Persistent, local-first long-term memory for GitHub Copilot Chat.
This extension adds:
@memorychat participant (Copilot Chat)- Language Model Tools for Copilot agent mode (auto-recall / auto-store)
- Memory sidebar (Activity Bar)
- Status bar memory counter
Privacy: memories are stored locally in a SQLite database. Embeddings are generated via OpenAI or a local Ollama fallback. If you configure an OpenAI key, text you embed is sent to OpenAI for embeddings.
@memoryparticipant in Copilot Chat: (screenshot placeholder)- Memory sidebar: (screenshot placeholder)
- Status bar count: (screenshot placeholder)
Use in Copilot Chat:
@memory /recall <query>— search memory@memory /store <text>— store a memory@memory /forget <query>— delete close matches@memory /stats— show stats@memory /index— index current workspace (best-effort)
- Memory: Store Memory (
superlocalmemory.store) - Memory: Search Memory (
superlocalmemory.search) - Memory: Forget Memory (
superlocalmemory.forget) - Memory: Index Project (
superlocalmemory.indexProject) - Memory: Memory Stats (
superlocalmemory.stats)
This extension contributes tools declared in package.json:
superlocalmemory_searchsuperlocalmemory_store
npm install
npm run buildThen press F5 in VS Code to run an Extension Development Host.
In Settings:
superlocalmemory.dbPath— path to SQLite DB (default:~/.superlocalmemory/vscode.db)superlocalmemory.openaiApiKey— OpenAI key for embeddingssuperlocalmemory.openaiEmbeddingModel— defaulttext-embedding-3-smallsuperlocalmemory.ollamaEndpoint— defaulthttp://localhost:11434superlocalmemory.ollamaEmbeddingModel— defaultnomic-embed-textsuperlocalmemory.autoCapture— capture a snippet on file savessuperlocalmemory.maxRecallResults— default5
You can also set OPENAI_API_KEY in your environment.
- All memories are stored locally in SQLite.
- Embeddings require an embedding provider:
- OpenAI (if configured via settings / env)
- Ollama fallback (local HTTP)
No data is uploaded anywhere else.
Copilot memory (when available) is typically scoped and opaque.
Superlocalmemory aims to be:
- local-first and inspectable (SQLite)
- structured (categories, sources, tags)
- cross-tool (same memory core can power other integrations)
- syncable (future: P2P / self-hosted)
AGPL-3.0.