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: 1509_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

Integrating AI with Open-Meteo API for Enhanced Weather Forecasting is a free n8n workflow on OpenRuna — 12 nodes connecting chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest. Download the JSON, view the flow diagram, and import it into your n8n instance.

What this workflow does

"Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" automates a chat-to-lmChatOpenAi flow in n8n across 12 nodes. 12 nodes · chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest It starts from a scheduled/triggered trigger and routes data through every node in order. OpenRuna renders the node graph and offers the JSON export so you can import and adapt it in minutes. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

Benchmark it against your current chat integration to spot missing steps or redundant nodes.
Fork it to add error handling, retries, or a notification step without designing the 12-node flow from scratch.
Share it with your team as a documented reference for scheduled/triggered automations in the chat stack.
Study how data moves from chat to lmChatOpenAi before building your own variant.

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. Set your credentials on the chat node, then verify each downstream node picks them up. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.

Tips by platform

Claude

Paste the exported JSON for "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" into Claude and ask it to explain each of the 12 nodes. Claude is good at suggesting credential mappings for chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest, plus error-handling and idempotency improvements before you run it in production.

ChatGPT

Have ChatGPT generate a checklist for adapting "Integrating AI with Open-Meteo API for Enhanced Weather Forecasting" to your accounts — which chat, lmChatOpenAi, agent, memoryBufferWindow, toolHttpRequest credentials to create and which node parameters to change first.

Cursor

Open the downloaded JSON in Cursor and use Agent mode to refactor hard-coded URLs into env vars, or to generate tests for any Code nodes. Keep the OpenRuna flow diagram open so you do not break connection order across the 12 nodes.

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.