Can I import a sanitized export without credentials?

Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own chat and lmChatOpenAi credentials in n8n after import.

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Integrating AI with Open-Meteo API for Enhanced Weather Forecasting

12 nodes · chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest

Engine: n8n
Nodes: 12
Trigger: scheduled/triggered
Source file: 1567_Stickynote_Automation_Webhook.json

Workflow flow

IntegrationsChatLmChatOpenAiAgentMemoryBufferWindowToolHttpRequest@n8n/n8n-nodes-langchain.chat@n8n/n8n-nodes-langchain.lmChatOpenAi@n8n/n8n-nodes-langchain.agent@n8n/n8n-nodes-langchain.memoryBufferWindow@n8n/n8n-nodes-langchain.toolHttpRequest

100%

TRIGGER

When chat message received

@n8n/n8n-nodes-langchain.chat

OpenAI Chat Model

@n8n/n8n-nodes-langchain.lmChatOpenAi

Chat Memory Buffer

@n8n/n8n-nodes-langchain.memoryBufferWindow

A tool to get the weather forecast based on geolocation

@n8n/n8n-nodes-langchain.toolHttpRequest

A tool for inputting the city and obtaining geolocation

@n8n/n8n-nodes-langchain.toolHttpRequest

Generic AI Tool Agent

@n8n/n8n-nodes-langchain.agent

6 steps · flows top to bottom, branches left to right

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chat lmChatOpenAi agent memoryBufferWindow toolHttpRequest

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Download n8n JSON Download sanitized JSON Download flow graph

Import into n8n

Download the n8n JSON file (full or sanitized).
In n8n: Workflows → Import from file.
Open each node and reconnect credentials to your accounts.
Test with manual execution before enabling triggers.

Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.

View source corpus ↗

Overview

Free n8n workflow: Integrating AI with Open-Meteo API for Enhanced Weather Forecasting. 12 nodes across chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest, with a downloadable JSON and a visual flow diagram on OpenRuna.

What this workflow does

This n8n workflow, "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting", wires together chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest in 12 nodes. 12 nodes · chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest A scheduled/triggered entry point kicks it off, and each node passes data to the next until the run completes. OpenRuna shows the full flow diagram, an integration breakdown, and a one-click JSON export for n8n's "Import from file". Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

Fork it to add error handling, retries, or a notification step without designing the 12-node flow from scratch.
Benchmark it against your current chat integration to spot missing steps or redundant nodes.
Import "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" into n8n, reconnect credentials for chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest, and run the same automation in your own workspace.
Use it as a starting template when wiring chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest — adjust node parameters to your accounts and record IDs.

Example output

Imported into n8n, "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" exposes 12 configured nodes. Trigger type: scheduled/triggered. Integrations: chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest. Reconnect the chat credential first, then walk the remaining nodes top to bottom. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.

Tips by platform

Claude

Ask Claude to turn the "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" JSON into a plain-English runbook, node by node. It can flag where chat rate limits or missing retries might bite before you go live.

ChatGPT

Drop the workflow JSON or this OpenRuna page URL into ChatGPT and ask for a step-by-step runbook: triggers, branches, and which fields to customise for chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest. Handy for onboarding teammates new to n8n.

Cursor

In Cursor, ask Agent mode to parameterise "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" — extract chat IDs and secrets into environment variables without changing the 12-node structure.

Frequently asked questions

How do I import "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" into n8n?: Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.
Which integrations does "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" use?: It uses: chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest. The flow diagram above shows how each service connects across the 12 nodes. Filter OpenRuna by integration to find related workflows and tools.
Can I import a sanitized export without credentials?: Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own chat and lmChatOpenAi credentials in n8n after import.
What trigger type does "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" use?: Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.
Is "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" free to use?: OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest.