Integrations

Connect Aurora Inference to coding agents, chat applications, and libraries through its OpenAI-compatible Chat Completions API. Choose a guide below, or use the shared connection settings to configure another compatible client.

Choose an integration

Coding tools and agent harnesses

Integration	Use it for
OpenCode	Terminal coding agent with custom providers
Pi	Extensible terminal coding harness
Hermes Agent	Persistent agent sessions, tools, and messaging
OpenClaw	A personal assistant with a local gateway and agent workspace
Aider	Pair programming and editing files in a Git repository
Continue	Chat, edits, and agent workflows in your IDE
Cline	Agentic coding in VS Code

Chat applications

Integration	Use it for
Chatbox AI	Desktop chat with your own Aurora token
Open WebUI	A self-hosted chat interface

SDKs and libraries

Integration	Use it for
OpenAI Python	Python applications and scripts
OpenAI JavaScript / TypeScript	Node.js and server-side TypeScript applications
Vercel AI SDK	Text generation and streaming in JavaScript applications
LangChain Python	Composable prompts and chat model workflows
LiteLLM	A common Python interface across model providers

Shared connection settings

Select Inference Endpoint

Choose preferred region.

Select inference Endpointhttps://ai.aur.lu/v1 - Aurora (default)

1. Create an API token with execute:inference:requests permission in the Aurora Portal. See Getting started.
2. Copy the selected base URL below. It already includes /v1.
3. Choose a model from the public catalog and keep its complete ID.

Setting	Value
API / provider type	OpenAI-compatible Chat Completions
API key	Your Aurora API token, without the Bearer prefix
Model ID used in these guides	deepseek/deepseek-v4-flash
Chat endpoint path	/chat/completions, appended to the base URL

Most clients append the chat endpoint themselves. Do not add a second /v1 or put /chat/completions in a base URL field unless the guide explicitly asks for it.

For terminal and SDK examples, set the token in the shell that launches the client:

export AURORA_API_TOKEN="<your-token>"

These shell examples use Bash/Zsh syntax. In PowerShell, use $env:AURORA_API_TOKEN = "<your-token>". Applications launched from a desktop icon or as a service may not inherit terminal environment variables. Use their credential settings or service environment instead. Keep tokens and secret files out of Git; server-side application tokens must not be bundled into browser code.

Discover models and verify authentication

List models (this endpoint is public):

curl --fail-with-body /models

A successful model listing does not verify your token. Test a chat request:

curl --fail-with-body /chat/completions \
-H "Authorization: Bearer $AURORA_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
  "model": "deepseek/deepseek-v4-flash",
  "messages": [{"role": "user", "content": "Say hello in one sentence."}]
}'

Expect a JSON response with text in choices[0].message.content. Availability can change; replace the example model everywhere in your client configuration if needed. See Models and pricing for current availability and costs.

API and feature compatibility

Aurora supports model discovery, Chat Completions, and text Completions. Streaming uses server-sent events (SSE). Compatibility is limited to the operations in the API reference.

• Select Chat Completions when a client offers multiple API modes. Aurora does not implement OpenAI Responses or Anthropic Messages.
• Tool calling, reasoning controls, structured output, and image input depend on the selected model. A client's capability toggle does not enable a server feature.
• For coding agents, choose a model suited to tool use and check a complete file-read → tool-result → answer cycle before starting a larger task.
• Context-window and output-limit settings tell the client how to budget tokens; they do not increase the model's server-side limits. Copy limits for the selected model rather than reusing another provider's defaults.
• Embeddings, speech, and image generation are not documented Aurora endpoints. Configure separate services for application features that need them.
• Agents may make additional calls for titles, summaries, or helper models. Check those assignments too. Client cost estimates may be absent or inaccurate for custom models; use the Aurora Portal to monitor billed usage.

What about Claude Code and Codex?

A custom base URL alone does not translate API protocols. Claude Code's gateway integration requires its supported gateway protocols, and current Codex provider configuration uses the Responses API (see the configuration reference). Neither is a direct Chat Completions integration. Use one of the coding harnesses above for a direct Aurora connection. A protocol-translating gateway would be a separate integration with its own compatibility requirements.

Troubleshooting

Symptom	What to check
401 or invalid API key	Use an Aurora token, remove surrounding whitespace, and ensure the application can read the credential.
403 or permission denied	Check the token's execute:inference:requests permission and the error details.
404 on a request	Check the full requested path for duplicate /v1, a repeated /chat/completions, or unsupported /responses or /messages.
Model not found	Copy the exact catalog ID, including its author prefix (for example deepseek/). Add client routing prefixes only where the guide shows them.
Models appear but chat fails	Discovery is public. Run the authenticated chat check above.
400 after enabling a feature	Check model support and the error body; remove unsupported reasoning, tool, image, or structured-output options.
Rate limit or capacity error	Reduce parallel requests and retry with backoff; inspect the error before retrying authentication or validation failures.
Context-limit error	Start a new session, reduce attachments/tool output, or correct the client's context budget.
Text appears all at once	Verify streaming is enabled and that your reverse proxy or application does not buffer SSE.

The guides describe upstream configuration interfaces and include checks you can run against your account. They are not a guarantee that every feature of every client works with every Aurora model.