Persistent architectural memory for AI coding agents via knowledge graphs and living specs.
OpenLore is worth checking the docs before setup with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
Repository setup guidance
gh repo view clay-good/OpenLore --webOpen the official README and confirm the supported install method.
Add the server entry to your MCP client config.
Restart your agent and verify that the server tools appear.
OpenLore helps AI coding agents remember your codebase structure across sessions. It builds a searchable knowledge graph from your code, so agents don't have to re-read files every time. This reduces token costs and speeds up complex tasks.
OpenLore is a developer tool that provides persistent architectural memory for AI coding agents. It transforms any evolving codebase into a navigable knowledge graph backed by OpenSpec living specifications. The tool maintains architectural context across agent sessions, including graph structure, specifications, decisions, drift state, and semantic retrieval. This allows agents to start each task already oriented, eliminating the need to re-discover the system from file reads. OpenLore features static analysis, living specs, automated drift detection, and graph-native MCP tools. It is designed to drastically reduce token costs and context decay, especially for large or unfamiliar codebases. The tool includes a CLI, MCP server, and agent benchmarking capabilities. It is open-source under the MIT license.
Strong trust signals; still review the README and permissions before production use.
Last commit was about 52 days ago.
160 GitHub stars indicate community interest.
3 open issues signal maintenance load.
MIT license detected.
Onboarding new AI agents to large, unfamiliar codebases by providing instant architectural context.
Reducing token costs and round-trips in multi-step coding tasks by maintaining persistent memory.
Detecting drift between code and specifications automatically to keep documentation up-to-date.
Enabling deep codebase queries like 'how does X flow through Y' without re-reading files.
Benchmarking agent performance with and without architectural memory to measure cost savings.
The tool requires access to the entire codebase to build the knowledge graph, which may expose sensitive code if not properly sandboxed.
MCP server exposes codebase information to connected AI agents; ensure agents are trusted and network access is controlled.
160
Stars
21
Forks
3
Issues
MIT
License
Local codebase intelligence CLI and MCP server for AI coding agents with change-safety gates and audit evidence.
An offline MCP server that indexes your codebase for semantic search, code search, and git history retrieval.
Official MCP reference servers from Anthropic. Includes servers for filesystem, GitHub, Postgres, Slack, and more.
Persistent architectural memory for AI coding agents via knowledge graphs and living specs.
AI agent developers building tools for code understanding and generation., Software engineers working on large or complex codebases who want to improve AI-assisted development., DevOps and platform engineers integrating AI agents into CI/CD pipelines.
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
The tool requires access to the entire codebase to build the knowledge graph, which may expose sensitive code if not properly sandboxed. MCP server exposes codebase information to connected AI agents; ensure agents are trusted and network access is controlled.
Similar or complementary options to evaluate include roam-code, code-memory, MCP Server Collection.
Jun 6, 2026
2 security/trust notes recorded.
Setup difficulty is 3/5.