Best Open Source Alternatives to a Data Catalog for AI Agents?
OpenMetadata, DataHub, Amundsen and the MCP layer that lets agents use them
The strongest open-source alternatives to a commercial data catalog for AI agents are OpenMetadata, DataHub and Amundsen. OpenMetadata is the most governance-oriented, DataHub has the deepest lineage and event model, and Amundsen is the simplest search-first catalog. What matters for agents is not the catalog UI but whether the metadata is reachable through an API or an MCP server.
Key Takeaways
- •OpenMetadata, DataHub and Amundsen are the three open-source catalogs most teams shortlist; all three publish their code on GitHub, so you can inspect and self-host them.
- •For AI agents the deciding factor is programmatic access: DataHub maintains an MCP server in the acryldata/mcp-server-datahub repository, which lets MCP clients query its metadata directly.
- •Quality tools (Great Expectations), observability tools (Monte Carlo) and diff tools (Datafold) are not catalogs; they answer different questions and sit beside one.
- •A catalog stores metadata; an agent layer has to read it, reason over it and act. Treat the two as separate purchases.
- •Start read-only. An agent that can only read catalog metadata cannot damage production, and you can widen permissions once you trust its answers.
We wrote this page for teams who already know why they want a catalog and are now asking what an agent needs from one. An agent asking "what's the blast radius if I drop this column?" needs three things: asset metadata, column-level lineage and ownership. Everything below is judged against those three.
What AI agents actually need from a catalog
A human uses a catalog by searching and clicking. An agent uses it by calling tools. That shifts the requirements toward stable APIs, structured responses and fine-grained access control. The Model Context Protocol specification (revision 2025-06-18) defines how a client discovers and calls tools exposed by a server, so any catalog that ships an MCP server can be queried by Claude Code, Cursor or another MCP client without custom glue.
Three questions separate catalogs that work for agents from catalogs that only work for people. Can an agent search by natural-language terms and get back typed results? Can it traverse lineage upstream and downstream programmatically? Can you scope what it sees, so an agent never reads a PII column it should not?
OpenMetadata
OpenMetadata is an open-source metadata platform with a unified metadata model, a REST API, ingestion connectors, data profiling, lineage and a governance workflow for glossary terms, tags and ownership. Its GitHub repository is the place to check current connector coverage and release notes. It fits teams that want governance and discovery in one product, and it is a good default when a single self-hosted service is easier to run than several.
The trade-off is operational weight. You run the application, a search backend and a relational store, and connector configuration is yours to maintain.
DataHub
DataHub, originally built at LinkedIn and now developed in the datahub-project/datahub repository, is built around a metadata graph and an event stream. That design suits lineage-heavy estates where many systems emit metadata continuously. For agents, the notable detail is the DataHub MCP server, which exposes search and lineage lookups as tools that an MCP client can call.
DataHub is the stronger choice when impact analysis is the main job. The cost is a larger deployment footprint than a search-only catalog.
Amundsen
Amundsen, in the amundsen-io/amundsen repository, started at Lyft as a search-and-popularity catalog. It is light on governance workflow and lineage compared with the other two, which is a feature if your need is "help analysts find the right table". For agent use you will likely write your own tool wrapper around its search and metadata services, and you should check recent commit activity before committing to it.
Comparison
| Tool | Approach | Where it fits | Limits |
|---|---|---|---|
| OpenMetadata | Open-source metadata platform: catalog, lineage, profiling, governance | Teams wanting discovery and governance in one self-hosted service | You operate the app, search index and database; confirm MCP support in current docs |
| DataHub | Open-source metadata graph with event streaming | Lineage-heavy estates and impact analysis; has an MCP server | Bigger deployment footprint; governance workflow is less central than lineage |
| Amundsen | Open-source search and discovery catalog | Helping analysts find tables with minimal setup | Lighter lineage and governance; agent access needs custom wrapping |
| Great Expectations | Open-source data quality assertions | Writing and running expectations on datasets in pipelines | Not a catalog; does not store broad asset metadata or lineage |
| Monte Carlo | Commercial data observability | Anomaly detection and incident alerting across warehouses | Closed-source and not a catalog; detection does not fix the issue |
| Datafold | Data diffing and CI for data changes | Reviewing the effect of code changes on tables before merge | Focused on diffs and lineage for change review, not general discovery |
| MLflow | Open-source ML experiment and model tracking | Tracking models, runs and artifacts | Catalogs ML assets, not warehouse tables and columns |
| Data Workers | Open-source MCP-native agents, including a Catalog Agent that federates existing catalogs | Letting engineers query OpenMetadata, DataHub or Amundsen metadata from their editor | Sits on top of a catalog rather than replacing the metadata store; the OSS edition is read-only by default |
The honest reading of this table is that you do not replace a catalog with an agent. You pick a metadata store, then pick how agents reach it. Quality, observability and diff tools complement the store; they are not substitutes for it.
Why open source helps agent workloads
Open-source catalogs remove per-seat licensing, which matters when agents multiply the number of callers. They also let you read the code that enforces access, which is useful when an auditor asks how an agent was prevented from reading sensitive columns. Licensing is explicit: projects released under the Apache License, Version 2.0 grant patent and redistribution rights, which simplifies embedding them in internal tooling.
Community-driven development also means the integration surface moves quickly, so pin versions and read changelogs before upgrading. Open source is not free of cost: you pay in operations and upgrade work instead of license fees.
Where Data Workers fits
We build Data Workers, an open-source set of MCP-native agents. Our Catalog Agent federates across OpenMetadata, DataHub, Amundsen, Unity Catalog and Atlan, so a question like "what tables haven't been queried in 90 days?" can be asked from Claude Code or Cursor without opening a catalog UI. It is not a replacement metadata store. If you have no catalog yet, choose one from the list above first. The open-source edition runs locally and is read-only by default, which is the safe place to start.
How to choose
- •Choose OpenMetadata if governance workflows and a single deployable product matter most.
- •Choose DataHub if lineage, event-driven ingestion and an existing MCP server matter most.
- •Choose Amundsen if you only need fast search and can accept lighter governance.
- •Add Great Expectations, Monte Carlo or Datafold for quality, detection and change review; none of them replaces a catalog.
Frequently Asked Questions
Is OpenMetadata or DataHub better for AI agents?
It depends on the agent's job. DataHub publishes an MCP server for search and lineage, which makes agent access straightforward today. OpenMetadata offers a broad REST API and governance features. Check each project's current documentation for MCP support before deciding.
Can Great Expectations or Monte Carlo replace a data catalog?
No. Great Expectations validates data against assertions, and Monte Carlo detects anomalies and incidents. Neither is a system of record for asset metadata, ownership and lineage, so they work alongside a catalog.
Do I need a catalog before I use MCP agents?
An agent can query warehouses directly, but answers about ownership, lineage and sensitivity depend on catalog metadata. Without one, agents guess from table names. We recommend standing up a catalog and giving agents read-only access first.
Why choose an open-source data catalog?
You avoid license fees, can inspect the code that enforces access, and can customize connectors. You take on hosting and upgrades, so budget engineering time for both.