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Fegis

Define AI tools in YAML with natural language schemas. All tool usage is automatically stored in Qdrant vector database, enabling semantic search, filtering, and memory…

Install

$docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
Claude Code
claude mcp add fegis -- docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
Cursor / Claude Desktop (mcp.json)
{
  "mcpServers": {
    "fegis": {
      "command": "docker",
      "args": [
        "run",
        "-d",
        "--name",
        "qdrant",
        "-p",
        "6333:6333",
        "-p",
        "6334:6334",
        "qdrant/qdrant:latest"
      ]
    }
  }
}

Install command taken from the project's README or registry entry. Always review code before running it.

Fegis

Fegis does 3 things:

  1. Easy to write tools - Write prompts in YAML format. Tool schemas use flexible natural language instructions.
  2. Structured data from tool calls saved in a vector database - Every tool use is automatically stored in Qdrant with full context.
  3. Search - AI can search through all previous tool usage using semantic similarity, filters, or direct lookup.

Quick Start

# Install uv
# Windows
winget install --id=astral-sh.uv -e

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone
git clone https://github.com/p-funk/fegis.git

# Start Qdrant
docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest

Configure Claude Desktop

Update claude_desktop_config.json:

{
  "mcpServers": {
    "fegis": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/fegis",
        "run",
        "fegis"
      ],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT_API_KEY": "",
        "COLLECTION_NAME": "fegis_memory",
        "EMBEDDING_MODEL": "BAAI/bge-small-en",
        "ARCHETYPE_PATH": "/absolute/path/to/fegis-wip/archetypes/default.yaml",
        "AGENT_ID": "claude_desktop"
      }
    }
  }
}

Restart Claude Desktop. You'll have 7 new tools available including SearchMemory.

How It Works

1. Tools from YAML

parameters:
  BiasScope:
    description: "Range of bias detection to apply"
    examples: [confirmation, availability, anchoring, systematic, comprehensive]
  
  IntrospectionDepth:
    description: "How deeply to examine internal reasoning processes"
    examples: [surface, moderate, deep, exhaustive, meta_recursive]
    
tools:
  BiasDetector:
    description: "Identify reasoning blind spots, cognitive biases, and systematic errors in AI thinking patterns through structured self-examination"
    parameters:
      BiasScope:
      IntrospectionDepth:
    frames:
      identified_biases:
        type: List
        required: true
      reasoning_patterns:
        type: List
        required: true
      alternative_perspectives:
        type: List
        required: true

2. Automatic Memory Storage

Every tool invocation gets stored with:

  • Tool name and parameters used
  • Complete input and output
  • Timestamp and session context
  • Vector embeddings for semantic search

3. SearchMemory Tool

"Use SearchMemory and find my analysis of privacy concerns"
"Use SearchMemory and what creative ideas did I generate last week?"  
"Use SearchMemory and show me all UncertaintyNavigator results"
"Use SearchMemory and search for memories about decision-making"

Available Archetypes

  • archetypes/default.yaml - Cognitive analysis tools (UncertaintyNavigator, BiasDetector, etc.)
  • archetypes/simple_example.yaml - Basic example tools
  • archetypes/emoji_mind.yaml - Symbolic reasoning with emojis
  • archetypes/slime_mold.yaml - Network optimization tools
  • archetypes/vibe_surfer.yaml - Web exploration tools

Configuration

Required environment variables:

  • ARCHETYPE_PATH - Path to YAML archetype file
  • QDRANT_URL - Qdrant database URL (default: http://localhost:6333)

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