip install vallm[all] # Everything
pip install vallm[llm] # Ollama + litellm for semantic review
pip install vallm[security] # bandit integration
pip install vallm[semantic] # CodeBERTScore
pip install vallm[graph] # NetworkX graph analysis

Quick Start

Validate Entire Project

# Install with LLM support
pip install vallm[llm]
# Setup Ollama (for semantic review)
ollama pull qwen2.5-coder:7b
ollama serve
# Validate entire project recursively
vallm batch . --recursive --semantic --model qwen2.5-coder:7b
# Fast validation for quick feedback (skip imports and complexity)
vallm batch . --recursive --no-imports --no-complexity
# Generate validation report in TOON format
vallm batch . --recursive --output toon > ./project/validation.toon

Project Structure & Files

Core Source Code:

Examples & Documentation:

Configuration Files:

Scripts & Tools:

Testing:

CI/CD & GitHub:

Project Analysis:

Python API

fromvallmimportProposal, validate, VallmSettingscode="""def fibonacci(n: int) -> list[int]: if n <= 0: return [] fib = [0, 1] for i in range(2, n): fib.append(fib[i-1] + fib[i-2]) return fib"""proposal=Proposal(code=code, language="python")
result=validate(proposal)
print(f"Verdict: {result.verdict.value}") # pass / review / failprint(f"Score: {result.weighted_score:.2f}")

CLI Commands Reference

# Batch validation (best for entire projects)
vallm batch . --recursive --semantic --model qwen2.5-coder:7b
vallm batch src/ --recursive --include "*.py,*.js" --exclude "*/test/*"
vallm batch . --recursive --format json --fail-fast
vallm batch . --recursive --verbose --show-issues # Detailed per-file results# Output formats for batch results
vallm batch . --recursive --format json # Machine-readable JSON
vallm batch . --recursive --format yaml # YAML format
vallm batch . --recursive --format toon # Compact TOON format
vallm batch . --recursive --format text # Plain text# Single file validation
vallm validate --file mycode.py --semantic --model qwen2.5-coder:7b
vallm validate --file app.js --security
vallm validate --file mycode.py --format json # JSON output# Quick syntax check only
vallm check mycode.py
vallm check src/main.go
# Configuration and info
vallm info

Real-World Usage Examples

1. Fast Project Validation (Recommended for CI/CD)

# Quick syntax check - excludes .git/ and other system files automatically
vallm batch . --recursive --no-imports --no-complexity
# Output: Excluded 30246 files by .gitignore, Validating 169 files...# ✓ 83 files passed, ✗ 115 files failed (mostly non-code files)

2. Generate Validation Report

# Save TOON format report to project directory
vallm batch . --recursive --output toon > ./project/validation.toon
# Save JSON report for CI/CD integration
vallm batch . --recursive --output json > ./project/validation.json
# Save detailed text report with security checks
vallm batch . --recursive --security --output text > ./project/validation-report.txt

3. Selective File Validation

# Validate only Python and JavaScript files
vallm batch . --recursive --include "*.py,*.js" --exclude "*/test/*"# Validate specific directory with custom patterns
vallm batch src/ --recursive --include "*.py" --exclude "*/__pycache__/*"# Validate with custom gitignore override
vallm batch . --recursive --no-gitignore --exclude "*.log,tmp/*"

4. Full Pipeline with LLM Review

# Complete validation with semantic analysis
vallm batch . --recursive --semantic --model qwen2.5-coder:7b --security
# Export full results with per-file details
vallm batch . --recursive --semantic --model qwen2.5-coder:7b --output json > full-validation.json

5. Development Workflow Integration

# Pre-commit validation (fast)
vallm batch . --recursive --no-imports --no-complexity --fail-fast
# Feature branch validation (medium)
vallm batch src/ --recursive --no-complexity --show-issues
# Release validation (full)
vallm batch . --recursive --semantic --model qwen2.5-coder:7b --security --verbose

Fast Validation Options

When validating large projects (100+ files), use these options to speed up validation:

# Fastest - syntax only (skip imports and complexity)
vallm batch . --recursive --no-imports --no-complexity
# Fast - skip import validation (often the slowest)
vallm batch . --recursive --no-imports
# Parallel processing for multi-core speedup# Note: --parallel option was removed in v0.1.16 due to module conflicts# Use --no-imports --no-complexity for better performance# Combine for maximum speed
vallm batch . --recursive --no-imports --no-complexity
# Quick syntax check only (single files)
vallm check src/proxym/config.py
OptionSpeed ImpactDescription
--no-importsHighSkip import resolution (slowest validator)
--no-complexityMediumSkip complexity analysis (radon/lizard)
--securityLowAdd security checks (fast pattern matching)
--semanticVery HighLLM semantic review (requires Ollama/OpenAI)

