AI Agent Database Memory

AI coding agents forget your database schema every session. beads (14 hours old): Dolt-powered version-controlled SQL memory. Memori BYODB. Never re-explain your schema again.

14 Hours Old Dolt-Powered Schema Memory

The Database Memory Problem

AI coding agents that work with databases face a specific memory challenge:

beads — Dolt-Powered Version-Controlled SQL Memory (14 hours ago!)

"Beads: A memory upgrade for your coding agent. Dolt-Powered: Version-controlled SQL database with cell-level merge, native branching, and built-in sync via Dolt remotes. Agent-Optimized: JSON output for easy LLM parsing. Your agent remembers every schema change." gastownhall/beads on GitHub, 14 hours ago

beads is the newest entry in AI agent memory — released just 14 hours ago. It uses Dolt (version-controlled SQL) to give coding agents persistent, version-aware database memory.

Master Claude Code in 2026 Guide (2 days ago)

"Claude inspects your live database structure and writes compatible SQL migrations, reducing schema errors. Prompt: 'Read the current schema, then generate a migration for the new feature.'" Medevel.com, 2 days ago

Memori BYODB — Bring Your Own Database (2 days ago)

"Memori BYODB: Turn your existing database into agent memory. Memori was evaluated on the LoCoMo benchmark for long-conversation memory — now works with your own PostgreSQL, SQLite, or MySQL." Memori BYODB, 2 days ago

Memora MCP — Database-Native Knowledge Graphs

"Memora MCP supports Dolt SQL schema: Use Dolt as a knowledge graph for your AI agent. Store schema, relationships, and migration history as queryable graph." agentic-box/memora on GitHub

Database Memory Solutions

Solution Type Version Control Encryption License
★ agent-memory MCP Memory AES-256 MIT
beads Dolt-backed SQL Git-native MIT
Memori BYODB BYODB
Memora MCP MCP Server

Why agent-memory Compliments Database Memory

beads handles SQL schema memory. agent-memory handles everything else — project architecture, API decisions, code patterns, tool usage. Together:

# Install agent-memory pip install agent-memory # Run MCP server for general project memory python -m agent_memory.mcp_server \ --storage json \ --path ./project-memory.json # Use beads for SQL schema memory # Use agent-memory for everything else # Combined: your AI agent remembers everything
agent-memory on GitHub beads on GitHub