"Information about project memory" matching MCP servers:

Provides AI assistants with progressive code intelligence to explore repository structure, find symbols, and assess the impact of changes using ast-grep.

MCP server for system monitoring, enabling users to ask about CPU, memory, disk, network, and processes on their system.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

MCP server exposing memory search, index, and stats tools for agents, with honesty guards to prevent re-litigation of settled decisions.

Analyzes Python project structure by exploring directory trees, extracting outlines from Python and Markdown files, and inspecting OpenAPI specifications.

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.

A read-only MCP server that lets Claude read and search source code from local project folders, with path containment security.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

A local-first MCP server that stores meeting transcripts and maintains a searchable markdown wiki of decisions, commitments, and contradictions, enabling Claude to recall context across meetings.

Persistent local semantic memory for MCP tools, enabling storing and searching notes by meaning using local embeddings and SQLite.

An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.

A public, source-linked notebook for AI agents and people. Search and read without a key; publish findings, questions, and corrections with permanent IDs and linked replies using an operator-controlled posting key.

A local-first MCP server that exposes personal notes and files as unified semantic context for AI agents via vector search and file monitoring.

Local-first agent memory & knowledge service: hybrid retrieval (vector + BM25/RRF) + memory governance (decay, dedup, freshness), REST + MCP dual protocol, fully offline without LLM. Python.

Enables LLM agents to use associative memory with key/value graph-based retrieval, supporting multi-hop traversal and human-like associative leaps beyond embedding similarity.

Hermetic memory for AI agents — one Rust binary, one SQLite file, zero network. Recall returns evidence with provenance or abstains: no code path for making things up.

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.

Local-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.

An MCP server that provides persistent memory capabilities for AI agents using Mem0, enabling storage, search, and management of contextual information across conversations with support for multiple backends and LLM providers.

A tool for detecting and cleaning Java memory shells via local or SSH remote execution. It enables AI agents to scan Java processes, analyze suspicious class code, and safely remove memory shells after user confirmation.