1rgs/claude-code-proxy

Features

• LLM Gateways - Functions as a centralized interface that aggregates multiple LLM providers into a single, translated API.
• Provider-Agnostic LLM Routing - Implements a gateway that routes requests to various LLM providers based on configurable tiers and capabilities independently of the provider API.
• API Translation Layers - Converts API calls from one provider's format into another compatible format via a dedicated translation layer.
• Model Compatibility Proxies - Provides a proxy layer that standardizes request and response formats between different proprietary LLM providers to ensure compatibility.
• Model Provider Abstractions - Provides a normalized interface that allows switching between AI providers without changing application code.
• Request-Response Translation Layers - Converts incoming API calls and outgoing responses between different provider schemas to maintain client compatibility.
• Response Translation Layers - Converts model outputs from various providers back into a standardized API response structure.
• Tool-Execution Loops - Orchestrates the request-response cycle between an AI model's tool request and the external execution result.
• Tool-Use Proxies - Provides a proxy to handle the execution and processing of model tool definitions and function calls.
• LLM Tool Proxies - Provides a middleman server that handles the execution and processing of model tool definitions and function calls.
• API Proxies - Routes requests from compatible clients to different model providers using a translation middleware.
• Conversation History Managers - Tracks and sequences message history to maintain dialogue state for stateful AI interactions.
• Conversation Context Tracking - Maintains a sequential history of messages to preserve dialogue state across multiple request cycles.
• Model Tier Selectors - Redirects requests to different model sizes or tiers based on a predefined configuration mapping.
• Streaming Interfaces - Ships an interface for translating and forwarding incremental model responses to clients to reduce perceived latency.
• LLM Response Streaming - Implements incremental delivery of model outputs to reduce perceived latency for the end user.
• Model Identifier Mappings - Redirects specific model requests to alternative provider backends based on a configurable mapping of capabilities.
• LLM Backend Mappings - Routes requests to alternative provider backends based on a configurable mapping of model capabilities and sizes.
• Real-Time Text Streaming - Implements low-latency incremental response delivery for AI chat interfaces.
• Token Stream Relays - Forwards incremental model outputs to the client in real time to minimize perceived latency.

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