GrantAi Memory

Name: GrantAi Memory
Author: solonai-com

by solonai-comversion 1.8.6i★ –iRuns on your computer

Registry namecom.solonai/grantai

Install

Overview

Description from the maker (original English)

Persistent memory for AI agents. Infinite context with sub-millisecond recall.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: GRANTAI_LICENSE_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: GRANTAI_LICENSE_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Download the maker’s .mcpb file

The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.

Run this in your terminal

claude mcp add grantai --env GRANTAI_LICENSE_KEY=your-value -- docker run -i --rm -e GRANTAI_LICENSE_KEY ghcr.io/solonai-com/grantai-memory:1.8.5

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "grantai": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GRANTAI_LICENSE_KEY",
        "ghcr.io/solonai-com/grantai-memory:1.8.5"
      ],
      "env": {
        "GRANTAI_LICENSE_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "grantai": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GRANTAI_LICENSE_KEY",
        "ghcr.io/solonai-com/grantai-memory:1.8.5"
      ],
      "env": {
        "GRANTAI_LICENSE_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

GRANTAI_LICENSE_KEY secret required env
Your GrantAi license key (get one free at solonai.com/grantai/signup)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain solonai.com (the registry checks domain ownership)
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7275 servers in this category
Published to the registry more recently than 4% of the other 7275 servers in this category
Basic checks equal to or better than 100% of the other 7275 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is GrantAi Memory safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: GRANTAI_LICENSE_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.8.6) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB, Docker on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.

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)

# 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/claude_desktop_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)

# 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:

# 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:

"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.](https://solonai.com/grantai/essay)

Documentation

Support

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

License

Free to try. Pricing & Terms

Get Started →

GrantAi Memory

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Free Edition — one command, no account

The Problem

The Solution

Why It Matters

Quick Start

macOS / Linux (Native)

2. Extract and install

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

Docker (All Platforms)

Supported Platforms

MCP Tools

Multi-Agent Memory Sharing

Basic shared memory (no setup required)

Any agent retrieves

With agent attribution (optional)

Retrieve from specific agent

When to use speaker

Framework integration

Built By

Why We Built This

Documentation

Support

License

When to use `speaker`