Landbot
No-code chatbot builder for conversational experiences
Hexabot
Build customizable AI chatbots with multi-channel deployment capabilities
Hexabot is an AI chatbot builder offering visual flow design, multi-channel support, NLU integration, and extensible plugin architecture for creating intelligent conversational agents.
Overview
Overview
Hexabot is a comprehensive AI chatbot and agent builder designed for developers and teams who need full control over their conversational AI infrastructure. Built with TypeScript, NestJS, and React, it combines visual flow design with powerful backend capabilities to create intelligent, multi-channel chatbot experiences.
Core Capabilities
The platform features a drag-and-drop visual editor for designing conversation flows, native integration with leading LLMs (Ollama, ChatGPT, Mistral, Gemini), and built-in NLU for intent detection and language recognition. Its extensible plugin system allows developers to add custom text-to-action capabilities and third-party integrations through a growing extension library.
Hexabot supports deployment across web, mobile, and social media channels while maintaining consistent user experiences. The knowledge base feature enables dynamic content integration, while contextual data collection powers personalized responses. Teams benefit from role-based access control, real-time inbox monitoring with human handover, subscriber segmentation through labels, and an analytics dashboard for performance tracking.
Deployment
The solution ships with Docker Compose configurations for straightforward deployment. A global CLI tool streamlines project creation, environment initialization, and development workflows, making it accessible for both rapid prototyping and production deployments.
Highlights
Visual drag-and-drop editor for designing conversation flows without coding
Multi-LLM support including Ollama, ChatGPT, Mistral, and Gemini with NLU capabilities
Extensible plugin system with library for text-to-action and third-party integrations
Multi-channel deployment across web, mobile, and social platforms with unified management
Pros
- Self-hosted solution providing full data control and customization flexibility
- Comprehensive visual editor reduces technical barriers for conversation design
- Built-in human handover and inbox for seamless agent escalation
- Docker-based deployment simplifies infrastructure setup and scaling
Considerations
- Requires Node.js, Docker, and MongoDB infrastructure knowledge for deployment
- AGPL-3.0 license requires source disclosure for modified network-accessible versions
- Smaller community compared to established commercial chatbot platforms
- Self-hosting demands ongoing maintenance and security management
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Fit guide
Great for
- Development teams requiring full customization and control over chatbot logic
- Organizations with data sovereignty requirements needing self-hosted solutions
- Businesses building multi-channel conversational experiences across platforms
- Teams wanting to integrate custom LLMs or proprietary NLU models
Not ideal when
- Non-technical users seeking fully managed, no-code SaaS chatbot platforms
- Projects requiring immediate deployment without infrastructure setup time
- Teams lacking resources for ongoing server maintenance and updates
- Use cases needing extensive pre-built industry-specific templates
How teams use it
E-commerce Customer Support
Deploy intelligent product recommendation chatbots across website and social channels with knowledge base integration for catalog queries and human handover for complex issues
Multilingual Enterprise Help Desk
Create internal support agents that detect user language, route inquiries based on intent, and escalate to appropriate departments while maintaining conversation context
Lead Qualification Automation
Build conversational agents that collect contextual data through natural dialogue, segment prospects with labels, and trigger CRM actions via custom plugins
Healthcare Appointment Scheduling
Implement HIPAA-compliant self-hosted chatbots that understand patient intent, check availability through API integrations, and hand off to staff when needed
Tech snapshot
TypeScript98%
SCSS1%
CSS1%
JavaScript1%
Dockerfile1%
Shell1%
Frequently asked questions
What LLM providers does Hexabot support?
Hexabot integrates with Ollama, ChatGPT, Mistral, Gemini, and other LLM providers. You can configure your preferred model through the platform's NLU settings and extend support via plugins.
Can I deploy Hexabot without Docker?
While Docker Compose is the recommended deployment method, you can run the NestJS API, React frontend, and MongoDB manually. Docker simplifies environment consistency and dependency management.
How does the plugin system work?
Hexabot's plugin architecture allows developers to create custom extensions for text-to-action capabilities, third-party integrations, and additional features. Extensions can be installed from the Extension Library or developed independently.
What channels are supported for chatbot deployment?
Hexabot supports web embedding via the live chat widget, mobile applications, and social media platforms. The multi-channel architecture ensures consistent conversation flows across all deployment targets.
Does Hexabot require coding knowledge?
The visual editor enables conversation design without coding. However, deployment, plugin development, and advanced customizations require TypeScript/JavaScript knowledge and familiarity with Node.js and Docker.
Project at a glance
Active- Stars
- 909
- Watchers
- 909
- Forks
- 193
LicenseAGPL-3.0
Repo age1 year old
Last commit23 hours ago
Self-hostingSupported
Primary languageTypeScript
Last synced 12 hours ago