{"id":"pi22by7-in-memoria","name":"In-Memoria","af_score":57.0,"security_score":23.2,"reliability_score":31.2,"what_it_does":"In Memoria is an on-machine (local-first) MCP server that learns from a codebase and exposes persistent “project blueprint” context, semantic/pattern search, file routing, and work-memory/pattern recommendations to AI coding assistants.","best_when":"You want local-first, persistent coding assistance where an MCP-capable agent repeatedly queries learned context, search, and routing for the same repository over time.","avoid_when":"You need a server with strong enterprise-grade access controls, documented rate limits, and guaranteed consistency/SLA; In Memoria is explicitly described as early-stage/WIP.","last_evaluated":"2026-03-30T13:40:35.442693+00:00","has_mcp":true,"has_api":false,"auth_methods":["None documented for MCP server in the README (local invocation via npx/CLI)"],"has_free_tier":false,"known_gotchas":["MCP tools are ignored unless the chat is in the agent/appropriate mode (not Ask/Edit)","MCP server must be running/configured (e.g., Claude Desktop/MCP config or VS Code .vscode/mcp.json)","Large codebases can be slow on first analysis; agents may time out or appear unresponsive during learning/ingestion","“Status: Work in Progress” and README notes documentation may be incomplete; tool behavior/edge cases may change"],"error_quality":0.0}