Choose Landbot if you need a polished no-code solution with enterprise features like team collaboration, role-based access, and built-in AI agents requiring minimal setup. Choose Typebot if you want full control over AI integration with any LLM provider, prefer a self-hosted open-source option, or need Git-compatible version control for your bot definitions. For AI-powered conversational forms specifically, Typebot’s approach of letting you choose and configure your own LLM provider delivers more flexibility, while Landbot gets non-technical teams to production faster.

Platform Overview

Landbot positions itself as a “conversational SaaS platform” with a focus on no-code visual building. Its recent AI additions include an AI Agent builder and natural language processing capabilities. Landbot emphasizes enterprise features, team collaboration, and integrations with marketing tools.

Typebot takes an open-source-first approach with strong emphasis on customizability. Built with a modern tech stack, Typebot provides a visual flow builder while exposing extensive customization through variables, logic jumps, and webhook integrations. Its AI capabilities come through integrations with OpenAI, Anthropic, and other LLM providers.

Visual Builder Experience

Both platforms use a node-based visual flow editor, but the developer experience varies.

Landbot’s Builder

Landbot’s builder uses a block-based system where each element (questions, conditions, integrations) is a drag-and-drop block. The interface feels polished and approachable for non-developers.

// Landbot webhook payload example
{
  "event": "flow_completed",
  "sessionId": "abc123",
  "answers": {
    "name": "John Doe",
    "email": "john@example.com",
    "preference": "product_inquiry"
  },
  "timestamp": "2026-03-15T10:30:00Z"
}

The platform handles data collection well but requires workarounds for complex logic. Conditional branching exists but can become nested and difficult to maintain in larger flows.

Typebot’s Builder

Typebot offers a more developer-friendly flow editor with better organization for complex bots. Variables are first-class citizens, and you can reference them throughout the flow.

// Typebot variable usage in conditions
{
  "type": "Condition",
  "items": [
    {
      "id": "cond_1",
      "logic": {
        "variable": "user.score",
        "operator": "greater_than",
        "value": 50
      }
    }
  ]
}

Typebot’s schema-based flow definition can be exported and version-controlled—a significant advantage for developers who want to track changes or generate flows programmatically.

AI Integration Capabilities

Landbot AI Features

Landbot includes built-in AI capabilities through its AI Agent feature. You can connect to language models without writing code, but the integration points are somewhat limited to predefined actions.

// Landbot AI Agent configuration
const agentConfig = {
  model: "gpt-4",
  prompt: "You are a customer support assistant for a SaaS product.",
  temperature: 0.7,
  maxTokens: 500
};

The AI features work well for simple Q&A flows but offer less flexibility for custom AI workflows. You cannot easily swap between different LLM providers or customize the inference pipeline.

Typebot AI Integration

Typebot’s approach to AI is fundamentally different—it provides integration blocks for external LLM services rather than bundling AI directly. This gives developers full control over their AI pipeline.

// Typebot OpenAI integration block
{
  "type": "ai",
  "provider": "openai",
  "model": "gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "Analyze the user's support request and categorize it."
    },
    {
      "role": "user",
      "content": "{{user.message}}"
    }
  ],
  "responseVariable": "ai.category"
}

You can connect to OpenAI, Anthropic, Google AI, Cohere, and other providers. The response can be captured in variables and used for subsequent logic:

// Typebot conditional logic based on AI response
{
  "type": "condition",
  "content": "Is {{ai.category}} equal to 'billing'?",
  "then": "jump_to_billing_flow",
  "else": "jump_to_general_support"
}

Typebot also supports function calling with OpenAI models, enabling more sophisticated AI workflows that can trigger external APIs directly.

Customization and Developer Features

Typebot Open Source Advantages

Typebot offers both a hosted version and a self-hosted option. The self-hosted version provides complete data control and unlimited bots without per-bot pricing.

# Running Typebot locally with Docker
docker run -p 3000:3000 \
  -e DATABASE_URL=postgresql://user:pass@db:5432/typebot \
  -e NEXTAUTH_URL=http://localhost:3000 \
  -e ENCRYPTION_SECRET=your-secret \
  -e DEFAULT_BOT_ID=your-bot-id \
  typebot.io/typebot:latest

This flexibility appeals to developers who need to embed conversational forms in privacy-sensitive applications or want to modify the underlying platform.

Landbot Enterprise Features

Landbot focuses on enterprise requirements with built-in team collaboration, role-based access control, and audit logs. These features matter for larger organizations but add complexity for simpler projects.

API and Integrations

Landbot API

Landbot provides a REST API for managing bots and retrieving submissions. The API requires authentication and works well for basic CRUD operations on flows and collecting responses.

// Landbot API: Get form submissions
const response = await fetch('https://api.landbot.io/v1/submissions', {
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  }
});

const submissions = await response.json();

Typebot API

Typebot exposes a more comprehensive API that includes both management and runtime operations. You can trigger Typebot flows programmatically and pass custom variables at runtime.

// Typebot API: Start a chat session with custom variables
const response = await fetch('https://typebot.io/api/v1/sessions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TYPEBOT_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    "typebotId": "your-typebot-id",
    "variables": {
      "source": "website",
      "campaign": "spring_promo",
      "userId": "user_123"
    }
  })
});

const session = await response.json();

Which Platform Should You Choose?

Choose Landbot if you need a polished, no-code solution with minimal setup. It works well for marketing teams building lead capture forms, customer service chatbots, and simple conversational flows. The enterprise features and support options justify the higher price point for organizations that need them.

Choose Typebot if you are a developer or technical team that wants full control over AI integration, prefers self-hosted options, or needs to version-control your bot definitions. The open-source nature and flexible API make it suitable for complex workflows where you want to integrate custom machine learning models or build sophisticated AI assistants.

For AI-powered conversational forms specifically, Typebot’s approach of letting you choose and configure your own LLM provider provides more flexibility. You can switch models, customize prompts, and handle responses programmatically without platform limitations.

Both platforms continue evolving their AI capabilities, so evaluating current needs while considering future requirements matters when making a long-term platform decision.

Related Reading

• AI Tools Guides Hub

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