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Documentation Index

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Xpander lets you build AI agents that use tools, remember context, and work across channels like Slack and Telegram. In this guide, you’ll create one from scratch, give it tools, and test it with a real task. Prerequisites: An xpander account. Sign up at app.xpander.ai if you don’t have one.

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1. Create Your Agent

1

Start a new Agent

Sign in to app.xpander.ai and click + New Agent in the top right.Xpander home page with New Agent button
2

Name and configure your Agent

Enter a name for your agent, something like “My Assistant.”Select xpander-cloud as the environment and OpenClaw as the runtime.Agent creation form with name input and environment/runtime selection
Why OpenClaw? OpenClaw is a fully managed runtime. Xpander handles all infrastructure so you can focus on configuring your agent. Other runtimes (Agno, Agno + Nvidia NeMo) give you more control but require additional setup. For your first agent, OpenClaw is the simplest option.
3

Skip channel setup for now

Xpander will take 15–30 seconds to start a container for your agent. Once ready, you’ll see options to connect Telegram and Slack.OpenClaw setup modal showing Telegram and Slack connection togglesClick Skip. You’ll configure channels after you’ve tested the agent.
You’re now in the Workbench, the main interface for building and monitoring your agent. It has two views:
  • Builder (left tab): Where you configure your agent’s personality, tools, and channels
  • Monitor (right tab): Where you view conversation logs, tool calls, and usage metrics
Workbench header showing Builder and Monitor tabs
For full details on the Workbench, see Core Concepts.

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2. Give Your Agent a Personality

Click the gear icon (top right of Builder) to open Agent Configuration. You’ll see three tabs: SOUL, Tools, and Channels. Start with the SOUL tab. SOUL (System Orchestration & User Logic) defines who your agent is and how it behaves.
SOUL configuration showing Who I Am, Core Truths, Boundaries, Vibe, Continuity sections
Xpander prefills all fields with sensible defaults. For now, try editing just two:
  • Who I Am: Change this to describe your agent’s role. Example: “You are a research assistant that finds and summarizes information.”
  • Vibe: Set the communication style. Example: “Concise and direct. Use bullet points for lists.”
The remaining fields (Core Truths, Boundaries, Continuity, and Current Focus) control deeper behavior. See Agent Instructions (SOUL) for what each field does.

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3. Enable Tools

Switch to the Tools tab. Tools let your agent take actions beyond chat: search the web, send emails, generate images, and more.
Tools tab showing built-in tools and connector options
For this quickstart, enable two built-in tools by clicking on them:
  1. Web Search: lets your agent find current information online
  2. Send Email: lets your agent compose and deliver emails
That’s all you need to try the first example below. You can always add more tools later from the connector catalog.

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4. Publish and Test

Click Publish in the top right corner. This deploys your configuration so the agent can use the tools you just enabled. Agent workbench dashboard showing Builder view with chat interface
Your agent is live. You’ll see a chat interface on the left side of the Workbench.
Click Publish every time you change your agent’s configuration (SOUL, tools, or channels). Changes don’t take effect until published.
Now try this prompt in the chat: 
Get the latest news about LLM reasoning models and send a summary to me via email
What should happen:
  1. The agent calls Web Search to find recent articles
  2. It summarizes the key findings
  3. It calls Send Email to deliver the summary to your inbox
If the agent responds with a summary and confirms the email was sent, it’s working. You can verify by checking your inbox.

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See what happened behind the scenes

Switch to the Monitor tab and click the conversation thread you just ran. This shows you exactly what your agent did: every message, every tool call, and the reasoning behind each step. Monitor Threads tab showing conversation logs with user messages, agent responses, and tool call details Each tool call is expandable, so you can inspect the request your agent sent and the response it got back. If the agent skipped a tool or returned something unexpected, this is where you’ll see why. For the full breakdown of Monitor features (metrics, task tracking, AI insights), see Core Concepts: Threads.

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5. Try More Examples

Now that you’ve seen the basic flow (configure → publish → prompt), try these:
Enable: Save CSV File (built-in, Tools tab)Prompt:
Create a CSV file with 10 rows of sample customer data: name, email, purchase amount, date
Expected result: The agent generates realistic data and returns a download link for the CSV file.
Enable: Code Interpreter, File Upload (built-in, Tools tab)Prompt:
Write a Python function that validates email addresses using regex, test it with 3 examples, and save it as a file
Expected result: The agent writes the function, runs test cases showing pass/fail results, and provides a downloadable .py file.
Enable: Generate Image from Text Prompt (built-in, Tools tab)Prompt:
Generate an image of a futuristic city skyline at sunset
Expected result: The agent returns a generated image. You can follow up with “make it more neon” or “add flying cars” to iterate.
Enable: Slack connector (Tools tab → + Add tools → search Slack). You’ll need to authorize your Slack workspace during setup.Prompt:
Send a message to #general saying "Hello from my xpander agent!"
Expected result: A message appears in your Slack channel from the agent. Once the connector is set up, you can also deploy your agent as a Slack bot so teammates can chat with it directly.

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Next Steps

Core Concepts

Understand agents, SOUL, memory, workflows, and tools

Agent Configuration

Deep dive into all agent settings

Tools & Connectors

Browse 100+ integrations in the catalog

Knowledge Bases & RAG

Upload documents for context-aware responses