Backpass is a tool that treats agent memory files as trainable weights, using session transcripts as loss signals to propose evidence-backed edits to AGENTS.md and project skills. It operates locally-first without APIs, requires human approval for all changes, and integrates with Claude Code and Codex harnesses via authenticated model calls.