"Using ChromaDB and Neo4j for persistent memory storage" matching MCP servers:

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

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Provides persistent local memory for any MCP-compatible AI agent, enabling agents to store, search, recall, forget, export, and manage preferences, rules, and facts with zero dependencies, no cloud, and no API keys.

Local MCP server giving AI coding agents (Claude Code, Cursor, VS Code/JetBrains Copilot) a shared, persistent memory of your projects and every bug/issue faced during development. Stateless, plain-file storage (AGENTS.md + issues.jsonl) — no database.

A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep

MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.

Local-first persistent memory MCP: shared SQLite key/value store, searchable, TTL-aware and secret-safe.

Provides AI agents with persistent, long-term memory via OKF-formatted markdown and SQLite indexing, enabling stateful storage, retrieval, and search across sessions.

Persistent memory for AI coding assistants.

A persistent, project-scoped memory layer for AI coding agents, providing 39 tools for capturing, searching, deduplicating, relating, and maintaining memory records across long-running coding sessions.

Persistent long-term memory for MCP clients. Store and recall semantic facts, episodic events, and task state with vector-based relevance search. Write what matters, retrieve what's needed — memory that persists across sessions and agents, backed by a local SQLite store with optional embeddings for meaning-based lookup. Simple file-based storage, no external services required.

An MCP server providing semantic vector search, dual memory engines, and Git version control for AI clients, enabling persistent memory with recall, forget, and rollback capabilities.

A hybrid long-term memory server for AI assistants that stores and retrieves memories, user facts, chat history, tasks, and reminders using PostgreSQL and ChromaDB with semantic search.

Enterprise-grade MCP server for persistent, intelligent memory management across Claude Code sessions.

Hand a project over — to your next session, to Cursor/Codex/Gemini, to your successor — with the reasons attached. Local-first memory MCP for coding agents: decisions re-injected before the agent acts, drift detection across every project you run. One SQLite file, nothing leaves your machine.

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.

Hosted memory for AI agents that learns from outcomes, with shared rooms. One key across Claude, Cursor & ChatGPT.

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.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.