FEGIS

# 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 ```bash # 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`: ```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 ```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) Optional environment variables: - `COLLECTION_NAME` - Qdrant collection name (default: fegis_memory) - `AGENT_ID` - Identifier for this agent (default: default-agent) - `EMBEDDING_MODEL` - Dense embedding model (default: BAAI/bge-small-en) - `QDRANT_API_KEY` - API key for remote Qdrant (default: empty) ## Requirements - Python 3.13+ - uv package manager - Docker (for Qdrant) - MCP-compatible client ## License MIT License - see LICENSE file for details.