github.com/timowhite88/Farns… 🧠 Farnsworth: Your Claude Companion AI Give Claude superpowers: persistent memory, model swarms, multimodal understanding, and self-evolution. 📷 📷 📷 📷 📷 📷 Documentation • Roadmap • Contributing • Docker 🎯 What is Farnsworth? Farnsworth is a companion AI system that integrates with Claude Code to give Claude capabilities it doesn't have on its own: Without FarnsworthWith Farnsworth🚫 Claude forgets everything between sessions✅ Claude remembers your preferences forever🚫 Claude is a single model✅ Model Swarm: 12 models collaborate via PSO🚫 Claude can't see images or hear audio✅ Multimodal: vision (CLIP/BLIP) voice (Whisper)🚫 Claude never learns from feedback✅ Claude evolves and adapts to you🚫 Single user only✅ Team collaboration with shared memory🚫 High RAM/VRAM requirements✅ Runs on <2GB RAM with efficient models All processing happens locally on your machine. Your data never leaves your computer. ✨ What's New in v0.5.0 🐝 Model Swarm - PSO-based collaborative inference with multiple small models 🔮 Proactive Intelligence - Anticipatory suggestions based on context and habits 🚀 12 New Models - Phi-4-mini, SmolLM2, Qwen3-4B, TinyLlama, BitNet 2B ⚡ Ultra-Efficient - Run on <2GB RAM with TinyLlama, Qwen3-0.6B 🎯 Smart Routing - Mixture-of-Experts automatically picks best model per task 🔄 Speculative Decoding - 2.5x speedup with draft verify pairs 📊 Hardware Profiles - Auto-configure based on your available resources Previously Added (v0.4.0) 🖼️ Vision Module - CLIP/BLIP image understanding, VQA, OCR 🎤 Voice Module - Whisper transcription, speaker diarization, TTS 📦 Docker Support - One-command deployment with GPU support 👥 Team Collaboration - Shared memory pools, multi-user sessions 🐝 Model Swarm: Collaborative Multi-Model Inference The Model Swarm system enables multiple small models to work together, achieving better results than any single model: Swarm Strategies StrategyDescriptionBest ForPSO CollaborativeParticle Swarm Optimization guides model selectionComplex tasksParallel VoteRun 3 models, vote on best responseQuality-criticalMixture of ExpertsRoute to specialist per task typeGeneral useSpeculative EnsembleFast model drafts, strong model verifiesSpeed qualityFastest FirstStart fast, escalate if confidence lowLow latencyConfidence FusionWeighted combination of outputsHigh reliability Supported Models (Jan 2025) ModelParamsRAMStrengthsPhi-4-mini-reasoning3.8B6GBRivals o1-mini in math/reasoningPhi-4-mini3.8B6GBGPT-3.5 class, 128K contextDeepSeek-R1-1.5B1.5B4GBo1-style reasoning, MIT licenseQwen3-4B4B5GBMMLU-Pro 74%, multilingualSmolLM2-1.7B1.7B3GBBest quality at sizeQwen3-0.6B0.6B2GBUltra-light, 100 languagesTinyLlama-1.1B1.1B2GBFastest, edge devicesBitNet-2B2B1GBNative 1-bit, 5-7x CPU speedupGemma-3n-E2B2B eff4GBMultimodal (text/image/audio)Phi-4-multimodal5.6B8GBVision speech reasoning Hardware Profiles Farnsworth auto-configures based on your hardware: minimal: # <4GB RAM: TinyLlama, Qwen3-0.6B cpu_only: # 8GB RAM, no GPU: BitNet, SmolLM2 low_vram: # 2-4GB VRAM: DeepSeek-R1, Qwen3-0.6B medium_vram: # 4-8GB VRAM: Phi-4-mini, Qwen3-4B high_vram: # 8GB VRAM: Full swarm with verification ⚡ Quick Start Option 1: Docker (Recommended) git clone github.com/timowhite88/Farns… cd Farnsworth docker-compose -f docker/docker-compose.yml up -d Option 2: Local Install git clone github.com/timowhite88/Farns… cd Farnsworth pip install -r requirements.txt # Install Ollama from ollama.ai, then: ollama pull deepseek-r1:1.5b # Optional: Add more models for swarm ollama pull phi4:mini ollama pull qwen3:0.6b ollama pull tinyllama:1.1b Configure Claude Code Add to your Claude Code MCP settings: { "mcpServers": { "farnsworth": { "command": "python", "args": ["-m", "farnsworth.mcp_server"], "cwd": "/path/to/Farnsworth" } } } Start Using! You: "Remember that I prefer TypeScript over JavaScript" Claude: ✓ I'll remember that preference. [Next week, new session] You: "What language should I use for this project?" Claude: "Based on your preference for TypeScript..." 