Getting Started with Openclaw: A Complete Guide

Openclaw is one of the most powerful AI orchestration platforms available today — but like any capable tool, it rewards teams that take the time to set it up correctly. This guide walks you through everything from initial installation to your first production pipeline, covering the decisions that matter and the mistakes worth avoiding.

What Is Openclaw?#

Openclaw is an AI workflow orchestration platform that lets teams connect large language models to their existing tools, data sources, and processes. Think of it as middleware between your AI models and the rest of your business infrastructure — handling context management, tool routing, memory, and the operational concerns that are tedious to build from scratch.

Its key strengths are:

• Composable pipelines — chain together models, tools, and human checkpoints in any order
• Built-in memory — persistent context that survives across sessions and agents
• Tool integrations — native connectors for common business tools (Slack, Notion, CRMs, databases)
• Observability — full trace logging so you can see exactly what the model did and why

Prerequisites#

Before you begin, make sure you have:

• Node.js 20+ or Python 3.11+
• An OpenAI or Anthropic API key (Openclaw is model-agnostic)
• Access to the Openclaw dashboard at app.openclaw.ai
• Your team's workspace credentials

Installation#

Install the Openclaw CLI globally:

npm install -g @openclaw/cli

# Verify installation
openclaw --version

# Authenticate with your workspace
openclaw auth login

Once authenticated, initialize a new project:

openclaw init my-first-pipeline
cd my-first-pipeline

This creates a standard project structure with a pipelines/ directory, a tools/ directory for custom integrations, and an openclaw.config.ts configuration file.

Configuring Your First Pipeline#

Pipelines in Openclaw are defined in YAML. Here's a minimal pipeline that takes a user query, searches your knowledge base, and returns a grounded response:

# pipelines/support-assistant.yaml
name: support-assistant
version: "1.0"
model: claude-3-5-sonnet
memory: persistent

steps:
  - id: retrieve
    type: tool
    tool: knowledge-base-search
    input: "{{ query }}"

  - id: respond
    type: llm
    system: |
      You are a helpful support assistant. Use the provided context
      to answer the user's question accurately and concisely.
      If the context doesn't contain the answer, say so honestly.
    context: "{{ retrieve.results }}"
    input: "{{ query }}"

Deploy it with:

openclaw deploy pipelines/support-assistant.yaml

Connecting Your Data Sources#

The most important configuration step is connecting Openclaw to the data your assistant needs to be useful. Openclaw supports several ingestion methods:

File Upload#

For static documentation, upload files directly via the dashboard or CLI:

openclaw ingest ./docs --pipeline support-assistant --chunk-size 512

Live Connectors#

For dynamic data, configure a connector in openclaw.config.ts:

export default {
  connectors: {
    notion: {
      token: process.env.NOTION_TOKEN,
      databases: ["your-database-id"],
      syncInterval: "1h",
    },
  },
};

Key Configuration Decisions#

When setting up Openclaw for a team, these decisions have the biggest impact on quality:

Chunk size for ingestion#

Larger chunks preserve more context but reduce retrieval precision. A chunk size of 400–600 tokens works well for most documentation. For structured data like FAQs or product specs, smaller chunks (200–300 tokens) typically perform better.

Memory scope#

Decide early whether memory should be per-user, per-session, or shared across the workspace. For internal tools, per-user memory usually delivers the best experience. For customer-facing assistants, per-session is safer from a privacy standpoint.

Human-in-the-loop checkpoints#

For any pipeline that takes real-world actions — sending emails, updating records, triggering external APIs — add a confirmation step:

- id: confirm-action
  type: human-approval
  message: "The assistant wants to send the following email. Approve?"
  timeout: 30m
  on-timeout: cancel

Testing Before You Ship#

Openclaw has a built-in evaluation runner. Create a test file with representative queries and expected behaviors:

[
  {
    "query": "What is your refund policy?",
    "expected_contains": "30 days",
    "expected_tone": "helpful"
  }
]

Run evals before every deployment:

openclaw eval --pipeline support-assistant --test-file evals/support.json

Common Setup Mistakes to Avoid#

• Skipping data cleanup. The quality of your ingested data is the ceiling for your assistant's accuracy. Spend time cleaning and structuring your knowledge base before ingestion.
• Too many tools at once. Start with one or two well-tested tools. Adding too many integrations before the core workflow is solid creates noise in the model's decision-making.
• No fallback handling. Define what happens when the model is uncertain. A clear "I don't know, here's how to reach a human" response is always better than a hallucinated answer.

Next Steps#

With your first pipeline deployed and tested, you're ready to iterate. The teams that get the most out of Openclaw are the ones that treat it as a living system — reviewing traces weekly, updating knowledge bases as the business changes, and expanding to new workflows incrementally.

For a production deployment with custom integrations and team training, get in touch — we've helped dozens of teams go from zero to fully operational Openclaw environments in under two weeks.