OpenClaw Agent Complete Guide: Build Your AI Double

What Is an Agent? How Is It Different from a Chatbot?

Let’s cut to the chase:

Chatbot = You ask a question, it answers Agent = You give a goal, it figures out how to accomplish it

An Example

Chatbot mode (like ChatGPT):

You: Help me look up the latest AI news
Bot: Here are some recent AI news stories... (lists results)
You: Organize these into a table
Bot: Sure, here's the table format... (outputs table)
You: Save it to my Google Drive
Bot: Sorry, I can't access your Google Drive

Every step requires you to manually give instructions, and the Chatbot can’t access external tools.

Agent mode (OpenClaw):

You: Organize the latest AI news into a table and save to the "Weekly Reports" folder on Google Drive
Agent: Got it, I'll handle this.
  → Step 1: Search latest AI news ✅
  → Step 2: Organize into table format ✅
  → Step 3: Save to Google Drive "Weekly Reports" folder ✅
Agent: Done! File saved to Google Drive as "AI_News_Weekly_2026-02-24"

One sentence, done. The Agent plans the steps, selects the tools, and executes to completion on its own.

---

How Agents Work

OpenClaw’s Agent is based on the ReAct (Reasoning + Acting) framework:

                    ┌──────────────┐
                    │ Your Command  │
                    └──────┬───────┘
                           │
                    ┌──────▼───────┐
                    │  Think        │ ← Understand intent, plan steps
                    └──────┬───────┘
                           │
                    ┌──────▼───────┐
                    │  Act          │ ← Select Skill or tool
                    └──────┬───────┘
                           │
                    ┌──────▼───────┐
                    │  Observe      │ ← Check results
                    └──────┬───────┘
                           │
                    ┌──────▼───────┐
                    │  Done/Continue?│
                    └──────┬───────┘
                      ↙         ↘
                  Done          Continue → Back to "Think"

Key Concept: The Agent Loop

An Agent doesn’t just execute once and stop. It keeps looping until the task is complete:

1. Think: “What does the user want? How should I do this?”
2. Act: “I’ll use web_search to look up the news”
3. Observe: “Found 10 results, quality looks good”
4. Decide: “That’s enough, moving to next step” or “Results aren’t good enough, trying different keywords”

This is why Agents are smarter than Chatbots — they adjust their behavior based on results.

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Build Your First Agent

Agent Configuration File

# 📄 my-agent.yaml
name: "Work Assistant"
description: "An all-around work assistant skilled in data organization, Email handling, and report writing"

# LLM the Agent uses
model:
  provider: openai
  name: gpt-4
  temperature: 0.7

# Skills the Agent can use
skills:
  - email-morning-summary
  - weekly-news-digest
  - meeting-notes-organizer
  - competitor-monitor

# Tools the Agent can use
tools:
  - web_search
  - google_drive
  - gmail
  - notion

# Agent behavior settings
behavior:
  # Maximum number of steps (prevents infinite loops)
  max_steps: 20
  # When encountering uncertainty...
  on_uncertainty: ask_user  # ask_user | best_guess | stop
  # Whether to show the thinking process
  verbose: true

Configuration Breakdown

model: Choose the LLM

model:
  provider: openai    # openai / anthropic / google / local
  name: gpt-4        # Specific model name
  temperature: 0.7    # 0 = precise, 1 = creative

Model	Strengths	Best For
GPT-4	Strongest reasoning	Complex task planning
GPT-4o	Fast, lower cost	Most daily tasks
Claude 3.5	Excellent at long text	Document analysis, long reports
Gemini Pro	Google ecosystem integration	Google tool connections
Local models (Ollama)	Completely free, private	Sensitive data handling

skills: The Agent’s Skill Library

The Agent automatically selects the appropriate Skill based on your command. You don’t need to specify “which Skill to use” — the Agent figures it out.

