GrantAi Memory MCP server

GrantAi

Deterministic Memory for AI Local. Private. Secure.

Website • Download • Documentation

Free Edition — one command, no account

The free edition is the full local memory engine as an MCP server: no email, no license key, no trial, and no network after the one-time model download (about 650 MB).

# macOS / Linux
curl -fsSL "https://solonai.com/grantai/install.sh?src=github" | bash

# Windows 10/11 (PowerShell)
irm "https://solonai.com/grantai/install.ps1?src=github" | iex

The installer verifies both downloads against a published SHA-256 manifest, installs under ~/.grantai/free (Windows: %LOCALAPPDATA%\GrantAi\free), and registers the server with Claude Code, Claude Desktop, Cursor, Gemini CLI and Codex where they are installed. Any other MCP client takes the same command and arguments. Homebrew: brew install solonai/grantai/grantai-free. Air-gapped installs and the full list of what runs where are on the developers page.

The Problem

Every AI system today has the same flaw: it guesses instead of remembers.

RAG (Retrieval-Augmented Generation) converts your documents into vectors — numerical approximations of meaning. When you query, it returns content that is mathematically similar to your question. Similar is not the same as correct.

Ask for "HIPAA encryption penalties" and RAG returns chunks that look like compliance content. Maybe the right section. Maybe adjacent paragraphs. Maybe hallucinated ranges. You pay for every token retrieved, whether relevant or not.

This is the Retrieval Tax:

• Re-retrieval — Same questions, same searches, same cost
• Over-retrieval — 20 chunks when you need 3
• Labor — Engineers tuning embeddings instead of building products
• Risk — Approximate answers in domains that require precision

Enterprise AI spends 85% of compute on inference. Most of that is wasted on retrieving content that doesn't answer the question.

The Solution

GrantAi is deterministic memory for AI agents.

Instead of similarity search, GrantAi uses direct addressing. Every piece of knowledge has a unique identifier. Retrieval is a lookup, not a search. You get the exact content you indexed — verbatim, with attribution, in milliseconds.

Result: 97% reduction in tokens sent to the LLM. Faster responses. Lower cost. No hallucination from retrieval.

Why It Matters

• Compliance — Exact citations, not paraphrased guesses
• Multi-Agent — Shared memory across your AI workforce with speaker attribution
• Cost — Pay for answers, not for searching
• Security — 100% local, AES-256 encrypted, zero data egress

Quick Start

macOS / Linux (Native)

# 1. Download from https://solonai.com/grantai/download
# 2. Extract and install
./install.sh

# 3. Restart your AI tool (Claude Code, Cursor, etc.)

Docker (All Platforms)

docker pull ghcr.io/solonai-com/grantai-memory:1.8.6

Add to your Claude Desktop config (~/.config/Claude/claudedesktopconfig.json):

{
  "mcpServers": {
    "grantai": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "--pull", "always",
               "-v", "grantai-data:/data",
               "ghcr.io/solonai-com/grantai-memory:1.8.6"]
    }
  }
}

Supported Platforms

Shortened. The full README is on GitHub.