This Langflow flow processes customer support messages to analyze their sentiment and urgency levels. The system takes JSON-formatted customer messages as input through either a text input component or webhook endpoint. It uses a parser to extract and format the message data before passing it to an AI agent for analysis.

The core processing happens through an OpenAI-powered agent that follows specific instructions to categorize each message. The agent analyzes the tone, keywords, and content to determine sentiment (positive, neutral, or negative), urgency level (low, medium, or high), and escalation risk (none, potential, or high). It also identifies any emotions present in the message such as anger, frustration, or gratitude.

The agent's analysis gets processed through a structured output component that formats the results into a consistent JSON schema. This ensures all responses contain the same fields: message content, sentiment classification, urgency level, escalation risk assessment, and detected emotions. The final structured data is then displayed through a chat output component, providing customer support teams with actionable insights for prioritizing and routing messages appropriately.

The flow can be extended using other Langflow nodes to create more sophisticated workflows. You could add embedding models and vector stores to incorporate customer history or SLA information into the analysis. API request components can automatically create tickets in help desk systems like Zendesk or send alerts to Slack channels. Prompt templates allow you to customize the classification criteria for different industries or use cases, while data type handling ensures smooth integration between components.