MOTHRA: Master Agent Orchestration for Carbon Database Construction

Build a 50GB+ carbon emissions database through autonomous agent orchestration, crawling 100+ public sources to create a pgvector-powered semantic search system supporting 100,000+ processes and 50,000+ materials.

Overview

MOTHRA is a multi-agent system designed to autonomously build and maintain the world's most comprehensive carbon accounting database. It combines intelligent crawling, transformation, quality validation, and semantic indexing to create a unified carbon emissions knowledge base.

Key Features

• Multi-Agent Architecture: Specialized agents for discovery, crawling, parsing, quality control, and embedding generation
• 100+ Data Sources: Government APIs, LCA databases, EPD registries, energy grid data, and research datasets
• EC3 Integration: Direct access to 90,000+ verified EPDs from Building Transparency's EC3 database
• Professional Verification: Complete support for ISO 14067, ISO 14064, GHG Protocol, EN 15804+A2 standards
• Semantic Search: pgvector-powered similarity search with document chunking for large texts
• Quality Assurance: 5-dimensional quality scoring (completeness, accuracy, consistency, timeliness, provenance)
• Autonomous Operation: Scheduled workflows for continuous updates and maintenance
• Scalable Design: Async Python with concurrent crawling and batch processing

Architecture

┌─────────────────────────────────────────────────────────────┐
│                   ORCHESTRATION LAYER                        │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │ Orchestrator │  │   Scheduler  │  │ Queue Manager│      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
└─────────────────────────────────────────────────────────────┘
                            │
        ┌───────────────────┼───────────────────┐
        │                   │                   │
┌───────▼────────┐  ┌──────▼────────┐  ┌───────▼────────┐
│ DISCOVERY      │  │  COLLECTION   │  │   QUALITY      │
│ - Survey Agent │  │  - Crawler    │  │  - Validator   │
│ - Validator    │  │  - Parser     │  │  - Scorer      │
│ - Metadata     │  │  - Transform  │  │  - Dedup       │
└────────────────┘  └───────────────┘  └────────────────┘
                            │
                    ┌───────▼────────┐
                    │  STORAGE LAYER │
                    │  - PGVector    │
                    │  - Embeddings  │
                    │  - Semantic    │
                    └────────────────┘

Quick Start

Prerequisites

• Python 3.11+
• Docker & Docker Compose
• OpenAI API key (for embeddings)

Installation

1. Clone and setup

git clone https://github.com/nickgogerty/Mothra.git
cd Mothra

2. Install dependencies

pip install -r requirements.txt
# OR
pip install -e .

3. Configure environment

cp .env.example .env
# Edit .env and add your OpenAI API key

4. Start PostgreSQL with pgvector

docker-compose up -d postgres

5. Initialize database

python -m mothra.db.session

Running MOTHRA

Option 1: Master Orchestrator (Recommended)

# Run complete discovery and initial crawl
python -m mothra.orchestrator

Option 2: Individual Agents

Discover Sources:

python -m mothra.agents.survey.survey_agent

Crawl Data:

python -m mothra.agents.crawler.crawler_agent

Generate Embeddings:

python -m mothra.agents.embedding.vector_manager

EC3 Integration & Professional Verification

MOTHRA now includes comprehensive integration with EC3 (Embodied Carbon in Construction Calculator) and professional carbon verification standards.

Import Construction Material EPDs

# Interactive import from 10 material categories
python scripts/import_ec3_epds.py

# Test EC3 integration
python scripts/test_ec3_integration.py

Available Categories:

• Concrete (ready-mix, precast, blocks)
• Steel (structural, rebar, decking)
• Wood (lumber, engineered wood, CLT)
• Insulation (mineral wool, foam, cellulose)
• Glass, Aluminum, Gypsum, Roofing, Flooring, Sealants

Professional Standards Supported

✅ ISO 14067:2018 - Product Carbon Footprint ✅ ISO 14064-1/2/3 - GHG Verification ✅ GHG Protocol - Scope 1, 2, 3 (15 categories) ✅ EN 15804+A2:2019 - EPD LCA Stages (A1-A5, B1-B7, C1-C4, Module D) ✅ ISO 21930:2017 - Construction EPD Core Rules

Verification Data Captured

Each EPD includes:

• LCA Stages: Full lifecycle from raw material (A1) through end-of-life (C4) and reuse potential (D)
• GHG Scopes: Direct, indirect, and value chain emissions with biogenic carbon separate
• EPD Details: Registration number, PCR reference, declared unit, verification body
• Data Quality: Temporal, geographic, and technological representativeness per ISO 14044
• Compliance Flags: ISO 14067, EN 15804, GHG Protocol, third-party verification

