Simple Agent -
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Simple Agent – Smart Medical Assistant

A flexible Langflow-powered chatbot designed to remember conversations, deliver structured responses, and connect seamlessly with backends or UIs for healthcare and utility applications.

Team

2–3 Members

Duration

2 Months

Technology

Python, Langflow

case5-agent

Product Overview

The Simple Agent is a lightweight, memory-aware AI chatbot designed using Langflow and OpenAI. It offers real-time, conversational interaction while delivering structured responses in JSON format for easy integration with backend systems. Built for flexibility, it allows developers to customize prompts, maintain session memory, and route outputs for various use cases; ranging from healthcare tools to internal support assistants.

How It Works

The chatbot takes user queries in real time, stores former sessions’ data with memory modules, and generates contextual responses with OpenAI. The outputs are ultimately delivered as natural text and JSON outputs, which are readable by humans and consumable by machines. Langflow manages the flow from input to the response, and custom components route the JSON data to connected systems or APIs.

User Cases

  • Healthcare Chat Assistants – Guide patients, answer queries, and automate routine interactions.
  • Medical Report Builders – Extract structured data from conversations to generate records.
  • Internal Workflow Agents – Support doctors or admins with structured task automation.
  • Triage Bots – Collect symptoms and prepare JSON reports for pre-diagnosis tools.
  • Voice or Text Virtual Agents – Enable omnichannel support with contextual memory.

Benefits

  • 85% Faster Triage Processing – Structured data enables faster decision-making.
  • Dual Output Format – JSON for backend, natural text for frontend.
  • 90% Query Context Retention – Memory ensures responses remain relevant in-session.
  • Plug-and-Play Workflow – Quick to deploy with reusable Langflow components.
  • Customizable Prompts – Easily adaptable for different medical domains or needs.
  • Chat Input Interface – Captures real-time user messages.
  • Session Memory Module – Stores and retrieves chat history dynamically.
  • Prompt Linker – Connects memory and input into a coherent prompt.
  • OpenAI Language Model – Generates conversational replies and data-rich outputs.
  • JSON Extractor Node – Converts LLM output into structured format for backend systems.

    Looking For A Job

    Challenges

    1
    Disconnected Conversations

    Without memory, chat responses felt fragmented and lacked context continuity.

    2
    Lack of Structured Output

    Responses weren’t formatted for backend systems or data usage.

    3
    Limited Prompt Control

    Developers had minimal flexibility to adjust or fine-tune chatbot behavior.

    4
    Missing Logic Routing

    Standard chat flows couldn’t handle dual output (user + system).

    Solutions

    1
    Memory Component Integration

    Enabled the chatbot to retain past messages for smoother multi-turn dialogue.

    2
    Custom JSON Output Generator

    Converted raw OpenAI responses into structured, machine-readable formats.

    3
    Prompt + History Fusion

    Designed flexible prompts that merge chat input with stored memory.

    4
    Dual Output Routing

    Sent responses both to the user interface and external systems for backend

    Outcome

    Technology Stack

    • openai
    • python
    • Langflow

    Features

    The Simple Agent enables intelligent, memory-driven chat with backend-ready responses. Key features include:

    Interactive-chat-during-live-sessions

    Session Memory

    output

    Dual Output

    access-control

    Custom Prompts

    Real-Time-Chat

    Backend Connectivity

    Real-Time-Chat

    Modular Flow

    mobile-agent-new-1

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