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🧠 KacheDB MCP Server

PyPI License Python

High-Speed Model Context Protocol (MCP) server for KacheDB — exposing sub-millisecond in-memory caching and SIMD semantic vector memory to Antigravity IDE, Claude Desktop, Cursor, and AI coding agents.


⚡ Why KacheDB for AI Agents?

AI coding assistants (like Antigravity IDE and Claude Desktop) repeatedly re-read large codebases, AST parses, and architecture plans, wasting thousands of tokens and adding hundreds of milliseconds of latency per turn.

kachedb-mcp connects your AI assistant directly to KacheDB's Megaslab pure-RAM cache:

  • 🚀 Sub-50 Microsecond Retrieval: $< 50\ \mu\text{s}$ cache hits in memory.
  • 🧠 SIMD Semantic Vector Memory: Natural language concept and code recall powered by ARM NEON & AVX2/FMA cosine similarity kernels.
  • 🪙 Massive Token & Cost Savings: Saves up to 80% of repetitive prompt tokens and tracks cumulative financial savings in real time.

🛠️ MCP Tools Exposed

Tool Type Description
kache_semantic_search 🧠 Vector Natural language semantic search over cached codebases, PR reviews, and past decisions (top_k, threshold).
kache_save_context 🧠 Vector Save an architectural pattern, bug solution, or file digest with SIMD vector embeddings.
kache_get Exact Sub-millisecond exact key retrieval for code chunks, ASTs, and tool outputs.
kache_set Exact Store string content in memory with optional TTL expiration.
kache_delete Exact Remove a key or vector from cache.
kache_stats 📊 Metrics Real-time connection health, active vector counts, and RAM footprint.
kache_telemetry 📊 Metrics Live cumulative tokens saved, latency saved (seconds), and hit ratios.

🚀 Quickstart

1. Start the KacheDB Server Daemon

Ensure your KacheDB daemon is running locally:

kachedb-server --port 6379

2. Configure in Antigravity IDE / Claude Desktop / Cursor

Add kachedb to your MCP configuration file (mcp_config.json or claude_desktop_config.json):

{
  "mcpServers": {
    "kachedb": {
      "command": "uvx",
      "args": ["kachedb-mcp"],
      "env": {
        "KACHEDB_HOST": "127.0.0.1",
        "KACHEDB_PORT": "6379",
        "KACHEDB_INDEX": "agent_semantic_memory",
        "KACHEDB_THRESHOLD": "0.80"
      }
    }
  }
}

Or run via Python pip:

pip install kachedb-mcp
{
  "mcpServers": {
    "kachedb": {
      "command": "kachedb-mcp"
    }
  }
}

⚙️ Environment Configuration

Variable Default Description
KACHEDB_HOST 127.0.0.1 KacheDB daemon hostname or IP
KACHEDB_PORT 6379 KacheDB daemon TCP port
KACHEDB_INDEX agent_semantic_memory Target vector index name for semantic memory
KACHEDB_THRESHOLD 0.80 Minimum cosine similarity (0.0 – 1.0) for semantic hits
KACHEDB_DEFAULT_TTL 86400 Default cache lifetime in seconds (24h)
KACHEDB_EMBEDDER auto Embedding provider (auto, fastembed, transformers, openai, mock)
OPENAI_API_KEY (optional) API key if using KACHEDB_EMBEDDER=openai

📄 License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.

Release files for kachedb-mcp 0.1.1

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