MultiMind SDK: The Future of AI Development

🚀 Multi-Model AI • RAG Systems • Vector Databases • Agent Framework • Fine-Tuning • Enterprise Compliance

Transparent, honest, and production-ready AI development toolkit

What is MultiMind SDK? • Key Features • Compliance • Quick Start • Documentation • Examples • Contributing

🤖 What is MultiMind SDK?

MultiMind SDK is a unified AI development framework that combines practical AI tools with a clean, extensible architecture. We're building a production-ready toolkit for AI developers, with transparency about what works today and what's coming next.

🌟 What Makes MultiMind SDK Special

• 🎯 Unified API: One interface for multiple AI models and providers
• 📚 Production-Ready RAG: Working RAG pipelines with popular vector databases
• 🤖 Agent Framework: Build AI agents with tools, memory, and orchestration
• ⚡ Multiple Vector DBs: Support for FAISS, Chroma, Weaviate, Qdrant, Pinecone, and more
• 🎨 Fine-Tuning Support: Tools for fine-tuning transformer and non-transformer models
• 🔐 Compliance Features: Basic compliance framework for healthcare and enterprise use

📋 Transparency: We're committed to honesty about feature status. See FEATURES.md for detailed status of all features, and ROADMAP.md for our development priorities.

🎯 For Beginners

• No AI Experience Required: Start building AI applications with simple Python code
• Pre-built Components: Use ready-made AI tools without understanding complex algorithms
• Step-by-step Examples: Learn AI development through practical examples
• Visual Interface: Use our web-based playground to experiment with AI

🚀 For Developers

• Unified Framework: One toolkit for all AI development needs
• Production Ready: Built-in monitoring, logging, and deployment tools
• Extensible: Add your own custom AI components easily
• Type Safe: Modern Python with full error checking and validation

🏢 For Enterprises

• Enterprise Compliance: Built-in support for HIPAA, GDPR, and other regulations
• Scalable Architecture: Handle millions of users and requests
• Cost Optimization: Intelligent resource management and cost tracking
• Security First: Authentication, encryption, and audit trails

✅ What Works Today

🎯 Core Features (Production Ready)

• ✅ Multi-Model AI Chat: OpenAI, Claude, Ollama, Mistral support
  • Example: examples/api/multi_model_wrapper.py
• ✅ Basic RAG Systems: FAISS, Chroma, and basic vector database support
  • Example: examples/rag/rag_example.py
• ✅ AI Agents: Basic agents with tools and memory
  • Example: examples/cli/basic_agent.py
• ✅ CLI Interface: Comprehensive command-line tools
  • Example: examples/cli/ (14/14 tests passing)
• ✅ Memory Management: Buffer, summary, and basic memory types
  • Example: examples/memory/basic_usage.py
• ✅ Basic Compliance: Healthcare compliance framework
  • Example: examples/compliance/healthcare_compliance_example.py
• ✅ Context Transfer: Transfer conversations between models
  • Example: examples/context_transfer/chrome_extension_example.py

📊 Full Status: See FEATURES.md for complete feature status with badges (✅ Stable | 🚧 Beta | 📋 Planned)

✨ Key Features

🧠 AI Model Management ✅ Stable / 🚧 Beta

• ✅ Model Integrations: OpenAI, Claude, Ollama, Mistral
  • Example: examples/api/model_wrapper.py
• ✅ Multi-Model Wrapper: Unified interface for multiple models
  • Example: examples/api/multi_model_wrapper.py
• 🚧 Model Routing: Basic routing between models
  • Example: examples/api/ensemble_api.py
• 🚧 Mixture-of-Experts (MoE): Basic implementation
  • Example: examples/moe/
• 📋 100+ Model Support: Many models planned, not yet implemented
• 📋 Federated Learning: Not implemented
• 📋 Model Compression: Basic support only

📚 RAG & Vector Databases ✅ Stable / 🚧 Beta

• ✅ FAISS: Fully functional local vector store
  • Example: examples/vector_store/
• ✅ Chroma: Complete implementation
  • Example: examples/rag/rag_example.py
• 🚧 Weaviate: Basic implementation
• 🚧 Qdrant: Core functionality
• 🚧 Pinecone: Working but basic
• 🚧 Milvus: Functional but limited
• 🚧 Elasticsearch: Basic implementation
• ✅ Basic RAG Pipeline: Core RAG with document processing
  • Example: examples/rag/rag_example.py
• 🚧 Advanced RAG: Enhanced retrieval features
  • Example: examples/rag/rag_advanced_example.py
• 📋 Hybrid RAG: Knowledge graph integration not functional
• 📋 60+ Vector Databases: Only ~8-10 actually implemented (see FEATURES.md)

