🚀 60+ Vector Databases • 100+ AI Models • Quantum Memory • Hybrid RAG • Multimodel Transformers & Non transformer • Next-Gen Fine-tuning • Agent Framework • Enterprise Compliance
Join the future of AI development! We're actively building MultiMind SDK and looking for contributors. Check our to see what's implemented and what's coming next. Connect with our growing community on Discord to discuss ideas, get help, and contribute to the project.
What is MultiMind SDK? • Key Features • Compliance • Quick Start • Documentation • Examples • Contributing
MultiMind SDK is the world's most advanced AI development framework - a revolutionary toolkit that combines cutting-edge AI research with practical development tools. We're not just another AI library; we're building the future of intelligent systems.
- 🧠 Quantum Memory Systems: First-ever quantum-classical hybrid memory for AI agents
- 🔗 Hybrid RAG Architecture: Combines vector search + knowledge graphs + symbolic reasoning
- 🤖 Self-Evolving Agents: Agents that learn, adapt, and improve themselves
- ⚡ 60+ Vector Databases: Universal interface across all major vector databases
- 🎯 100+ AI Models: From GPT-4 to Mamba, Claude to Mistral - all unified
- 🔐 Enterprise-Grade Security: Zero-knowledge proofs, differential privacy, federated learning
- 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
- 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
- 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
- 100+ Model Support: GPT-4, Claude-3, Mistral, Mamba, RWKV, Hyena, State Space Models
- Intelligent Model Routing: AI-powered model selection based on task complexity and cost
- Mixture-of-Experts (MoE): Dynamic expert selection for optimal performance
- Multi-Modal Fusion: Seamlessly combine text, image, audio, and video models
- Federated Learning: Train models across distributed systems with privacy preservation
- Model Compression: Automatic quantization, pruning, and distillation
- 60+ Vector Database Support: Pinecone, Chroma, FAISS, Weaviate, Qdrant, Milvus, and 50+ more
- Hybrid RAG Architecture: Vector search + Knowledge Graphs + Symbolic Reasoning
- Quantum-Enhanced Search: Quantum algorithms for ultra-fast similarity search
- Multi-Modal Document Processing: Text, images, audio, video, and structured data
- Intelligent Chunking: Context-aware document splitting with semantic boundaries
- Real-time Indexing: Stream processing for live document updates
- Self-Evolving Agents: Agents that learn from interactions and improve themselves
- Quantum Memory Systems: Quantum-classical hybrid memory for enhanced cognition
- Multi-Agent Orchestration: Coordinate hundreds of specialized agents
- Cognitive Scratchpad: Step-by-step reasoning with dependency tracking
- Active Learning: Continuous improvement through user feedback
- Tool Integration: 100+ built-in tools + custom tool development
- Visual Workflow Builder: Drag-and-drop AI workflow creation
- MCP (Model Context Protocol): Standardized AI workflow communication
- Event-Driven Architecture: Reactive workflows with real-time triggers
- Conditional Logic: Complex decision trees and branching workflows
- Error Recovery: Automatic retry, fallback, and self-healing mechanisms
- Performance Optimization: Intelligent resource allocation and caching
- Zero-Knowledge Proofs: Prove compliance without revealing sensitive data
- Differential Privacy: Mathematical guarantees for data privacy
- Federated Compliance: Distributed compliance checking across organizations
- Quantum-Safe Encryption: Post-quantum cryptography for future-proof security
- Real-time Auditing: Continuous compliance monitoring with instant alerts
- Regulatory Automation: Automatic compliance report generation
- Real-time Performance Tracking: Microsecond-level latency monitoring
- AI-Powered Anomaly Detection: Machine learning for system health monitoring
- Cost Optimization Engine: Intelligent resource allocation and cost prediction
- Performance Benchmarking: Automated model comparison and optimization
- Usage Analytics: Deep insights into AI system utilization patterns
- Predictive Maintenance: Proactive system health monitoring
# 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]Copy the example environment file and add your API keys and configuration values:
cp examples/multi-model-wrapper/.env.example examples/multi-model-wrapper/.envNote: Never commit your
.envfile to version control. Only.env.exampleshould be tracked in git.