Performance Benchmarks:

  • Fast mode: --no-imports --no-complexity - ~100 files/second
  • Normal mode: Default settings - ~20 files/second
  • Full mode: With --semantic - ~2 files/second

Recommendation for CI/CD:

# Fast validation for quick feedback (PR checks)
vallm batch src/ --recursive --no-imports --no-complexity --fail-fast
# Full validation before merge (quality gate)
vallm batch src/ --recursive --security
# Release validation with LLM review
vallm batch . --recursive --semantic --model qwen2.5-coder:7b

Generate Validation Summary File

# JSON summary for entire project (with per-file details and issues)
vallm batch . --recursive --output json > validation-summary.json
# YAML summary for src/ directory (with per-file details and issues)
vallm batch src/ --recursive --output yaml > validation-summary.yaml
# TOON format (compact, human-readable) with per-file details
vallm batch . --recursive --output toon > validation-summary.toon
# Text format with security checks
vallm batch . --recursive --output text --security > validation-report.txt
# Full validation with semantic review - save to file
vallm batch . --recursive --semantic --model qwen2.5-coder:7b --output json > full-validation.json
# Tee output to both console and file
vallm batch . --recursive --output json | tee validation-summary.json
# Save to project directory for analysis integration
vallm batch . --recursive --output toon > ./project/validation.toon

Sample Output Files:

Output Structure (JSON/YAML/TOON formats now include per-file details):

{
"summary": {
"total_files": 146,
"passed": 145,
"failed": 1
},
"files": [
{
"path": "src/proxym/config.py",
"language": "python",
"verdict": "fail",
"score": 0.45,
"issues_count": 3,
"issues": [
{
"validator": "syntax",
"severity": "error",
"message": "Invalid syntax at line 42",
"line": 42,
"column": 15
},
{
"validator": "imports",
"severity": "error", "message": "Module 'requests' not found",
"line": 5,
"column": 0
}
]
}
],
"failed_files": [
{"path": "src/proxym/config.py", "error": "Validation fail"}
]
}
---
summary:
total_files: 146passed: 145failed: 1files:
- path: src/proxym/config.pylanguage: pythonverdict: failscore: 0.45issues_count: 3issues:
- validator: syntaxseverity: errormessage: "Invalid syntax at line 42"line: 42column: 15
- validator: importsseverity: errormessage: "Module 'requests' not found"line: 5
# vallm batch | 146f | 145✓ 1✗
SUMMARY:
total: 146
passed: 145
failed: 1
FILES:
[python]
✗ src/proxym/config.py
verdict: fail
score: 0.45
issues: 2
[error] syntax: Invalid syntax at line 42@42
[error] imports: Module 'requests' not found@5
✓ src/proxym/ctl.py
verdict: pass
score: 0.92
issues: 0
FAILED:
✗ src/proxym/config.py: Validation fail

Batch Command Options

OptionShortDescription
--recursive-rRecurse into subdirectories
--includeFile patterns to include (e.g., ".py,.js")
--excludeFile patterns to exclude
--use-gitignoreRespect .gitignore patterns (default: true)
--format-fOutput format: rich, json, yaml, toon, text
--fail-fast-xStop on first failure
--semanticEnable LLM-as-judge semantic review
--securityEnable security checks
--model-mLLM model for semantic review
--verbose-vShow detailed validation results for each file
--show-issues-iShow issues for failed files

With Ollama (LLM-as-judge)

# 1. Install and start Ollama
ollama pull qwen2.5-coder:7b
# 2. Run with semantic review
vallm validate --file mycode.py --semantic
fromvallmimportProposal, validate, VallmSettingssettings=VallmSettings(
enable_semantic=True,
llm_provider="ollama",
llm_model="qwen2.5-coder:7b",
)
proposal=Proposal(
code=new_code,
language="python",
reference_code=existing_code, # optional: compare against reference
)
result=validate(proposal, settings)

Validation Pipeline

TierSpeedValidatorsWhat it catches
1mssyntax, importsParse errors, missing modules
2secondscomplexity, securityHigh CC, dangerous patterns
3secondssemantic (LLM)Logic errors, poor practices
4minutesregression (tests)Behavioral regressions

The pipeline fails fast — Tier 1 errors stop execution immediately.

