anvaiops-mcp
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- FlicenseNot gradedqualityAmaintenanceEnables Claude Code to run a self-hosted persistent memory server backed by Qdrant, Neo4j, and Ollama, exposing 11 MCP tools for adding, searching, updating, deleting, and organizing memories and knowledge-graph entities. Supports Anthropic or local Ollama LLMs, automatic session hooks, and cross-session memory management.-
- AlicenseNot gradedqualityCmaintenanceProvides AI coding agents with deterministic, attestable org memory via an MCP server, enabling automatic recall of relevant facts on every prompt, loss-proof capture of learnings, and curated promotion of verified facts to a tenant-isolated memory store.Apache 2.0
- FlicenseNot gradedqualityAmaintenanceEnables Claude Code to persistently store, search, update, and delete memories backed by self-hosted Qdrant, Neo4j, and Ollama, using Anthropic or a fully local LLM. Exposes 11 MCP tools plus session hooks that automatically inject relevant context on startup and save session summaries on exit.-
- FlicenseNot gradedqualityAmaintenanceEnables Claude Code to maintain persistent cross-session memory using self-hosted Qdrant, Neo4j, and Ollama, with optional Anthropic or local LLM backends, and exposes MCP tools for adding, searching, updating, deleting, and graphing memories.-
- AlicenseAqualityAmaintenanceGives any MCP client (Claude, ChatGPT, Cursor, and others) one shared knowledge-graph memory where agents record decisions, problems, and approaches together with their reasoning, then retrieve them by meaning rather than keyword. Spreading activation pulls in linked context and decay lets unused knowledge fade without deleting it.918Apache 2.0
- FlicenseNot gradedqualityAmaintenanceEnables Claude Code to maintain cross-session memory using self-hosted Qdrant vector storage, optional Neo4j knowledge graphs, and Ollama embeddings, with Anthropic or Ollama as the main LLM. It exposes MCP tools for adding, searching, updating, deleting, and graphing memories, plus hooks for automatic context injection and session summarization.-
TDQS
Scored across 3 tools
Each tool targets a clearly distinct operation on the memory resource: search (read), write (create), and status (health check). There is no overlap in purpose and an agent can unambiguously select the right one.
All three tools follow the identical memory_<verb> pattern (memory_search, memory_write, memory_status), a fully predictable and consistent convention across the set.
Three tools is on the thin side for an operational-memory system; the narrow scope keeps it coherent, but the surface feels minimal given the domain implies richer lifecycle management.
Read (search) and create (write) are covered plus a health check, but there is no update, delete, or get-by-id operation, leaving notable gaps in the memory lifecycle as the description itself hints at an experimental/async model.