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

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

A lightweight MCP data broker that stores large binary payloads under a UUID, enabling MCP clients and agents to exchange massive results without flooding the LLM context window.

Enables secure file and bucket management operations with Supabase Storage through enterprise-grade security features, batch uploads/downloads, and comprehensive file management capabilities. Supports both local file paths and base64 data with advanced transformations and auto-download functionality.

Enables AI assistants to create and manage persistent terminal sessions that continue running even after disconnection, with features like smart output management, web UI visualization, and automatic bug fixing with Codex integration.

A fork of the Neo4j Model Context Protocol server that enables interaction with Neo4j knowledge graphs through environment variable configuration and improved options for remote connections.

This read-only MCP Server allows you to connect to Neo4J data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Neo4J (https://www.cdata.com/drivers/neo4j/download/mcp).

Extracts structured operator data from Neo4j query plans and provides rich context for MCP clients to deliver intelligent optimization recommendations.

A local, persistent memory system for AI coding assistants that stores decisions, patterns, and session context via MCP tools. It enables cross-session memory management using SQLite and optional vector search without external dependencies or cloud storage.

An MCP server that exposes ChromaDB vector database operations, enabling AI assistants to perform collection management and semantic document searches. It supports HTTP, persistent, and in-memory connection modes along with various embedding providers including OpenAI and HuggingFace.

Auto-captured, auto-recalled, path-scoped memory for AI coding agents and teams.

MCP server that provides persistent long-term memory for Claude Code, enabling storage, search, and retrieval of project knowledge across sessions.

MCP server for managing project memory in AGENTS.md. Allows agents to record and remove durable facts using save/forget tools.

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.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

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.

Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.

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

Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.

Persistent memory and automatic git snapshots for Claude Code, capturing decisions, patterns, and architecture across sessions.