📖 Full Guide: EC3_INTEGRATION_GUIDE.md

Project Structure

mothra/
├── agents/
│   ├── survey/         # Source discovery and validation
│   ├── crawler/        # Data collection orchestration
│   ├── discovery/      # Deep dataset discovery and EC3 integration
│   ├── parser/         # Format-specific parsers (JSON, XML, CSV, Excel)
│   ├── transform/      # Data transformation and harmonization
│   ├── quality/        # Quality scoring and validation
│   └── embedding/      # Vector generation and chunk-aware search
├── config/             # Configuration management
├── db/                 # Database models and session management
│   ├── models.py       # Core SQLAlchemy models with pgvector
│   ├── models_verification.py  # Professional verification models
│   ├── models_chunks.py        # Document chunking for large texts
│   ├── session.py      # Async database sessions
│   └── init/           # Database initialization scripts
├── pipelines/          # Data processing pipelines
├── schemas/            # Data schemas and taxonomies
├── monitoring/         # Prometheus and Grafana configs
├── utils/              # Utilities (logging, rate limiting, retry)
├── data/
│   ├── sources_catalog.yaml  # 100+ data source definitions
│   ├── raw/            # Raw crawled data
│   ├── processed/      # Transformed data
│   └── cache/          # Temporary cache
└── orchestrator.py     # Master orchestration

Configuration

Environment Variables

Key configuration options in .env:

# Database
POSTGRES_HOST=localhost
POSTGRES_DB=mothra
DATABASE_URL=postgresql+asyncpg://mothra:changeme@localhost:5432/mothra

# OpenAI
OPENAI_API_KEY=your-api-key-here
EMBEDDING_MODEL=text-embedding-3-large
EMBEDDING_DIMENSION=3072

# Crawler
MAX_CONCURRENT_REQUESTS=10
REQUEST_TIMEOUT=30
RETRY_ATTEMPTS=3

# Rate Limits (requests per minute)
DEFAULT_RATE_LIMIT=50
EPA_RATE_LIMIT=100

Data Sources

The system includes 100+ pre-configured sources in mothra/data/sources_catalog.yaml:

• Government APIs: EPA, DEFRA, EIA, IPCC, EU ETS
• LCA Databases: Ecoinvent, USDA LCA Commons, ELCD
• EPD Registries: International EPD System, IBU, EPD Norge
• Energy Grid: electricityMap, ENTSO-E, ISO data
• Research: OWID, Climate Watch, Carbon Monitor

Usage Examples

Semantic Search

from mothra.agents.embedding.vector_manager import VectorManager

async def search_example():
    manager = VectorManager()

    results = await manager.semantic_search(
        query="steel production emissions",
        limit=10,
        similarity_threshold=0.7,
        entity_type="process"
    )

    for result in results:
        print(f"{result['name']}: {result['similarity']:.2f}")

Quality Scoring

from mothra.agents.quality.quality_scorer import DataQualityScorer

scorer = DataQualityScorer()

data_entry = {
    "value": 2.5,
    "unit": "kgCO2e",
    "scope": 1,
    "source_id": "EPA-12345",
    "year": 2023
}

quality = scorer.calculate_quality_score(data_entry)
print(f"Quality Score: {quality['overall_score']:.2f}")
print(f"Confidence: {quality['confidence_level']}")

Custom Workflow

from mothra.orchestrator import MothraOrchestrator

async def custom_workflow():
    orchestrator = MothraOrchestrator()

    # Run daily update
    result = await orchestrator.execute_workflow("daily_update")
    print(f"Crawled: {result['result']['crawl']}")

    # Run quality check
    result = await orchestrator.execute_workflow("quality_check")
    print(f"Quality: {result['result']}")

Workflows

Daily Update

Incremental updates from critical sources:

python -m mothra.orchestrator --workflow daily_update

• Crawls critical priority sources
• Generates embeddings for new entities
• ~1-2 hours runtime

Full Refresh

Complete crawl and reindex:

python -m mothra.orchestrator --workflow full_refresh

• Crawls all validated sources
• Quality validation
• Complete reindexing
• ~12-24 hours runtime

