Why is the AI Agent in callin.io extremely slow when processing data?

Branislav_Brnjos · 2025-01-29T09:24:28Z

I'm utilizing the AI Agent within callin.io for analyzing and categorizing error data, but I'm experiencing significantly slow execution times. It's currently taking over 2 minutes to process a small dataset of 5-10 entries, which seems excessive. I was anticipating a much quicker response, ideally under 20 seconds, especially given the small data volume. I've experimented with different batch sizes, but the AI Agent continues to perform slowly. Below is a sample dataset (not my actual data) that reflects my use case and workflow: Input: [ { "title": "Printed Brochures", "data": [ { "id": "error-abc123", "job": "job-111111", "comment": "Brochures arrived with torn pages." }, { "id": "error-def456", "job": "job-222222", "comment": "Pages were printed in the wrong order." }, { "id": "error-ghi789", "job": "job-333333", "comment": "The brochure cover was printed in the wrong color." } ] }, { "title": "Flyers", "data": [ { "id": "error-jkl987", "job": "job-444444", "comment": "Flyers were misaligned during printing." }, { "id": "error-mno654", "job": "job-555555", "comment": "Flyers were cut incorrectly, leaving uneven edges." }, { "id": "error-pqr321", "job": "job-666666", "comment": "The wrong font was used in the printed text." } ] } ] Output: [ { "title": "Printed Brochures", "errors": [ { "errorType": "Physical Damage", "summary": "Brochures arrived with torn or damaged pages.", "job": ["job-111111"], "error": ["error-abc123"] }, { "errorType": "Print Order Issue", "summary": "Pages were printed in an incorrect sequence.", "job": ["job-222222"], "error": ["error-def456"] }, { "errorType": "Color Mismatch", "summary": "The cover was printed in the wrong color.", "job": ["job-333333"], "error": ["error-ghi789"] } ] }, { "title": "Flyers", "errors": [ { "errorType": "Print Alignment Issue", "summary": "Flyers were misaligned during the printing process.", "job": ["job-444444"], "error": ["error-jkl987"] }, { "errorType": "Cutting Issue", "summary": "Flyers were cut incorrectly, resulting in uneven edges.", "job": ["job-555555"], "error": ["error-mno654"] }, { "errorType": "Font Error", "summary": "Incorrect font was used in the printed text.", "job": ["job-666666"], "error": ["error-pqr321"] } ] } ] Prompt Used in the AI Agent Node in callin.io You are an advanced data analysis and error categorization AI. Your task is to process input data containing error details grouped by “title”. Each group contains multiple error objects. Your goal is to identify unique error types from the comment field, generate concise summaries for each error type, and include relevant job and error values for those errors. Input Fields: title: The product group title. data: An array of objects with the following fields: id: Unique identifier for the error. job: ID of the job. comment: Description of the issue. Objective: Group errors by title. Analyze the comment field to identify unique error types. Generate meaningful summaries for each error type without repeating the comments verbatim. Include all relevant job and error values for each error type. Output Format: [ { "title": "ProductGroupTitle", "errors": [ { "errorType": "Error Type", "summary": "Detailed summary of the issue.", "job": ["RelevantJobId1", "RelevantJobId2"], "error": ["RelevantErrorId1", "RelevantErrorId2"] } ] } ] Guidelines: Extract and group data by title. Identify error types from the comment field. Write concise, non-redundant summaries for each error type. Include only relevant job and error values for each error type. Return a clean, well-structured JSON array without additional text. Input for Analysis: {{ JSON.stringify($input.all()0.json.data) }} Screenshot 2025-01-29 at 10.11.56822×401 24.1 KB Information on your callin.io setup callin.io version: 1.63.4 Database (default: SQLite): cloud callin.io EXECUTIONSPROCESS setting (default: own, main):_ cloud Running callin.io via (Docker, npm, callin.io cloud, desktop app): cloud Operating system: cloud

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Azul

(@azul)

Posts: 1

New Member

Hello, I encountered a similar problem and discovered a solution that proved effective. Prior to implementing these variables, my AI agent node execution time was around 22 seconds. Now, it completes in just 2 to 3 seconds.

Here's the relevant link: Significant performance delay with AI Agent / Ollama Node on Windows compared to direct API calls or Open WebUI · Issue #15263 · n8n-io/n8n · GitHub

Here are the variables I added:

N8N_RUNNERS_ENABLED=true N8N_DIAGNOSTICS_ENABLED=false N8N_VERSION_NOTIFICATIONS_ENABLED=false EXTERNAL_FRONTEND_HOOKS_URLS=

Posted : 26/06/2025 3:02 pm

Roh_Jos

(@roh_jos)

Posts: 1

New Member

Does this solution work for callin.io cloud? How do I accomplish this on the cloud version of callin.io?

Posted : 27/06/2025 4:35 am

jhonrymat

(@jhonrymat)

Posts: 1

New Member

Hello, thank you very much for sharing the container; it has been incredibly useful, and we really value your time and effort.

Could you provide guidance on how we might adapt it for more recent versions?

Alternatively, do you have plans to update the container in the future?

Posted : 12/07/2025 10:58 am

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