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mcp-client-chatbot

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Basic Information

Better Chatbot is an open-source chatbot template and reference app for building modern AI chat interfaces. It is built with Next.js and the Vercel AI SDK and integrates the Model Context Protocol (MCP) so language models can call external tools and services. The repo demonstrates how to wire multiple LLM providers, host locally or with Docker and Vercel, and persist data with PostgreSQL. It provides examples for creating custom visual workflows that become callable tools in chat, configuring MCP servers, and adding OAuth sign-in options. The project is intended both as a deployable assistant and as a developer starter kit for building tool-enabled conversational experiences, voice assistants, browser automation, and reusable multi-step workflows.

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App URL
https://github.com/cgoinglove/mcp-client-chatbot
Github Repository
https://github.com/cgoinglove/mcp-client-chatbot/blob/main/README.md

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App Details

Features
Integrates MCP to let the model call external tools and servers and chain tool calls into multi-step tasks. Visual workflow editor lets you publish workflows as chat-callable tools. Playwright MCP example shows browser automation driven by the LLM. Realtime voice assistant demo uses a realtime LLM API with full MCP tool integration. Quick tool mentions via @ to bind tools per response and tool presets for organized tool sets. Tool Choice Mode supports Auto, Manual, or None to control tool autonomy. Default tools include Exa-powered web search, a JS executor, HTTP client, and interactive data-visualization tools like sortable tables and charts. Includes Docker and Vercel deployment guidance, Postgres support, and OAuth configuration.
Use Cases
This repository accelerates building practical conversational agents by providing a ready-made UI, deployment scripts, and clear guides for integrating tools and LLM providers. Developers can prototype assistants that perform real actions such as web search, browser automation, API calls, and data visualization without building orchestration from scratch. Visual workflows make complex multi-step logic reusable and callable from chat, which simplifies automation and reduces manual prompting. Tool mentions and presets let teams optimize token usage and control which tools are available per conversation. Built-in guides, environment templates, and examples for Docker, Vercel, and PostgreSQL lower the barrier to self-hosting and production deployment. The project also includes tips for system prompts, OAuth, and adding new LLM providers to customize behavior.

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