Evaluate AI agent response correctness with OpenAI and RAGAS methodology

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Important notice

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

1. Workflow Overview

This n8n template demonstrates how to calculate the evaluation metric "Correctness" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths....

Best for

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

Tools used

@n8n/n8n-nodes-langchain.chainllm, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.evaluationtrigger, n8n-nodes-base.set, n8n-nodes-base.evaluation, n8n-nodes-base.noop, @n8n/n8n-nodes-langchain.agent

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Evaluate AI agent response correctness with OpenAI and RAGAS methodology
Workflow name: Evaluate AI agent response correctness with OpenAI and RAGAS methodology

This n8n template demonstrates how to calculate the evaluation metric "Correctness" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths.

The scoring approach is adapted from the open-source evaluations project RAGAS and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py

How it works

This evaluation works best where the agent's response is allowed to be more verbose and conversational.
For our scoring, we classify the agent's response into 3 buckets: True Positive (in answer and ground truth), False Positive (in answer but not ground truth) and False Negative (not in answer but in ground truth).
We also calculate an average similarity score on the agent's response against all ground truths.
The classification and the similarity score is then averaged to give the final score.
A high score indicates the agent is accurate whereas a low score could indicate the agent has incorrect training data or is not providing a comprehensive enough answer.

Requirements

n8n version 1.94+
Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

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 - Correctness Classifier

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.7

Block 2 - Examples1

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

Block 3 - OpenAI Chat Model

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

Block 4 - OpenAI Chat Model1

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

Block 5 - When fetching a dataset row

Type / Role: n8n-nodes-base.evaluationTrigger - evaluationTrigger
Config choices: Version 4.6

Block 6 - Remap Input

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

Block 7 - Evaluation

Type / Role: n8n-nodes-base.evaluation - evaluation
Config choices: Version 4.6

Block 8 - Set Input Fields

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

Block 9 - No Operation, do nothing

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

Block 10 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 11 - When chat message received

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

Block 12 - Merge

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.1

Block 13 - Calculate F1 Score

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

Block 14 - Correctness Score

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

Block 15 - Sticky Note1

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

Block 16 - Sticky Note

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

Block 17 - Update Metrics

Type / Role: n8n-nodes-base.evaluation - evaluation
Config choices: Version 4.6

Block 18 - Update Outputs

Type / Role: n8n-nodes-base.evaluation - evaluation
Config choices: Version 4.6

Block 19 - Sticky Note3

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

Block 20 - Get Embeddings

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

Block 21 - GroundTruth to Items

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

Block 22 - Get Embeddings1

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

Block 23 - Aggregate

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

Block 24 - Remap Embeddings

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

Showing the first 24 of 27 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Evaluate AI agent response correctness with OpenAI and RAGAS methodology
Complexity	advanced
Nodes	27
Categories	Engineering, AI Summarization
Author	Jimleuk
Published	27 May 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/4424/4424.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 Evaluate AI agent response correctness with OpenAI and RAGAS methodology do?

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.

Jimleuk

100 workflows

Nodes

@n8n/n8n-nodes-langchain.chainllm @n8n/n8n-nodes-langchain.outputparserstructured @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.evaluationtrigger n8n-nodes-base.set n8n-nodes-base.evaluation n8n-nodes-base.noop @n8n/n8n-nodes-langchain.agent

Complexity

advanced

Published 27 May 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/4424/4424.json

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Evaluate AI agent response correctness with OpenAI and RAGAS methodology

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

This n8n template demonstrates how to calculate the evaluation metric "Correctness" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths.

How it works

Requirements

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Correctness Classifier

Block 2 - Examples1

Block 3 - OpenAI Chat Model

Block 4 - OpenAI Chat Model1

Block 5 - When fetching a dataset row

Block 6 - Remap Input

Block 7 - Evaluation

Block 8 - Set Input Fields

Block 9 - No Operation, do nothing

Block 10 - AI Agent

Block 11 - When chat message received

Block 12 - Merge

Block 13 - Calculate F1 Score

Block 14 - Correctness Score

Block 15 - Sticky Note1

Block 16 - Sticky Note

Block 17 - Update Metrics

Block 18 - Update Outputs

Block 19 - Sticky Note3

Block 20 - Get Embeddings

Block 21 - GroundTruth to Items

Block 22 - Get Embeddings1

Block 23 - Aggregate

Block 24 - Remap Embeddings

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