Skip to main content

🧠 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 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for kachedb-mcp 0.2.0
File Size Uploaded
kachedb_mcp-0.2.0.tar.gz 17.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for kachedb-mcp 0.2.0
File Interpreter ABI Platform
kachedb_mcp-0.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 31.9 kB

Release files / kachedb_mcp-0.2.0.tar.gz

Download URL kachedb_mcp-0.2.0.tar.gz
Size 17.1 kB
Tags Source
SHA-256 checksum
How to use checksums
6f634acae9bee300d298c20bdbe4ef605ae3cfd9cd38308aa7a56b04608672e2
BLAKE2b-256 checksum
How to use checksums
58c3269bd9f05521728df0b0fca86be982cc92a3be7c29b590189f64e48da072
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 9, 2026.

Transparency log

Release files / kachedb_mcp-0.2.0-py3-none-any.whl

Download URL kachedb_mcp-0.2.0-py3-none-any.whl
Size 14.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6e13e89570d328c11320934f145b2de9ebe52454e069415d53419d28f5877afe
BLAKE2b-256 checksum
How to use checksums
fd8bbc9880bba581e2e40e08b4fc7c5a064dc8b5217fa372ba1b3971706c7b37
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 9, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.2.0 This release

2 release files

0.1.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page