"Tools and frameworks for development environment memory-bank with GitHub Copilot compatibility" matching MCP servers:

A local MCP server that provides persistent, project-scoped semantic memory for GitHub Copilot in VS Code, using ChromaDB and embeddings for RAG-based memory retrieval across sessions.

Enables LLMs to generate and retrieve long-term memories using Vertex AI Memory Bank, with tools for memory initialization, generation, retrieval, creation, deletion, and listing.

Enables agents to query a git-native markdown corpus for the specs governing a file in about two reads, plus supports lexical topic search with expansion. Includes maintenance loops that surface uncovered code and stale governing docs to keep the corpus honest.

Enables MCP-based hybrid semantic code search over repositories, providing tools for searching code by meaning or keyword, retrieving symbol definitions, finding usages, reading chunks, and listing indexed repos.

Integrates Microsoft 365 enterprise data into GitHub Copilot and Claude Desktop using Microsoft 365 Copilot APIs. It enables natural language queries across SharePoint, OneDrive, emails, calendars, and Teams meetings while maintaining full enterprise permission enforcement.

Provides Claude Co-work with MCP tools to search and record organizational memory, including search, read, query, ingest, remember, sync, and status operations.

MCP server providing durable Substrate organizational memory tools, including search, read, query, ingest, remember, and sync. It integrates with Codex to automatically capture completed turns and session boundaries for persistent memory.

Enables any MCP client to run a complete data-science pipeline on a CSV: exploration, cleaning, model comparison, training, tuning, and plain-language analysis. Exposes nine tools that turn raw data into trained models and reports.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

MCP server that connects Claude Code to Substrate organizational memory, providing tools for searching, reading, querying, and writing memory, with automatic capture of conversation turns.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

A plugin that allows Dify to connect to multiple MCP (Model Control Protocol) servers using HTTP with Server-Sent Events transport, supporting custom configurations for URLs, headers, and timeouts.

Exposes code search and file reading tools over the Model Context Protocol, enabling any MCP-compatible client to query a codebase with natural language.

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

Self-hosted personal knowledge graph for Claude that persists across sessions, devices, and tools. Built on Neo4j with local semantic embeddings; OAuth 2.1 lets Claude Code, Claude Desktop, and claude.ai web all hit the same graph.

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.

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

A Model Context Protocol server that connects GitHub code to Claude.ai. This server utilizes the Pera1 service to extract code from GitHub repositories and provide better context to Claude.

Exposes tools that let an agent search a local Markdown-based memory store, fetch full cards by id, write new knowledge cards that are indexed immediately, and inspect or rebuild the SQLite index. All retrieval runs locally over Chinese-friendly full-text search with graded exact/prefix matching, so agents without a memory system can recall prior notes while existing Markdown files stay read-only.

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.