databridge-ai 0.49.2

DataBridge AI Community Edition - An open-source, MCP-native data reconciliation engine with tools for hierarchy management, data quality, and analytics

DataBridge AI - Community Edition

Open-source, MCP-native data reconciliation engine for comparing, profiling, and managing data quality.

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Overview

DataBridge AI Community Edition is a free, open-source data reconciliation toolkit built on the Model Context Protocol (MCP). It provides essential tools for:

• Data Comparison - Hash-based row comparison, orphan detection, conflict identification
• Fuzzy Matching - Find approximate matches between datasets using RapidFuzz
• Data Profiling - Statistical analysis and quality metrics for your data
• PDF/OCR Extraction - Extract text from PDFs and images
• dbt Integration - Generate dbt projects from your data
• Data Quality - Create and run data validation rules

Installation

# Basic installation
pip install databridge-ai

# With PDF support
pip install databridge-ai[pdf]

# With OCR support
pip install databridge-ai[ocr]

# With all optional dependencies
pip install databridge-ai[all]

Quick Start

As an MCP Server

DataBridge AI works as an MCP server, making its tools available to AI assistants like Claude:

# Run the MCP server
databridge-mcp

Using the Dashboard

# Start the web dashboard
databridge-ui

Then open http://localhost:5050 in your browser.

The dashboard includes a dedicated Hierarchy Builder page for visually creating and managing multi-level hierarchy projects, importing from CSV, and exporting deployment scripts.

Python API

from databridge_ai import load_csv, profile_data, fuzzy_match_columns

# Load and profile a CSV file
result = load_csv("data.csv")
profile = profile_data("data.csv")

# Find fuzzy matches between two files
matches = fuzzy_match_columns(
    source_file="source.csv",
    target_file="target.csv",
    source_column="name",
    target_column="customer_name",
    threshold=80
)

Available Tools (~106 tools)

Category	Tools	Description
File Discovery	3	find_files, get_working_directory, stage_file
Data Loading	3	load_csv, load_json, query_database
Data Profiling	2	profile_data, profile_book_sources
Hashing & Comparison	3	compare_hashes, compare_table_data, get_data_comparison_summary
Fuzzy Matching	2	fuzzy_match_columns, fuzzy_deduplicate
PDF/OCR	3	extract_text_from_pdf, ocr_image, parse_table_from_text
Workflow	4	analyze_request, save_workflow_step, get_workflow, clear_workflow
Transform	2	transform_column, convert_sql_format
Documentation	1	get_application_documentation
Templates	10	list_financial_templates, get_template_details, get_skill_prompt, etc.
Diff Utilities	6	diff_text, diff_dicts, diff_lists, explain_diff, generate_patch, find_similar_strings
dbt Integration	8	create_dbt_project, generate_dbt_model, generate_dbt_schema, validate_dbt_project, etc.
Data Quality	7	generate_expectation_suite, run_validation, create_data_contract, add_column_expectation, etc.
License	1	get_license_status

Editions

DataBridge AI is available in four editions:

	Community (CE)	Pro	Pro Examples	Enterprise
Tools	~106	~297	Tests & Tutorials	361+
Price	Free	Licensed	Licensed Add-on	Custom
Distribution	Public PyPI	GitHub Packages	GitHub Packages	Private Deploy
Data Reconciliation	✅	✅		✅
Fuzzy Matching	✅	✅		✅
Data Profiling	✅	✅		✅
PDF/OCR	✅	✅		✅
dbt Basic	✅	✅		✅
Data Quality	✅	✅		✅
UI Dashboard	✅	✅		✅
Diff Utilities	✅	✅		✅
Templates (Basic)	✅	✅		✅
Hierarchy Builder (49 tools)		✅		✅
Wright Pipeline (31 tools)		✅		✅
Cortex AI Agent (26 tools)		✅		✅
Data Catalog (19 tools)		✅		✅
Faux Objects (18 tools)		✅		✅
Connections (16 tools)		✅		✅
AI Orchestrator (16 tools)		✅		✅
Data Observability (15 tools)		✅		✅
Data Versioning (12 tools)		✅		✅
Git/CI-CD (12 tools)		✅		✅
Lineage Tracking (11 tools)		✅		✅
PlannerAgent (11 tools)		✅		✅
GraphRAG Engine (10 tools)		✅		✅
Unified AI Agent (10 tools)		✅		✅
Hierarchy-Graph Bridge (5 tools)		✅		✅
Console Dashboard (5 tools)		✅		✅
Schema Matcher (5 tools)		✅		✅
Data Matcher (4 tools)		✅		✅
47 Tests + 19 Tutorials			✅
Custom Agents				✅
White-label				✅
SLA Support				✅
On-premise Deploy				✅

Upgrade to Pro

# Set your license key
export DATABRIDGE_LICENSE_KEY="DB-PRO-YOURKEY-20260101-signature"

# Install Pro (from GitHub Packages)
pip install databridge-ai-pro --extra-index-url https://[e-mail]/datanexum/DATABRIDGE_AI/main/

# Install Pro Examples (tests & tutorials, requires Pro key)
pip install databridge-ai-examples                # CE tests + beginner tutorials
pip install databridge-ai-examples[pro]           # + Pro tests + advanced tutorials

License Key Format: DB-{TIER}-{CUSTOMER_ID}-{EXPIRY}-{SIGNATURE}

Contact [e-mail] for pricing and license keys.

Configuration

Create a .env file in your project root:

# Database connection (optional)
DATABRIDGE_DATABASE_URL=postgresql://user:pass@localhost/db

# OCR settings (optional)
DATABRIDGE_TESSERACT_PATH=/usr/bin/tesseract

# Fuzzy matching threshold (default: 80)
DATABRIDGE_FUZZY_THRESHOLD=80

# Max rows to display (default: 10)
DATABRIDGE_MAX_ROWS_DISPLAY=10

Plugin System

Extend DataBridge AI with custom plugins:

plugins/
├── my_plugin/
│   ├── __init__.py
│   └── mcp_tools.py  # Must have register_tools(mcp)

# plugins/my_plugin/mcp_tools.py
def register_tools(mcp):
    @mcp.tool()
    def my_custom_tool(param: str) -> str:
        """My custom tool description."""
        return f"Processed: {param}"

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Links

• Documentation: github.com/datanexum/DATABRIDGE_AI/wiki
• Commercialization Guide: docs/COMMERCIALIZATION.md
• Issues: github.com/datanexum/DATABRIDGE_AI/issues
• Pro Features: Pro Features Wiki