📖 Full Installation Guide → 🌟 Key Features 🧠 Advanced Memory System Claude finally remembers! Multi-tier hierarchical memory: Memory TypeDescriptionWorking MemoryCurrent conversation contextEpisodic MemoryTimeline of interactions, "on this day" recallSemantic Layers5-level abstraction hierarchyKnowledge GraphEntities, relationships, temporal edgesArchival MemoryPermanent vector-indexed storageMemory DreamingBackground consolidation during idle time 🤖 Agent Swarm (11 Specialists) Claude can delegate tasks to AI agents: Core AgentsDescriptionCode AgentProgramming, debugging, code reviewReasoning AgentLogic, math, step-by-step analysisResearch AgentInformation gathering, summarizationCreative AgentWriting, brainstorming, ideationAdvanced Agents (v0.3 )DescriptionPlanner AgentTask decomposition, dependency trackingCritic AgentQuality scoring, iterative refinementWeb AgentIntelligent browsing, form fillingFileSystem AgentProject understanding, smart searchCollaboration (v0.3 )DescriptionAgent DebatesMulti-perspective synthesisSpecialization LearningSkill development, task routingHierarchical TeamsManager coordination, load balancing 🖼️ Vision Understanding (v0.4 ) See and understand images: CLIP Integration - Zero-shot classification, image embeddings BLIP Integration - Captioning, visual question answering OCR - Extract text from images (EasyOCR) Scene Graphs - Extract objects and relationships Image Similarity - Compare and search images 🎤 Voice Interaction (v0.4 ) Hear and speak: Whisper Transcription - Real-time and batch processing Speaker Diarization - Identify different speakers Text-to-Speech - Multiple voice options Voice Commands - Natural language control Continuous Listening - Hands-free mode 👥 Team Collaboration (v0.4 ) Work together with shared AI: Shared Memory Pools - Team knowledge bases Multi-User Support - Individual profiles and preferences Permission System - Role-based access control Collaborative Sessions - Real-time multi-user interaction Audit Logging - Compliance-ready access trails 📈 Self-Evolution Farnsworth learns from your feedback and improves automatically: Fitness Tracking - Monitors task success, efficiency, satisfaction Genetic Optimization - Evolves better configurations over time User Avatar - Builds a model of your preferences LoRA Evolution - Adapts model weights to your usage 🔍 Smart Retrieval (RAG 2.0) Self-refining retrieval that gets better at finding relevant information: Hybrid Search - Semantic BM25 keyword search Query Understanding - Intent classification, expansion Multi-hop Retrieval - Complex question answering Context Compression - Token-efficient memory injection Source Attribution - Confidence scoring 🛠️ Architecture ┌─────────────────────────────────────────────────────────────┐ │ Claude Code │ │ (Your AI Programming Partner) │ └─────────────────────────────────────────────────────────────┘ │ MCP Protocol ▼ ┌─────────────────────────────────────────────────────────────┐ │ Farnsworth MCP Server │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Memory │ │ Agent │ │Evolution │ │Multimodal│ │ │ │ Tools │ │ Tools │ │ Tools │ │ Tools │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ │ │ │ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Memory │ │ Agent │ │ Multimodal │ │ System │ │ Swarm │ │ Engine │ │ │ │ │ │ │ │ • Episodic │ │ • Planner │ │ • Vision │ │ • Semantic │ │ • Critic │ │ (CLIP/BLIP)│ │ • Knowledge │ │ • Web │ │ • Voice │ │ Graph v2 │ │ • FileSystem │ │ (Whisper) │ │ • Archival │ │ • Debates │ │ • OCR │ │ • Sharing │ │ • Teams │ │ • TTS │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Evolution │ │Collaboration │ │ Storage │ │ Engine │ │ System │ │ Backends │ │ │ │ │ │ │ │ • Genetic │ │ • Multi-User │ │ • FAISS │ │ Optimizer │ │ • Shared │ │ • ChromaDB │ │ • Fitness │ │ Memory │ │ • Redis │ │ Tracker │ │ • Sessions │ │ • SQLite │ │ • LoRA │ │ • Permissions│ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ └────────────────┴────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Model Swarm (v0.5 ) │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ PSO Collaborative Engine │ │ │ │ • Particle positions = model configs │ │ │ │ • Velocity = adaptation direction │ │ │ │ • Global/personal best tracking │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Phi-4 │ │DeepSeek │ │ Qwen3 │ │ SmolLM2 │ │ │ │ mini │ │ R1-1.5B │ │ 0.6B/4B │ │ 1.7B │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │TinyLlama │ │ BitNet │ │ Gemma │ │ Cascade │ │ │ │ 1.1B │ │ 2B(1-bit)│ │ 3n-E2B │ │ (hybrid) │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ 🔧 Tools Available to Claude Once connected, Claude has access to these tools: ToolDescriptionfarnsworth_remember(content, tags)Store information in long-term memoryfarnsworth_recall(query, limit)Search and retrieve relevant memoriesfarnsworth_delegate(task, agent_type)Delegate to specialist agentfarnsworth_evolve(feedback)Provide feedback for system improvementfarnsworth_status()Get system health and statisticsfarnsworth_vision(image, task)Analyze images (caption, VQA, OCR)farnsworth_voice(audio, task)Process audio (transcribe, diarize)farnsworth_collaborate(action, ...)Team collaboration operationsfarnsworth_swarm(prompt, strategy)NEW: Multi-model collaborative inference 📦 Docker Deployment Multiple deployment profiles available: # Basic deployment docker-compose -f docker/docker-compose.yml up -d # With GPU support docker-compose -f docker/docker-compose.yml --profile gpu up -d # With Ollama ChromaDB docker-compose -f docker/docker-compose.yml --profile ollama --profile chromadb up -d # Development mode (hot reload debugger) docker-compose -f docker/docker-compose.yml --profile dev up -d See docker/docker-compose.yml for all options. 📊 Dashboard Farnsworth includes a Streamlit dashboard for visualization: python main.py --ui # Or with Docker: docker-compose -f docker/docker-compose.yml --profile ui-only up -d 📸 Dashboard Features Memory Browser - Search and explore all stored memories Episodic Timeline - Visual history of interactions Knowledge Graph - 3D entity relationships Agent Monitor - Active agents and task history Evolution Dashboard - Fitness metrics and improvement trends Team Collaboration - Shared pools and active sessions Model Swarm Monitor - PSO state, model performance, strategy stats 🚀 Roadmap See ROADMAP.md for detailed plans. Completed ✅ v0.1.0 - Core memory, agents, evolution v0.2.0 - Enhanced memory (episodic, semantic, sharing) v0.3.0 - Advanced agents (planner, critic, web, filesystem, debates, teams) v0.4.0 - Multimodal (vision, voice) collaboration Docker v0.5.0 - Model Swarm 12 new models hardware profiles Coming Next 🎬 Video understanding and summarization 🔐 Encryption at rest (AES-256) ☁️ Cloud deployment templates (AWS, Azure, GCP) 📊 Performance optimization (<100ms recall) 💡 Why "Farnsworth"? Named after Professor Hubert J. Farnsworth from Futurama - a brilliant inventor who created countless gadgets and whose catchphrase "Good news, everyone!" perfectly captures what we hope you'll feel when using this tool with Claude. 