MemoTrail – Persistent memory for AI coding assistants (100% local) (github.com)

MemoTrail has been unveiled as a groundbreaking persistent memory layer for AI coding assistants, aimed at addressing the common issue of forgetting context between sessions. Traditionally, AI coding tools, like Claude Code, lack the ability to remember past interactions, often leading to inefficiencies as users have to reiterate previous decisions and context. With MemoTrail, every session is recorded, decisions are searchable, and meaningful context is retained. This means that when a user queries about future architectural choices, the AI can provide relevant details from past sessions, enhancing productivity and collaboration. The significance of MemoTrail lies in its local, secure architecture—ensuring that all data remains on the user's machine without relying on the cloud. The system employs the all-MiniLM-L6-v2 model for embedding conversations, storing vector representations in ChromaDB along with metadata in SQLite. This allows for efficient and semantic searches across thousands of coding sessions, enabling developers to quickly retrieve discussions or decisions made previously. MemoTrail operates seamlessly within the Claude Code environment, offering a suite of features such as session summarization and decision extraction, ultimately transforming the way developers interact with AI coding assistants.