Coil

AI coding agents often struggle with retaining information between sessions, leading to repetitive debugging and a lack of consistent context. Traditional memory solutions, whether static instruction files or semantic search-based MCP servers, fall short by either requiring manual maintenance, generating false-positive retrievals, lacking a quality signal for useful information, or suffering from cross-project contamination. Coil addresses these critical issues by implementing a structured memory system that leverages typed schemas, allowing only relevant information to be stored, and structured SQL queries against a local SQLite database for precise, context-aware retrieval. This is augmented by a unique usage-based utility scoring mechanism that automatically promotes proven knowledge and filters out noise, ensuring agents recall what truly matters.

主な機能

ユースケース