📋 Requirements MinimumRecommendedWith Full SwarmPython 3.10 Python 3.11 Python 3.11 4GB RAM8GB RAM16GB RAM2-core CPU4-core CPU8-core CPU5GB storage20GB storage50GB storage-4GB VRAM8GB VRAM Supported Platforms: Windows 10 , macOS 11 , Linux Optional Dependencies: ollama - Local LLM inference (recommended) llama-cpp-python - Direct GGUF inference torch - GPU acceleration transformers - Vision/Voice models playwright - Web browsing agent whisper - Voice transcription 📄 License Farnsworth is dual-licensed: Use CaseLicensePersonal / Educational / Non-commercialFREECommercial (revenue > $1M or enterprise)Commercial License Required See LICENSE for details. For commercial licensing, contact via GitHub. 🤝 Contributing We welcome contributions! See CONTRIBUTING.md for guidelines. Priority Areas: Video understanding module Cloud deployment templates Performance benchmarks Additional model integrations Documentation improvements 📚 Documentation 📖 User Guide - Complete usage documentation 🗺️ Roadmap - Future plans and features 🤝 Contributing - How to contribute 📜 License - License terms 🐳 Docker Guide - Container deployment 🐝 Model Configs - Supported models and swarm configs 🔗 Research References Model Swarm implementation inspired by: Model Swarms: Collaborative Search via Swarm Intelligence Harnessing Multiple LLMs: Survey on LLM Ensemble Small Language Models - MIT Tech Review ⭐ Star History If Farnsworth helps you, consider giving it a star! ⭐

Major Update to Farnsworth Today v0.05.0 is now Live 🧠 Farnsworth: Your Claude Companion AI Give Claude superpowers: persistent memory, model swarms, multimodal understanding, and self-evolution. 📷 📷 📷 📷 📷 📷 Documentation • Roadmap • Contributing • Docker 🎯 What is Farnsworth? Farnsworth is a companion AI system that integrates with Claude Code to give Claude capabilities it doesn't have on its own: Without FarnsworthWith Farnsworth🚫 Claude forgets everything between sessions✅ Claude remembers your preferences forever🚫 Claude is a single model✅ Model Swarm: 12 models collaborate via PSO🚫 Claude can't see images or hear audio✅ Multimodal: vision (CLIP/BLIP) voice (Whisper)🚫 Claude never learns from feedback✅ Claude evolves and adapts to you🚫 Single user only✅ Team collaboration with shared memory🚫 High RAM/VRAM requirements✅ Runs on <2GB RAM with efficient models All processing happens locally on your machine. Your data never leaves your computer. ✨ What's New in v0.5.0 🐝 Model Swarm - PSO-based collaborative inference with multiple small models 🔮 Proactive Intelligence - Anticipatory suggestions based on context and habits 🚀 12 New Models - Phi-4-mini, SmolLM2, Qwen3-4B, TinyLlama, BitNet 2B ⚡ Ultra-Efficient - Run on <2GB RAM with TinyLlama, Qwen3-0.6B 🎯 Smart Routing - Mixture-of-Experts automatically picks best model per task 🔄 Speculative Decoding - 2.5x speedup with draft verify pairs 📊 Hardware Profiles - Auto-configure based on your available resources Previously Added (v0.4.0) 🖼️ Vision Module - CLIP/BLIP image understanding, VQA, OCR 🎤 Voice Module - Whisper transcription, speaker diarization, TTS 📦 Docker Support - One-command deployment with GPU support 👥 Team Collaboration - Shared memory pools, multi-user sessions 🐝 Model Swarm: Collaborative Multi-Model Inference The Model Swarm system enables multiple small models to work together, achieving better results than any single model: Swarm Strategies StrategyDescriptionBest ForPSO CollaborativeParticle Swarm Optimization guides model selectionComplex tasksParallel VoteRun 3 models, vote on best responseQuality-criticalMixture of ExpertsRoute to specialist per