MCP server for persistent, compounding memory that automatically captures corrections and insights across AI sessions, enabling agents to learn and improve over time.
An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.
MCP server that captures and recalls coding session memory (failures, decisions, diffs) for AI agents, enabling cross-agent continuity and preventing repeated mistakes.
MCP server enabling AI agents to submit plans and designs for human review, with append-only decision records that store both approvals and rejections, so agents can query past decisions before proposing new changes.