ManualTrigger Automate

For n8n, this manual-triggered workflow automates the mapping and storage of workflow credentials into a SQLite database, enabling users to query and retrieve credential information efficiently using an AI agent. It simplifies the management of workflow credentials, allowing for quick searches and insights, such as identifying workflows that utilize specific applications.

7/8/2025
13 nodes
Medium
manualmediumsticky notelangchainn8nadvanced
Categories:
Manual TriggeredMedium Workflow
Integrations:
Sticky NoteLangChainN8n

Target Audience

Who Should Use This Workflow


- Developers looking for a way to manage and store workflow credentials securely.
- Data Analysts who need to query and analyze workflow credentials without exposing sensitive information.
- Automation Engineers interested in integrating various tools like Sticky Note, LangChain, and N8n for advanced automation solutions.
- Project Managers needing to oversee workflow efficiency and credential usage across different applications.

Problem Solved

What Problem Does This Workflow Solve


This workflow addresses the challenge of managing and securing workflow credentials across multiple integrations. It enables users to:
- Store workflow credentials in a SQLite database securely.
- Retrieve and query these credentials easily using an AI agent, ensuring that sensitive information is not directly exposed.
- Provide a user-friendly interface for querying credentials, making it accessible for non-technical users.

Workflow Steps

Detailed Explanation of the Workflow Process


1. Manual Trigger: The workflow starts when the user clicks the "Test workflow" button.
2. Map Workflows & Credentials: This step collects the workflow ID, name, and credentials from the current n8n instance and prepares them for storage.
3. Save to Database: The extracted credentials are saved into a SQLite database named n8n_workflow_credentials.db, ensuring that the credentials are stored securely and can be retrieved later.
4. Chat Trigger: This element sets up a webhook that allows users to interact with the AI agent.
5. OpenAI Chat Model: Integrates OpenAI's chat capabilities, allowing the AI agent to process user queries.
6. Window Buffer Memory: This stores the context for the AI agent, enabling it to maintain a conversation state.
7. Workflow Credentials Helper Agent: This AI agent helps users find specific workflow credentials based on their queries.
8. Query Workflow Credentials Database: This step enables the AI agent to execute SQL queries against the SQLite database to retrieve relevant credentials based on user requests.

Customization Guide

How Users Can Customize and Adapt This Workflow


- Adjust Database Schema: Users can modify the database schema in the Save to Database step to include additional fields or tables as required by their specific use cases.
- Modify AI Agent Responses: Customize the systemMessage in the Workflow Credentials Helper Agent to change how the AI agent interacts with users, tailoring it to specific queries or workflows.
- Change Trigger Conditions: Users can adapt the Manual Trigger to other types of triggers (e.g., scheduled triggers) based on their needs.
- Enhance Query Capabilities: Customize the Query Workflow Credentials Database step to allow for more complex SQL queries, enhancing the agent's ability to retrieve specific information.