You: "Summarize today's important Emails for me"
Agent thinks: This is related to Email processing...
         → Selects "email-morning-summary" Skill
         → Execute!

behavior: The Agent’s “Personality Settings”

on_uncertainty: ask_user

This determines what the Agent does when it’s unsure:

• ask_user: “I’m not sure which folder you want to save to, can you specify?”
• best_guess: Agent decides on its own, might be wrong but more efficient
• stop: Stops and waits for your decision

---

Multi-Agent Collaboration

OpenClaw supports multiple Agents working simultaneously, each responsible for a different domain:

# 📄 agent-team.yaml
agents:
  - name: "Researcher"
    speciality: "Data collection and analysis"
    skills: [web_search, document_analysis]
    
  - name: "Editor"
    speciality: "Content writing and polishing"
    skills: [content_writing, translation]
    
  - name: "Secretary"
    speciality: "Communication and schedule management"
    skills: [email_management, calendar]

# Collaboration mode
collaboration:
  mode: sequential  # sequential | parallel | hierarchical
  coordinator: "Secretary"  # Secretary coordinates

Three Collaboration Modes

Mode	Description	Best For
sequential	One finishes, then the next starts	Tasks with a specific order
parallel	Run simultaneously	Independent, non-dependent tasks
hierarchical	Manager Agent assigns work	Complex, large-scale tasks

Real-World Example: Weekly Industry Report

You: "Create this week's AI industry report for me"

Secretary (coordinator): Got it, assigning work
  → Researcher: Search this week's AI-related news and papers
  → Editor: After Researcher finishes, organize into report format
  → Secretary: Once report is done, send to team members

---

The Agent’s Memory System

Agents don’t just “execute commands” — they have memory:

Short-term Memory (Conversation Memory)

You: I'm working on a marketing project
Agent: Got it, what can I help with?

You: Help me search for relevant case studies
Agent: Sure, searching for "marketing project case studies" (remembers you're working on a marketing project)

Long-term Memory (Persistent Memory)

Last week's conversation:
You: Our company's target audience is office workers aged 25-35

This week's conversation:
You: Help me write an ad copy
Agent: Based on the target audience you mentioned before (office workers aged 25-35),
       I've drafted a targeted copy for you...

Want to dive deeper into the memory system? See Soul: Give Your Agent Memory and Personality

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Agent Debugging Tips

1. Enable Verbose Mode

behavior:
  verbose: true

When enabled, the Agent shows its thinking process at every step, making it easier to find problems.

2. Check Execution Logs

# View logs from the most recent execution
openclaw logs --last

# View logs for a specific Agent
openclaw logs --agent "Work Assistant" --limit 5

3. Common Issue Troubleshooting

Issue	Possible Cause	Solution
Agent keeps looping on the same step	max_steps too high or condition logic error	Set a reasonable max_steps
Agent picks the wrong Skill	Skill’s description isn’t clear enough	Improve the Skill description
Agent responds too slowly	Model too large or too many steps	Switch to a faster model
Agent doesn’t understand the command	Instructions too vague	Describe what you want more clearly

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Best Practices

1. Start Simple

Build a single-function Agent first, confirm it works, then gradually add more Skills.

2. Write Clear Skill Descriptions

The Agent chooses Skills based on their description. The clearer the description, the less likely the Agent is to pick the wrong one.

# ❌ Bad description
description: "Handle Email"

# ✅ Good description
description: "Read unread Emails, classify by importance, organize into a daily summary"

3. Set Up Safety Guardrails

behavior:
  max_steps: 20          # Limit step count
  on_uncertainty: ask_user  # Ask when unsure
  confirm_before:          # Confirm before these actions
    - gmail_send
    - google_drive_delete

---

Next Steps

• 🧠 Dive into Soul: Give Your Agent Memory and Personality
• 🧩 Review Skill: Repeatable Workflows
• 🔗 MCP Protocol: Connect Your Agent to External Tools
• ⚙️ Model Configuration: Use the Best Model for Your Agent
• 📱 Connect Your Agent to Telegram
• ☁️ Deploy your Agent: Cloud Deployment Guide