A high-performance AI gateway written in Go, providing a unified OpenAI-compatible API for OpenAI, Anthropic, Gemini, xAI, Groq, OpenRouter, Z.ai, Azure OpenAI, Oracle, Ollama, and more.

Quick Start - Deploy the AI Gateway

Step 1: Start GoModel

docker run --rm -p 8080:8080 \
  -e LOGGING_ENABLED=true \
  -e LOGGING_LOG_BODIES=true \
  -e LOG_FORMAT=text \
  -e LOGGING_LOG_HEADERS=true \
  -e OPENAI_API_KEY="your-openai-key" \
  enterpilot/gomodel

Pass only the provider credentials or base URL you need (at least one required):

docker run --rm -p 8080:8080 \
  -e OPENAI_API_KEY="your-openai-key" \
  -e ANTHROPIC_API_KEY="your-anthropic-key" \
  -e GEMINI_API_KEY="your-gemini-key" \
  -e GROQ_API_KEY="your-groq-key" \
  -e OPENROUTER_API_KEY="your-openrouter-key" \
  -e ZAI_API_KEY="your-zai-key" \
  -e XAI_API_KEY="your-xai-key" \
  -e AZURE_API_KEY="your-azure-key" \
  -e AZURE_BASE_URL="https://your-resource.openai.azure.com/openai/deployments/your-deployment" \
  -e AZURE_API_VERSION="2024-10-21" \
  -e ORACLE_API_KEY="your-oracle-key" \
  -e ORACLE_BASE_URL="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/v1" \
  -e ORACLE_MODELS="openai.gpt-oss-120b,xai.grok-3" \
  -e OLLAMA_BASE_URL="http://host.docker.internal:11434/v1" \
  enterpilot/gomodel

⚠️ Avoid passing secrets via -e on the command line - they can leak via shell history and process lists. For production, use docker run --env-file .env to load API keys from a file instead.

Step 2: Make your first API call

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5-chat-latest",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

That's it! GoModel automatically detects which providers are available based on the credentials you supply.

Supported LLM Providers

Example model identifiers are illustrative and subject to change; consult provider catalogs for current models. Feature columns reflect gateway API support, not every individual model capability exposed by an upstream provider.

✅ Supported ❌ Unsupported

For Z.ai's GLM Coding Plan, set ZAI_BASE_URL=https://api.z.ai/api/coding/paas/v4. For Oracle, set ORACLE_MODELS=openai.gpt-oss-120b,xai.grok-3 when the upstream /models endpoint is unavailable.

Alternative Setup Methods

Running from Source

Prerequisites: Go 1.26.2+

1. Create a .env file:

   cp .env.template .env

2. Add your API keys to .env (at least one required).

3. Start the server:

   make run

Docker Compose

Infrastructure only (Redis, PostgreSQL, MongoDB, Adminer - no image build):

docker compose up -d
# or: make infra

Full stack (adds GoModel + Prometheus; builds the app image):

cp .env.template .env
# Add your API keys to .env
docker compose --profile app up -d
# or: make image

Building the Docker Image Locally

docker build -t gomodel .
docker run --rm -p 8080:8080 --env-file .env gomodel

OpenAI-Compatible API Endpoints

Gateway Configuration

GoModel is configured through environment variables and an optional config.yaml. Environment variables override YAML values. See .env.template and config/config.example.yaml for the available options.

Key settings:

Quick Start - Authentication: By default GOMODEL_MASTER_KEY is unset. Without this key, API endpoints are unprotected and anyone can call them. This is insecure for production. Strongly recommend setting a strong secret before exposing the service. Add GOMODEL_MASTER_KEY to your .env or environment for production deployments.

Response Caching

GoModel has a two-layer response cache that reduces LLM API costs and latency for repeated or semantically similar requests.

Layer 1 - Exact-match cache

Hashes the full request body (path + Workflow + body) and returns a stored response on byte-identical requests. Sub-millisecond lookup. Activate by environment variables: RESPONSE_CACHE_SIMPLE_ENABLED and REDIS_URL.

Responses served from this layer carry X-Cache: HIT (exact).

Layer 2 - Semantic cache

Embeds the last user message via your configured provider’s OpenAI-compatible /v1/embeddings API (cache.response.semantic.embedder.provider must name a key in the top-level providers map) and performs a KNN vector search. Semantically equivalent queries - e.g. "What's the capital of France?" vs "Which city is France's capital?" - can return the same cached response without an upstream LLM call.

Expected hit rates: ~60–70% in high-repetition workloads vs. ~18% for exact-match alone.

Responses served from this layer carry X-Cache: HIT (semantic).

Supported vector backends: qdrant, pgvector, pinecone, weaviate (set cache.response.semantic.vector_store.type and the matching nested block).

Both cache layers run after guardrail/workflow patching so they always see the final prompt. Use Cache-Control: no-cache or Cache-Control: no-store to bypass caching per-request.

See DEVELOPMENT.md for testing, linting, and pre-commit setup.

Roadmap to 0.2.0

Must Have

• Intelligent routing
• Broader provider support: Oracle model configuration via environment variables, plus Cohere, Command A, Operational, and DeepSeek V3
• Budget management with limits per user_path and/or API key
• Editable model pricing for accurate cost tracking and budgeting
• Full support for the OpenAI /responses and /conversations lifecycle
• Prompt cache visibility showing how much of each prompt was cached by the provider
• Guardrails hardening: better UI, simpler architecture, easier custom guardrails, and response-side guardrails before output reaches the client
• Passthrough for all providers, beyond the current OpenAI and Anthropic beta
• Fix failover charts in the dashboard

Should Have

• Cluster mode

Community

Join our Discord to connect with other GoModel users.

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