task typeGeneral useSpeculative EnsembleFast model drafts, strong model verifiesSpeed qualityFastest FirstStart fast, escalate if confidence lowLow latencyConfidence FusionWeighted combination of outputsHigh reliability Supported Models (Jan 2025) ModelParamsRAMStrengthsPhi-4-mini-reasoning3.8B6GBRivals o1-mini in math/reasoningPhi-4-mini3.8B6GBGPT-3.5 class, 128K contextDeepSeek-R1-1.5B1.5B4GBo1-style reasoning, MIT licenseQwen3-4B4B5GBMMLU-Pro 74%, multilingualSmolLM2-1.7B1.7B3GBBest quality at sizeQwen3-0.6B0.6B2GBUltra-light, 100 languagesTinyLlama-1.1B1.1B2GBFastest, edge devicesBitNet-2B2B1GBNative 1-bit, 5-7x CPU speedupGemma-3n-E2B2B eff4GBMultimodal (text/image/audio)Phi-4-multimodal5.6B8GBVision speech reasoning Hardware Profiles Farnsworth auto-configures based on your hardware: minimal: # <4GB RAM: TinyLlama, Qwen3-0.6B cpu_only: # 8GB RAM, no GPU: BitNet, SmolLM2 low_vram: # 2-4GB VRAM: DeepSeek-R1, Qwen3-0.6B medium_vram: # 4-8GB VRAM: Phi-4-mini, Qwen3-4B high_vram: # 8GB VRAM: Full swarm with verification ⚡ Quick Start Option 1: Docker (Recommended) git clone github.com/timowhite88/Farns… cd Farnsworth docker-compose -f docker/docker-compose.yml up -d Option 2: Local Install git clone github.com/timowhite88/Farns… cd Farnsworth pip install -r requirements.txt # Install Ollama from ollama.ai, then: ollama pull deepseek-r1:1.5b # Optional: Add more models for swarm ollama pull phi4:mini ollama pull qwen3:0.6b ollama pull tinyllama:1.1b Configure Claude Code Add to your Claude Code MCP settings: { "mcpServers": { "farnsworth": { "command": "python", "args": ["-m", "farnsworth.mcp_server"], "cwd": "/path/to/Farnsworth" } } } Start Using! You: "Remember that I prefer TypeScript over JavaScript" Claude: ✓ I'll remember that preference. [Next week, new session] You: "What language should I use for this project?" Claude: "Based on your preference for TypeScript..." 📖 Full Installation Guide → 🌟 Key Features 🧠 Advanced Memory System Claude finally remembers! Multi-tier hierarchical memory: Memory TypeDescriptionWorking MemoryCurrent conversation contextEpisodic MemoryTimeline of interactions, "on this day" recallSemantic Layers5-level abstraction hierarchyKnowledge GraphEntities, relationships, temporal edgesArchival MemoryPermanent vector-indexed storageMemory DreamingBackground consolidation during idle time 🤖 Agent Swarm (11 Specialists) Claude can delegate tasks to AI agents: Core AgentsDescriptionCode AgentProgramming, debugging, code reviewReasoning AgentLogic, math, step-by-step analysisResearch AgentInformation gathering, summarizationCreative AgentWriting, brainstorming, ideationAdvanced Agents (v0.3 )DescriptionPlanner AgentTask decomposition, dependency trackingCritic AgentQuality scoring, iterative refinementWeb AgentIntelligent browsing, form fillingFileSystem AgentProject understanding, smart searchCollaboration (v0.3 )DescriptionAgent DebatesMulti-perspective synthesisSpecialization LearningSkill development, task routingHierarchical TeamsManager coordination, load balancing 🖼️ Vision Understanding (v0.4 ) See and understand images: CLIP Integration - Zero-shot classification, image embeddings BLIP Integration - Captioning, visual question answering OCR - Extract text from images (EasyOCR) Scene Graphs - Extract objects and relationships Image Similarity - Compare and search images 🎤 Voice Interaction (v0.4 ) Hear and speak: Whisper Transcription - Real-time and batch processing Speaker Diarization - Identify different speakers Text-to-Speech - Multiple voice options Voice Commands - Natural language control Continuous Listening - Hands-free mode 👥 Team Collaboration (v0.4 ) Work together with