When building workflows with autonomous coding agents (like Claude Code, Cursor, or OpenCode), agent execution loops issue dozens of rapid LLM calls per minute. Standard Python-based proxies introduce 40ms–100ms of latency per hop, balloon to hundreds of megabytes in memory, and easily choke during parallel agent streaming.
To solve this, I built GoRoute (https://github.com/ziuus/goroute) — an ultra-lightweight, high-throughput AI gateway and dynamic LLM router written in Go.
The Latency & Reliability Problem
In autonomous coding agent sessions:
Compounding Proxy Latency : In a 30-step agent loop, 60ms of routing overhead adds 1.8 seconds of pure wait time. 2. Provider Rate Limits : Hitting a 429 on Anthropic or OpenAI kills the entire agent trajectory unless failovers are instantaneous. 3. Protocol Mismatches : Tool definitions and message schemas differ between OpenAI (function_call / tools) and Anthropic (messages API).
Architecture & Key Features
Sub-0.8ms Local Routing : Zero-allocation hot path, standard library HTTP + lightweight routing, baseline footprint <12MB RAM. #
Universal Protocol Translation : Transparently translate OpenAI format to Anthropic messages format and vice-versa, allowing any coding tool to talk to any upstream model. #
Dynamic Failover Cascades : If Claude 3.5 Sonnet hits rate-limits or 5xx errors, GoRoute cascades automatically to DeepSeek-V3 or GPT-4o with zero lost tokens. #
Multi-Account Token Pooling : Distribute requests round-robin across multiple API keys to maximize quota without manual key rotation. #
Native MCP Compatibility : Designed to sit cleanly alongside Model Context Protocol servers.
Quick Start
Run locally via Go:
Or via Docker:
Point your agent or SDK to http://localhost:8080/v1 and you are ready.
Open Source
GoRoute is 100% open source under the MIT License.
Feedback, issue reports, and benchmarks are warmly welcomed! If you are building agentic tools or multi-model pipelines, give it a try.h