A specialized skill for automating message object construction and dynamic conversation history management using LangChain.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install langchain-chat-prompt-template
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 langchain-chat-prompt-template 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 LangChain ChatPromptTemplate Skill?
This skill provides a comprehensive guide to implementing ChatPromptTemplate and MessagesPlaceholder within the LangChain framework. It is designed to help developers build sophisticated conversational interfaces without the overhead of manually constructing complex message objects for every interaction. By utilizing this approach within Openclaw Skills, you can create more maintainable and scalable AI assistants that handle stateful conversations naturally.
The core value of this skill lies in its ability to abstract the structural requirements of chat-based models. Instead of hardcoding message arrays, you define a template that can dynamically accept history at runtime, ensuring your LLM has the necessary context to provide relevant and coherent responses.
LangChain ChatPromptTemplate Skill Use Cases
- Developing interactive chatbots that need to recall previous user queries and AI responses.
- Creating career coaching bots that maintain context across multiple user sessions.
- Orchestrating complex multi-turn dialogues in AI-driven customer support tools.
- Implementing dynamic prompt injection for agents using the Openclaw Skills methodology.
How LangChain ChatPromptTemplate Skill Works
- Import the necessary components from langchain_core, specifically ChatPromptTemplate and MessagesPlaceholder.
- Define a template structure using the from_messages factory method, incorporating SystemMessage and HumanMessage definitions.
- Integrate a MessagesPlaceholder into the template to act as a dynamic variable for conversation history.
- Invoke the template by passing a list of historical message objects (HumanMessage and AIMessage) at runtime.
- Pass the formatted output directly to a Chat Model for processing.
LangChain ChatPromptTemplate Skill Setup
To get started with this implementation, ensure you have the core LangChain package installed in your development environment:
pip install langchain-core
Once installed, you can integrate these templates into your custom agents or scripts following the Openclaw Skills standards for modular AI development.
LangChain ChatPromptTemplate Skill Data Schema & Taxonomy
| Element | Type | Purpose |
|---|---|---|
| ChatPromptTemplate | Object | Defines the overall structure and sequence of messages sent to the LLM. |
| MessagesPlaceholder | Variable | Reservse a specific location in the prompt for dynamic history lists. |
| HumanMessage | Class | Wraps user-provided text into a format the chat model recognizes. |
| AIMessage | Class | Wraps model-generated responses to maintain accurate history. |
| Runtime Variables | Dictionary | A collection of keys (like 'history') mapped to lists of message objects. |
LangChain ChatPromptTemplate Skill Advanced Features
- Support for persistent memory integration allowing bots to remember context over days or weeks.
- Multi-agent compatibility where history from different specialized agents can be merged into a single placeholder.
- Modular prompt partials that can be swapped dynamically based on user intent.
- Seamless integration into broader Openclaw Skills workflows for automated AI coding tasks.
SKILL.md
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