shared AI: Shared Memory Pools - Team knowledge bases Multi-User Support - Individual profiles and preferences Permission System - Role-based access control Collaborative Sessions - Real-time multi-user interaction Audit Logging - Compliance-ready access trails 📈 Self-Evolution Farnsworth learns from your feedback and improves automatically: Fitness Tracking - Monitors task success, efficiency, satisfaction Genetic Optimization - Evolves better configurations over time User Avatar - Builds a model of your preferences LoRA Evolution - Adapts model weights to your usage 🔍 Smart Retrieval (RAG 2.0) Self-refining retrieval that gets better at finding relevant information: Hybrid Search - Semantic BM25 keyword search Query Understanding - Intent classification, expansion Multi-hop Retrieval - Complex question answering Context Compression - Token-efficient memory injection Source Attribution - Confidence scoring 🛠️ Architecture ┌─────────────────────────────────────────────────────────────┐ │ Claude Code │ │ (Your AI Programming Partner) │ └─────────────────────────────────────────────────────────────┘ │ MCP Protocol ▼ ┌─────────────────────────────────────────────────────────────┐ │ Farnsworth MCP Server │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Memory │ │ Agent │ │Evolution │ │Multimodal│ │ │ │ Tools │ │ Tools │ │ Tools │ │ Tools │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ │ │ │ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Memory │ │ Agent │ │ Multimodal │ │ System │ │ Swarm │ │ Engine │ │ │ │ │ │ │ │ • Episodic │ │ • Planner │ │ • Vision │ │ • Semantic │ │ • Critic │ │ (CLIP/BLIP)│ │ • Knowledge │ │ • Web │ │ • Voice │ │ Graph v2 │ │ • FileSystem │ │ (Whisper) │ │ • Archival │ │ • Debates │ │ • OCR │ │ • Sharing │ │ • Teams │ │ • TTS │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Evolution │ │Collaboration │ │ Storage │ │ Engine │ │ System │ │ Backends │ │ │ │ │ │ │ │ • Genetic │ │ • Multi-User │ │ • FAISS │ │ Optimizer │ │ • Shared │ │ • ChromaDB │ │ • Fitness │ │ Memory │ │ • Redis │ │ Tracker │ │ • Sessions │ │ • SQLite │ │ • LoRA │ │ • Permissions│ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ └────────────────┴────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Model Swarm (v0.5 ) │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ PSO Collaborative Engine │ │ │ │ • Particle positions = model configs │ │ │ │ • Velocity = adaptation direction │ │ │ │ • Global/personal best tracking │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Phi-4 │ │DeepSeek │ │ Qwen3 │ │ SmolLM2 │ │ │ │ mini │ │ R1-1.5B │ │ 0.6B/4B │ │ 1.7B │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │TinyLlama │ │ BitNet │ │ Gemma │ │ Cascade │ │ │ │ 1.1B │ │ 2B(1-bit)│ │ 3n-E2B │ │ (hybrid) │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ 🔧 Tools Available to Claude Once connected, Claude has access to these tools: ToolDescriptionfarnsworth_remember(content, tags)Store information in long-term memoryfarnsworth_recall(query, limit)Search and retrieve relevant memoriesfarnsworth_delegate(task, agent_type)Delegate to specialist agentfarnsworth_evolve(feedback)Provide feedback for system improvementfarnsworth_status()Get system health and statisticsfarnsworth_vision(image, task)Analyze images (caption, VQA, OCR)farnsworth_voice(audio, task)Process audio (transcribe, diarize)farnsworth_collaborate(action, ...)Team collaboration operationsfarnsworth_swarm(prompt, strategy)NEW: Multi-model collaborative inference 📦 Docker Deployment Multiple deployment profiles available: # Basic deployment docker-compose -f docker/docker-compose.yml up -d # With GPU support docker-compose -f docker/docker-compose.yml --profile gpu up -d # With Ollama ChromaDB docker-compose -f docker/docker-compose.yml --profile ollama --profile chromadb up -d # Development mode (hot reload debugger) docker-compose -f docker/docker-compose.yml --profile dev up -d See docker/docker-compose.yml for all options. 