general MCP Server
openlore provides persistent architectural memory for AI coding agents by turning codebases into queryable knowledge graphs featuring static analysis, living specs, automated drift detection, and graph-native MCP tools to eliminate context decay and drastically slash orientation token costs.
Discovered via github-topic:mcp and last synced 3mo ago.
Install instructions not detected yet
Check the source repository for the latest setup steps.
What it does
[docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md)
Trigger
load ≥ 30, age ≥ 15min, or cross-module density ≥ 0.15
load ≥ 60, age ≥ 30min, git hash divergence, or density ≥ 0.30
Doc
✓
[docs/ALGORITHMS.md](docs/ALGORITHMS.md)
You can use layer 1 alone to give agents structural context. Add layer 2 for semantic intent and architectural governance through OpenSpec-compatible living specifications. Layer 3 keeps that context continuously accessible through graph-native MCP tools once `openlore mcp` is running. --- ## openlore vs. Alternatives
[docs/PHILOSOPHY.md](docs/PHILOSOPHY.md)
openlore