Hexabot

Provides a drag-and-drop interface to construct conversational flows without writing code, using a node-based graph system where users connect intent recognition, response logic, and action nodes. The builder compiles visual workflows into executable bot logic that routes user inputs through decision trees and conditional branches, supporting multi-turn conversations with state management across dialogue turns.

Integrates natural language understanding to classify user inputs into predefined intents and extract structured entities, supporting multiple languages through language-agnostic tokenization and embedding-based similarity matching. The system allows custom entity definitions (regex patterns, lookup lists, ML models) that are applied post-classification to extract domain-specific information from recognized intents.

Enforces rate limits and usage quotas at the user, channel, or global level to prevent abuse and manage costs. Supports multiple rate-limiting strategies (token bucket, sliding window) and quota types (messages per hour, API calls per day, LLM tokens per month). Includes configurable responses when limits are exceeded (error messages, queue for later processing, or graceful degradation).

Abstracts multiple LLM providers (OpenAI, Anthropic, local models) behind a unified interface, allowing users to swap providers or route requests based on cost/latency without changing bot logic. Includes a prompt templating engine that injects conversation context, user variables, and entity data into LLM calls, with support for few-shot examples and system prompts configured via the visual editor.

Routes bot responses to multiple messaging platforms (Telegram, WhatsApp, Slack, Discord, web chat, etc.) with automatic format conversion. The system abstracts platform-specific constraints (character limits, rich text support, media types) and converts generic bot responses into platform-native formats (Slack blocks, Telegram inline keyboards, WhatsApp templates) without requiring channel-specific logic in the bot definition.

Provides a plugin system allowing developers to extend bot capabilities with custom code (JavaScript/TypeScript or Python) for actions, integrations, and custom NLU models. Extensions are registered in the visual editor and can be invoked from bot workflows, receiving conversation context and returning results that flow back into the dialogue. The architecture supports both synchronous actions (API calls) and asynchronous workflows (background jobs).

Maintains conversation state across multiple dialogue turns, storing user variables, extracted entities, and dialogue history in a context object that persists for the duration of a session. State is accessible to all workflow nodes (intents, actions, LLM calls) and can be modified by extensions or bot logic, enabling multi-turn conversations that reference previous exchanges and maintain user-specific data without external databases.

Automatically logs all conversation events (user messages, intent recognition, bot responses, action execution) with structured metadata (timestamps, confidence scores, latency, user IDs, channel) into a queryable event store. Provides dashboards for conversation metrics (volume, intent distribution, resolution rates) and allows filtering/searching conversations by user, intent, or time range for debugging and analytics.

Provides tools to train intent classifiers by collecting user utterances, labeling them with intents, and retraining the NLU model. Includes active learning features that identify low-confidence predictions or out-of-distribution inputs and suggest them for labeling, accelerating model improvement without manual data collection. Training can be triggered on-demand or scheduled periodically.

Supports versioning of bot workflows, allowing multiple versions to coexist and be deployed to different user segments or channels. Includes A/B testing capabilities to route users to different bot versions and measure performance differences (intent recognition accuracy, user satisfaction, conversion rates) without requiring external experimentation platforms.

Enables seamless escalation from bot to human agents, transferring the full conversation context (history, extracted information, user variables) to the agent interface. Supports integration with helpdesk systems (Zendesk, Intercom, etc.) and includes queue management, agent assignment, and conversation resumption if the bot regains capability to handle the request.