Agent Memory Optimizerv1.0.0

A solo developer runs a persistent AI assistant that writes daily memory notes into memory/*.md, causing context bloat and rising API costs. Running the optimizer monthly archives notes into memory/archive/YYYY-MM/ while keeping active prompts lean. The assistant stays responsive without manual file cleanup.

A SaaS support team uses an AI agent that logs each day's conversation summaries as markdown files for continuity. Over time, the active memory directory slows retrieval and inflates prompt tokens. The optimizer monthly archive keeps only current-month notes active, reducing token overhead and maintaining clean operational directories.

An academic researcher uses an OpenClaw agent to capture daily literature notes and experiment logs as markdown. The growing memory folder degrades prompt efficiency and clutters context. Scheduling the optimizer to archive older notes preserves recent context while keeping historical records organized in monthly folders for later retrieval.

A content marketer runs a daily AI agent that drafts and records editorial notes, trends, and drafts in memory markdown files. Without cleanup, the agent's prompt context becomes stale and expensive. A weekly dry-run then monthly archive operation keeps active memory focused on the current month's content tasks.

An engineering team deploys an on-call AI agent that writes incident summaries and runbook notes daily to memory. Retaining all daily logs in active context increases prompt costs and slows response times. Running the optimizer after each month archives older incident notes, keeping on-call context tight and relevant.

The core optimizer script is published free on ClawHub under an open-source license, driving adoption among OpenClaw users. A paid hosted tier offers scheduled archiving, retention policies, multi-workspace dashboards, and audit logs for teams that don't want to manage cron jobs themselves.

Instead of selling the script alone, bundle it into a broader managed memory service that automates archiving, retrieval indexing, and token-usage analytics across many agents. The optimizer acts as the ingestion and cleanup pipeline, while the platform charges for storage, search, and observability.

Offer the optimizer as part of a consulting package where experts audit a company's agent memory setup, configure archiving policies, and integrate token-usage reporting. The free skill serves as a lead magnet, while implementation, training, and custom retention-policy work generate service revenue.