pyspur/docs/chatbots/concepts.mdx

---
title: 'Concepts'
description: 'Understanding Chatbots in PySpur'
---

# Chatbot Concepts

PySpur allows you to create two types of Spurs: standard workflows and chatbots. This guide explains what chatbots are in PySpur, how they differ from standard workflows, and why you might want to use them.

## What Are Chatbots in PySpur?

In PySpur, a chatbot is a special type of workflow designed to handle conversational interactions. Unlike standard workflows that process data in a one-time execution flow, chatbots:

- Maintain conversation history across multiple interactions
- Process user messages and generate assistant responses
- Handle user sessions to keep conversations separate
- Support conversational context and state management

## How Chatbots Differ from Standard Workflows

| Feature | Standard Workflow | Chatbot |
| ------- | ---------------- | ------- |
| Input/Output Structure | Flexible, user-defined | Fixed structure with specific fields |
| Session Management | Not built-in | Automatic session tracking |
| Message History | Not available | Automatically maintained |
| Execution Model | One-time processing | Conversational, multi-turn |
| Primary Use Case | Data processing, automation | User interactions, conversations |

### Required Input/Output Fields

Chatbots in PySpur have a predefined structure to support conversations:

**Required Input Fields:**
- `user_message` (string): The message from the user
- `session_id` (string): A unique identifier for the conversation session
- `message_history` (array): Previous messages in the conversation (automatically managed)

**Required Output Fields:**
- `assistant_message` (string): The response message from the chatbot

## When to Use Chatbots

Choose a chatbot Spur when you need to:

- Create conversational interfaces for your users
- Build customer support or information retrieval systems
- Develop virtual assistants that remember context
- Design interactive Q&A systems

Choose a standard workflow when you need to:
- Process data in a one-time operation
- Build automation pipelines without conversation
- Create custom data transformations with flexible inputs/outputs

In the next section, we'll walk through how to create and configure a chatbot in PySpur.