What Features Does Hexabot Provide for AI Workflow Automation?

Hexabot is a self-hostable AI workflow automation platform for teams that need to build agents and business automations while keeping production data under their own control. Its feature set covers the full path from trigger to action: workflows can start from chat, forms, schedules, or events; call models, tools, APIs, and back-office systems; and pause for human judgment when automation isn't enough. The features below are the ones to evaluate if you're deciding whether Hexabot fits your operational use case.

How workflows are triggered and what they can call

Hexabot workflows are not limited to conversational input. A workflow can be started by:

• A chat message
• A form submission
• A schedule
• An event

Once running, a workflow can call language models, tools, APIs, and back-office systems. This makes the platform suited to service operations where a conversation needs to produce a real action — a lookup, a ticket, a routed request — rather than just a reply.

How flows are modeled and validated

Hexabot supports two ways to define a flow:

Both are backed by typed contracts and schema validation, so the shape of inputs and outputs is defined rather than implied. For teams that treat workflows as code, the YAML option is the relevant one; for teams that need non-engineers to inspect a flow, the graph view matters more.

Reusable actions and capability bindings

Workflows are assembled from reusable actions, LLM calls, and capability bindings. Each has a clear contract for inputs, outputs, and settings. The practical effect is that an action built once — say, a call to an internal system — can be reused across workflows without redefining its interface each time. When evaluating the platform, check whether your existing systems can be expressed as actions with clean input/output contracts; that is where most integration effort goes.

Memory, RAG, and MCP integration

Hexabot gives workflows managed memory, retrieval-augmented context, and MCP integration points for sanctioned tools. In practice:

• Memory lets a workflow retain context across interactions rather than treating each turn as isolated.
• RAG supplies retrieved context to model calls, so answers can draw on your knowledge rather than only the model's training.
• MCP provides a defined integration point for tools you have approved, rather than ad hoc connections.

If your automation depends on remembering prior interactions or grounding responses in internal documents, these are the features to test first.

Human review and control flow

Automation rarely covers every case. Hexabot includes:

• Conditionals and loops for branching logic
• Segmentation for dividing audiences or cases
• Inbox workflows for queued human handling
• Live handoff when a conversation needs a person

This is the part to scrutinize if your use case involves complex or sensitive requests. The Orange success story on the site describes exactly this pattern: handling frequent requests automatically while routing complex cases and keeping the customer experience consistent across website and social channels.

Multichannel delivery and team operations

Hexabot runs multilingual assistants across web, messaging, social, and custom channels, with roles and analytics for team control. That means the same workflow runtime can serve different channels, and access can be governed by role rather than shared credentials.

Self-hosting and getting started

The platform is self-hosted, which is the basis for the data-control claim: you run the runtime and keep production data under your control. The documented local path is:

1. Install the CLI: npm install -g @hexabot-ai/cli
2. Create a project: hexabot create my-project
3. Enter the workspace: cd my-project
4. Run the local stack: hexabot dev

The starter template produces a working automation workspace where you can define workflows, connect channels, and extend the runtime with actions and tools.

Choosing based on your situation

Hexabot is a reasonable fit if you need self-hosted operation, workflows that both converse and act, and a mix of visual and version-controlled flow definitions. It is less obviously the right choice if you only need a simple chatbot without actions or memory, or if you cannot run and maintain your own stack.

Pricing is tiered — the site lists Starter, Pro, and Unlimited plans and describes starting free before upgrading as users, workflows, and license activations become production signals. Check the pricing page for current terms rather than assuming any tier's limits.