Best tools to build AI chatbots and assistants
Combine an app framework, a RAG pipeline, and a model API or local runtime to ship a chatbot that answers from your knowledge base.
Recommended tools
Dify LLM App Development Platform
Dify is an open-source LLM application development platform that combines visual workflow orchestration, a built-in RAG engine, and Agent capabilities. It lets developers and non-technical users quickly build, test, and deploy AI-powered apps with support for multiple model providers and seamless API integration.
Open WebUI
Open WebUI is a feature-rich, self-hosted AI chat interface that supports Ollama and OpenAI-compatible APIs out of the box. It runs fully offline, offers a polished multi-model conversation experience, and includes role management, markdown support, and plugin extensibility for teams and individuals.
LangChain Framework Tutorial
An in-depth tutorial on the LangChain framework, walking through its architecture and core modules—Chains, Agents, and Memory—with hands-on examples. It also covers RAG integration, custom tool development, and debugging strategies, making it a solid starting point for developers ready to move beyond simple prompts and build production-grade LLM applications.
LlamaIndex Framework Tutorial
A hands-on tutorial for LlamaIndex, the framework designed for building RAG applications that connect LLMs to custom data sources. It covers data ingestion pipelines, indexing strategies, query engines, and agent-based retrieval, with practical examples for PDF, web, and database connectors. Aimed at developers who want to build domain-specific AI assistants grounded in their own documents.
Deploying Local LLMs with Ollama
A cross-platform tool that lets you download and run open-source LLMs like Llama, Mistral, and Phi locally on your own machine. It provides a CLI, a local API server, and an OpenAI-compatible endpoint, so you can experiment with models privately — no GPU or cloud account required. Ideal for developers who want offline AI capabilities or need to test prompts before deploying to production.