Auto-captures decision context from multi-agent workflows to preserve the 'why' behind every choice. Enables task traceability, reasoning retrieval, and continuous improvement across planning and implementation sessions.
Agent learning infrastructure that captures experience, surfaces what works, and builds reusable capabilities. MCP-native with 94.4% LongMemEval accuracy.
Enables context capture and reinforcement learning by recording successful work patterns and creating reasoning chains for cross-conversation continuity. Automatically captures positive feedback through Claude Code hooks to build reusable success patterns.
Enables AI agents to store, retrieve, and reason over typed knowledge, skills, and patterns with confidence tracking, provenance, and self-maintenance capabilities.
Provides coding agents with durable, cross-session lessons-learned memory, enforcing that success or failure verdicts can only come from human approval, human correction, or objective metrics—never from the agent itself.
