# NeuroCore ## Context NeuroCore is a modern AI framework for building context-aware AI applications. It provides essential building blocks for creating sophisticated systems with memory, context management, reasoning capabilities, and plugin-based extensibility. The project is currently in early development (v0.0.1). ## Main Content NeuroCore aims to solve common challenges in building advanced AI applications by providing a modular, well-structured framework with components that work together to create more intelligent, context-aware AI systems. ### Core Philosophy The framework follows several key design principles: 1. **Modularity**: Components are designed to work independently but integrate seamlessly 2. **Abstraction**: Provider-agnostic interfaces that work across different LLM vendors 3. **Explainability**: Making AI reasoning and decisions transparent and inspectable 4. **Extensibility**: Designed for customization and extension with plugins 5. **Context-awareness**: Deep integration of memory, knowledge, and user context ### Key Components  1. **Memory System** - Stores and retrieves information with semantic search - Supports different memory types (messages, facts, knowledge, etc.) - [[202504121408 - AI Memory Systems]] 2. **Context Builder** - Intelligently selects relevant context for LLM prompts - Prioritizes different context sources based on relevance - Manages token limitations efficiently 3. **Reasoning System** - Implements structured reasoning methods (Chain of Thought, etc.) - Represents reasoning as a graph with steps and connections - Supports task planning for complex problems - [[202504121408 - Reasoning Methods in AI]] - [[202504121408 - Chain of Thought Reasoning]] 4. **Model Context Protocol (MCP)** - Intent-based system for AI interactions - Abstracts provider-specific implementations - Routes requests to appropriate handlers - [[202504121408 - Model Context Protocol]] 5. **Action System** - Defines and executes concrete operations - Provides authorization and validation - Creates a standardized interface for actions 6. **RAG System** - Enhances responses with knowledge retrieval - Preprocesses and chunks documents - Enables semantic search across knowledge bases 7. **Goal Management** - Tracks objectives for agents and users - Monitors progress toward goals - Integrates with reasoning and context systems ### Current Implementation Status - ✅ Chain of Thought Reasoning - Fully implemented - ✅ Anthropic Provider - Fully implemented - ✅ Model Context Protocol - Core functionality implemented - 🚧 Memory System - Basic functionality implemented - 🚧 Context Builder - Works with limitations - 🚧 Action System - Core functionality works - 🚧 RAG System - Basic implementation - 🚧 Goal Management - Core tracking works - 📝 Additional Reasoning Methods - Planned - 📝 OpenAI Provider - Planned - 📝 Relationship System - Planned ## References & Links - [[projects/NeuroCore]] - Project showcase page - [[202504121408 - Chain of Thought Reasoning]] - CoT implementation - [[202504121408 - Model Context Protocol]] - MCP details - [[202504121408 - AI Memory Systems]] - Memory system details - [[202504121408 - Context-Aware AI Applications]] - Applications built with NeuroCore - [[202504121408 - Reasoning Methods in AI]] - Overview of reasoning methods ## Personal Insights NeuroCore represents an important step toward more structured, maintainable AI application development. While many AI applications today are built as one-off implementations with limited reuse, NeuroCore brings software engineering best practices to AI development through modularity, abstraction, and standardized interfaces. The focus on context-awareness is particularly valuable, as it addresses one of the key limitations of current AI systems: their inability to maintain consistent context across interactions and leverage relevant past information. The reasoning system's approach to making thinking explicit rather than treating AI as a black box also contributes significantly to making AI more trustworthy and explainable. ## Questions & Ideas - How might NeuroCore be extended to support multi-agent systems where multiple AI entities collaborate? - What performance considerations come into play with the additional abstraction layers? - How could the framework be adapted for edge deployment with smaller models? - What opportunities exist for standardizing prompt engineering best practices within the framework? - How might the system evolve to support fine-tuning and adaptation based on user feedback? ## Related Topics - AI Application Frameworks - Software Architecture for AI Systems - Context-Aware Computing - Modular AI Design - Provider Abstraction - AI Memory Management