🚀 Local multi-LLM testing & performance tracker

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Important notice

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

1. Workflow Overview

Local Multi LLM Testing & Performance Tracker This workflow is perfect for developers, researchers, and data scientists benchmarking multiple LLMs with LM Studio. It dynamically fetches active m...

Best for

Engineering automation workflows
AI Summarization automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.stickynote, n8n-nodes-base.httprequest, @n8n/n8n-nodes-langchain.chattrigger, n8n-nodes-base.datetime, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.code, n8n-nodes-base.googlesheets, n8n-nodes-base.set

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: 🚀 Local multi-LLM testing & performance tracker
Workflow name: 🚀 Local multi-LLM testing & performance tracker

🚀 Local Multi-LLM Testing & Performance Tracker

This workflow is perfect for developers, researchers, and data scientists benchmarking multiple LLMs with LM Studio. It dynamically fetches active models, tests prompts, and tracks metrics like word count, readability, and response time, logging results into Google Sheets. Easily adjust temperature 🔥 and top P 🎯 for flexible model testing.

Level of Effort:

🟢 Easy – Minimal setup with customizable options.

Setup Steps:

Install LM Studio and configure models.
Update IP to connect to LM Studio.
Create a Google Sheet for result tracking.

Key Outcomes:

Benchmark LLM performance.
Automate results in Google Sheets for easy comparison.

Version 1.0

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 - Sticky Note

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

Block 2 - Get Models

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 3 - Sticky Note1

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

Block 4 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 5 - Sticky Note2

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

Block 6 - Sticky Note3

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

Block 7 - Get timeDifference

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

Block 8 - Sticky Note4

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

Block 9 - Sticky Note5

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

Block 10 - Sticky Note6

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

Block 11 - Run Model with Dunamic Inputs

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

Block 12 - Analyze LLM Response Metrics

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

Block 13 - Save Results to Google Sheets

Type / Role: n8n-nodes-base.googleSheets - googleSheets
Config choices: Version 4.5

Block 14 - Capture End Time

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

Block 15 - Capture Start Time

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

Block 16 - Prepare Data for Analysis

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 17 - Extract Model IDsto Run Separately

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

Block 18 - Sticky Note7

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

Block 19 - Add System Prompt

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 20 - LLM Response Analysis

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.4

Block 21 - Sticky Note8

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

3. Summary Table

Workflow	🚀 Local multi-LLM testing & performance tracker
Complexity	advanced
Nodes	21
Categories	Engineering, AI Summarization
Author	Wildkick
Published	28 Sept 2024

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2442/2442.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

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

Download workflow JSON Original n8n.io source Engineering workflows AI Summarization workflows

Frequently asked questions

What does 🚀 Local multi-LLM testing & performance tracker do?

Local Multi LLM Testing & Performance Tracker This workflow is perfect for developers, researchers, and data scientists benchmarking multiple LLMs with LM Studio. It dynamically fetches active m...

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.

Wildkick

1 workflow

Nodes

n8n-nodes-base.stickynote n8n-nodes-base.httprequest @n8n/n8n-nodes-langchain.chattrigger n8n-nodes-base.datetime @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.code n8n-nodes-base.googlesheets n8n-nodes-base.set

Complexity

advanced

Published 28 Sept 2024

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/2442/2442.json

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🚀 Local multi-LLM testing & performance tracker

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

🚀 Local Multi-LLM Testing & Performance Tracker

Level of Effort:

Setup Steps:

Key Outcomes:

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Sticky Note

Block 2 - Get Models

Block 3 - Sticky Note1

Block 4 - When chat message received

Block 5 - Sticky Note2

Block 6 - Sticky Note3

Block 7 - Get timeDifference

Block 8 - Sticky Note4

Block 9 - Sticky Note5

Block 10 - Sticky Note6

Block 11 - Run Model with Dunamic Inputs

Block 12 - Analyze LLM Response Metrics

Block 13 - Save Results to Google Sheets

Block 14 - Capture End Time

Block 15 - Capture Start Time

Block 16 - Prepare Data for Analysis

Block 17 - Extract Model IDsto Run Separately

Block 18 - Sticky Note7

Block 19 - Add System Prompt

Block 20 - LLM Response Analysis

Block 21 - Sticky Note8

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions