Create Your First OpenClaw AI Agent in 10 Minutes

Most people overthink their first AI agent. They read twelve Medium posts about autonomous reasoning loops, watch a YouTube video about multi-agent orchestration, and then spend three weekends trying to wire together a system that reliably does... basically nothing.

I know because that was me four months ago.

Here's what I wish someone had told me at the start: your first agent doesn't need to be impressive. It needs to work. Consistently. On one task. Without draining your wallet or making you want to throw your laptop out a window.

That's exactly what OpenClaw is built for, and that's what this post is about. In roughly ten minutes, you're going to have a functioning agent that actually does something useful. Not a toy demo. Not a "look what AI can do" parlor trick. A real agent that handles a real task.

Let's get into it.

Why Most First Agents Fail

Before we build anything, let me save you from the mistakes I made and that I see repeated constantly in forums and Discord servers.

Mistake #1: Starting with a multi-step, multi-tool workflow. You want an agent that reads your email, searches the web for context, drafts a reply, and logs the conversation to Notion. That's a month-two project. That's not day one.

Mistake #2: Ignoring cost until it's too late. Every reasoning step is a language model call. A ten-step task can easily burn through $2–5 in API credits, and when you're iterating and testing, those costs compound fast. I've seen people in the OpenClaw Discord post screenshots of $40 bills from a single afternoon of development. Don't be that person.

Mistake #3: No visibility into what's happening. The agent fails, and you have no idea why. Was it the prompt? The tool definition? Did the model hallucinate a parameter? Without observability, debugging an agent is like debugging a program where you can't see the logs, the stack trace, or the source code. You're just guessing.

Mistake #4: Treating the agent like magic. An agent is just a loop: perceive, decide, act, observe the result, repeat. If you understand the loop, you can fix the loop. If you treat it like a black box, you'll be stuck forever.

OpenClaw addresses all of these problems by design, which is why it's become my go-to for building agents. The architecture keeps things simple, the cost controls are explicit, and the observability is actually good. Let me show you what I mean.

What We're Building

We're going to build a daily summary agent. It does one thing:

Takes a list of RSS feed URLs you give it.
Fetches the latest posts from those feeds.
Summarizes the key headlines and takeaways into a clean, readable digest.
Outputs that digest as structured text you can pipe wherever you want — email, Slack, a text file, whatever.

This is a genuinely useful agent. I still use a version of this every morning. And it's simple enough to build in ten minutes while being complex enough to teach you the core OpenClaw concepts you'll need for everything else.

Step 1: Install OpenClaw and Set Up Your Environment

First, get OpenClaw installed. Open your terminal:

pip install openclaw

Once that's done, initialize a new project:

openclaw init daily-summary-agent
cd daily-summary-agent

This creates a project structure that looks like this:

daily-summary-agent/
├── agent.yaml
├── skills/
│   └── (empty for now)
├── tools/
│   └── (empty for now)
└── main.py

The key file is agent.yaml. This is where you define what your agent is, what it can do, and how it behaves. OpenClaw is opinionated about configuration-first development, which I initially found annoying but now appreciate deeply. It forces you to think about your agent's design before you start writing code.

Set your API key:

export OPENCLAW_API_KEY=your_key_here

Step 2: Define Your First Skill

In OpenClaw, a skill is a discrete capability your agent has. It's not a tool (we'll get to those), it's a higher-level description of something the agent knows how to do. Think of skills as job descriptions and tools as the specific instruments used to execute them.

