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Basic Information

Arch Gateway is a modular edge and AI gateway designed to act as a smart proxy server for agentic applications. The repository provides infrastructure that handles low-level plumbing for agents, including routing and handoff between agents, unifying access to different LLM providers, applying guardrails to inputs, clarifying vague user prompts, and converting prompts into API or tool calls. It is language and framework agnostic and is built to run alongside application servers as a containerized process on top of Envoy. The project includes a CLI, Docker and Docker Compose based deployment guidance, configuration via arch_config.yaml, quickstart examples and demos such as a currency exchange agent and a weather forecast agent. The repo also documents observability, debugging, and practices for running or hosting a purpose-built function calling model used in routing and function invocation scenarios.

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Features
Arch exposes several core capabilities for building and operating agentic apps. It provides routing to specialized agents and fast prompt routing using a purpose-built small LLM for agent selection and handoff. It unifies routing to upstream LLM providers via model-based and preference-based routing policies so you can map plain-language preferences to models without retraining. Centralized prompt guardrails let operators enforce safety and validate inputs. Built-in function and tools support converts clarified prompts to API calls. Observability is supported with W3C-compatible tracing, request metrics and integrations for tracing dashboards. The gateway is implemented on Envoy for scalability, includes a Python CLI for lifecycle operations, containerized images, debug logging and build commands, and ships demos and sample configs to illustrate common use cases.
Use Cases
Arch reduces developer effort by removing repetitive plumbing needed to run agentic systems so teams can focus on higher-level logic. It simplifies adding and switching LLMs, centralizes safety and input validation, and automates intent clarification and function calling to reduce manual prompt engineering. Preference-based routing adapts to intent drift and supports multi-turn flows without brittle rule trees. Observability and tracing give production-ready insights into prompt flows, latencies and model selection. The containerized design and CLI enable easy local development and deployment with Docker and Docker Compose. Included demos, quickstart guides and configuration examples accelerate building practical agents such as API-backed chat agents and domain-specific operators.

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