Mnemo
Mnemo is an MCP service that provides extended memory for AI assistants. Through the context caching function of Gemini, it allows assistants to load large code libraries, documents, PDFs and other materials and conduct natural - language queries, achieving perfect information recall with low cost and low latency.
2.5 points
7.9K

What is Mnemo?

Mnemo (Greek: Memory) is an innovative AI assistant extension tool that leverages Google Gemini AI's 1 million token context window and context caching function to provide AI assistants like Claude with the ability to access large knowledge bases. Traditional AI assistants are usually limited by the finite context length and cannot handle large code libraries or document sets. Mnemo allows AI assistants to query and understand a large amount of information without a complex retrieval system by loading the entire knowledge base into Gemini's cache at once.

How to use Mnemo?

Using Mnemo is very simple: 1. Load your code library, documents or PDFs into Gemini's cache. 2. Set a memorable alias for the cache. 3. Query the cached content through natural language. 4. AI assistants (such as Claude) will use the information in the cache to answer your questions. Mnemo supports three deployment methods: local server, self - hosted Cloudflare Worker or hosted service to meet the needs of different users.

Use cases

Mnemo is particularly suitable for the following scenarios: • Developers need AI assistants to understand the entire code library. • Researchers need to analyze a large number of documents or academic papers. • Technical support teams need to access complete product documentation. • Any scenario where AI needs to process large information sets beyond the normal context limit.

Main Features

Multi - source data loading
Supports loading data from multiple sources: GitHub repositories (public and private), any URL (documents, articles), PDF documents, JSON APIs, local file directories, and multi - page crawling.
Perfect information recall
Through Gemini's context caching, a 100% information recall rate is achieved without the need for chunking or retrieval, ensuring that AI can access all the loaded content.
Cost optimization
By leveraging Gemini's caching function, the cost of cached tokens is 75 - 90% lower than that of regular input tokens, significantly reducing the usage cost.
Flexible deployment options
Provides three deployment methods: local development server (supports all functions), self - hosted Cloudflare Worker (suitable for use with Claude.ai), hosted service (for VIP customers).
MCP protocol integration
Fully compatible with the Model Context Protocol, it can be seamlessly integrated with AI assistants such as Claude Desktop and Claude.ai.
Intelligent page crawling
Supports intelligent crawling based on token targets, automatically controlling the crawling depth and scope to ensure the most relevant content is obtained within the token limit.
Advantages
Perfect recall: AI can access all the loaded content without chunking or retrieval.
Cost - effective: The cost of cached tokens is 75 - 90% lower than that of regular input.
Low latency: Cached content can be provided quickly, resulting in a shorter response time.
Easy to use: No complex vector databases or retrieval logic is required.
Flexible deployment: Supports multiple deployment methods including local, self - hosted and hosted.
Limitations
Depends on the Gemini API: A Google Gemini API key is required.
Cache time limit: The cache has a TTL (Time - To - Live) limit, with a default of 1 hour.
Token limit: Limited by Gemini's 1 million token context window.
Cloudflare Worker limit: The self - hosted version has a 40 - page crawling limit.
Requires network connection: A stable network connection is required to load remote resources.

How to Use

Get an API key
First, you need to obtain a Google Gemini API key, which can be created in Google AI Studio.
Choose a deployment method
Select a deployment method according to your needs: local development, self - hosted Cloudflare Worker or hosted service.
Configure the AI assistant
Configure the MCP server connection in Claude Desktop or Claude.ai.
Load data
Use the context_load tool to load data into the cache.
Query data
Query the data in the cache using natural language.

Usage Examples

Code library analysis
Developers need AI assistants to help understand the code structure of a large open - source project.
Technical documentation query
Technical support engineers need to quickly find specific function descriptions in product documentation.
Academic research
Researchers need to analyze the methods and results in multiple academic papers.
API integration
Developers need to know how to integrate third - party API services.

Frequently Asked Questions

What is the difference between Mnemo and traditional RAG (Retrieval - Augmented Generation)?
How much does it cost to use Mnemo?
Can I use Mnemo in Claude.ai?
How long will the cached data be saved?
How to load a private GitHub repository?
What file formats does Mnemo support?
Do I need programming knowledge to use Mnemo?
How is data security ensured?

Related Resources

Official GitHub Repository
The source code, latest version and issue tracking of Mnemo
Gemini API Documentation
The official documentation and guide for the Google Gemini API
Model Context Protocol
The official specification and documentation of the MCP protocol
Cloudflare Workers
The deployment and configuration guide for Cloudflare Workers
Claude MCP Configuration Guide
How to configure and use the MCP server in Claude
Logos Flux Official Website
The official website of the Mnemo development team

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "mnemo": {
      "type": "http",
      "url": "http://localhost:8080/mcp"
    }
  }
}

{
  "mcpServers": {
    "mnemo": {
      "type": "http",
      "url": "https://mnemo.<your-subdomain>.workers.dev/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_AUTH_TOKEN"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

A
Airweave
Airweave is an open - source context retrieval layer for AI agents and RAG systems. It connects and synchronizes data from various applications, tools, and databases, and provides relevant, real - time, multi - source contextual information to AI agents through a unified search interface.
Python
15.1K
5 points
V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
9.4K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
9.0K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
16.0K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
6.3K
4 points
P
Paperbanana
Python
8.0K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
9.3K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
8.8K
5 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
22.4K
4.5 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
27.7K
4.3 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
77.5K
4.3 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
36.3K
5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
67.7K
4.5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
36.3K
5 points
C
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
103.2K
4.7 points
M
Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
52.7K
4.8 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
© 2026AIBase