This project is currently under development and will be updated regularly including this readme file. Stay tuned for an installation guide, Web UI, and more exciting features!

Introduction • System Architecture • Capabilities • Development Process • Future Enhancements

The project now ships with an experimental Model Context Protocol (MCP) server that exposes a thin wrapper around the Notion REST API. This allows large language models to search, read, and update Notion content in a structured and auditable way.

• The server is implemented with FastAPI and lives in pluggins/notion_mcp/server.py.
• A lightweight NotionClient centralizes request signing, error handling, and frequently used API calls.
• An /mcp/invoke endpoint accepts an action plus parameters, executes the corresponding Notion call, and returns a normalized payload so that tool-using LLMs can reason about the results.

1. Create a Notion integration and copy its Internal Integration Token.
2. Set the environment variable: export NOTION_API_KEY=<token>.
3. Start the server with python -m pluggins.notion_mcp.server --host 0.0.0.0 --port 8080.
4. Send MCP requests such as:

   curl -X POST http://localhost:8080/mcp/invoke \
     -H "Content-Type: application/json" \
     -d '{
           "action": "query_database",
           "parameters": {
             "database_id": "<database>",
             "page_size": 5
           }
         }'
       

The MCP surface currently supports common workflows (searching, listing databases, retrieving pages, creating or updating pages, and appending blocks) and is designed so additional Notion actions can be registered with minimal boilerplate.

Quantum HiveMind is an ambitious artificial intelligence project focused on creating a next-generation AI system capable of autonomous operation, efficient parallel processing, and complex coordination between specialized modules.

The overarching vision is to develop an advanced AI agent that can perceive its environment, reason about options, make decisions, and take actions to accomplish goals with minimal human intervention. Key characteristics of the system include:

• Autonomy: The system will be driven by self-determined goals and exhibit a high degree of self-directed behavior. It will take proactive actions based on internal reasoning and motivations.
• Efficiency: The system will leverage parallel processing capabilities to optimize speed and throughput. Asynchronous processes, concurrency, and distributed architectures will be employed.
• Coordination: Various modules with specialized capabilities will seamlessly coordinate processing. This requires complex communication protocols, task assignment, and resource allocation mechanisms.
• Adaptability: The system will continuously learn from new data and experiences. This will allow it to adapt to changing conditions and improve its performance over time.
• Resilience: The system will implement robust error handling, redundancy, and fail-over mechanisms to ensure continuity of operations.

To realize this vision, cutting-edge AI techniques will be utilized, including state-of-the-art large language models, knowledge graphs, optimization algorithms, and predictive modeling.

This document serves as the technical specification guiding the design, architecture, capabilities, implementation, and testing of the Quantum HiveMind system. It establishes requirements, proposes structural organization, outlines development processes, and defines the scope for current and future capabilities.

The specification will evolve over time as the project progresses through iterative prototyping and incremental refinement. It is intended to provide a comprehensive technical reference for all contributors to ensure a shared understanding of the system.

• Powered by state-of-the-art GPT-4 architecture
• Responsible for high-level reasoning, strategic planning, priority setting
• Assigns tasks and sub-goals to Worker Bees based on capabilities
• Receives and analyzes results from Worker Bees
• Maintains operation logs and system health metrics
• Leverages short-term memory for task context
• Leverages long-term memory via Knowledge Graph
• Implements redundancy and fail-over mechanisms
• Contains extensive error handling logic

• Encapsulated modules focused on specific capabilities
• Operate asynchronously for optimal parallelization
• Leverage threading and multiprocessing
• Examples: data retrieval, text generation, web scraping
• Designed for high performance and scalability
• Expose common interface to receive tasks from Queen
• Return results and status codes back to Queen Bee

• Powered by graph database like Neo4j or Stardog
• Stores entities and relationships relevant to the AI system
• Enables complex reasoning by Queen Bee
• Maintains long-term memory and knowledge

• Relational database like PostgreSQL
• Stores transient data like tasks, logs, metrics
• Enables data analysis and insight generation

• Enables external software integration via APIs
• Swagger/OpenAPI documentation for discoverability
• Authentication and access control capabilities
• Caching and rate limiting features
• Versioning to maintain backwards compatibility

• Python 3.11 runtime
• Utilizes async/await for asynchronous operations
• Leverages multi-threading and multi-processing
• Structured into packages/modules for organization
• Containerization via Docker
• Encapsulates dependencies and configurations
• Simplifies deployment and orchestration
• Promotes reproducibility and portability
• Graphviz diagrams
• Visualize architecture for documentation
• Diagram component interactions and data flows
• GPT-4 integration
• Leverage callable API provided by Anthropic
• Integrate stateful context for continuous operation
• Incremental development
• Start with core Queen Bee module
• Gradually build out additional components
• Enables iterative refinement

• Strategic reasoning and planning
• Dynamic optimization of resource allocation
• Natural language task assignment
• Measurement and tracking of KPIs
• Time-series analysis of performance data
• Root cause analysis for errors
• Automated failure and recovery mechanisms

• Information retrieval from web and APIs
• Data scraping, cleansing, transformation
• Data visualization and dashboarding
• Image and video processing
• Document summarization
• Text generation and translation
• Anomaly detection algorithms
• Predictive modeling
• Predictive modeling and forecasting

• Kanban boards via Trello
• Track development status
• Visualize work in progress
• GitHub for version control
• Branch management
• Code reviews and approvals
• Issue tracking
• Documentation
• Architecture diagrams
• API documentation
• Operations guide
• Workflow diagrams
• Testing
• Unit testing for components
• Integration testing
• Performance and load testing
• Automated regression testing

• Ideas for extending capabilities:
• Semantic analysis
• Optimization algorithms
• Improved reasoning
• Potential additional features:
• Mobile application
• Conversational interface
• Reporting dashboard
• Simulation environment