"How to find and troubleshoot a memory leak in software" matching MCP servers:

Enables MCP clients to search locally indexed text files, PDFs, and images using natural-language descriptions, and to index folders and inspect index statistics.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

Enhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.

Enables AI clients like Claude Desktop and Cursor to persist project knowledge across sessions and tools by extracting durable facts from conversations, deduplicating them, resolving contradictions, and retrieving only the most relevant, token-budgeted facts for the current task. Adds PII redaction, project scoping, TTL-based staleness handling, and an append-only audit log so shared context stays current and safe.

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

A portable, self-hosted semantic memory layer for LLM agents, enabling them to search and add memories by meaning over the Model Context Protocol.

Local-first memory for Claude Code and any MCP client: hybrid vector + keyword search and a bi-temporal knowledge graph in one SQLite file. Local embeddings, no API key, $0/token.

Enables persistent memory management with semantic vector recall, BM25 keyword search, and 3-way fused retrieval combining lexical, semantic, and tag-based signals.

An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.

A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.

A high-performance, persistent memory system for the Model Context Protocol (MCP) providing vector search capabilities and efficient knowledge storage using libSQL as the backing store.

An MCP server for neuroanatomically-inspired memory consolidation using Neo4j and semantic search, enabling episodic memory encoding, semantic retrieval, and graph operations.

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

Provides intelligent, persistent memory for AI assistants with semantic search, natural language queries, and OAuth-based team collaboration, enabling context-aware conversations across multiple clients.

Provides persistent, semantic memory storage for LLMs across sessions using vector embeddings and FAISS search. Features bio-inspired memory consolidation, intelligent forgetting, and semantic retrieval without API costs.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with persistent storage and semantic similarity matching.

Local-first MCP server for long-term memory of dev sessions. It indexes session metadata with embeddings in Postgres/pgvector, enables semantic search over past sessions, and resolves only relevant session files for context.

MCP Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

Enables agents to search local Chinese text memories and fetch individual records through stdio tools, using bigram FTS5 recall with optional vector rank fusion.

A fully-local AI memory system that saves notes, code, and terminal output to a vector database via Ollama and LanceDB, enabling context retrieval from terminal, API calls, or inside Claude Code.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

Enables AI assistants to remember user information and preferences across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

Enables MCP clients to retrieve relevant memories and save confirmed durable facts through authenticated access backed by PostgreSQL provenance and Qdrant semantic search.

Enables MCP hosts and AI agents to store, recall, and forget private local long-term memories using hybrid semantic/full-text search, workspace isolation, recency and importance ranking, and automatic PII/secret redaction.