Enables AI agents to store, retrieve, and self-improve procedural memories (lessons learned) based on relevance to the current task, pruning unused memories to reduce context load and prevent repetition of past mistakes.
Verified memory for AI agents — agents propose memories that are quarantined until verified against evidence, and recall() returns only trusted, fresh, and in-scope facts, preventing poisoned or hallucinated data from spreading.
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.
Enables AI agents to learn from their work by recording tasks, extracting patterns, detecting mistakes, and proactively surfacing insights, all using the agent's own model through a cooperative intelligence pattern.
Agent learning infrastructure that captures experience, surfaces what works, and builds reusable capabilities. MCP-native with 94.4% LongMemEval accuracy.
