Tutorials: Chat

In this post, we will see how we can build a NodeJS server that will allow frontend chat SDKs to start and stop an assistant for a given channel in Stream Chat. Building polished AI assistants can be challenging. Features like streaming responses, table components, and code generation require complex implementation across SDKs and the

Large Language Models (LLMs) are becoming increasingly prevalent, and many developers are integrating various LLM providers, such as OpenAI, Anthropic, Gemini, and others into their Stream Chat applications across multiple SDKs. To help developers get started and accelerate their development with Stream Chat and AI, we've created a platform-specific, step-by-step guide. To follow along, we

This tutorial will demonstrate how easy it is to build an AI assistant with Stream React Native Chat SDK. While this tutorial features Anthropic and OpenAI APIs as the LLM provider, you can integrate any LLM service with Stream and still benefit from the same features, such as generation indicators, markdown support, tables, etc. No

In this tutorial, we will demonstrate how easy it is to build an AI assistant for iOS using the Stream Flutter Chat SDK on both the client and server sides. For this example, we will use the Anthropic and OpenAI APIs as the LLM service, but you can use any LLM service with Stream Chat.

In this tutorial, we will demonstrate how easy it is to create an AI assistant for iOS using Stream Chat. In this example, we will use the Anthropic and OpenAI APIs as our example LLM; however, developers are free to use whichever LLM provider they like and still benefit from Stream’s rich UI support for

This tutorial guides you through building an AI assistant seamlessly integrated with the Stream Chat SDK for Jetpack Compose. You'll learn how to handle interactions on both the client and server sides by setting up and running your own simple backend. The AI assistant leverages Stream's edge network for optimal performance and uses APIs from

In this tutorial, we will demonstrate how easy it is to build an assistant integrated into Stream’s React Chat SDK and learn how to incorporate the interaction on both the client and server sides. We will use the Anthropic and OpenAI APIs as the out-of-the-box examples, but using this method, developers can integrate any LLM

Geographically dispersed teams often have a hard time scheduling meetings that work for all participants. Human Resources departments also face this challenge when working with existing employees and job candidates alike. Employees have the benefit of a defined and somewhat uniform computing environment, job applicants are a whole different challenge. Each candidate uses whatever computer