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MultiMind SDK - Development Roadmap

This roadmap outlines our development priorities and future features. For current feature status, see FEATURES.md.

🎯 Current Status (July 2026)

✅ What Works Today

  • Core AI model integrations (OpenAI, Claude, Ollama)
  • RAG pipelines with FAISS and Chroma
  • Basic AI agents with tools (keyword-based tool routing)
  • Non-transformer inference: Mamba and RWKV (HuggingFace-backed)
  • CLI interface
  • Basic memory management
  • Compliance policy modeling (GDPR/HIPAA) with audit trails

🚧 In Active Development

  • Additional LLM providers via OpenAI-compatible APIs
  • Enhanced RAG features
  • Advanced memory systems
  • Fine-tuning improvements

⚠️ Honest Status Note

A July 2026 audit found ~44 of the ~60 vector-store backends are real client-library-backed implementations; the 11 remaining stubs now raise NotImplementedError instead of silently failing (see FEATURES.md for the full breakdown).

📋 Planned Features

🔴 High Priority

Vector Database Expansion

  • Goal: Implement the most popular vector databases; carried over from 2025, still unimplemented
  • Priority Backends:
    • MongoDB Atlas (vector search)
    • Neo4j Vector
    • OpenSearch Vector Search
    • Supabase Vector
    • LanceDB (full implementation)
    • DeepLake
    • Azure Cognitive Search
    • AWS OpenSearch
    • Google Vertex AI Matching Engine
    • Milvus (advanced features)
    • Weaviate (advanced features)
    • Qdrant (full feature set)
    • Pinecone (full feature set)

Advanced RAG Features

  • Hybrid search (vector + keyword/BM25)
  • Knowledge graph integration
  • Multi-modal document processing
  • Advanced chunking strategies
  • Query optimization and reranking
  • Real-time indexing

Enhanced Agent Framework

  • Multi-agent orchestration improvements
  • Advanced tool integration
  • Agent-to-agent communication
  • Hierarchical agent systems

Memory System Improvements

  • Graph-based memory (knowledge graphs)
  • Temporal memory with timestamps
  • Memory deduplication
  • Memory merging and conflict resolution
  • Memory scoring and relevance ranking

🟡 Medium Priority

Advanced Fine-Tuning

  • QLoRA implementation
  • Advanced optimization techniques
  • RAG fine-tuning with synthetic data
  • Hyperparameter optimization
  • Multi-task fine-tuning

Compliance & Security Enhancements

  • Differential privacy implementation
  • Homomorphic encryption (current code is a placeholder; not working today)
  • Zero-knowledge proofs (current code is a placeholder; when dependencies available)
  • Regulatory change detection
  • Advanced audit logging
  • Self-healing compliance systems

Monitoring & Observability

  • Real-time performance tracking
  • Cost optimization engine
  • AI-powered anomaly detection
  • Predictive maintenance
  • Advanced analytics dashboard

Workflow & Orchestration

  • Visual workflow builder
  • Event-driven architecture
  • Advanced error recovery
  • Workflow templates
  • YAML/JSON workflow definitions

🟢 Future Innovations

Experimental Features

  • Quantum Memory (real quantum hardware integration)
    • Note: Current implementation is classical simulation only
    • Requires access to quantum computing hardware
    • Research phase

Advanced Features

  • Self-evolving agents with learning mechanisms
  • Federated learning support
  • Advanced model compression
  • Model watermarking
  • Multi-modal fusion improvements

Enterprise Features

  • Enterprise integration hub
  • Plugin system (Slack, Notion, Salesforce)
  • Database connectors
  • Real-time data integration (Kafka, MQTT)
  • Edge deployment toolkit

Developer Experience

  • No-code visual builder
  • Agent marketplace
  • Enhanced documentation
  • Interactive tutorials
  • Developer tools and debuggers

🚫 Removed from Roadmap

These features were claimed in the README but are not feasible or will not be implemented:

  • ❌ 60+ Vector Databases - Original claim retracted; a 2026 audit confirmed ~44 working client-backed backends, with the core ~18 recommended for production
  • ❌ Quantum Memory (Hardware) - Requires quantum hardware access. Keeping simulation only for educational purposes
  • ❌ 100+ AI Models - Focusing on quality over quantity. Supporting major providers and popular models
  • ❌ Self-Evolving Agents (Fully Autonomous) - Research phase, not production-ready
  • ❌ Zero-Knowledge Proofs (Full Implementation) - Dependent on external library support

📊 Development Priorities

Phase 1: Stability (current, H2 2026)

  1. Truthful docs and honest feature status
  2. Stabilize the core: providers, RAG pipeline, agents
  3. Improve test coverage
  4. Fix bugs and improve error handling

Phase 2: Expansion (after Phase 1)

  1. Additional providers and vector database backends
  2. Enhance agent framework
  3. Improve memory systems
  4. Advanced fine-tuning features

Phase 3: Enterprise (later)

  1. Compliance enhancements
  2. Monitoring and observability
  3. Workflow automation
  4. Enterprise integrations

Phase 4: Innovation (future)

  1. Experimental features
  2. Research integrations
  3. Advanced capabilities
  4. Developer experience improvements

The Phase 2-4 items were originally scheduled for 2025 and did not ship on that timeline. Phases are now sequenced by dependency, not by calendar quarter.

🤝 Contributing to the Roadmap

We welcome contributions! If you'd like to work on any of these features:

  1. Check FEATURES.md for current status
  2. Review CONTRIBUTING.md for guidelines
  3. Open an issue or discussion to coordinate
  4. Submit a pull request

📝 Notes

  • Realistic Timeline: We're committed to honest, realistic timelines
  • Quality Over Quantity: Better to have fewer, well-implemented features
  • Community Driven: Roadmap evolves based on community needs
  • Transparency: We'll update this roadmap as priorities change

Last Updated: July 2026
Next Review: Quarterly