OpenLore
Persistent architectural memory for AI coding agents using queryable codebase knowledge graphs and MCP tools.
OpenLore overview
OpenLore is an open-source memory layer for AI coding agents. It turns codebases into queryable knowledge graphs with static analysis, living specs, drift detection, and MCP tools so agents can recover architectural context instead of re-discovering it every session.
Architecture memory
OpenLore focuses on persistent architectural context for coding agents.
Architecture decisions and code relationships are often the context agents need most.Queryable codebase graph
The project describes codebases as queryable knowledge graphs with static analysis.
Graph structure can expose relationships that flat notes or chat summaries miss.MCP tool surface
OpenLore includes graph-native MCP tools for agent access.
MCP makes codebase memory easier to connect to multiple agent hosts.When to use OpenLore
Repository orientation
Help agents understand architecture and code relationships before editing.
Living specs
Use living specs and drift detection to keep project memory aligned with code.
MCP codebase context
Expose structured repository context to agent environments through MCP.
How it compares
Compare it with nearby memory systems by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
Questions
Is OpenLore open source?
Yes. The GitHub repository is listed under the MIT license.
What kind of memory does OpenLore provide?
It focuses on architectural and codebase memory for coding agents, including queryable code relationships and MCP tools.