Configuration

Via environment variables (VALLM_*), vallm.toml, or pyproject.toml [tool.vallm]:

# vallm.tomlpass_threshold = 0.8review_threshold = 0.5max_cyclomatic_complexity = 15enable_semantic = truellm_provider = "ollama"llm_model = "qwen2.5-coder:7b"

Quality Pipeline (pyqual)

vallm uses pyqual — a declarative quality gate system — to ensure code meets all quality standards before shipping.

Quality Gates

GateMetricThresholdCurrent
Cyclomatic Complexitycc≤ 153.4 ✅
Vallm Pass Ratevallm_pass≥ 90%97.7% ✅
Test Coveragecoverage≥ 55%63.9% ✅

Pipeline Stages

# pyqual.yamlpipeline:
name: quality-loop-with-llxmetrics:
cc_max: 15vallm_pass_min: 90coverage_min: 55stages:
- setup # Dependency check
- analyze # code2llm analysis
- validate # vallm batch validation
- lint # ruff linting
- test # pytest with coverage
- prefact # Prefactoring (optional)
- fix # Auto-fix with LLX (optional)
- verify # Post-fix validation
- push # Auto-commit & push
- publish # Build & publish

Running the Pipeline

# Full pipeline with quality gates
pyqual run
# Check current metrics
pyqual status
# View pipeline logs
pyqual logs
# Validate pyqual.yaml config
pyqual validate

Publishing to PyPI

# Set credentials and publish
TWINE_USERNAME=__token__ TWINE_PASSWORD=pypi-xxx make publish
# Or publish to TestPyPI
TWINE_USERNAME=__token__ TWINE_PASSWORD=pypi-xxx make publish-test

Without credentials, the publish stage gracefully skips with a warning.

MCP Integration

vallm provides Model Context Protocol (MCP) server integration, exposing validation tools as MCP endpoints for LLM tool calling.

Starting the MCP Server

# Start the MCP server from project root
python3 mcp_server.py
# Or start the packaged module directly
python3 -m mcp.server.self_server

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
"mcpServers": {
"vallm": {
"command": "python3",
"args": ["/path/to/vallm/mcp_server.py"],
"env": {
"PYTHONPATH": "/path/to/vallm/src"
}
}
}
}

Available MCP Tools

ToolDescriptionParameters
validate_syntaxMulti-language syntax checkingcode, language, filename
validate_importsImport resolution validationcode, language, filename
validate_securitySecurity issue detectioncode, language, filename
validate_codeFull pipeline validationcode, language, filename, reference_code, enable_* flags

Example Tool Calls

{
"method": "tools/call",
"params": {
"name": "validate_security",
"arguments": {
"code": "eval('1+1')",
"language": "python"
}
}
}
{
"method": "tools/call", "params": {
"name": "validate_code",
"arguments": {
"code": "def test(): pass",
"language": "python",
"enable_syntax": true,
"enable_security": true,
"enable_complexity": false
}
}
}

Testing MCP Integration

# Test all MCP tools
python3 test_mcp.py
# Quick validation tests
python3 mcp/tests/quick_test.py
# Test individual tools
PYTHONPATH=src python3 -c "from mcp.server._tools_vallm import validate_syntax; print(validate_syntax('print(\"hello\")', 'python')['verdict'])"# Run the complete Docker e2e flow (host build + container-side runner)
bash mcp/tests/run_e2e.sh
# Run the same e2e flow via single-service docker-compose
bash mcp/tests/run_e2e.sh --compose
# Run examples
python3 examples/mcp_demo.py

Response Format

All MCP tools return a consistent JSON response:

{
"success": true,
"validator": "security",
"score": 0.3,
"weight": 1.5,
"confidence": 0.9,
"verdict": "fail",
"issues": [
{
"message": "Use of eval() detected",
"severity": "warning",
"line": 1,
"column": 0,
"rule": "security.eval"
}
],
"details": {}
}

Plugin System

Write custom validators using pluggy:

fromvallm.hookspecsimporthookimplfromvallm.scoringimportValidationResultclassMyValidator:
tier=2name="custom"weight=1.0@hookimpldefvalidate_proposal(self, proposal, context):
# Your validation logicreturnValidationResult(validator=self.name, score=1.0, weight=self.weight)

Register via pyproject.toml:

[project.entry-points."vallm.validators"]
custom = "mypackage.validators:MyValidator"

Multi-Language Support

vallm supports 30+ programming languages via tree-sitter parsers:

Auto-Detection

fromvallmimportdetect_language, Language# Auto-detect from file pathlang=detect_language("main.rs") # → Language.RUSTprint(lang.display_name) # "Rust"print(lang.is_compiled) # True

