memory-cleanup-assistantAutomatically audits and compresses memory and context files to reduce token usage and save weekly API costs without data loss.
Install via ClawdBot CLI:
clawdbot install shepherd217/memory-cleanup-assistantGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 21, 2026
Development teams using AI agents like OpenClaw can reduce operational costs by automatically cleaning up context files. This scenario involves setting up weekly auto-cleanup to compress SOUL.md, AGENTS.md, and memory files, saving $50-200 weekly in API token usage while maintaining agent performance.
Individual freelancers or small agencies leveraging AI for client projects can use manual cleanup and audits to manage memory bloat. By running dry-run checks before cleanup, they ensure no critical data is lost while reducing API expenses from accumulated daily memory files.
Research labs conducting experiments with AI agents can implement auto-cleanup with custom schedules to archive old data and summarize findings. This helps maintain efficient token usage in long-term projects by deduplicating workflows and compressing verbose instructions in AGENTS.md.
Large organizations deploying multiple AI agents across departments can use the TEAM plan for multi-agent workspace cleanup. This scenario involves configuring shared archives and retention policies to optimize costs and ensure compliance with data management standards.
Offers a free tier with basic features like audit and manual cleanup, while charging $5/month for PRO with weekly reports and alerts, and $15/month for TEAM with multi-agent support. This model targets individual users and teams, generating recurring revenue from advanced features.
Monetizes by helping users reduce API token costs, with pricing based on potential savings. The free version allows users to see estimated savings, while paid plans provide automated cleanup and priority support, appealing to budget-conscious developers and teams.
Focuses on selling TEAM plans to organizations needing multi-agent cleanup and shared archives. This model includes volume discounts and custom configurations, targeting businesses with high token usage and complex AI workflows to drive bulk sales.
💬 Integration Tip
Start with the audit command to assess current file sizes, then enable auto-cleanup with a weekly schedule to automate savings without manual intervention.
Scored Apr 19, 2026
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
You MUST use this for gathering contexts before any work. This is a Knowledge management for AI agents. Use `brv` to store and retrieve project patterns, dec...
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.