Hexabot

Provides a drag-and-drop interface for constructing multi-turn conversation flows without writing code. Uses a node-based graph architecture where conversation states, conditions, and actions are represented as connected nodes, enabling non-technical users to define branching logic, user input validation, and response routing through visual composition rather than imperative programming.

Integrates natural language understanding to classify user messages into predefined intents and extract structured entities across multiple languages. Uses either built-in NLU models or integrates with external NLU providers, enabling the chatbot to understand user intent beyond exact keyword matching and extract relevant data (names, dates, amounts) from conversational input for downstream processing.

Enables seamless escalation from chatbot to human agent when conversation requires human intervention. Implements queue management, agent routing, and conversation context transfer to ensure agents have full conversation history and user information. Supports multiple handoff triggers (user request, intent confidence threshold, conversation timeout) and integrates with common helpdesk platforms (Zendesk, Intercom, etc.).

Implements rate limiting and throttling mechanisms to prevent abuse and control resource consumption. Supports per-user, per-channel, and global rate limits with configurable thresholds and enforcement strategies (reject, queue, or degrade). Integrates with LLM provider rate limits to prevent exceeding quota and implements backpressure mechanisms to gracefully handle traffic spikes.

Implements content filtering and safety mechanisms to prevent chatbot from generating harmful, offensive, or inappropriate responses. Uses configurable filters for detecting and blocking unsafe content in both user inputs and chatbot responses. Integrates with external safety APIs (OpenAI Moderation, Perspective API) and supports custom filtering rules based on domain-specific policies.

Routes conversation flows across multiple messaging platforms (Slack, WhatsApp, Facebook Messenger, web chat, etc.) while maintaining conversation state and context across channels. Implements a channel abstraction layer that normalizes message formats, handles platform-specific constraints (character limits, media types), and ensures a single conversation thread can span multiple channels with consistent state synchronization.

Abstracts multiple LLM providers (OpenAI, Anthropic, Ollama, local models) behind a unified interface, enabling chatbot responses to be generated by different language models without changing conversation logic. Implements provider-agnostic prompt templating, token counting, and cost tracking across different model families with different API signatures and capabilities.

Provides SDK and plugin architecture for developers to extend chatbot capabilities with custom code (actions, integrations, middleware). Extensions can hook into conversation lifecycle events, implement custom logic for specific intents, or integrate with external APIs. Uses a standardized extension interface that abstracts platform details and enables extensions to be packaged, versioned, and shared across chatbot instances.

Manages conversation state across turns, including user profile data, conversation history, and intermediate processing results. Implements session storage with configurable persistence backends (in-memory, Redis, database) and automatic state serialization/deserialization. Enables conversation resumption after interruptions and maintains context for multi-turn interactions spanning hours or days.

Provides real-time dashboards and analytics for monitoring chatbot performance, including conversation metrics (success rate, average turns, user satisfaction), intent distribution, common failure patterns, and user engagement trends. Collects telemetry from all conversations and surfaces actionable insights for improving chatbot quality through data visualization and anomaly detection.

Enables developers to write and execute test cases for chatbot conversations, simulating user inputs and validating expected responses and state transitions. Supports batch testing of conversation flows, regression testing after model updates, and automated quality checks. Implements conversation replay and debugging tools for analyzing failed test cases.

Enables exporting chatbot conversation flows and configurations to portable formats (JSON, YAML) and importing from external sources. Supports version control integration for tracking changes to conversation logic over time, enabling rollback to previous versions and collaborative editing with conflict resolution. Implements diff visualization for understanding what changed between versions.

Automatically validates conversation flow definitions for common errors and anti-patterns, including unreachable nodes, infinite loops, missing intent handlers, and incomplete response configurations. Provides linting rules that can be customized and enforced as part of development workflow. Generates warnings and errors that guide developers toward correct conversation design patterns.