Optimization suite for OpenClaw agents to prevent token leaks and context bloat. Use when an agent needs to implement background task isolation (Cron) or a Reset & Summarize workflow (RAG).

Token Optimizer Skill This skill provides the procedural knowledge to keep your OpenClaw instance lean and efficient. Quick Reference Problem Solution Background tasks bloating context Cron isolation ( sessionTarget: "isolated" ) Reading entire history every turn Local RAG with memory search Context exceeds 100k tokens Reset & Summarize protocol Finding old conversations Session transcript indexing Workflow 1: Periodic Task Isolation To prevent background tasks from bloating your main conversation context, always isolate them. Steps 1. Locate your openclaw.json config. 2. In the cron.jobs array, set sessionTarget: "isolated" for any task that doesn't need to be part of the main chat history. 3. Use the message tool within the task's payload if human intervention is required. Example Config Key Points sessionTarget: "isolated" runs the task in a separate, transient session Use deliver: true to send results back to the main channel Isolated sessions don't pollute your main context with heartbeat/check history Workflow 2: Reset & Summarize (The "Digital Soul" Protocol) When your context usage (visible via 📊 session status ) exceeds 100k tokens, perform a manual consolidation. Steps 1. Check Context : Run 📊 session status to see current token usage 2. Scan History : Review the current session for new facts, preferences, or project updates 3. Update MEMORY.md : Append these new facts to your long term memory file 4. Daily Log : Ensure memory/YYYY MM DD.md is up to date with today's events 5. Restart : Run openclaw gateway restart to clear the active history When to Trigger Context 100k tokens Session running for several days Noticeably slower responses User explicitly requests a "fresh start" Workflow 3: Local RAG Configuration For efficient recall without token burn, configure local embeddings. Configuration ( openclaw.json ) Usage Use memory search to retrieve context from your logs instead of loading everything: The tool returns relevant snippets with file paths and line numbers. Use memory get to pull specific sections. Workflow 4: Session Transcript Indexing (Advanced) Index your session transcripts ( .jsonl files) for searchable conversation history. How It Works OpenClaw stores session transcripts in ~/.openclaw/sessions/ . These can be indexed for semantic search, allowing you to find old conversations without loading them into context. Configuration Add transcript paths to memorySearch.extraPaths : Best Practices Index selectively (recent sessions, important conversations) Use date based filtering to limit search scope Archive old transcripts to cold storage after indexing Workflow 5: Hybrid Search (Vector + BM25) Combine semantic search with keyword matching for more accurate retrieval. Why Hybrid? Search Type Strengths Weaknesses Vector (semantic) Finds conceptually similar content May miss exact terms BM25 (keyword) Finds exact matches Misses synonyms/paraphrases Hybrid Best of both worlds Slightly more compute How to Use When memory search returns low confidence results: 1. Try the search with different phrasing (semantic variation) 2. Search for exact keywords you remember (BM25 behavior) 3. Combine results manually if needed Future Enhancement OpenClaw's RAG system may support native hybrid search in future versions. For now, run multiple queries when precision matters. Troubleshooting "My context is growing too fast" 1. Check cron jobs: Are they isolated? 2. Check heartbeat frequency: Too frequent = more tokens 3. Are you loading large files unnecessarily? "memory search returns nothing" 1. Verify memorySearch is configured in openclaw.json 2. Check that the embedding model is downloaded 3. Ensure memory files exist and have content "Restart didn't clear context" The restart clears the session history, but: System prompt is always loaded Workspace files (MEMORY.md, etc.) are injected fresh This is by design for continuity Credits Pépère (shAde) — Original concept and documentation Zayan (Clément) — Implementation and testing Built for the OpenClaw community. 🦦😸

token-optimizer