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ENTERPILOT/GoModel

AI gateway / AI control plane / AI proxy written in Go. Unified OpenAI-compatible and Anthropic-compatible API for OpenAI, Anthropic, Gemini, Groq, xAI, Ollama, vLLM and more. A LiteLLM alternative with observability, guardrails, streaming, cost tracking, intelligent routing, sticky sessions, failover, real-time logs and usage tracking. Prod ready. observed · 2026-09-03

github.com/ENTERPILOT/GoModel · homepage · Go · MIT (permissive) observed · 2026-09-03

Health v2 · maintenance only

80/100

  • Activity 100
  • Release rhythm 88
  • Longevity 19
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 1.0
  • age_days: 271
  • days_rel: 0
  • days_push: 0
  • n_releases_24m: 87

Full methodology

Adoption not part of the score

1107 stars · 92 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GoModel is an open-source AI gateway and control plane written in Go that exposes a unified OpenAI-compatible and Anthropic-compatible API across providers like OpenAI, Anthropic, Gemini, Groq, xAI, Ollama, and vLLM. It adds observability, response caching, guardrails, cost tracking, intelligent routing, failover, sticky sessions, and real-time usage logs, positioning itself as a production-ready LiteLLM alternative.

Use cases

  • route LLM requests to multiple providers through one OpenAI-compatible endpoint
  • track token usage and LLM spending per model, path, or user
  • cache identical LLM responses to cut costs and latency
  • add guardrails and audit paths to AI traffic
  • fail over between LLM providers automatically
  • swap model providers without changing application code
  • self-host a LiteLLM alternative in Go
  • debug AI calls with real-time request logs

When to choose

  • you need a fast, resource-efficient self-hosted AI gateway in Go
  • you want provider-agnostic LLM routing with cost tracking and observability
  • you want OpenAI and Anthropic SDK compatibility against many backends
  • you need caching, guardrails, and failover at the gateway layer

When to avoid

  • you need a client-side SDK embedded in your app rather than a proxy service
  • you require a long-proven enterprise gateway with a large community
  • your stack is Python-centric and you prefer LiteLLM's Python ecosystem

Facets

service · maturity active

llm-inference proxy api-gateway caching monitoring analytics auth rate-limiting logging middleware large-language-models artificial-intelligence apis self-hosted developer-tools analytics backend windows self-hosted go cross-platform ai-gateway llm-gateway ai-proxy openai-compatible anthropic-compatible litellm-alternative cost-tracking guardrails intelligent-routing failover usage-tracking observability provider-routing response-caching ai-agents linux macos docker web-server

4 sources

Member repositories

RepositoryRoleHealth v2
ENTERPILOT/GoModelmain80

For agents

markdown · JSON · MCP: product_card(name="ENTERPILOT/GoModel")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem