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.
rating : 2.5 points
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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

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