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Dolt

by Community

Dolt – Git for Data

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OSS

Dolt

Added 1 June 2026

#agent-memory #agent-memory-server #ai-agents #ai-database #data-version-control #data-versioning #database #database-version-control

Overview

Dolt is a SQL database with Git-like version control built in. It tracks schema and data changes as commits, enabling branching, merging, and full history inspection. Developers can clone databases, create branches for experiments, and merge changes back with conflict resolution.

Best for

Best for
Teams needing audit trails and collaborative workflows on structured data

Use cases

  • Tracking data lineage and auditing changes across database versions
  • Collaborating on database schema and data modifications in parallel branches
  • Rolling back corrupted or incorrect data to a known good state

Notes

Dolt is a SQL database with Git-like version control built in. It tracks schema and data changes as commits, enabling branching, merging, and full history inspection. Developers can clone databases, create branches for experiments, and merge changes back with conflict resolution.

22,967 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Tracking data lineage and auditing changes across database versions
  • Collaborating on database schema and data modifications in parallel branches
  • Rolling back corrupted or incorrect data to a known good state

Pros

  • Native Git workflow for databases eliminates separate version control tooling
  • Full commit history and blame tracking for data changes
  • SQL-compatible interface reduces learning curve for database users

Cons

  • Performance overhead compared to standard SQL databases due to version tracking
  • Smaller ecosystem and community support than PostgreSQL or MySQL
  • Merging complex data conflicts requires manual intervention

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Native Git workflow for databases eliminates separate version control tooling
  • Full commit history and blame tracking for data changes
  • SQL-compatible interface reduces learning curve for database users

Cons

  • Performance overhead compared to standard SQL databases due to version tracking
  • Smaller ecosystem and community support than PostgreSQL or MySQL
  • Merging complex data conflicts requires manual intervention