What is the Model Context Protocol (MCP)?

Fegis implements the Model Context Protocol (MCP), extending it to create semantically rich, internally defined LLM tools using YAML archetypes.

How does Fegis use memory?

Fegis features a hybrid memory system that combines vector embeddings with structured metadata, creating an emergent knowledge graph for persistent memory and semantic retrieval.

How do I get started with Fegis?

Clone the Fegis repository from GitHub, start Qdrant using Docker, and configure the Claude Desktop MCP server.

Fegis is a semantic tool building framework and compiler that transforms YAML specifications into structured, reusable tools for large language models (LLMs).

What can I build with Fegis?

Fegis can be used to create thinking frameworks, web exploration interfaces, optimization systems, and symbolic reasoning tools for LLMs.

Fegis

Name: Fegis
Author: p-funk

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Data Science & ML

Compiles YAML specifications into semantic LLM tools with structured memory and an emergent knowledge graph.

Fegis is a semantic tool building framework and compiler that transforms YAML specifications—called Archetypes—into structured, reusable tools for large language models (LLMs). Built on the Model Context Protocol (MCP), Fegis compiles each Archetype into schema-validated interfaces, where field names and parameters act as semantic directives that guide content generation. Every tool invocation is preserved in a hybrid memory system combining vector embeddings with structured metadata—forming an emergent knowledge graph that enables persistent memory, semantic retrieval, and exploration of interconnected data.

Key Features

01Compiles YAML Archetypes into LLM tools.

02Features a hybrid memory system with vector embeddings and structured metadata.

03Provides a semantic programming framework.

0416 GitHub stars

05Implements the Model Context Protocol (MCP).

06Enables the creation of an emergent knowledge graph.

Use Cases

01Creating web exploration interfaces.

02Building thinking frameworks for LLMs.

03Developing optimization systems inspired by biological networks.