🤖 AI Agents ✅ Stable / 🚧 Beta

• ✅ Basic Agents: Agent class with tool support
  • Example: examples/cli/basic_agent.py
• ✅ Agent Registry: Agent registration and management
  • Example: examples/agents/agent_registry_example.py
• ✅ ReAct Toolchain: ReAct pattern implementation
  • Example: examples/agents/react_toolchain_example.py
• 🚧 Multi-Agent Orchestration: Basic coordination
• 📋 Self-Evolving Agents: Learning mechanisms not implemented
• 📋 Cognitive Scratchpad: Advanced features missing

🧠 Memory Systems ✅ Stable / 🚧 Beta / 📋 Planned

• ✅ Buffer Memory: Working conversation buffer
• ✅ Summary Memory: Working summarization
• ✅ Agent Memory: Agent state management
  • Example: examples/memory/basic_usage.py
• 🚧 Vector Store Memory: Working but limited
• 🚧 Episodic Memory: Basic implementation
• 🚧 Hybrid Memory: Multi-memory routing
  • Example: examples/memory/advanced_memory_manager.py
• 📋 Quantum Memory: Simulation only (not real quantum hardware)
  • Note: Educational/research use only
  • Example: examples/memory/quantum_memory.py

🔄 Fine-Tuning 🚧 Beta

• 🚧 Basic LoRA: Basic LoRA support
  • Example: examples/fine_tuning/
• 🚧 Non-Transformer Models: Mamba, RWKV, Hyena support
  • Example: examples/non_transformer/
• 📋 QLoRA: Placeholder only
• 📋 Advanced Optimization: Many techniques not implemented

🛡️ Compliance & Security ✅ Stable / 🚧 Beta / 📋 Planned

• ✅ Basic Compliance: Healthcare compliance framework
  • Example: examples/compliance/healthcare/
• 🚧 GDPR Support: Basic features
• 📋 Zero-Knowledge Proofs: Dependencies not available
• 📋 Differential Privacy: Not implemented
• 📋 Federated Compliance: Not implemented
• 📋 Quantum-Safe Encryption: Not implemented

🔄 Workflow & Orchestration ✅ Stable / 🚧 Beta / 📋 Planned

• ✅ Prompt Chains: Basic chaining
  • Example: examples/cli/prompt_chain.py
• ✅ Task Runner: Simple task execution
  • Example: examples/cli/task_runner.py
• 🚧 MCP (Model Context Protocol): Basic executor
  • Example: examples/mcp/
• 🚧 Pipeline Builder: Basic pipeline construction
  • Example: examples/pipeline/pipeline_example.py
• 📋 Visual Workflow Builder: Not implemented
• 📋 Event-Driven Architecture: Not fully implemented

📊 Monitoring 🚧 Beta / 📋 Planned

• 🚧 Basic Logging: TraceLogger and basic metrics
• 🚧 Usage Tracking: Basic usage tracking
  • Example: examples/cli/usage_tracking.py
• 📋 Real-time Performance Tracking: Not implemented
• 📋 AI-Powered Anomaly Detection: Not implemented
• 📋 Cost Optimization Engine: Not implemented

🚀 Quick Start

Installation

# Basic installation
pip install multimind-sdk

# With compliance support
pip install multimind-sdk[compliance]

# With development dependencies
pip install multimind-sdk[dev]

# With gateway support
pip install multimind-sdk[gateway]

# Full installation with all features
pip install multimind-sdk[all]

Environment Setup

Copy the example environment file and add your API keys and configuration values:

cp examples/multi-model-wrapper/.env.example examples/multi-model-wrapper/.env

Note: Never commit your .env file to version control. Only .env.example should be tracked in git.

🎯 Simple Examples for Everyone

For Beginners: Multi-Model AI Chat

from multimind.models import OpenAIModel, ClaudeModel

# Create AI models
gpt_model = OpenAIModel(model="gpt-3.5-turbo")
claude_model = ClaudeModel(model="claude-3-sonnet")

# Chat with AI
response = await gpt_model.generate("Explain AI in simple terms")
print(response)

For Developers: Basic RAG System

from multimind.rag import RAGPipeline
from multimind.vector_store import ChromaVectorStore
from multimind.models import OpenAIModel

# Create a RAG system with Chroma
rag = RAGPipeline(
    vector_store=ChromaVectorStore(),
    model=OpenAIModel(model="gpt-3.5-turbo")
)

