Langflow

What if your AI always returned clean, predictable outputs? The Structured Output feature allows you to define an explicit output schema for your AI workflows, specifying the exact structure and data types the model must return. Instead of dealing with free-form text and fragile parsing, you define clear fields, types, and lists, and the model is guided to comply with that structure. This turns LLMs into reliable system components, making AI workflows easier to integrate, validate, and run in production. Ideal for: - APIs and backend integrations - Data extraction and normalization - Automation workflows - Reducing parsing errors and edge cases With Structured Output, AI responses follow a contract (not best-effort text) enabling predictable, production-ready workflows in Langflow. Learn more about Langflow: https://lnkd.in/diHc5mWn utm_medium=organic&utm_campaign=feature_structured_output

Langflow Use Case: Lead Scoring System Automatically score and classify Facebook leads using AI, turning raw lead data into prioritized, sales-ready opportunities. This Langflow workflow analyzes incoming Facebook leads, enriches them with company and contextual data, evaluates fit and intent, and assigns quality tiers based on predefined criteria. The results are delivered as structured data directly to your CRM, enabling smarter routing, faster follow-ups, and more efficient sales handoffs. Ideal for scaling paid media. Ready for real production use. 🔗 Template: https://lnkd.in/d3jxq_HU

In our first episode of the year, we talk with Ruben Casas from Postman about how Postman has embraced AI with support for MCP, flows, and agents.

How to Build a File-Aware Agent in Langflow This tutorial shows how to build an agent that reads uploaded files and uses them as context. By default, agents only see user messages. With this setup, the agent can: - Read file content - Use that content as context - Answer questions grounded on documents Components Used: Chat Input Read File Prompt Template Agent Chat Output Step-by-step Setup: Read File Use the Read File component to load the content of uploaded files. Prompt Template Create a prompt that injects the file content as context. Agent Connect the Prompt Template to the Agent, define the instructions, and select a model. Chat Output Send the agent response to Chat Output. This setup allows agents to reason over uploaded documents in a clean and practical way. That’s how you build agents that understand files, not just prompts. Learn more about Langflow: https://lnkd.in/dp-HCu_b

Turn your AI agent into a Google Calendar assistant With Composio components in Langflow, you can connect Google Calendar directly into your flow and let your agent: - Read calendar availability - Create and update events - Schedule meetings based on context - Reschedule or cancel events automatically - Trigger workflows from calendar events One powerful example is conversation-driven automated scheduling. Your agent can auto-schedule meetings right after an email reply, checking availability and creating the calendar event automatically. The same logic applies to conversations in tools like WhatsApp or Slack, creating calendar events directly from the conversation. This is how agents stop managing prompts and start orchestrating real workflows across communication and time. 👉 Learn more about Langflow: https://lnkd.in/dAut_7Pu

Agent skills are taking over from MCP servers, but MCP is bringing out UIs. Some believe MCP is finished already, so we dig into the two sides in this week's AI++ newsletter.

What if your workflow could automatically choose the best LLM for each task? The LLM Selector component allows a flow to dynamically select the most appropriate language model at runtime, based on the request context and OpenRouter model specifications. Instead of hardcoding a single model, the LLM Selector uses a judge LLM and an optimization goal to decide which model should handle each request. You can optimize for: Quality → prioritize higher-quality, reasoning-focused models Speed → prioritize faster responses Cost → prioritize lower-cost models Balanced → trade off quality, speed, and cost Whenever a workflow needs to decide which LLM should handle a task, the selector evaluates the input and routes execution automatically without manual switching. Ideal for: - Cost-aware production workflows - Mixing fast and reasoning-heavy models - Dynamic fallback between providers - Multi-LLM systems that adapt per request The LLM Selector is a key feature for building scalable, efficient, and production-ready AI workflows, available in Langflow. Learn more about Langflow: https://lnkd.in/dcqzGy4S

Build and test Langflow components directly in VS Code A community-built VS Code extension makes it possible to edit Langflow component code, sync changes back to your local instance, and run flows, all without leaving your IDE. With this extension, developers can: - Browse Langflow projects and flows inside VS Code - Open and edit component Python code - Save and sync changes back to Langflow - Execute flows via API and stream outputs in real time Designed for developers who want a tighter feedback loop when working on custom Langflow components, especially alongside AI-assisted coding tools. Built by Samuel Matioli 👏 🔗 VS Code Marketplace https://lnkd.in/dcKzFYAb 🎥 Demo https://lnkd.in/dwgHVNwz

Langflow Use Case: Document Classification Simplify document organization by automatically classifying contracts, invoices, and business files by type and content. Ideal for finance, legal, operations, and any team handling large volumes of documents. This Langflow workflow creates a document classification system that processes uploaded files, extracts text, and uses AI to intelligently categorize them. From there, documents can be organized, routed to the right teams, and integrated with other systems through structured outputs. 🔗 Template: https://lnkd.in/dBMabhCi

How to Build an Agent That Can Access Websites in Langflow A basic agent can generate responses, but it cannot browse the web or access external data by default. To interact with websites, the agent needs tools. Why use a URL Tool - Fetches content from specific websites - Reads and analyzes external pages - Uses real, up-to-date information in responses Instead of guessing, the agent can consult the source directly. How Langflow Enables This (Tool Mode) In Langflow, components can be turned into tools by enabling Tool Mode. When enabled: - The component becomes callable by the agent - The agent decides when to use it during reasoning You give the agent a capability, not a script. Step-by-step: Agent with Website Access Components Chat Input Agent Chat Output URL component (Tool Mode enabled) Enabling the URL Component as a Tool After adding the URL component to the canvas, enable Tool Mode in its settings. This allows the agent to call the URL tool whenever it needs external information. Depth Parameter (URL Tool) The Depth parameter controls how far the agent can navigate within a website. Depth = 1 → fetch only the initial URL Depth ≥ 1 → includes internal pages within the same site Result: With this setup, the agent can receive a user prompt, fetch content from one or more website pages, and generate informed, grounded responses. That’s how you build agents that actually interact with the real world. Learn more about Langflow: https://lnkd.in/diHc5mWn utm_medium=organic&utm_campaign=agent_url_tool_tutorial