Help Desk Ticketing System
Build a help desk ticketing system with an SLA-aware queue, threaded replies, ownership, internal notes, and resolution workflows.
Operations
AI Chatbot
Build an AI chatbot and chat app with a modern chatbot template, threaded conversations, starter prompts, and local responses that need no API.
AI
Use templateAbout This Template
AI Chatbot is a Reflex template for creating a modern conversational assistant or building a chat app around another use case. The AI chatbot template opens with realistic conversation history and supports the complete chat workflow from the first screen. Users can select or create a thread, compose a multiline message, send it, view a brief typing state, receive a relevant local response, and copy the reply. Conversation search and responsive navigation make the chat app easy to explore, while renaming and confirmed deletion keep thread management predictable. Starter prompts help begin a conversation, and retry behavior makes loading, error, and success states easy to inspect. Deterministic local responses let the chatbot template work without credentials or an external model API. Used as a chat box maker, it gives you the composer, typing state, and message stream without wiring them together yourself; used as a messaging app creator starting point, the same threaded structure extends to an AI assistant or a customer support widget. Teams that want to make a chat app can adapt the response rules, prompts, participants, storage boundary, and visual presentation. A model provider and persistent database can be connected later when the AI chatbot needs live generation, authenticated users, or durable conversation history.
Frequently Asked Questions
An AI chatbot holds a threaded conversation, keeps history, and answers in context. Building one in Python needs four parts: a message store, a composer, a response boundary, and a streaming view. This AI chatbot template ships all four with threaded conversations, searchable history, starter prompts, multiline composition, retry behavior, and typing feedback, so you replace the response function instead of building the interface.
Yes. The template ships with deterministic local responses so it runs with no credentials, and the response boundary can be swapped for any provider. Teams commonly connect Anthropic's Claude API, OpenAI, Google Gemini, Groq, or a self-hosted model, then keep the same threaded conversation UI.
Yes. Teams build a chat app by adapting the participants, starter prompts, response rules, conversation examples, navigation, and message presentation. The chatbot template retains its working thread-management flow while those product details change.
You can create a chat app interface for team conversations with the existing threaded layout. To make a chat app truly multi-user, add identity, delivery, presence, authorization, and persistent storage.
Conversations live in Reflex state and persist to SQLite by default. Point the same models at Postgres or Supabase through SQLModel for shared history, then add authentication so each account sees only its own threads.