Fluxion Bus: Local MCP Gateway for AI Tasks

A local MCP gateway for delegating AI tasks across models (Codex, Claude Code, Antigravity) with quota monitoring.

Fluxion Bus provides a local, unified gateway for developers to manage and delegate AI tasks across multiple models. Designed for efficiency and control, it integrates seamlessly into your development workflow, allowing you to leverage the strengths of different AI providers without complex individual API integrations. This tool is particularly useful for building sophisticated AI-powered applications where task-specific model selection and resource management are critical.

What Fluxion Bus Does

Fluxion Bus acts as a central hub for your AI interactions. It allows you to send AI requests to a variety of models, including Codex, Claude Code, and Antigravity, through a single, consistent interface. The primary function is to abstract away the complexities of interacting with individual AI APIs, providing a simplified layer for task delegation. Furthermore, it includes built-in quota monitoring, enabling you to track and manage your usage across different AI services, preventing unexpected costs and ensuring efficient resource allocation.

Key Features

Unified API Access: Interact with multiple AI models (Codex, Claude Code, Antigravity) via a single local gateway.
Task Delegation: Easily route AI tasks to the most suitable model based on your requirements.
Quota Monitoring: Track and manage API usage across integrated AI services.
Local Gateway: Operates locally, offering greater control and potentially lower latency for development and testing.
MCP Integration: Designed to work within the MCP ecosystem, facilitating integration with other developer tools and agents.

Who Fluxion Bus Is For

Fluxion Bus is an essential tool for AI developers building complex applications. It is ideal for those who need to:

Integrate multiple AI models into a single application.
Manage and monitor API quotas to control costs.
Develop and test AI functionalities locally before deployment.
Streamline their AI task delegation process.
Work within an MCP-based development environment.