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An AI-powered DataHub agent that detects metadata, lineage, governance, and data-quality issues, explains their business impact, and generates actionable SQL fixes.
Build your AI team. Orchestrate specialized agents to code, research, analyze, reason, and create using real AI models in one powerful workspace.
An agent that assesses a schema change, writes the dbt fix, verifies its own fix, and won't write to DataHub unless it passes — then sweeps the whole catalog and ranks what's safe to change.
Ripple AI: Autonomous Self-Healing Data Pipeline
Sherlock turns DataHub metadata into disciplined investigations, challenging hypotheses, exposing missing evidence, and guiding teams toward the next best action.
A model that scores 100% is usually cheating. Hindsight uses DataHub column lineage to prove a feature knew the answer before the decision was made, and blocks the release.
Data quality tools watch your data. Nobody watches whether the documentation is still true. This open-source DataHub agent does, judging context, not schema diffs.
Your migration copilot
Generates dbt staging models, tests and docs from DataHub metadata over MCP — and proposes the metadata first. A PII tag in the catalog becomes a masked column in the generated SQL
DataHub provides trusted metadata, MCP connects the agent to that metadata, and AI turns that context into preventive change-risk decisions.
An agent that reads real DataHub schema before writing dbt code, validates its own SQL against it, opens a PR, and writes back to the catalog, generated pipelines you can trust, not guesses.
AI agent that predicts which downstream consumers will silently produce wrong data after a schema change, then writes governance warnings back into DataHub autonomously.
An agentic reliability control room using DataHub lineage, quality, and ownership context to stop healthcare data and ML pipeline issues before they impact clinical operations.
A tested Windows Quickstart audit and documentation fix that replaces Unix-only commands, aligns Python guidance with the CLI, and adds native PowerShell setup examples.
Turn a whiteboard photo into a validated dbt + Airflow PR — grounded in DataHub via the MCP Server, tested in DuckDB before merge, with column-level lineage written back to the catalog.
A PR bot that reports the real downstream impact of a dbt schema change — dashboards, ML models, and owners — before you merge, then writes the verdict back into DataHub.
An AI agent that detects data governance gaps, reasons over DataHub context and lineage, and writes approved fixes back to the catalog.
Tells you whether a metric dropped because customers left or because the instrument broke. Every claim cites a DataHub URN, and an external scorer reads only the run ledger.
Agent-native x402 payment gateway for autonomous AI agent transactions on-chain
Governance proxy for AI agents writing to DataHub
An evidence-first agent that audits your DataHub catalog against itself: it finds where two sources disagree, traces what breaks downstream, and hands a steward one exact governed fix.
DataHub shows what depends on a dataset. Tally finds the exact code to change, builds an evidence-backed plan, and refuses to guess where the proof ends.
Autonomous agents that catch silent data changes breaking production ML models — trace DataHub lineage, measure the AUC drop, open a fix PR, and write the warning back via the MCP Server.
FTIR spectral agent retrieval kernel optimized for Arm64 — real cross-architecture benchmark, reproducible on GitHub Actions ARM runners.
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