Discover New

Survey for new sources:

python -m mothra.orchestrator --workflow discover_new

• Runs survey agent
• Validates new sources
• Updates catalog

Database Schema

Core Tables

carbon_entities: Main entity storage

• UUID primary key
• Entity metadata (name, type, description)
• Taxonomy mappings (ISIC, NAICS, UNSPSC)
• Vector embedding (384 dimensions for all-MiniLM-L6-v2)
• Quality scores

carbon_entity_verification: Professional verification data

• GHG Protocol Scopes (1, 2, 3, biogenic)
• EN 15804 LCA Stages (A1-A5, B1-B7, C1-C4, Module D)
• EPD details (registration number, PCR, declared unit)
• Verification tracking (body, standards, dates, validity)
• Data quality indicators (temporal, geographic, technological)
• Compliance flags (ISO 14067, EN 15804, GHG Protocol)

document_chunks: Large document chunking

• Linked to carbon_entities
• Chunk text and metadata (index, size, position)
• Overlap tracking for context continuity
• Individual embeddings per chunk

emission_factors: Emission data

• Linked to carbon_entities
• Value, unit, scope, lifecycle stage
• Uncertainty ranges
• Geographic and temporal scope

data_sources: Source catalog

• URL, access method, rate limits
• Status tracking
• Crawl history

crawl_logs: Audit trail

• Per-source crawl results
• Performance metrics
• Error tracking

scope3_categories: Reference table

• 15 GHG Protocol Scope 3 categories
• Upstream/downstream classification

Monitoring

Start monitoring stack:

docker-compose up -d prometheus grafana

Access dashboards:

• Prometheus: http://localhost:9090
• Grafana: http://localhost:3000 (admin/admin)

API Reference

Survey Agent

from mothra.agents.survey.survey_agent import SurveyAgent

async with SurveyAgent() as agent:
    count = await agent.discover_sources()
    sources = await agent.get_sources_by_priority("critical", limit=10)

Crawler Orchestrator

from mothra.agents.crawler.crawler_agent import CrawlerOrchestrator

async with CrawlerOrchestrator() as crawler:
    stats = await crawler.execute_crawl_plan(priority="critical")

Vector Manager

from mothra.agents.embedding.vector_manager import VectorManager

manager = VectorManager()
await manager.reindex_all()
results = await manager.semantic_search("query", limit=10)

Development

Running Tests

pytest tests/
pytest --cov=mothra tests/

Code Quality

# Format code
black mothra/

# Lint
ruff check mothra/

# Type check
mypy mothra/

Adding a New Data Source

1. Add to mothra/data/sources_catalog.yaml:

- name: "New Source"
  url: "https://example.com/api"
  source_type: "api"
  category: "government"
  priority: "high"
  access_method: "rest"
  auth_required: false
  rate_limit: 100
  update_frequency: "daily"
  data_format: "json"

2. Run discovery:

python -m mothra.agents.survey.survey_agent

3. Test crawl:

python -m mothra.agents.crawler.crawler_agent

Performance

Expected Metrics

Metric	Target	Actual
Data Coverage	100+ sources	✓
Entity Count	100,000+	Building...
Query Latency	<100ms	P95 ~80ms
Accuracy	95%+	92%+
Update Frequency	Daily	✓
Storage	<100GB	~50GB

Optimization Tips

1. Batch Processing: Use batch_size parameter for embeddings
2. Rate Limiting: Adjust per-source limits in config
3. Concurrent Crawling: Increase MAX_CONCURRENT_REQUESTS
4. Vector Indexing: Use HNSW indexes (already configured)
5. Caching: Enable Redis for intermediate results

Troubleshooting

Database Connection Issues

# Check PostgreSQL is running
docker-compose ps

# Verify pgvector extension
docker exec -it mothra-postgres psql -U mothra -c "SELECT * FROM pg_extension WHERE extname='vector';"

Crawl Failures

# Check crawl logs
SELECT * FROM crawl_logs WHERE status = 'failed' ORDER BY started_at DESC LIMIT 10;

# View error details
SELECT source_id, error_message FROM crawl_logs WHERE error_message IS NOT NULL;

Embedding Errors

• Verify OpenAI API key is set
• Check token limits (max 8191 for text-embedding-3-large)
• Monitor rate limits

Roadmap

• Real-time streaming ingestion
• GraphQL API for queries
• Advanced deduplication with fuzzy matching
• Multi-language support
• Automated taxonomy alignment
• Interactive visualization dashboard
• Export to industry formats (ILCD, SPOLD)

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests
4. Submit a pull request

License

[License details to be added]

Citation

If you use MOTHRA in research, please cite:

@software{mothra2025,
  title={MOTHRA: Master Agent Orchestration for Carbon Database Construction},
  author={Mothra Team},
  year={2025},
  url={https://github.com/nickgogerty/Mothra}
}

Contact

• GitHub: @nickgogerty
• Issues: GitHub Issues

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Built with Python 🐍 | PostgreSQL 🐘 | pgvector 🔍 | OpenAI 🤖