# Add documents
await rag.add_documents([
    "MultiMind SDK is a powerful AI development toolkit",
    "It supports multiple vector databases and AI models",
    "RAG systems help retrieve relevant context for AI responses"
])

# Query with context
results = await rag.query("What is MultiMind SDK?")
print(results)

For Enterprises: Healthcare Compliance

from multimind.compliance import ComplianceMonitor
from multimind.compliance.healthcare import HIPAACompliance

# Create a compliance monitor
compliance = ComplianceMonitor(
    organization_id="your_org",
    regulations=[HIPAACompliance()]
)

# Check compliance
is_compliant = await compliance.check_compliance(data)
if not is_compliant:
    violations = compliance.get_violations()
    print(f"Compliance violations: {violations}")

📊 Test Coverage & Current Status

✅ Testing Results (Latest)

• Python Version Tested: 3.10.10 ✅
• Total Tests: 200
• Passed: 157 (78.5%) ✅
• Failed: 10 (5%)
• Skipped: 37 (18.5%)
• Success Rate: 78.5% ✅

🧪 Test Categories Performance

• Core Functionality: ✅ 100% working
• CLI Examples: ✅ 14/14 tests passing
• API Examples: ✅ 15/16 tests passing
• Compliance Examples: ⚠️ 12/15 tests passing
• Advanced Features: ⚠️ 70% working

🚀 Production-Ready Features (✅ Stable)

• ✅ Multi-model AI chat with OpenAI, Claude, Ollama, Mistral
• ✅ Basic AI agents with memory and tools
• ✅ RAG (Retrieval-Augmented Generation) systems with FAISS and Chroma
• ✅ Basic vector database integrations (FAISS, Chroma, Annoy)
• ✅ CLI interface for easy interaction (14/14 tests passing)
• ✅ Basic model conversion and fine-tuning
• ✅ Basic compliance and security features
• ✅ Context transfer between models
• ✅ Basic memory management systems

📋 For detailed feature status: See FEATURES.md for complete status of all features with badges.

🔧 Quick Start for Developers

1. Install MultiMind SDK

# Basic installation
pip install multimind-sdk

# With all features
pip install multimind-sdk[all]

# Development installation
git clone https://github.com/multimind-dev/multimind-sdk.git
cd multimind-sdk
pip install -e ".[dev]"

2. Set Up Environment

# Create .env file with your API keys
echo "OPENAI_API_KEY=your_openai_api_key" > .env
echo "ANTHROPIC_API_KEY=your_anthropic_api_key" >> .env
echo "MISTRAL_API_KEY=your_mistral_api_key" >> .env

3. Test Basic Functionality

# Quick test - Basic AI chat
from multimind import OpenAIModel

model = OpenAIModel(model="gpt-3.5-turbo")
response = await model.generate("Hello, world!")
print(response)

4. Try Working Examples

# Basic agent example
python examples/cli/basic_agent.py

# Multi-model chat
python examples/cli/chat_with_gpt.py

# RAG system
python examples/rag/example_rag.py

# Context transfer
python examples/context_transfer/chrome_extension_example.py

5. Tested and Working Examples

# CLI Examples (14/14 tested and working)
python examples/cli/basic_agent.py
python examples/cli/chat_with_gpt.py
python examples/cli/chat_ollama_cli.py

# API Examples (15/16 tested and working)
python examples/api/ensemble_api.py
python examples/api/compliance_example.py

# Compliance Examples (12/15 tested and working)
python examples/compliance/healthcare/ehr_compliance.py
python examples/compliance/healthcare/clinical_trial_compliance.py

🎯 Developer-Friendly Examples

Simple Multi-Model Chat

from multimind.models import OpenAIModel, ClaudeModel

# Create models
models = {
    "gpt": OpenAIModel(model="gpt-3.5-turbo"),
    "claude": ClaudeModel(model="claude-3-sonnet")
}

# Use models directly
response = await models["gpt"].generate("Hello, world!")
print(response)

AI Agent with Tools

from multimind import Agent, CalculatorTool, OpenAIModel

# Create agent with calculator tool
agent = Agent(
    model=OpenAIModel(model="gpt-3.5-turbo"),
    tools=[CalculatorTool()],
    system_prompt="You are a helpful AI assistant that can perform calculations."
)

# Run tasks
response = await agent.run("What is 123 * 456?")
print(response)

RAG System

from multimind.rag import RAGPipeline
from multimind.vector_store import ChromaVectorStore

# Create RAG system
rag = RAGPipeline(
    vector_store=ChromaVectorStore(),
    model=OpenAIModel(model="gpt-3.5-turbo")
)