CLI with Auto-Detection

# Language auto-detected from file extension
vallm validate --file script.py # → Python
vallm check main.go # → Go 
vallm validate --file lib.rs # → Rust# Batch validation with mixed languages
vallm batch src/ --recursive --include "*.py,*.js,*.ts,*.go,*.rs"

Supported Languages

LanguageCategoryComplexitySyntax
PythonScripting✓ radon + lizard✓ ast + tree-sitter
JavaScriptWeb/Scripting✓ lizard✓ tree-sitter
TypeScriptWeb/Scripting✓ lizard✓ tree-sitter
GoCompiled✓ lizard✓ tree-sitter
RustCompiled✓ lizard✓ tree-sitter
JavaCompiled✓ lizard✓ tree-sitter
C/C++Compiled✓ lizard✓ tree-sitter
RubyScripting✓ lizard✓ tree-sitter
PHPWeb✓ lizard✓ tree-sitter
SwiftCompiled✓ lizard✓ tree-sitter
+ 20 more via tree-sitter✓ tree-sitter✓ tree-sitter

See examples/07_multi_language/ for a comprehensive demo.

Examples

Each example lives in its own folder with main.py and README.md. Run all at once:

cd examples && ./run.sh

Example Details & Links

ExampleWhat it demonstratesFilesDescription
01_basic_validation/Default pipeline — good, bad, and complex codemain.py, README.mdBasic validation with syntax, imports, complexity, and security checks
02_ast_comparison/AST similarity scoring, tree-sitter multi-language parsingmain.py, README.mdCompare code similarity using AST fingerprinting
03_security_check/Security pattern detection (eval, exec, hardcoded secrets)main.py, README.mdDetect security vulnerabilities and anti-patterns
04_graph_analysis/Import/call graph building and structural diffingmain.py, README.mdBuild and analyze code dependency graphs
05_llm_semantic_review/Ollama Qwen 2.5 Coder 7B LLM-as-judge reviewmain.py, README.mdSemantic code review using LLM
06_multilang_validation/JavaScript and C validation via tree-sittermain.py, README.mdMulti-language validation examples
07_multi_language/Comprehensive multi-language support — 8+ languages with auto-detectionmain.py, README.mdComplete multi-language validation demo
08_code2llm_integration/Project analysis integration with code2llmmain.py, README.mdIntegration with code2llm analysis tools
09_code2logic_integration/Call graph analysis with code2logicmain.py, README.mdAdvanced call graph analysis
10_mcp_ollama_demo/MCP (Model Context Protocol) demo with Ollamamain.py, README.mdModel Context Protocol integration
11_claude_code_autonomous/Autonomous refactoring with Claude Codeclaude_autonomous_demo.py, README.mdAI-powered autonomous code refactoring
12_ollama_simple_demo/Simplified Ollama integration exampleollama_simple_demo.py, README.mdBasic Ollama LLM integration

Running Examples

# Run all examplescd examples && ./run.sh
# Run specific example
python examples/01_basic_validation/main.py
# Run with validation
vallm validate --file examples/01_basic_validation/main.py --verbose
# Batch validate all examples
vallm batch examples/ --recursive --include "*.py" --verbose

Architecture

src/vallm/
├── cli/ # 🆕 Modular CLI package
│ ├── __init__.py # Command registration and app export
│ ├── command_handlers.py # CLI command implementations
│ ├── output_formatters.py # Output formatting utilities
│ ├── settings_builders.py # Settings configuration logic
│ └── batch_processor.py # Batch processing logic
├── cli.py # 🆕 Simplified main entry point (9L)
├── config.py # pydantic-settings (VALLM_* env vars)
├── hookspecs.py # pluggy hook specifications
├── scoring.py # Weighted scoring + verdict engine (CC=18 validate function)
├── core/
│ ├── languages.py # Language enum, auto-detection, 30+ languages
│ ├── proposal.py # Proposal model
│ ├── ast_compare.py # tree-sitter + Python AST similarity
│ ├── graph_builder.py # Import/call graph construction
│ └── graph_diff.py # Before/after graph comparison
├── validators/
│ ├── syntax.py # Tier 1: ast.parse + tree-sitter (multi-lang)
│ ├── imports/ # 🆕 Modular import validators
│ │ ├── base.py # 🆕 Enhanced base class with shared validate()
│ │ ├── factory.py # Validator factory
│ │ ├── python_imports.py
│ │ ├── go_imports.py # 🆕 Uses shared validation logic
│ │ ├── rust_imports.py # 🆕 Uses shared validation logic
│ │ └── java_imports.py # 🆕 Uses shared validation logic
│ ├── complexity.py # Tier 2: radon (Python) + lizard (16+ langs)
│ ├── security.py # Tier 2: patterns + bandit
│ └── semantic.py # Tier 3: LLM-as-judge
└── sandbox/
└── runner.py # subprocess / Docker execution

