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Hivechat

HiveChat is an AI chat application designed for small and medium - sized teams. It supports multiple large - model service providers and provides functions such as group management, Token limits, and third - party login.
Artificial intelligence chatbotsDeveloper tools#Team AI#Multi - model#Group management#Intelligent chat.TypeScript
5 points
10.7K
Open Site

Overview

Content Details

Alternatives

What is the MCP server?

The MCP server is a lightweight, high - performance middleware used to manage and optimize the context interaction of large AI models. It allows users to call multiple AI models through a unified interface, simplifies complex configurations, and improves work efficiency.

How to use the MCP server?

You can complete the configuration and startup in just a few steps: install dependencies, initialize the database, configure models and service providers, and finally run the service. You can quickly get started without in - depth knowledge of underlying technologies.

Applicable scenarios

It is suitable for small and medium - sized teams, educational institutions, or individual developers, especially for scenarios that require cross - model collaboration and unified management of AI resources.

Main features

Multi - model support
It is compatible with mainstream AI models such as OpenAI, Claude, Gemini, etc., and supports custom addition of other models.
Context management
Intelligently save and restore conversation context to ensure continuity and consistency.
Permission control
Allocate model access permissions and Token limits by user groups to flexibly meet team needs.
MCP protocol
Based on the standardized MCP protocol, it enables efficient data transmission and model interaction.
Advantages
Easy to integrate and expand
Unified management of multiple AI models
Support for custom configuration
Efficient context processing capabilities
Limitations
Optimization may be required for high - concurrency requests
Some advanced features require additional payment
A certain learning cost is required for initial configuration

How to use

Install dependencies
Clone the project code and install the required dependency libraries.
Initialize the database
Run the initialization script to create the database table structure.
Configure models and service providers
Edit the.env file and fill in the API key and relevant configurations.
Start the service
Run in development mode or build for the production environment.

Usage examples

Query weather forecasts
Call an AI model through MCP to obtain the weather conditions of a specified city.
Chain - of - thought reasoning
Leverage DeepSeek's chain - of - thought ability to solve complex problems.

Frequently Asked Questions

Which models does the MCP server support?
How to add a new AI model?
Does it support multi - language input?

Related resources

GitHub repository
Project source code and documentation
Online demonstration
Experience the actual effect of the MCP server
User manual
Detailed operation guide

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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