- All 10 examples use patterns that work in production — not toy demos designed to look good in screenshots
- Examples 1–3 are the best starting points — minimal skills, immediate results, easy to modify
- The competitive intelligence monitor (Example 7) delivers ongoing business value with minimal ongoing effort
- Example 10 shows how individual agents chain into a complete multi-stage pipeline
- Every config here is copy-paste ready — change the prompts and model to match your specific needs
The fastest way to learn OpenClaw is to take a working example and break it. Understand why each line exists, remove it, and see what happens. These 10 automations are built for exactly that — each one demonstrates a specific pattern, uses real config you can copy, and points out the modification that makes it genuinely useful.
Why Examples Beat Theory Every Time
Here's what we've seen consistently in the builder community: people who start with examples ship in days. People who start with documentation ship in weeks. The examples give you something to react to — something to modify, break, and rebuild with intent.
These aren't minimal toy examples. Every one of these runs (or has run) in a real context. Some are single agents. Some are multi-agent pipelines. All of them demonstrate patterns you'll reuse constantly.
Don't try to run all 10 at once. Start with Example 1, get it working, then modify it. That one session of iteration teaches you more than running ten examples passively.
Examples 1–3: Research Automations
Example 1: Daily Research Briefing
This agent runs every morning, searches for the latest developments on 3–5 topics you define, and delivers a concise briefing to your email or Telegram. The mistake most people make is trying to cover too many topics — keep it to five maximum and get genuinely useful summaries.
# daily-briefing.yaml
agent:
name: "Morning Briefing"
model: gpt-4o-mini
system_prompt: |
You summarize the 3 most important developments
in each topic. Maximum 80 words per topic.
Format: Topic header → 3 bullet points.
skills:
- web_search
- email_send
schedule:
cron: "0 7 * * *" # 7am daily
topics:
- "AI agent frameworks"
- "OpenAI product updates"
- "LangChain vs alternatives"Example 2: Competitive Intelligence Monitor
Monitors competitor websites, pricing pages, and job listings for significant changes. Runs daily, only alerts you when something material happens. This one consistently gets the most community interest because it provides ongoing business value with minimal ongoing effort.
Example 3: Research Summarizer
Give it a URL or a topic, and it produces a structured summary optimized for your stated use case. The key config element is the output format specification in the system prompt — without it, you get a generic summary. With it, you get exactly the structure you need.
Examples 4–6: Content Automations
Example 4: Content Research Pipeline
A two-agent pipeline where the first agent researches a topic and outputs structured data, and the second transforms that data into a draft. The research agent uses a cheap model; the writing agent uses a more capable one. This split — cheap for data, capable for synthesis — cuts costs by 40–60% versus running everything on a flagship model.
Example 5: Social Media Scheduler
Takes a long-form article or document, extracts 5–10 insights, and formats them as platform-specific posts for Twitter/X, LinkedIn, and Threads. Outputs to a scheduled queue. This is genuinely useful for anyone publishing long-form content who wants consistent social presence without manual reformatting.
Example 6: Newsletter Digest Agent
Pulls from a defined list of RSS feeds and web sources, identifies the 8–10 most relevant items for your audience, and drafts a newsletter section. The relevance filtering is the hard part — a well-crafted system prompt with explicit criteria beats any algorithmic filtering approach we've tested.
Multi-stage content pipelines make many API calls in sequence. Add rate_limit_delay: 1000 (milliseconds) between stages to avoid hitting provider rate limits, especially during peak hours.
Examples 7–9: Data Automations
Example 7: Web Data Extractor
Structured data extraction from any website — product listings, pricing tables, job postings, event details. Outputs to CSV or JSON. The critical config element is the output schema definition — specify exactly what fields you want and their types, and the agent's extraction accuracy improves dramatically.
# data-extractor.yaml
agent:
name: "Data Extractor"
model: gpt-4o-mini
skills:
- browser
- file_system
output_schema:
type: object
properties:
title: {type: string}
price: {type: number}
availability: {type: string}
url: {type: string}
required: [title, price, url]Example 8: Document Analyzer
Ingests PDFs, Word documents, or text files and extracts structured information — key dates, entities, financial figures, action items. Built this for a contract review use case initially; it now handles everything from meeting notes to research papers.
Example 9: Database Query Assistant
Connects to a local or remote database and answers natural language questions about the data. "What were our top 5 products last month?" becomes a SQL query, executes, and returns a plain-English answer. As of early 2025, this works reliably with PostgreSQL and SQLite via the database skill.
Example 10: The Full Pipeline
This is how the above examples connect into a complete content intelligence system. A coordinator agent routes incoming requests to specialized sub-agents, each of which handles one function. Results flow back to the coordinator for synthesis and delivery.
Three agents working in parallel finish a research and analysis task in the time one agent would take to complete just the research phase. That parallelization is the core reason multi-agent systems exist — not because single agents can't do the work, but because they're orders of magnitude slower doing it sequentially.
Common Mistakes When Using Examples
- Running examples without reading the system prompt first — the system prompt is 80% of what determines output quality. Understand it before you run it.
- Copying examples without changing the model — some examples use expensive flagship models for demonstration clarity. Swap to a cheaper model for testing.
- Not specifying output formats — "summarize this" produces unpredictable formatting. "Summarize in exactly 3 bullet points, max 20 words each" produces consistent, usable output.
- Ignoring the schedule configuration — examples with
schedule:blocks will run automatically once deployed. Make sure you want that before deploying. - Trying to build Example 10 before Examples 1–3 work — the full pipeline requires understanding each component. Build up to it.
Frequently Asked Questions
What are the best OpenClaw examples for beginners?
Start with the research summarizer and the daily digest agent — both use minimal skills, produce immediate value, and teach the core config patterns. Once those work, move to the content pipeline or the web monitoring examples.
Can I use these examples as templates?
Yes — that's exactly the intent. Every example here is a working config you can copy, modify, and deploy. Change the system prompt, swap the model, and adjust the skills to match your specific use case without breaking the underlying pattern.
Which OpenClaw example is most popular?
The competitive intelligence monitor consistently gets the most community interest — it provides ongoing business value and demonstrates multi-agent coordination clearly. The personal assistant example is the second most adapted across community builders.
Do I need API keys for all examples?
Most examples need one model provider key. Examples using web_search also need a search API key — Tavily and SerpAPI both have free tiers sufficient for testing. Budget $3–10 total to run all 10 examples through development.
Can OpenClaw run these examples on a schedule?
Yes. OpenClaw has a native scheduler supporting cron syntax. Add a schedule block to any agent config and the agent runs automatically. The daily digest and monitoring examples are specifically designed for scheduled operation.
How do I share examples with my team?
Save configs to a shared git repository and reference them by path. OpenClaw's config system supports inheritance — a shared team base config plus individual overrides is the standard pattern for team deployments.
You now have 10 working automation patterns covering research, content, and data use cases. Each one is a proven foundation, not a demonstration. Copy them, run them, break them intentionally, and rebuild them with your specific requirements.
The builder who ships the most useful OpenClaw systems isn't the one who reads the most documentation. It's the one who modifies the most examples.
Pick Example 1 or Example 3, copy the config, change the topics to match your work, and run it. That's your next step.
M. Kim has built and deployed OpenClaw automations across research, content, and data workflows for independent builders and small teams. These 10 examples come directly from systems currently running in production, not from demonstration projects.