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📚Complete Guide

Relevance AI Tutorial: Get Started in 5 Minutes [2026]

Master Relevance AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.

Get Started with Relevance AI →Full Review ↗
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Getting Started with Relevance AI

1

Sign up at app.relevanceai.com and explore the dashboard Create your first agent using the visual builder or clone a template from the Marketplace Connect knowledge sources by uploading documents or syncing from Google Drive/SharePoint Build custom tools using the no

2

code tool builder to integrate with your APIs and databases Test your agent in the chat interface and configure autonomy levels and approval workflows Deploy via triggers (webhook, scheduled, API) or embed as a chat widget

💡 Quick Start: Follow these 2 steps in order to get up and running with Relevance AI quickly.

🔍 Relevance AI Features Deep Dive

Explore the key features that make Relevance AI powerful for agent workflows.

Visual Agent Builder

What it does:

Configure agents with skills, knowledge bases, and conversation patterns through a drag-and-drop interface. Define agent roles, set autonomy levels, and deploy without writing code.

Use case:

Creating a sales development agent that researches prospects using LinkedIn data, personalizes outreach emails, and logs activities in HubSpot — all configured visually.

Custom Tool Builder

What it does:

Create agent tools by visually chaining API calls, data transformations, conditional logic, and LLM processing steps. Tools become reusable capabilities across agents.

Use case:

Building a custom tool that queries your internal pricing database, applies discount logic, and generates a formatted quote — without writing a single line of code.

Multi-Agent Workforces

What it does:

Connect multiple specialized agents on a visual canvas to handle complex, multi-step workflows. Each agent focuses on specific tasks while the workforce handles coordination and handoffs.

Use case:

Building a lead qualification workforce where one agent researches prospects, another scores leads, and a third agent updates CRM records and triggers email sequences.

Knowledge Base Management

What it does:

Upload documents (PDF, DOCX, CSV), scrape websites, or connect databases as agent knowledge. Automatic chunking, embedding, and retrieval for RAG-powered responses.

Use case:

Creating a product knowledge base from documentation, FAQ pages, and training materials that agents use to answer customer questions accurately.

Business Tool Integrations

What it does:

Pre-built connectors for HubSpot, Salesforce, Google Workspace, Slack, Intercom, and other business platforms. Agents can read, create, and update records in connected tools.

Use case:

Building an agent that monitors Intercom conversations, identifies high-priority issues, creates HubSpot tickets, and notifies the team via Slack.

Deployment & Trigger Options

What it does:

Deploy agents as chat interfaces, API endpoints, scheduled tasks, or webhook-triggered automations. Embed agents in websites or connect them to existing tools via API.

Use case:

Deploying a support agent as a chat widget on your website that also runs scheduled checks on open tickets every hour.

❓ Frequently Asked Questions

Do I need coding skills to use Relevance AI?

No. The visual agent builder and tool builder are designed for non-technical users. You can create agents, build custom tools, connect knowledge bases, and deploy automations without writing code. For advanced customization, the API and SDK are available for developers.

How does Relevance AI compare to building agents with code frameworks?

Relevance AI trades flexibility for accessibility. Code frameworks (CrewAI, LangGraph) offer unlimited customization but require Python expertise. Relevance AI gets you to a working agent faster without code but limits complex orchestration patterns. Choose Relevance AI for business automation; code frameworks for custom agent architectures.

What LLM providers does Relevance AI support?

Relevance AI supports OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), and Google (Gemini) models. Model selection is configurable per agent and per tool step. The platform handles API key management and model routing.

Can Relevance AI agents handle sensitive business data securely?

Relevance AI offers SOC 2 Type II compliance, GDPR compliance, data encryption at rest and in transit, and configurable data retention policies. Enterprise plans include SSO, RBAC, and multi-region deployment for data residency requirements.

What's the difference between agents and workforces?

Agents are individual AI workers that perform specific tasks. Workforces are multi-agent teams where specialized agents collaborate on complex workflows. For example, a lead generation workforce might include research agents, qualification agents, and outreach agents working together.

🎯

Ready to Get Started?

Now that you know how to use Relevance AI, it's time to put this knowledge into practice.

✅

Try It Out

Sign up and follow the tutorial steps

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Start Using Relevance AI Today

Follow our tutorial and master this powerful agent tool in minutes.

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Tutorial updated March 2026