Stop your AI agents from
solving the same problems twice

• Captures reasoning that works When an agent solves a task successfully, we extract the reasoning strategy — not just the answer.
• Pattern-matches new tasks New tasks are compared against stored patterns to find relevant prior solutions.
• Injects proven paths Before your agent explores from scratch, it receives context about how similar problems were solved.
• Reduces costs and errors Fewer wasted LLM calls. Fewer dead ends. More consistent outcomes.

Currently focused on coding agents (Cursor, Devin, Claude Code, internal tools).

1. New task arrives Your agent receives a coding task, bug fix, or feature request.
2. Pattern match against stored reasoning We compare the task signature against successfully completed prior tasks.
3. Inject relevant strategy If a match is found, the reasoning pattern is added to the agent's context.
4. Execute and store success Successful completions are captured for future reuse.

Benchmarked on coding, debugging, and math tasks with Claude Sonnet 4. Performance varies by use case.