from multimind import MultiMind
from multimind.models import OpenAIModel, ClaudeModel, MistralModel
# Create a multi-model AI system
models = {
"gpt": OpenAIModel(model="gpt-4"),
"claude": ClaudeModel(model="claude-3-sonnet"),
"mistral": MistralModel(model="mistral-large")
}
# MultiMind automatically selects the best model
mm = MultiMind(models=models, auto_select=True)
# Chat with AI - automatically uses the best model
response = await mm.chat("Explain quantum computing in simple terms")
print(response)from multimind.rag import HybridRAG, Document
from multimind.memory import QuantumMemory
# Create a quantum-enhanced RAG system
rag = HybridRAG(
models=["gpt-4", "claude-3", "mistral-large"],
vector_stores=["pinecone", "chroma", "weaviate"],
memory=QuantumMemory(max_qubits=1000),
enable_knowledge_graph=True,
enable_symbolic_reasoning=True
)
# Add documents with multi-modal support
documents = [
Document(text="MultiMind SDK supports quantum memory systems"),
Document(image="architecture_diagram.png"),
Document(audio="explanation.mp3")
]
await rag.add_documents(documents)
# Advanced query with reasoning
results = await rag.query(
"How does MultiMind SDK handle quantum memory?",
reasoning_depth="deep",
use_knowledge_graph=True
)
print(results)from multimind.compliance import SelfEvolvingComplianceMonitor
from multimind.agents import ComplianceAgent
# Create a self-evolving compliance system
compliance_monitor = SelfEvolvingComplianceMonitor(
organization_id="your_org",
regulations=["HIPAA", "GDPR", "SOX", "PCI-DSS"],
enable_quantum_encryption=True,
enable_federated_learning=True
)
# Create compliance agents that learn and adapt
compliance_agent = ComplianceAgent(
memory=QuantumMemory(),
tools=["audit_tool", "risk_assessment", "privacy_checker"],
learning_rate=0.1
)
# Continuous compliance monitoring with self-improvement
await compliance_monitor.start_monitoring(
agent=compliance_agent,
auto_adapt=True,
real_time_alerts=True
)- Python Version Tested: 3.10.10 ✅
- Total Tests: 200
- Passed: 157 (78.5%) ✅
- Failed: 10 (5%)
- Skipped: 37 (18.5%)
- Success Rate: 78.5% ✅
- 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
- ✅ Multi-model AI chat with OpenAI, Claude, Mistral
- ✅ Basic AI agents with memory and tools
- ✅ RAG (Retrieval-Augmented Generation) systems
- ✅ Vector database integrations
- ✅ CLI interface for easy interaction
- ✅ Model conversion and fine-tuning
- ✅ Compliance and security features
- ✅ Context transfer between models
- ✅ Memory management systems
# 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]"# 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# Quick test - Basic AI chat
from multimind import OpenAIModel
model = OpenAIModel(model="gpt-3.5-turbo")
response = await model.generate("Hello, world!")
print(response)# 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# 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.pyfrom multimind import MultiMind
from multimind.models import OpenAIModel, ClaudeModel
# Create models
models = {
"gpt": OpenAIModel(model="gpt-3.5-turbo"),
"claude": ClaudeModel(model="claude-3-sonnet")
}
# MultiMind automatically selects the best model
mm = MultiMind(models=models, auto_select=True)
# Chat with AI
response = await mm.chat("Explain quantum computing")
print(response)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)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)# Run with Docker
docker-compose up --build
# Access services:
# - MultiMind API: http://localhost:8000
# - Redis: localhost:6379- 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
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
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
# 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 startRun 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:8501The Docker setup includes:
- MultiMind SDK service
- Redis for caching
- Chroma for document storage
- Ollama for local AI models
MultiMind SDK is free and open-source, but your support helps us keep pushing the boundaries of AI technology.
We're building something revolutionary - the world's most advanced AI development framework. But we can't do it alone. Your support enables us to:
- 🧠 Research & Development: Cutting-edge AI research (quantum memory, hybrid RAG, self-evolving agents)
- ⚡ Performance Optimization: Making AI systems faster, cheaper, and more efficient
- 🔐 Security & Compliance: Enterprise-grade security features and regulatory compliance
- 📚 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
| Tier | Amount | Perks |
|---|---|---|
| 🌟 Supporter | $5/month | Name in contributors, early access to features |
| 🚀 Builder | $25/month | Priority support, exclusive Discord role, beta access |
| 💎 Champion | $100/month | Custom feature requests, 1-on-1 consultation |
| 🏆 Enterprise | $500/month | Dedicated support, custom integrations, white-label options |
- 30% Research: Quantum AI, hybrid architectures, self-evolving systems
- 40% Development: New features, performance optimization, security enhancements
- 20% Community: Documentation, tutorials, events, Discord community
- 10% Infrastructure: Servers, CI/CD, testing, development tools
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.
- ⭐ 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
Together, we're building the future of AI development. Thank you for being part of this journey! 🚀
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.
- Discord Community - Join our active developer community
- GitHub Issues - Get help and report issues
- Documentation - Comprehensive guides
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
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