📊 Dashboard Farnsworth includes a Streamlit dashboard for visualization: python main.py --ui # Or with Docker: docker-compose -f docker/docker-compose.yml --profile ui-only up -d 📸 Dashboard Features Memory Browser - Search and explore all stored memories Episodic Timeline - Visual history of interactions Knowledge Graph - 3D entity relationships Agent Monitor - Active agents and task history Evolution Dashboard - Fitness metrics and improvement trends Team Collaboration - Shared pools and active sessions Model Swarm Monitor - PSO state, model performance, strategy stats 🚀 Roadmap See ROADMAP.md for detailed plans. Completed ✅ v0.1.0 - Core memory, agents, evolution v0.2.0 - Enhanced memory (episodic, semantic, sharing) v0.3.0 - Advanced agents (planner, critic, web, filesystem, debates, teams) v0.4.0 - Multimodal (vision, voice) collaboration Docker v0.5.0 - Model Swarm 12 new models hardware profiles Coming Next 🎬 Video understanding and summarization 🔐 Encryption at rest (AES-256) ☁️ Cloud deployment templates (AWS, Azure, GCP) 📊 Performance optimization (<100ms recall) 💡 Why "Farnsworth"? Named after Professor Hubert J. Farnsworth from Futurama - a brilliant inventor who created countless gadgets and whose catchphrase "Good news, everyone!" perfectly captures what we hope you'll feel when using this tool with Claude. 📋 Requirements MinimumRecommendedWith Full SwarmPython 3.10 Python 3.11 Python 3.11 4GB RAM8GB RAM16GB RAM2-core CPU4-core CPU8-core CPU5GB storage20GB storage50GB storage-4GB VRAM8GB VRAM Supported Platforms: Windows 10 , macOS 11 , Linux Optional Dependencies: ollama - Local LLM inference (recommended) llama-cpp-python - Direct GGUF inference torch - GPU acceleration transformers - Vision/Voice models playwright - Web browsing agent whisper - Voice transcription 📄 License Farnsworth is dual-licensed: Use CaseLicensePersonal / Educational / Non-commercialFREECommercial (revenue > $1M or enterprise)Commercial License Required See LICENSE for details. For commercial licensing, contact via GitHub. 🤝 Contributing We welcome contributions! See CONTRIBUTING.md for guidelines. Priority Areas: Video understanding module Cloud deployment templates Performance benchmarks Additional model integrations Documentation improvements 📚 Documentation 📖 User Guide - Complete usage documentation 🗺️ Roadmap - Future plans and features 🤝 Contributing - How to contribute 📜 License - License terms 🐳 Docker Guide - Container deployment 🐝 Model Configs - Supported models and swarm configs 🔗 Research References Model Swarm implementation inspired by: Model Swarms: Collaborative Search via Swarm Intelligence Harnessing Multiple LLMs: Survey on LLM Ensemble Small Language Models - MIT Tech Review ⭐ Star History If Farnsworth helps you, consider giving it a star! ⭐

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#thoughtoftheday #MotivationalQuotes #morningvibes #morningmotivation #knowledge #education #learningskill #learningskillsbypractice

#thoughtoftheday #MotivationalQuotes #morningvibes #morningmotivation #knowledge #education #learningskill #educationdevloplearningskill

The month of January Osprey focused on #learningskill INITIATIVE. Chosen by their teachers, these students consistently demonstrate curiosity & interest in new learning with a positive mindset and a willingness to take risks. #OspreyProud #developingworkhabits @BluewaterDSB