Skip to main content

OAuth2 settings finder with OpenRouter chat model and Llama 3.3

Workflow preview

Workflow preview
100%
OAuth2 settings finder with OpenRouter chat model and Llama 3.3 preview
Open on n8n.io

Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Find OAuth URIs with AI Llama Overview: The AI agent identifies: Authorization URI Token URI Audience Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data: Sc...

Best for

  • Engineering automation workflows
  • AI Summarization automation workflows
  • intermediate n8n builders looking for reusable templates

Tools used

n8n-nodes-base.executeworkflowtrigger, @n8n/n8n-nodes-langchain.chainllm, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatopenrouter, n8n-nodes-base.code, n8n-nodes-base.stickynote

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Hendriekus.

Original n8n.io source

1.1 Workflow description

Title
OAuth2 settings finder with OpenRouter chat model and Llama 3.3
Workflow name
OAuth2 settings finder with OpenRouter chat model and Llama 3.3

Find OAuth URIs with AI Llama

Overview: The AI agent identifies:

  • Authorization URI
  • Token URI
  • Audience

Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data:

  • Score Range: 0 < x ≤ 1
  • Score Granularity: 0.01 increments

Model Details: Leveraging the Wayfarer Large 70b Llama 3.3 model.

How it works:

This template is designed to assist users in obtaining OAuth2 settings using AI-powered insights. It is ideal for developers, IT professionals, or anyone working with APIs that require OAuth2 authentication. By leveraging the AI agent, users can simplify the process of extracting and validating key details such as the authorization_url, token_url, and audience.

Set up instructions:

1. Configuration Nodes

  • Structured Output Node: Parses the AI model's output using a predefined JSON schema. This ensures the data is structured for downstream processing.
  • Code Node: If the AI model’s output does not match the required format, use the Code node to re-arrange and transform the data. Example code snippets are provided below for common scenarios.

2. AI Model Prompt

The prompt for the AI model includes:

  • A detailed structure and objectives of the query.
  • Flexibility for the model to improvise when accurate results cannot be determined.

3. Confidence Scoring

The AI model assigns a confidence score (0 < x ≤ 1) to indicate the reliability of the extracted data. Scores are provided in increments of 0.01 for granularity.

Adaptability

Customize this template:

  • Update the AI model prompt with details specific to your API or OAuth2 setup.
  • Adjust the JSON schema in the Structured Output node to match the data format.
  • Modify the Code logic to suit the application's requirements.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When Executed by Another Workflow

Type / Role
n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
Config choices
Version 1.1

Block 2 - LLM Bus

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.5

Block 3 - Structured Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 4 - OpenRouter Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 5 - Conform JSON

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 6 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 7 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 8 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 9 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 10 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 11 - Sticky Note5

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 12 - Sticky Note6

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

3. Summary Table

Workflow OAuth2 settings finder with OpenRouter chat model and Llama 3.3
Complexity intermediate
Nodes 12
Categories Engineering, AI Summarization
Author Hendriekus
Published 21 Mar 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/3279/3279.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does OAuth2 settings finder with OpenRouter chat model and Llama 3.3 do?

Find OAuth URIs with AI Llama Overview: The AI agent identifies: Authorization URI Token URI Audience Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data: Sc...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI Summarization use case.