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FEGIS (Schema Driven Memory)
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FEGIS (Schema Driven Memory)

FEGIS is a structured cognitive framework based on the Anthropic model context protocol, supporting the definition of cognitive tools through patterns and the persistent storage of cognitive products.
Knowledge management and memoryDeveloper tools#Cognitive Framework#Thinking Tool#Semantic Storage.Python
2.5 points
8.4K
Open Site

Overview

Installation

Content Details

Alternatives

What is the MCP server?

The MCP server is a framework that allows you to define, register, and invoke the cognitive patterns of language models. It enables efficient knowledge management and retrieval through vectorized storage and semantic context.

How to use the MCP server?

Through simple configuration and instructions, you can easily enable the MCP server to enhance the cognitive ability of language models.

Applicable Scenarios

The MCP server is suitable for application scenarios that require efficient knowledge management, cognitive tool integration, and cross - model compatibility, such as knowledge - intensive tasks and complex problem - solving.

Main Features

Dynamic Registration
Supports dynamic registration of new cognitive patterns at runtime.
Persistent Storage
Saves cognitive results in a structured form, supporting long - term access and reuse.
Semantic Retrieval
Quickly finds relevant information through content similarity.
Model Agnosticism
Ensures seamless migration of your cognitive data between different models.
Advantages
Enhance the cognitive ability of language models.
Support persistent storage and an efficient retrieval mechanism.
Cross - model compatibility, no need to worry about data loss caused by model switching.
High flexibility and strong customizability.
Limitations
Requires a certain technical background for setup and maintenance.
Some advanced features may require additional computing resources.
The initial configuration may be relatively complex.

How to Use

Install Dependencies
First, ensure that the necessary tools and dependencies are installed, such as the Python package manager and Docker.
Start the Qdrant Database
Run the Qdrant database to support vector storage.
Configure Claude Desktop
Edit the configuration file to integrate the MCP server.

Usage Examples

Case 1: Knowledge Retrieval
Search for relevant content in the history records by keywords.
Case 2: Reuse of Cognitive Results
Obtain previously stored cognitive results through the UUID.

Frequently Asked Questions

Does the MCP server support multiple languages?
How to ensure data security?
What should I do if I encounter technical problems?

Related Resources

Official Documentation
Detailed usage guides and technical documentation.
GitHub Code Repository
Open - source code and example projects.
Community Forum
A communication platform with other users.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "mcp-fegis-server": {
      "command": "uv",
      "args": [
        "--directory",
        "<FEGIS_PATH>",
        "run",
        "fegis"
      ],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT_API_KEY": "",
        "COLLECTION_NAME": "cognitive_archive",
        "FAST_EMBED_MODEL": "nomic-ai/nomic-embed-text-v1.5",
        "CONFIG_PATH": "<FEGIS_PATH>/archetypes/example.yaml"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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