meta-harness 1.0.0

Agent Output Quality Evaluation and Experience Tracking System

Meta-Harness

Agent Output Quality Evaluation and Experience Tracking System

Overview

Meta-Harness is an enterprise-grade Agent quality assurance system, based on the Stanford paper Meta-Harness: End-to-End Optimization of Model Harnesses.

Core Features

Feature	Description
Auto Evaluation	Multi-dimensional scoring after each Agent output
Experience Storage	SQLite-based structured storage for all task executions
Smart Indexing	Auto-mark high/low score experiences
Statistics	Success rate, tool effectiveness analysis

Evaluation Dimensions

Dimension	Weight	Description
Correctness	30%	Syntax, logic correctness
Completeness	20%	Requirements coverage
Efficiency	15%	Time/space complexity
Maintainability	15%	Code structure
Security	10%	No injection risks
Test Coverage	10%	Has test cases

Installation

# PyPI (recommended)
pip install meta-harness

# From source
pip install .

Quick Start

1. Evaluate Output

from meta_harness import quick_evaluate

# Evaluate Agent output
result = quick_evaluate("Implement user login", login_code)

print(f"Score: {result.overall_score}")
print(f"Dimensions: {result.scores}")
print(f"Feedback: {result.feedback}")

2. Record Experience

from meta_harness import ExperienceTracker

# Create tracker (auto-creates DB at ~/.meta_harness/)
tracker = ExperienceTracker()

# Record execution experience
record = tracker.record(
    task="Implement user login",
    output=login_code,
    evaluation={"overall_score": 85},
    tools_used=["code", "file_writer"],
    success=True,
    duration_seconds=30
)

print(f"Record ID: {record.id}")

3. Statistics

# Get overall statistics
stats = tracker.get_stats(days=30)
print(f"Total: {stats['total']}")
print(f"Success Rate: {stats['success_rate']}%")
print(f"Average Score: {stats['avg_score']}")

# Tool effectiveness analysis
tool_stats = tracker.analyze_tool_effectiveness()
for tool, stat in tool_stats.items():
    print(f"{tool}: {stat['success_rate']} success rate")

Advanced Features

Batch Evaluation

from meta_harness import batch_evaluate

pairs = [
    ("Task 1", "Output 1"),
    ("Task 2", "Output 2"),
    ("Task 3", "Output 3"),
]

results = batch_evaluate(pairs)
for r in results:
    print(f"{r.task}: {r.overall_score}")

Experience Search

# Search similar tasks
similar = tracker.search_similar("user auth", limit=5)
for r in similar:
    print(f"- {r.task} (score: {r.evaluation.get('overall_score', 'N/A')})")

# Get low score records (need improvement)
low_score = tracker.get_low_score_records(threshold=60)

Data Export

# Export to JSON
tracker.export_json("backup.json", days=30)

# Archive old records
tracker.archive_old(days=90)

# Cleanup (keep only recent 1000)
tracker.cleanup(keep_recent=1000)

CoPaw Integration

For integration with CoPaw Agent framework, see INTEGRATION.md

Project Structure

meta-harness/
├── pyproject.toml
├── README.md           # English (this file)
├── README_zh.md        # 中文
├── LICENSE
├── CONTRIBUTING.md
├── CHANGELOG.md
├── src/
│   └── meta_harness/
│       ├── __init__.py
│       ├── evaluator/
│       └── tracker/
├── tests/
└── docs/
    └── INTEGRATION.md

Dependencies

• Python >= 3.10
• SQLAlchemy >= 2.0

Optional:

• memorycoreclaw - For memory system integration

License

MIT License

Related Links

• MemoryCoreClaw - Human-like memory system
• CoPaw - Agent framework

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