Workflow Examples

This page describes common workflow patterns you can build in FlowTrux. Each example outlines the node sequence and configuration approach.

1. Slack Notification from AI Summary

Pattern: Manual Trigger --> Agent --> Action:MCP

A simple three-node workflow that takes input text, summarizes it with an AI agent, and posts the summary to Slack.

Nodes:

1. Trigger (Manual) -- static data containing the text to summarize:

   { "content": "Paste your long document here..." }
   

2. Agent -- configured with a system prompt like "You are a concise summarizer" and user prompt:

   Summarize the following in 3 bullet points: {{trigger.data.content}}
   

3. Action:MCP -- Slack server, send_message tool:

   {
     "channel": "general",
     "text": "{{steps.agent-1.output.response}}"
   }
   

2. Data Pipeline: API to Google Sheets

Pattern: Webhook --> Action:HTTP --> Agent --> Action:MCP

Receive data via webhook, fetch additional details from an API, analyze with an agent, and write results to a Google Sheet.

Nodes:

1. Trigger (Webhook, minimal response mode) -- external service posts a payload with identifiers.

2. Action:HTTP (GET) -- fetch full data from an external API:

   URL: https://api.example.com/data/{{trigger.data.id}}
   

3. Agent -- analyze the fetched data:

   Analyze this data and extract key metrics: {{steps.http-1.output.data}}
   

4. Action:MCP -- Google Workspace server, sheets_append tool:

   {
     "spreadsheetId": "your-sheet-id",
     "range": "Sheet1!A:D",
     "values": "{{steps.agent-1.output.response}}"
   }
   

3. Uptime Monitoring with Alerts

Pattern: Cron --> Action:HTTP --> Logic:If-Else --> Action:MCP

Check a service health endpoint on a schedule and send an alert if it is down.

Nodes:

1. Trigger (Cron) -- expression */5 * * * * to check every 5 minutes.

2. Action:HTTP (GET) -- call the health check endpoint:

   URL: https://api.example.com/health
   

3. Logic:If-Else -- condition:

   {{steps.http-1.output.status}} !== 200
   

4. Action:MCP (true branch) -- Telegram server, send_message tool:

   {
     "text": "ALERT: Service is down. Status: {{steps.http-1.output.status}}"
   }
   

The false branch (service healthy) can be left unconnected or connected to a logging node.

4. Loop Processing

Pattern: Webhook --> Agent --> Logic:Loop --> Action:MCP --> Aggregator:Concat

Process a list of items individually by looping over an array extracted by an agent.

Nodes:

1. Trigger (Webhook, lastNode response mode) -- receives a batch payload.

2. Agent -- extract structured items from the input:

   Extract all action items from this text as a JSON array of objects
   with "title" and "priority" fields: {{trigger.data.content}}
   

3. Logic:Loop -- condition set to the agent's output array:

   {{steps.agent-1.output.response}}
   

4. Action:MCP (inside loop body) -- process each item. For example, create a YouTrack issue:

   {
     "summary": "{{item.title}}",
     "priority": "{{item.priority}}"
   }
   

5. Aggregator:Concat -- collects all loop iteration results into a single array.

5. Scheduled Backup with Notification

Pattern: Cron --> Action:MCP --> Action:MCP --> Action:MCP

Run a database backup on a schedule, write it to the filesystem, and send a notification.

Nodes:

1. Trigger (Cron) -- expression 0 2 * * * to run daily at 2:00 AM.

2. Action:MCP -- filesystem server or custom server to execute the backup command.

3. Action:MCP -- filesystem server, write the output to a backup file.

4. Action:MCP -- Telegram server, send_message tool:

   {
     "text": "Backup completed at {{steps.action-2.output.path}}"
   }
   

6. Multi-Agent Research Pipeline

Pattern: Trigger --> Agent --> Parallel[Agent, Agent] --> Aggregator:Merge --> Action:MCP

Use multiple agents in parallel to produce different analyses, then merge and deliver the results.

Nodes:

1. Trigger (Manual) -- provide a research topic:

   { "topic": "Impact of AI on healthcare diagnostics" }
   

2. Agent (Researcher) -- gather information:

   Research the following topic and provide detailed findings: {{trigger.data.topic}}
   

3. Logic:Parallel -- splits execution into two concurrent branches:

   • Branch A -- Agent (Summarizer):

     Write a concise executive summary of these findings: {{steps.researcher.output.response}}
     

   • Branch B -- Agent (Sentiment Analyst):

     Analyze the sentiment and key concerns in these findings: {{steps.researcher.output.response}}
     

4. Aggregator:Merge -- combines both branch outputs into a single object.

5. Action:MCP -- Slack server, send_formatted_report tool:

   {
     "channel": "research",
     "title": "Research Report: {{trigger.data.topic}}",
     "sections": [
       { "heading": "Summary", "content": "{{steps.summarizer.output.response}}" },
       { "heading": "Sentiment", "content": "{{steps.sentiment.output.response}}" }
     ]
   }
   

Tips for Building Workflows

• Start small. Build and test one or two nodes at a time before adding complexity.
• Use manual triggers during development. Switch to webhook or cron once the workflow logic is validated.
• Check node outputs in execution history. Expand individual node logs to inspect the exact data being passed between nodes.
• Use the Transform action to reshape data between nodes when the output format of one node does not match the expected input of the next.
• Use minimal webhook mode for integrations that do not need the workflow result. This avoids holding the HTTP connection open.