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  3. GrantAi Memory
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Health: Not checked yetWe have not completed a health check for this listing yet.Last checked 8/10/2026, 11:33:20 PM

GrantAi Memory

Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Install Config Generator

Choose your client
claude_desktop_config.json
{
  "mcpServers": {
    "grantai-memory": {
      "command": "npx",
      "args": [
        "-y",
        "grantai-memory"
      ]
    }
  }
}

πŸ’‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

GrantAi

Deterministic Memory for AI
Local. Private. Secure.

Website β€’ Download β€’ Documentation

GrantAi Demo


The Problem

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

RAG (Retrieval-Augmented Generation) converts your documents into vectors β€” 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 β€” Same questions, same searches, same cost
  • Over-retrieval β€” 20 chunks when you need 3
  • Labor β€” Engineers tuning embeddings instead of building products
  • Risk β€” 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 β€” verbatim, with attribution, in milliseconds.

RAGGrantAi
Returns similar contentReturns the exact content
10-20 chunks, hope one is right1-3 sentences, always right
Slows down as corpus growsMilliseconds regardless of size
No attributionFull audit trail
ApproximateDeterministic

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

Why It Matters

  • Compliance β€” Exact citations, not paraphrased guesses
  • Multi-Agent β€” Shared memory across your AI workforce with speaker attribution
  • Cost β€” Pay for answers, not for searching
  • Security β€” 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

PlatformMethodStatus
macOS (Apple Silicon)Nativeβœ…
Linux (x64)Nativeβœ…
WindowsNativeβœ…
All PlatformsDockerβœ…

MCP Tools

GrantAi provides these tools to your AI:

ToolDescription
grantai_inferQuery memory for relevant context
grantai_teachStore content for future recall
grantai_learnImport files or directories
grantai_healthCheck server status
grantai_summarizeStore session summaries
grantai_projectTrack project state
grantai_snippetStore code patterns
grantai_gitImport git commit history
grantai_captureSave 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

ScenarioUse speaker?Why
Shared knowledge baseNoAll contributions equal, no filtering needed
Session continuityNoSame context, just persist and retrieve
Research β†’ Code handoffYesCoder filters for researcher's findings only
Role-based trustYesSecurity 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

  • Installation Guide
  • Troubleshooting

Support

  • Issues β€” Open an issue
  • Email β€” support@solonai.com

License

Free to try. Pricing & Terms


Get Started β†’

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Frequently Asked Questions about GrantAi Memory

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "grantai-memory": { "command": "npx", "args": ["-y", "GrantAi Memory"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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