Load Prompts from GitHub Repo and Auto-Populate n8n Expressions

Workflow Name

Load Prompts from GitHub Repo and Auto-Populate n8n Expressions

Key Features and Highlights

This workflow automatically loads prompt text files from a specified GitHub repository, dynamically extracts variable placeholders within the prompts, and replaces them with preset variable values to generate complete prompt content for subsequent AI model invocation. It incorporates a variable validation mechanism to ensure all required variables are assigned; otherwise, it proactively throws an error and halts execution. Additionally, by integrating the Ollama chat model with the LangChain AI Agent, it achieves a closed-loop automated process from text prompts to intelligent responses.

Core Problems Addressed

• Automated management and invocation of text prompt templates stored in GitHub
• Dynamic replacement of variables in prompts to avoid inefficiencies and errors from manual editing
• Variable completeness validation to ensure prompt accuracy
• Directly driving AI models with structured prompts for intelligent text generation or interaction

Application Scenarios

• AI assistants or chatbots dynamically generating conversation prompts from version-controlled template libraries
• Content creators or marketing teams quickly producing customized copy based on project-specific parameters
• R&D teams building prompt-driven automated testing or documentation generation workflows
• Any scenario requiring intelligent text processing that combines version control systems with AI models

Main Workflow Steps

1. Manually trigger the workflow to start execution
2. Use the “setVars” node to preset variables such as GitHub username, repository name, file path, and business-related variables (company, product, features, etc.)
3. Connect to the GitHub node to read prompt text files from the specified path
4. Extract file content and store it in the “SetPrompt” node
5. Use the code node “Check All Prompt Vars Present” to dynamically scan variable placeholders in the prompt and verify all are defined in “setVars”
6. Use the conditional node “If” to decide whether to continue with variable replacement or stop and throw an error based on validation results
7. Execute variable replacement in the code node “replace variables,” substituting placeholders with actual values
8. Save the fully replaced prompt text
9. Invoke the LangChain AI Agent combined with the Ollama chat model to perform AI text generation or interaction using the generated prompt
10. Output the final AI response

Systems or Services Involved

• GitHub: Storage and version control platform for prompt templates
• n8n Built-in Nodes: Manual trigger, variable setting, conditional logic, code execution, error handling, text extraction, etc.
• LangChain AI Agent: AI task execution driven by prompts
• Ollama Chat Model: Local or cloud-based AI language model service

Target Users and Value

• AI Product Developers: Quickly manage and invoke prompt templates to enhance development efficiency
• Content Marketing and Copywriting Teams: Automatically generate customized content, reducing repetitive work
• Automation Engineers: Build intelligent text processing pipelines
• Any teams or individuals combining code version control with AI text interaction, facilitating efficient, controllable prompt management and AI generation workflows

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By seamlessly integrating GitHub with AI models, this workflow ensures prompt template version consistency and variable accuracy, significantly enhancing the flexibility and reliability of prompt-driven AI applications.