Real-time chatbots are now the default interface for customer support, internal tooling, and autonomous agents. The difference between a chatbot that feels instant and one that feels broken usually comes down to three engineering decisions: how you stream tokens, how you manage conversation history, and how you structure tool use. This guide walks through the architecture and code patterns you need to build a production-ready chatbot, and where your inference provider fits into the stack.
Architecture of a Real-Time LLM Chatbot
A real-time chatbot is typically composed of four layers: the client interface, a stateful session manager, the LLM inference layer, and optional tool services. The client communicates over WebSockets or Server-Sent Events (SSE) to receive tokens as they are generated. The session manager stores conversation history, handles retries, and enforces rate limits. The inference layer executes the model, and tool services execute functions on behalf of the model.
Oxlo.ai provides an OpenAI-compatible inference layer with request-based pricing. Instead of scaling cost with token count, you pay a flat rate per API request. For chatbots that maintain long system prompts or multi-turn history, this
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