Real Problems After Using OpenClaw for 30 Days

By 小陈师傅c_z

Duration: 20:00

player.exe -v "real-problems-postmortem"

overview.md

An honest 30-day postmortem from a developer who lived with OpenClaw daily. No hype, no demo magic — just the real problems encountered: memory leaks, unexpected API bills, skill conflicts, and the hard-won lessons from a month of autonomous agent debugging.

memory_bills_conflicts.md

Memory Leaks, API Bills & Skill Conflicts

The first problem surfaced on day three: a memory leak. The vector database kept growing as the agent stored every observation, every thought, every intermediate result. By week two, retrieval was slow and noisy — the agent was pulling in irrelevant memories and making worse decisions. The fix involved aggressive pruning and setting a retention window, but it was a manual, painful process.

The second shock was the API bill. An autonomous agent in a reasoning loop makes far more LLM calls than a chatbot. A single complex task — debugging a failing test suite — burned through 40 reasoning iterations. At $0.005 per call, that's $0.20 per task. Run that 50 times a day and you're at $300/month before you notice.

When Skills Collide

The third problem was skill conflicts. The creator had installed a "file_manager" skill and a "git_ops" skill, both of which could read and write files. When asked to "commit the changes," the agent sometimes invoked file_manager to manually copy files instead of using git. The descriptions overlapped just enough to confuse the agent's skill selection logic.

// 30-day cost & issue log

Day 3:

Memory DB hit 2GB, retrieval latency > 5s, decision quality degraded

Day 14:

API bill reached $180 — autonomous loops burning tokens invisibly

Day 21:

Skill conflict: agent used file_manager instead of git_ops for commits

Day 30 Fix:

Pruning cron job, per-task token budget, explicit skill priority config

lessons_learned.md

What I Learned

The creator's lessons are practical and unsentimental. First: set a token budget per task and hard-stop the agent when it's exceeded. Second: prune the memory database on a schedule — old observations are noise, not knowledge. Third: namespace your skills and write descriptions that don't overlap. If two skills can do the same thing, the agent will get confused.

The final takeaway: OpenClaw is powerful but it is not set-and-forget. It's a system that demands operational discipline — monitoring, budgeting, pruning, and careful skill design. Treat it like a service you operate, not a magic box you deploy. Do that, and it genuinely transforms how you work. Don't, and you'll learn these lessons the expensive way.

$ ssh clawdbot.space --loading...