Turbo Quant Memory

{
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "name": "io.github.Lexus2016/turbo-quant-memory",
  "description": "Local-first memory and knowledge graph for coding agents. Compact retrieval, no network.",
  "title": "Turbo Quant Memory",
  "repository": {
    "url": "https://github.com/Lexus2016/turbo_quant_memory",
    "source": "github"
  },
  "version": "0.30.0",
  "websiteUrl": "https://github.com/Lexus2016/turbo_quant_memory#readme",
  "packages": [
    {
      "registryType": "pypi",
      "registryBaseUrl": "https://pypi.org",
      "identifier": "turbo-quant-memory",
      "version": "0.30.0",
      "runtimeHint": "uvx",
      "transport": {
        "type": "stdio"
      },
      "packageArguments": [
        {
          "description": "Run the memory server over stdio.",
          "isRequired": true,
          "value": "serve",
          "type": "positional"
        }
      ],
      "environmentVariables": [
        {
          "description": "Snowball stemmer for the BM25 lane. Use Russian for Cyrillic-dominant corpora.",
          "default": "English",
          "name": "TQMEMORY_FTS_LANGUAGE"
        },
        {
          "description": "Embedder backend. Default fastembed (ONNX); sentence-transformers needs the [torch] extra.",
          "default": "fastembed",
          "name": "TQMEMORY_EMBEDDING_BACKEND"
        },
        {
          "description": "Set to 1 to apply pending schema migrations automatically on startup.",
          "default": "0",
          "name": "TQMEMORY_MIGRATE_ON_STARTUP"
        },
        {
          "description": "Argon2id passphrase for the secrets vault on headless hosts. Omit to use the OS keyring.",
          "isSecret": true,
          "name": "TQMEMORY_SECRETS_PASSPHRASE"
        }
      ]
    }
  ]
}