# Add documents
await rag.add_documents(["MultiMind SDK is a powerful AI development toolkit"])

# Query with context
results = await rag.query("What is MultiMind SDK?")
print(results)

🐳 Docker Quick Start

# Run with Docker
docker-compose up --build

# Access services:
# - MultiMind API: http://localhost:8000
# - Redis: localhost:6379

📚 Documentation

• FEATURES.md ⭐ - Honest feature status with badges (✅ Stable | 🚧 Beta | 📋 Planned)
• ROADMAP.md - Development priorities and future features
• Getting Started Guide - Your first steps with MultiMind SDK
• API Reference - Complete API documentation
• Examples - Ready-to-use code examples
• Compliance Guide - Enterprise compliance features
• Architecture - How MultiMind SDK works
• Contributing Guide - Join our development team

📁 Project Structure

multimind-sdk/
├── multimind/                    # Core SDK package
│   ├── core/                    # Core AI components
│   ├── models/                  # AI model integrations
│   ├── rag/                     # Document AI system
│   ├── agents/                  # AI agent framework
│   ├── memory/                  # Memory management
│   ├── compliance/              # Enterprise compliance
│   ├── cli/                     # Command-line tools
│   └── gateway/                 # Web API gateway
├── examples/                    # Ready-to-use examples
│   ├── basic/                   # Simple examples for beginners
│   ├── advanced/                # Complex examples for experts
│   ├── compliance/              # Compliance examples
│   └── streamlit-ui/            # Web interface
├── docs/                        # Documentation
└── tests/                       # Test suite

🤝 Contributing

We love your input! We want to make contributing to MultiMind SDK as easy and transparent as possible.

• Contributing Guide - How to contribute
• Code of Conduct - Community guidelines
• Issue Tracker - Report bugs or request features

Development Setup

# Clone the repository
git clone https://github.com/multimind-dev/multimind-sdk.git
cd multimind-sdk

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Start documentation
cd multimind-docs
npm install
npm start

🐳 Docker Setup

Run MultiMind SDK with Docker for easy deployment:

# Start all services
docker-compose up --build

# Access the web interface
# MultiMind API: http://localhost:8000
# Web Playground: http://localhost:8501

The Docker setup includes:

• MultiMind SDK service
• Redis for caching
• Chroma for document storage
• Ollama for local AI models

💖 Support MultiMind SDK

🚀 Why Support MultiMind SDK?

We're building a practical, production-ready AI development framework. Your support enables us to:

• ⚡ Core Development: Complete vector database integrations and improve existing features
• 🔐 Security & Compliance: Enhance compliance features and security
• 📚 Documentation & Education: Better tutorials, examples, and learning resources
• 🌍 Community Growth: Supporting our growing global community of AI developers
• 🛠️ Infrastructure: Servers, CI/CD, testing, and development tools
• 🧪 Quality & Testing: Improve test coverage and code quality

💎 Support Tiers

🎯 What Your Support Funds

• 50% Development: New features, vector database integrations, performance optimization
• 25% Community: Documentation, tutorials, events, Discord community
• 15% Quality: Testing, code quality, bug fixes
• 10% Infrastructure: Servers, CI/CD, testing, development tools

🌟 Join Our Mission

Help us democratize AI development and build the future of intelligent systems.

Every contribution, no matter the size, helps us push the boundaries of what's possible with AI.

🙏 Other Ways to Support

• ⭐ Star the Repository: Show your love on GitHub
• 💬 Join Discord: Help other developers and share your ideas
• 🐛 Report Issues: Help us improve by reporting bugs
• 📝 Contribute Code: Submit pull requests and improve the codebase
• 📚 Write Documentation: Help make MultiMind SDK more accessible
• 🌍 Spread the Word: Share MultiMind SDK with your network

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

For more information about the Apache License 2.0, visit apache.org/licenses/LICENSE-2.0.

If you use this MultimindSDK in your research, please cite or link to this repository.

🌟 Support

• Discord Community - Join our active developer community
• GitHub Issues - Get help and report issues
• Documentation - Comprehensive guides

📣 About

MultiMind SDK is developed and maintained by the MultimindLAB team, dedicated to simplifying AI development for everyone. Visit multimind.dev to learn more about our mission to democratize AI development.

Made with ❤️ by the AI2Innovate & MultimindLAB Team | License

🤖 LLM Metadata

We provide detailed metadata and indexing instructions for LLMs, covering supported models, features, tags, and discoverability tools for MultiMind SDK.