GrantAi

Deterministic Memory for AI
Local. Private. Secure.

GrantAi Demo

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).

bash

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

powershell

# 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.

RAG	GrantAi
Returns similar content	Returns the exact content
10-20 chunks, hope one is right	1-3 sentences, always right
Slows down as corpus grows	Milliseconds regardless of size
No attribution	Full audit trail
Approximate	Deterministic

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)

bash

# 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)

Terminal

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

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

config.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

Platform	Method	Status
macOS (Apple Silicon)	Native	✅
Linux (x64)	Native	✅
Windows	Native	✅
All Platforms	Docker	✅

MCP Tools

GrantAi provides these tools to your AI:

Tool	Description
`grantai_infer`	Query memory for relevant context
`grantai_teach`	Store content for future recall
`grantai_learn`	Import files or directories
`grantai_health`	Check server status
`grantai_summarize`	Store session summaries
`grantai_project`	Track project state
`grantai_snippet`	Store code patterns
`grantai_git`	Import git commit history
`grantai_capture`	Save conversation turns for continuity

Multi-Agent Memory Sharing

Multiple agents can share knowledge through GrantAi's memory layer.

Basic shared memory (no setup required)

python

# Any agent stores
grantai_teach(
    content="API rate limit is 100 requests/minute.",
    source="api-notes"
)

# Any agent retrieves
grantai_infer(input="API rate limiting")

All agents read from and write to the same memory pool. No configuration needed.

With agent attribution (optional)

Use speaker to track which agent stored what, and from_agents to filter retrieval:

python

# Store with identity
grantai_teach(
    content="API uses Bearer token auth.",
    source="api-research",
    speaker="researcher"  # optional
)

# Retrieve from specific agent
grantai_infer(
    input="API authentication",
    from_agents=["researcher"]  # optional filter
)

When to use `speaker`

Scenario	Use speaker?	Why
Shared knowledge base	No	All contributions equal, no filtering needed
Session continuity	No	Same context, just persist and retrieve
Research → Code handoff	Yes	Coder filters for researcher's findings only
Role-based trust	Yes	Security agent's input treated differently

Framework integration

GrantAi works with any MCP-compatible client. Point your agents at the same GrantAi instance:

config.json

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

All agents using this config share the same memory volume (grantai-data).

Built By

GrantAi is built by Lawrence Grant, founder of SolonAI.

Background: Harvard, IBM, AI architecture and security work for Blackstone, Goldman Sachs, and Vanguard. Author of Mergers and Acquisitions Cybersecurity: The Framework For Maximizing Value.

Why We Built This

Read the full case for deterministic memory: Your AI Has Amnesia. You're Paying. Blame the Architecture.

Documentation

Support

Issues — Open an issue
Email — support@solonai.com

License

Free to try. Pricing & Terms

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