An enterprise-grade dialogue system providing intent recognition, entity extraction, and context-aware conversation management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chatbot-engine
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install chatbot-engine using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Chatbot Engine?
Chatbot Engine is a sophisticated conversational framework designed to empower AI agents with human-like interaction capabilities. As a core component of Openclaw Skills, it provides a robust infrastructure for handling complex, multi-turn dialogues while maintaining state and context across interactions.
This skill bridges the gap between raw LLM outputs and structured business logic. It integrates advanced Natural Language Processing (NLP) techniques, including vector-based knowledge base retrieval and hybrid intent classification, to ensure that user queries are understood accurately and answered with relevant, grounded information.
Chatbot Engine Use Cases
- Building automated customer support bots that require multi-step troubleshooting flows.
- Implementing booking and reservation systems that need to extract specific entities like dates, locations, and names.
- Creating context-aware personal assistants that remember previous user inputs during a session.
- Developing FAQ bots that utilize vector-based retrieval to provide precise answers from large knowledge bases.
How Chatbot Engine Works
- The system receives a raw text input from the user through the DialogueManager interface.
- The Intent Classifier analyzes the text to determine the user's primary goal using machine learning or rule-based models.
- The Entity Extractor identifies and labels key information such as names, dates, and locations (NER).
- The Dialogue Management layer checks the current session state and determines if additional information is needed from the user.
- If required, the Knowledge Base performs a vector search to fetch relevant context or documentation.
- The engine synthesizes a response and updates the conversation state for the next turn in the Openclaw Skills workflow.
Chatbot Engine Setup
To integrate this skill into your environment, install the necessary dependencies using the following command:
pip install -r requirements.txt
You can then initialize the engine in your Python script:
from scripts.dialogue_manager import DialogueManager
bot = DialogueManager()
response = bot.process("I want to book a hotel in Beijing tomorrow")
print(response)
Chatbot Engine Data Schema & Taxonomy
The Chatbot Engine organizes its logic and metadata through a structured directory hierarchy within the Openclaw Skills framework:
| Component | File/Path | Description |
|---|---|---|
| Core Logic | scripts/dialogue_manager.py |
Orchestrates intent, entities, and state. |
| NLP Models | scripts/intent_classifier.py |
Handles categorization of user goals. |
| NER Data | scripts/entity_extractor.py |
Manages extraction of structured entities. |
| Knowledge | scripts/knowledge_base.py |
Interface for vector search and RAG. |
| Documentation | SKILL.md |
Skill definition and metadata. |
Chatbot Engine Advanced Features
- Context-Aware State Tracking: Maintains a history of the conversation to handle ambiguous follow-up questions.
- Vector-Based RAG: Seamlessly integrates with knowledge bases for highly accurate information retrieval.
- Hybrid Intent Recognition: Combines deterministic rules with machine learning for high-precision classification.
- Modular Architecture: Easily swap or extend the entity extraction and intent classification modules within Openclaw Skills.
SKILL.md
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