Cut OpenClaw Boot Tokens 43% by Slimming Tool & Memory Files
A user on r/openclaw slimmed their agent's boot context from ~9,457 to ~5,400 tokens — a 43% reduction — by cleaning up TOOLS.md and MEMORY.md.
Problems Found
- TOOLS.md bloat: Every tool added usage notes, commands, examples, and edge cases. Useful but not needed every session.
- Memory promotion overgrowth: Promotion candidates were added directly to main memory, causing
MEMORY.mdto grow indefinitely.
Fixes Applied
- Changed
TOOLS.mdto an index (auto-injected, so can't remove). Detailed notes moved totools/; agent reads them only when that tool is invoked. - New memory flow:
daily notes → promotion candidates → curated long-term memory. Promotion candidates go to a separate file; main memory holds only a pointer. Only durable facts needed often stay in boot memory.
Results
After: same agent, same tools, same behavior — just less loaded on every wake-up.
📖 Read the full source: r/openclaw
👀 See Also
Running MiniMax M2.7 Q8_0 128K on 2x3090 with CPU Offloading – Real-World Benchmarks and Config
A user successfully runs MiniMax M2.7 at Q8_0 with 128K context on two RTX 3090s plus DDR4 RAM, achieving ~50 tps prompt processing and ~10 tps token generation, and shares their llama-server flags.
Claude Code: Context Management Over Prompt Engineering
A developer shares that after a year of using Claude Code, the key skill isn't prompt wording or model selection, but providing comprehensive project context upfront to get better results.
Cron Jobs with AI Fallback Can Incur Unexpected API Costs When Tools Hang
A user reported that a cron job in OpenClaw checking an email inbox every 10 minutes using himalaya burned through ~$60 in API credits when the IMAP connection started hanging, triggering Claude agents on each timed-out run despite instructions to only engage AI for inbound emails.
Automated QA and Testing with AI: A New Era for Software Testing
antirez describes using LLM agents for automated QA by writing a markdown file that instructs the agent to perform manual testing on new releases. Applied to DwarfStar and Redis Arrays, this approach raises software quality without compromising on thoroughness.