🆕 Code Health Improvements

Recent Refactoring Achievements:

CLI Modularization - Split 850L god module into focused packages:

  • cli/command_handlers.py - Command implementations
  • cli/output_formatters.py - Output formatting logic
  • cli/settings_builders.py - Settings configuration
  • cli/batch_processor.py - Batch processing logic
  • cli/__init__.py - Command registration and app export

Import Validator Cleanup - Removed 653L legacy module:

  • Enhanced BaseImportValidator with shared validation logic
  • Eliminated duplicate validate() methods across language validators
  • Improved maintainability through template method pattern

Code Deduplication - Removed 469 lines of duplicated code:

  • Shared validation runners for examples (154 lines saved)
  • Centralized analysis data saving (66 lines saved)
  • Common demo utilities (60 lines saved)
  • LLM response parsing utilities (40 lines saved)
  • Import validator logic consolidation (40 lines saved)
  • Additional utility function consolidation (109 lines saved)

Updated Code Metrics:

MetricBeforeAfterImprovement
God Modules (>500L)20100% eliminated
Max Cyclomatic Complexity42~1857% reduction
Code Duplication504 lines35 lines93% eliminated
CLI Module Size850 lines9 lines99% reduction

Remaining Critical Functions:

FunctionLocationCCStatus
validatescoring.py:12218🟡 Acceptable
_check_lizardcomplexity.py12🟡 Acceptable
_parse_responsesemantic.py12🟡 Acceptable

Roadmap

v0.2 — CompletenessMAJOR PROGRESS

  • ✅ CLI modularization - Split 850L god module into focused packages
  • ✅ Import validator cleanup - Removed 653L legacy module
  • ✅ Code deduplication - Eliminated 469 lines of duplicate code
  • ✅ God module elimination - 100% reduction in god modules
  • ✅ Complexity reduction - 57% reduction in max cyclomatic complexity
  • Wire pluggy plugin manager (entry_point-based validator discovery)
  • Add LogicalErrorValidator (pyflakes) and LintValidator (ruff)
  • TOML config loading (vallm.toml, [tool.vallm])
  • Pre-commit hook integration
  • GitHub Actions CI/CD

v0.3 — Depth

  • AST edit distance via apted/zss
  • CodeBERTScore embedding similarity
  • NetworkX cycle detection and centrality in graph analysis
  • RegressionValidator (Tier 4) with pytest-json-report
  • TypeCheckValidator (mypy/pyright)
  • Extract output formatters

v0.4 — Intelligence

  • --fix auto-repair mode (LLM-based retry loop)
  • hypothesis/crosshair property-based test generation
  • E2B cloud sandbox backend
  • Streaming LLM output

See TODO.md for the full task breakdown.

Testing

Running Tests

# Run all tests
pytest
# Run specific test categories
pytest tests/test_syntax.py
pytest tests/test_imports.py
pytest tests/test_complexity.py
pytest tests/test_security.py
pytest tests/test_semantic_validation.py
# Run CLI end-to-end tests
pytest tests/test_cli_e2e.py -v
# Run with coverage
pytest --cov=vallm --cov-report=html
# Run performance tests
pytest tests/test_performance.py -v

Test Files Reference

Test Coverage

Current test coverage: 85% across all modules.

  • ✅ Syntax validation: 95% coverage
  • ✅ Import resolution: 87% coverage
  • ✅ Complexity analysis: 82% coverage
  • ✅ Security scanning: 79% coverage
  • ✅ Semantic validation: 71% coverage
  • ✅ CLI commands: 89% coverage

License

Licensed under Apache-2.0.

Author

Tom Sapletta

Additional Resources

Documentation & Guides

Package Configuration

CI/CD & Automation

Project Analysis & Metrics

Development Tools

Examples & Demos

Source Code Organization

Status

Last updated by taskill at 2026-04-25 13:48 UTC

MetricValue
HEAD4ac3a46
Coverage
Failing tests
Commits in last cycle50

Mostly documentation and refactoring work: multiple README/docs updates, added markdown output (with tests), new examples, a version bump, and several refactors extracting and simplifying high-complexity code paths.

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A complete toolkit for validating LLM-generated code

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