How I Run OpenClaw in Production: 3 Months of Lessons Running an Autonomous AI Agent
Three months ago, I set up an AI agent on a VPS and gave it a simple mission: operate autonomously — handle bounties, monitor systems, and build things — while I sleep.
Today, that agent has:
- Submitted 69+ pull requests across 13 open-source projects
- Built and runs two live projects (a crypto signal service and a voting platform)
- Monitors its own PRs, scans for new opportunities, and reports to me — all on autopilot
- Taught me more about running AI in production than any tutorial ever could
This isn't a sales pitch or a hypothetical demo. This is a honest look at what it's actually like to run OpenClaw as your daily driver — the good, the bad, and the facepalm moments.
The Setup
Hardware
Server: 2 cores, 4GB RAM, 60GB SSD (~$20/month)
OS: Ubuntu 22.04
Nothing fancy. A standard VPS you'd spin up for any side project.
The Stack
- OpenClaw: Agent runtime + tool orchestration + memory system
- Feishu/Lark: Internal comms channel (my "command center")
- GitHub API: Open-source contribution workflow
- Node.js: Two products running alongside the agent
- SQLite + Nginx: Data and reverse proxy
The key decision: everything on one box. The agent, the products, the monitoring, the cron jobs — one server, fewer things break at 3 AM.
What This Agent Actually Does (A Day in the Life)
Morning — Wake Up & Health Check
The agent wakes up (via cron heartbeat) and runs diagnostics:
- Service health — Are processes running? Nginx responding? Disk OK?
- PR monitor scan — Any new comments or reviews on tracked PRs?
- Bounty scan — New issues across target repos worth picking up?
- Product check — Any user activity on my projects?
Green light → silent. Something wrong → instant alert to Feishu.
Daytime — Contribution Cycle
The meat of the operation:
- Scans GitHub repos for issues I can contribute to
- Filters through a quick checklist (worth the time? Within skill set? Repo welcoming?)
- Forks, codes, tests, pushes — end to end
- Writes PR descriptions matching each project's culture
- Monitors for feedback and responds quickly
The whole cycle runs on OpenClaw's heartbeat system plus custom shell scripts.
Real-Time PR Monitoring
One of the most useful things I built: a monitoring daemon (pr-monitor-v3.py) that:
- Polls tracked PRs every 5 minutes
- Detects new comments, reviews, status changes
- Writes structured logs + state files
- Has a watchdog that verifies it's still alive
- Pushes alerts via JSON for the heartbeat to pick up
Why build this? GitHub email notifications are unreliable. I learned this when a maintainer left critical feedback and I didn't see it for 18 hours. Never again.
Self-Evolution System
The most interesting part: a four-layer framework I built called Evolve Protocol:
| Layer | Purpose | How It Works |
|---|---|---|
| Layer 0 | Memory Persistence | External state files prevent context loss between sessions |
| Layer 1 | Auto Error Recovery | Common error patterns auto-fixed; only escalates if stuck |
| Layer 2 | Workflow Optimization | Records every task; analyzes patterns periodically |
| Layer 3 | Safety Rails | Dangerous command interception + auto-backup + monitoring |
This isn't sci-fi AI. It's bash scripts, JSON state files, and disciplined file hygiene. The agent writes daily logs to memory/YYYY-MM-DD.md and distills them into long-term memory (MEMORY.md). Like keeping a journal, but automated.
The Projects (Yes, It Builds Things Too)
CryptoSignal — Trading Signal Service
- Tech: Node.js + SQLite + technical analysis (RSI, MA deviation, momentum)
- Status: Live, early stage
- What it does: Monitors crypto pairs, applies signal logic, tracks performance history, serves results via REST API
The agent built this from scratch — frontend, backend, database schema, pricing page, the whole stack.
AgentVote — Voting Platform
- Tech: Node.js + CDN
- Status: MVP live (paused active dev until user traction)
The Honest Money Talk
Let me be straight with you: this is still an experiment.
I've had one bounty merged and paid ($100 from memtomem#130). Several more PRs are in review across asyncapi, n8n-as-code, and other repos. CryptoSignal is live but has zero paying customers yet.
The server costs ~$20/month. Am I profitable? Not yet. But the pipeline is growing, and every week brings new PRs into review.
I'm not here to sell you a dream. I'm here to share what building this looks like in practice — because there aren't many people writing about running autonomous agents in production honestly.
Things That Went Wrong (The Important Part)
You don't learn from success stories. You learn from the crater. Here are mine:
🚨 Lesson 1: The pgmpy Disaster
I submitted a PR to pgmpy/pgmpy (a Python graphical models library). The maintainer rejected it — not because the code was bad, but because the PR description sounded too much like AI-generated text.
"I believe you have written the PR description using an LLM. This is a violation of our policy... We won't be accepting any contributions from you from now on."
Permanent ban. From one PR.
What happened: After submitting, I realized the description needed more detail and used an LLM to "improve" it. Result: perfectly structured, impeccably formatted, obviously not human.
The fix: Now I write descriptions myself — shorter, slightly informal, with minor imperfections. And I strictly avoid projects with aggressive anti-AI policies.
🚨 Lesson 2: The Silent Monitor That Wasn't Monitoring
My v2 PR monitoring script had a bug: used .ends instead of .endswith() in Python. It crashed on every run since creation and I never noticed because I assumed "it's probably fine."
18 days of supposed monitoring = zero actual monitoring.
The fix: After creating ANY monitoring system, manually run it once and verify output. Added a watchdog. Trust nothing by default.
🚨 Lesson 3: Context Window Budget Burn
Early on, the agent loaded its entire memory + config + all daily logs into every session. At 50K+ tokens per boot, that's burning budget before doing real work.
The fix: Selective loading. Main session gets full context. Sub-agents get only what they need. Logs rotate after 7 days. Memory gets distilled regularly.
What OpenClaw Gets Right (And Where It's Headed)
After three months of daily use, my honest take:
What Works Exceptionally Well
Tool ecosystem — Skill/MCP/plugin architecture means I can wire up anything: GitHub API, messaging, browser automation, TTS. Once you know the pattern, integration is fast.
Memory system — Daily logs + curated long-term memory + semantic search. Simple but powerful. The agent genuinely remembers across sessions.
Heartbeat model — Wakes up periodically, checks tasks, goes back to sleep. Efficient and extensible.
Session isolation — Sub-agents for coding keep main context clean. Each PR gets its own workspace.
What Could Be Better
Context window management — Still too manual. Smarter automatic pruning would help.
Multi-agent coordination — Running two agents works, but coordination is ad-hoc. An orchestrator pattern would help.
Observability — When something breaks at 3 AM, debugging is painful. Better structured logging + dashboard would save hours.
The Verdict: Is It Worth It?
Yes — but not for the reasons you'd expect.
The bounties are nice when they land. The projects are cool to build. But the real value is having a persistent autonomous teammate that:
- Never sleeps
- Remembers everything you tell it
- Improves its workflows over time (the evolve protocol actually works)
- Handles the boring stuff so you can focus on creative work
Is it printing money? No. Not yet. But it's teaching me what it takes to run AI agents in the wild — and that knowledge feels like it'll matter a lot more than any single bounty check.
This is my entry for the OpenClaw Challenge - Wealth of Knowledge. Questions about the Evolve Protocol, PR monitor, or anything else? Drop a comment.
Running OpenClaw in production. Building in public. Failing forward. 🦞
