Persistent semantic memory for AI agents using local ChromaDB vector search. No cloud required.
Most knowledge and memory work still happens through a UI a human drives. Copilot Memory MCP MCP server moves it into the conversation instead. Persistent semantic memory for AI agents using local ChromaDB vector search. No cloud required.
A local MCP server that gives Copilot durable, project-scoped memory across sessions. Memories are stored in an embedded ChromaDB vector database with all-MiniLM-L6-v2 embeddings, enabling semantic retrieval (RAG). Everything runs in a single Docker container — no cloud services required.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
The server publishes 5 tools. What each one is for:
create_memory — Store a new memory with title, content, optional project scope and tagssearch_memories — Semantic vector search; filter by project and/or tagsupdate_memory — Update an existing memory by ID; re-embeds on changedelete_memory — Permanently delete a memory by IDlist_memories — Browse memories with pagination (lightweight, no content)Configuration is passed through the environment: CHROMA_PATH. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the knowledge and memory options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Copilot Memory MCP's toolset — create_memory, search_memories, update_memory and 2 more — is a fair guide to whether it matches your workflow. It is maintained by LabForgeDev; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Copilot Memory MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| create_memory | Store a new memory with title, content, optional project scope and tags |
| search_memories | Semantic vector search; filter by project and/or tags |
| update_memory | Update an existing memory by ID; re-embeds on change |
| delete_memory | Permanently delete a memory by ID |
| list_memories | Browse memories with pagination (lightweight, no content) |
| Variable | Description | Required |
|---|---|---|
| CHROMA_PATH | Filesystem location the server is allowed to use. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.