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Documentation Index

Fetch the complete documentation index at: https://docs.stagewise.io/llms.txt

Use this file to discover all available pages before exploring further.

Custom endpoints connect stagewise to any model, anywhere — including models running locally on your machine. stagewise supports multiple API styles: OpenAI-compatible chat completions, OpenAI Responses, Anthropic, Google, Azure OpenAI, Amazon Bedrock, and Google Vertex AI.

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Creating a custom endpoint

1

Open Models & Providers

Go to Settings → Agent → Models & Providers.
2

Open custom providers

Click Custom Providers in the top-right corner of the built-in providers section.
3

Add a provider

Click Add Provider.
4

Configure the provider

Unless your provider states otherwise, use OpenAI (Chat Completions) — the de facto standard for self-hosted and proxy services.
Fill in:
  • Name — Display name
  • Provider type — The API specification your endpoint implements
  • Base URL — The endpoint URL

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Supported API specifications

SpecExample services
openai-chat-completionsOllama, LM Studio, vLLM, LiteLLM, Together AI
anthropicSelf-hosted Claude, Anthropic-compatible proxies
openai-responsesOpenAI Responses API endpoints
googleSelf-hosted Gemini-compatible services
azureAzure OpenAI Service
amazon-bedrockAWS Bedrock
google-vertexGoogle Vertex AI

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Cloud platform configuration

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Azure OpenAI

Additional fields: Resource name and API version (e.g., 2024-02-01).

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Amazon Bedrock

Choose an Authentication Method:
MethodWhat to enter
Access KeysAWS Region, Access Key ID, and Secret Access Key
Named ProfileOptional AWS Region override and an AWS Profile from ~/.aws/config or ~/.aws/credentials
Default Credential ChainOptional AWS Region override. stagewise resolves credentials from environment variables, shared credentials, ECS/EC2 metadata, and SSO
SSO profiles require an active session. If requests fail with an expired-token error, run aws sso login --profile <name>.

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Google Vertex AI

Additional fields: Project ID, Location (e.g., us-central1), and Google credentials (service account JSON).

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Local models via Ollama

A typical setup for running models locally:
  1. Run Ollama on your machine: ollama serve
  2. Pull a model: ollama pull llama3
  3. Create a custom endpoint in stagewise:
    • Provider type: openai-chat-completions
    • Base URL: http://localhost:11434/v1
  4. Add a custom model with the model ID Ollama expects (e.g., llama3)
  5. Select the model from the model picker

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Model ID mapping

Some endpoints use different model identifiers. Use Model ID Mapping to remap: 
{
  "claude-sonnet-4-6": "claude-v2"
}
This tells stagewise: “when Sonnet 4.6 is selected, request claude-v2 from this endpoint.”

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Using your custom endpoint

After creating the endpoint, go to any built-in provider’s configuration and switch to Custom endpoint mode. Select your endpoint from the dropdown. All models for that provider route through your custom endpoint.