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MCP verified Apache-2.0 Self-run

Chat Ui

mcp-ai-ql-chat-ui · by AI-QL

Single-File AI Chatbot UI with Multimodal & MCP Support: An All-in-One HTML File for a Streamlined Chatbot Conversational Interface

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Install

$ agentstack add mcp-ai-ql-chat-ui

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

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Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Chat UI

[](https://vuejs.org) [](https://vuetifyjs.com)

[](https://hub.docker.com/repository/docker/aiql/chat-ui/tags?page=1&ordering=last_updated) [](https://github.com/AI-QL/chat-ui/blob/main/LICENSE)

The UI of Chat is becoming increasingly complex, often encompassing an entire front-end project along with deployment solutions.

This repository aims to construct the entire front-end UI using a single HTML file, aiming for a minimalist approach to create a chatbot.

By simplifying the structure and key functions, developers can quickly set up and experiment with a functional chatbot, adhering to a slimmed-down project design philosophy.

Features

  • Supports OpenAI-format requests, enabling compatibility with various backends such as HuggingFace Text Generation Inference (TGI), vLLM, etc.
  • Automatically supports multiple response formats without additional configuration, including standard OpenAI response formats, Cloudflare AI response formats, and plain text responses
  • Support various backend endpoints through custom configurations, providing any project with a universal frontend chatbot
  • Support the download of chat history, interrupt the current generation, and repeat the previous generation to quickly test the backend inference capabilities
  • Support MCP (Model Context Protocol) by acting as a renderer and facilitating community interactions with the backend main process via IPC
  • Inquiries with image inputs can be made using multimodal vision models
  • Support for toggling between original format and Markdown format display
  • Support internationalization and localization i18n

How to use

Option 1: Chat with demo AIQL

> The demo will use Llama-3.3-70B-Instruct by default

> Multimodal image upload is only supported for vision models

> MCP tools call necessitates a desktop backend and LLM support in OpenAI format, referencing Chat-MCP

Option 2: Download [Index](./index.html) and open it locally (recommended)
Option 3: Download [Index](./index.html) and deploy it by python
cd /path/to/your/directory
python3 -m http.server 8000

> Then, open your browser and access http://localhost:8000

Option 4: fork this repo and link it to Cloudflare pages

> Demo: https://www2.aiql.com

Option 5: Deploy your own Chatbot by Docker
docker run -p 8080:8080 -d aiql/chat-ui
Option 6: Deploy within Huggingface

> Don't forget add app_port: 8080 in README.md

Option 7: Deploy within [K8s](#k8s-section)
Option 8: Integrated as a renderer within the desktop application

> Demo: Chat-MCP

How to config

By default, the Chatbot will use API format as OpenAI ChatGPT.

You can insert your OpenAI API Key and change the Endpoint in configuration to use API from any other vendors

You can also download the config template from [example](./example/config) and insert your API Key, then use it for quick configuration

Trouble Shooting

If you're experiencing issues opening the page and a simple refresh isn't resolving the issue, please take the following steps:

Reset Interface Configuration

  1. Click Refresh icon on the upper right of Interface Configuration

Reset All Configuration

  1. Click hidden botton on the right side of the index page
  2. Click Reset All Config icon

Reset Cache

  1. Right-click your browser page and go to the Network section.
  2. Right-click on section table and clear your browser's cache and cookies to ensure you have the latest version of the page.
  3. Additionally, inspect the browser's Network section to see which resources are failing to load due to your location. This will provide you with more specific information about the issue.

K8s

  1. Introduce the image as sidecar container

```yaml spec: template: metadata: labels: app: my-app spec: containers:

  • name: chat-ui

image: aiql/chat-ui ports:

  • containerPort: 8080

```

  1. Add service

```yaml apiVersion: v1 kind: Service metadata: name: chat-ui-service spec: selector: app: my-app ports:

  • protocol: TCP

port: 8080 targetPort: 8080 type: LoadBalancer ```

  1. You can access the port or add other ingress

```yaml apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: my-app-ingress annotations: nginx.ingress.kubernetes.io/rewrite-target: /$1 spec: rules:

  • host: chat-ui.example.com

http: paths:

  • path: /

pathType: Prefix backend: service: name: chat-ui-service port: number: 8080 ```

Demo

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.