Create your first skill file:

touch skills/summarize_feeds.yaml

Now open it and add this:

name: summarize_feeds
description: >
  Fetches recent posts from a list of RSS feed URLs
  and produces a concise daily summary of key headlines
  and takeaways.

inputs:
  - name: feed_urls
    type: list[string]
    description: List of RSS feed URLs to process

output:
  type: string
  description: A formatted daily digest summarizing the latest posts

behavior:
  max_steps: 5
  cost_limit: 0.10
  on_failure: return_partial

A few things to notice:

max_steps: 5 — This is your guardrail against infinite loops. The agent gets five reasoning/action steps to complete this skill. If it can't finish in five, something is wrong with your design, not the agent. Start tight and loosen later.

cost_limit: 0.10 — You're capping this skill at ten cents per execution. OpenClaw tracks token usage in real time and will gracefully stop if you hit the limit. This is the single feature that saved me the most money when I was learning.

on_failure: return_partial — If something goes wrong, give me whatever you've got so far instead of just dying silently. This is invaluable for debugging.

Step 3: Register Your Tool

Skills describe what to do. Tools describe how to do it. We need a tool that can actually fetch RSS feeds.

touch tools/rss_fetcher.py

import feedparser
from openclaw.tools import tool

@tool(
    name="fetch_rss",
    description="Fetches and parses an RSS feed URL. Returns a list of recent post titles and summaries.",
    params={
        "url": {
            "type": "string",
            "description": "The RSS feed URL to fetch"
        }
    }
)
def fetch_rss(url: str) -> dict:
    feed = feedparser.parse(url)

    if feed.bozo and not feed.entries:
        return {"error": f"Failed to parse feed: {url}"}

    posts = []
    for entry in feed.entries[:10]:
        posts.append({
            "title": entry.get("title", "No title"),
            "summary": entry.get("summary", "No summary available")[:300],
            "link": entry.get("link", ""),
            "published": entry.get("published", "Unknown date")
        })

    return {
        "feed_title": feed.feed.get("title", "Unknown Feed"),
        "post_count": len(posts),
        "posts": posts
    }

You'll need feedparser installed:

pip install feedparser

The @tool decorator is doing the heavy lifting here. It tells OpenClaw exactly what this function does, what it expects, and what it returns. The agent uses this metadata to decide when and how to call the tool. The better your description and parameter docs, the more reliably the agent will use the tool correctly. This is not the place to be lazy.

Step 4: Wire It All Together

Now open agent.yaml and connect everything:

name: daily-summary-agent
version: "0.1"

persona: >
  You are a sharp, concise news curator. You read feeds carefully,
  identify the most important and interesting stories, and write
  summaries that are informative without being verbose. No fluff.
  No filler. Just the signal.

skills:
  - summarize_feeds

tools:
  - rss_fetcher

settings:
  model: gpt-4o-mini
  temperature: 0.3
  verbose: true

A few decisions here that matter:

Model choice: gpt-4o-mini — For a summarization task, you do not need the most expensive model. gpt-4o-mini is fast, cheap, and more than capable of reading RSS content and producing clean summaries. You can always upgrade later. Start cheap.

Temperature: 0.3 — We want consistency, not creativity. Low temperature means the agent will make more predictable, repeatable choices. For a daily digest, that's exactly what you want.

Verbose: true — This is your observability toggle during development. With this on, OpenClaw logs every reasoning step, every tool call, every decision the agent makes. You can see exactly why it did what it did. Turn this off in production, but keep it on while you're building. Always.

Step 5: Write Your Entry Point

Open main.py:

from openclaw import Agent

agent = Agent.from_config("agent.yaml")

result = agent.run(
    skill="summarize_feeds",
    inputs={
        "feed_urls": [
            "https://feeds.arstechnica.com/arstechnica/technology-lab",
            "https://hnrss.org/frontpage",
            "https://www.techmeme.com/feed.xml"
        ]
    }
)

print(result.output)
print(f"\n--- Stats ---")
print(f"Steps taken: {result.steps}")
print(f"Total cost: ${result.cost:.4f}")
print(f"Status: {result.status}")

Now run it:

python main.py

If you've set everything up correctly, you'll see the agent's reasoning steps scroll by in real time (thanks to verbose: true), followed by a clean daily digest of the latest posts from those three feeds.

The stats at the bottom will show you something like 3–4 steps taken and a cost well under your $0.10 limit. That's the whole point. Controlled, observable, cheap.

