memex
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- FlicenseNot gradedqualityAmaintenanceA self-hosted, temporal knowledge-graph memory for AI coding agents — shared across projects, queryable across time, written and read via MCP by any Claude Code session.-

litopysofficial
AlicenseNot gradedqualityBmaintenancePersistent graph-based memory for AI agents, stored as plain markdown — no vector DB. Typed nodes and 11 relation types via 5 MCP tools (search, get, create, link, related), stdio and HTTP/SSE transports.3MIT- AlicenseNot gradedqualityCmaintenanceLocal-first code intelligence and safety layer for AI coding agents. MCP server exposes dependency graph, impact analysis, and AST-compressed repo context, backed by typed local memory, patch-scope safety gates, and git-independent transaction rollback.1MIT
- AlicenseAqualityDmaintenanceA persistent, event-sourced knowledge graph MCP server for AI coding agents that enables semantic search, tiered context retrieval, and git-based version control of AI memory.31300 PyPI2MIT
- AlicenseNot gradedqualityAmaintenancem1nd is a local MCP runtime that gives coding agents graph-native memory of a codebase: structure, docs, decisions, change impact, recovery state, and investigation continuity.74 npm21MIT
- FlicenseNot gradedqualityAmaintenanceMemtrace is a persistent memory layer for coding agents, built as a bi‑temporal structural knowledge graph over your codebase (AST‑driven symbols and relationships, plus temporal evolution and cross‑service API topology)476-
TDQS
Scored across 14 tools
Tools are largely distinct: each getter targets a specific aspect (decisions, problems, project, symbol, stale context, engineering context), and write/action tools are clearly separate (record, resolve, invalidate, explain, predict). Minor overlap exists between get_project_context and get_context_briefing, both providing project summaries, but they differ in focus and usage timing.
All tool names follow a consistent verb_noun pattern in snake_case (get_recent_decisions, record_decision, resolve_problem, invalidate_edge, explain_change, predict_impact). The verb clearly indicates the action, and nouns describe the target resource, making the naming predictable and uniform.
14 tools is well within the expected range for a knowledge/context management server. Each tool addresses a distinct operation (retrieval, recording, mutations, analysis) without redundancy, and the count feels appropriate for the scope of the domain.
The surface covers key lifecycle operations: decision recording (with supersede/force), problem creation and resolution, edge invalidation, context retrieval via multiple projections, search, and analytical tools. Minor gaps include no explicit update tool for decisions or problems (though supersede covers decision updates), but agents can work around with existing tools.