TinyBrain

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TinyBrain is a robust, security-focused memory storage system designed to provide Large Language Models (LLMs) with persistent, intelligent memory capabilities via the Model Context Protocol (MCP). It specializes in managing and organizing information crucial for tasks such as security code review, penetration testing, and exploit development. With features like specialized categories for vulnerabilities and exploits, priority/confidence tracking, advanced relationship mapping, and AI-enhanced semantic search, TinyBrain ensures LLMs have access to context-aware, highly relevant data. Its SQLite backend guarantees high performance and reliability, while a comprehensive set of 40 MCP tools provides extensive control over memory, session, and task management, all aligned with industry security standards.

Características Principales

• Security-Focused Design with specialized categories (e.g., vulnerability, exploit) and standards compliance (OWASP, CWE, NIST).
• Intelligent Memory Management offering context-aware storage, advanced multi-strategy search, and context summaries.
• High Performance and Reliability via an SQLite backend with optimized queries, transaction safety, and concurrent access.
• AI-Enhanced Search and Intelligence, including semantic search, embedding generation, and similarity calculations for conceptual understanding.
• Real-Time Notifications and Alerts for memory events, high-priority items, and duplicate detection.
• 1 GitHub stars

Casos de Uso

• Supporting penetration testing operations with context-aware memory and multi-stage task progress tracking.
• Aiding exploit development by mapping relationships between vulnerabilities, exploits, and payloads for comprehensive understanding.
• Enhancing LLM capabilities for security code review by providing relevant vulnerability and exploit data.