What Just Happened Under the Hood

Let me walk through the agent's reasoning so you understand the loop:

Step 1: The agent reads the skill definition, sees it has a list of feed URLs and a fetch_rss tool, and decides to call fetch_rss for the first URL.
Step 2: It calls fetch_rss for the second URL.
Step 3: It calls fetch_rss for the third URL.
Step 4: Now it has all the raw feed data. It synthesizes everything into a formatted digest, selecting the most important stories across all three feeds.

Four steps. Clean. Predictable. No hallucinated tools, no infinite loops, no random tangents.

This is what good agent design looks like. The skill is scoped tightly, the tools are well-defined, and the guardrails (max_steps, cost_limit) prevent disaster. The agent doesn't need to be "smart" — it needs to be constrained enough to succeed.

Customizing and Extending

Once you've got the basic version working, here are some natural next steps:

Add more feeds. Just extend the list in your inputs. The agent handles it automatically (though you might need to bump max_steps if you add more than 5–6 feeds).

Change the output format. Update the persona in agent.yaml to specify markdown, bullet points, or whatever format you prefer. The persona is surprisingly powerful for controlling output style.

Add a delivery tool. Create a new tool that sends the digest via email or posts it to Slack. Register it in agent.yaml, and update the skill to include a delivery step. Now you've got a two-tool agent.

Schedule it. Throw python main.py in a cron job or a GitHub Action. Five minutes of setup, and you've got an autonomous daily briefing that runs on autopilot.

Skipping the Setup: Felix's OpenClaw Starter Pack

If you followed along and everything worked — great, you're off to the races.

But if you're the type who'd rather start with something pre-configured and working, then tweak from there, check out Felix's OpenClaw Starter Pack on Claw Mart. It's $29 and includes a set of pre-built skills — including a feed summarizer very similar to what we just built, plus a web scraper skill, a file organizer, and a few others.

The main value isn't the code itself (you can build all of it from scratch as we just demonstrated). The value is that the skill definitions, tool configurations, and guardrail settings are already dialed in. Felix clearly spent time tuning the prompts, step limits, and cost caps so things work reliably out of the box. When I was starting out, I would have gladly paid $29 to skip the three days I spent tweaking my persona prompts and tool descriptions to get consistent output.

It's a solid starting point. Grab it, run the examples, break them apart, learn from the config choices, and then build your own.

Common Issues and How to Fix Them

Agent keeps calling the same tool with the same input: Your tool description is probably too vague. The agent doesn't understand that it already has the data it needs. Make the tool's return description more explicit, or add a note in the persona like "Do not re-fetch a feed you've already fetched."

Output is messy or inconsistent: Lower the temperature. Tighten the persona description. Be specific about format — "Use markdown headers for each feed, bullet points for stories, bold the headline" works better than "make it look nice."

Hitting the cost limit before finishing: Either increase the cost_limit or reduce the scope of the skill. If you're feeding in 15 RSS feeds, that's a lot of content for the model to process. Split it into batches or increase your budget.

feedparser returning empty results: Some feeds require specific User-Agent headers or don't support standard RSS parsing. Test the URL in a browser first. If the feed is valid but feedparser struggles, you might need to add request headers to the tool.

Where to Go From Here

You have a working agent. That's more than most people who start exploring this space ever achieve. Here's your roadmap:

Run this daily for a week. See how it holds up. Note what breaks or drifts.
Add a second tool. Email delivery or Slack posting is the natural next step.
Build a second agent. Something completely different — a file organizer, a research assistant, a data cleanup tool. The patterns you learned here transfer directly.
Explore multi-skill agents. Once you're comfortable with single-skill agents, OpenClaw supports chaining skills together in sequences and conditional flows.

The key insight from building agents is that simplicity wins. The most useful agents I've built are also the most boring. They do one thing, they do it consistently, and they cost pennies to run. The flashy multi-agent orchestration demos are fun to watch but miserable to maintain.

Start simple. Get it working. Then, and only then, make it more complex.

Now go build something.