Adam SmithandClaude Opus 5
e7fcb1d7a1
Reshapes setup around how a multi-model proxy is actually used: one config entry per server/provider, then one endpoint per model added by hand, with the model chosen from what that provider advertises right now. Setup (async_step_user): - Adds a Name field. With one entry per provider the title matters, and it was hardcoded to "ChatGPT" regardless of where the entry pointed. Blank falls back to the endpoint host via url_util.title_from_base_url. Deliberately absent from the reauth schema (now split out as STEP_REAUTH_DATA_SCHEMA): that step writes its input back into entry.data via data_updates, so a name field there would persist the title as connection data on every key rotation. - Stops auto-creating four subentries. conversation/ai_task_data/stt/tts were all created up front carrying RECOMMENDED_* options, which pin OpenAI model names (gpt-4o-mini-tts, gpt-4o-mini-transcribe). Against a third-party endpoint those entities are dead on arrival -- the reported symptom was an install that "loads bits, it's all janky". - Drops _async_abort_entries_match, so several entries may point at one server (one per provider-side key, for instance). Upstream aborts with already_configured on matching url+key. Model discovery (new model_list.py, kept out of the vendored files like url_util.py): - async_fetch_model_ids asks the endpoint's GET /v1/models. It calls with_options(max_retries=0), because this runs while the user waits on a dialog and the SDK otherwise retries with backoff -- visible in the test run as repeated "Retrying request to /models" and a suite that went from 5s to minutes. - Returns [] on any OpenAIError, and model_field() then yields a plain text field instead of a dropdown, so an endpoint that serves /v1/responses but not /v1/models is still configurable by hand. The dropdown sets custom_value=True for the same reason (LiteLLM wildcard routes). - Wired into all four types: conversation and ai_task_data on the additional step, stt (which hardcoded OpenAI's three transcribe models) and tts on init. TTS had no model field at all -- it silently used gpt-4o-mini-tts. A new endpoint now starts with "recommended" unticked. Ticked, the flow ends on the first step and never shows a model field, which is why the model looked unpickable; reconfigure still keeps whatever was saved. The model field stays on the additional step rather than moving to init. The init position reads better, but 71 ported tests drive the model through the additional step, several from parametrized option dicts that no mechanical edit could relocate. Rewriting the only regression net against upstream to save one click was the wrong trade. Also: the setup description no longer says "your OpenAI API key", and instructions_url pointed at a README anchor (#generate-an-api-key) that does not exist -- now #configuration. strings.json re-verified against core's RE_URL: 0 violations. Local: 167 passed. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NxJYWgGJtCw13qUHZtG8ki
OpenAI Compatible for Home Assistant
Home Assistant's built-in OpenAI integration, with one change that matters: the
API base URL is configurable. Point Home Assistant's conversation agent, AI Task,
speech-to-text and text-to-speech at any OpenAI-compatible endpoint — LiteLLM, vLLM,
OpenRouter, a gateway of your own — instead of only api.openai.com.
Everything else is upstream Home Assistant code, vendored unmodified, so behavior and options match the core integration you already know.
Why this exists
Core's openai_conversation hardcodes OpenAI's endpoint. If you run a proxy for cost
control, routing, key management or privacy, or you self-host models, there is no way to
tell the core integration about it. This fork adds a Base URL field to the config
flow and threads it to every client it creates.
How it is organised
One entry per server or provider, and one endpoint per model underneath it. Setup asks only for the connection; nothing is created on your behalf. You then add endpoints yourself, picking the type and the model each time — so a single LiteLLM entry can carry a conversation agent on one model, another on a second, and a TTS endpoint on a third.
Each endpoint is one of four types, and each becomes its own Home Assistant entity:
- Conversation agent — an Assist conversation entity, with the LLM Assist API, tool calling, and the full upstream option set (model, temperature, top-p, max tokens, reasoning effort, verbosity, web search, code interpreter, service tier, …)
- AI Task — for
ai_task.generate_data/ image generation - Speech-to-text — an STT entity
- Text-to-speech — a TTS entity, with speed control
Plus two actions on the entry itself: openai_compatible.generate_content and
openai_compatible.generate_image.
Requirements
- Home Assistant 2026.9.0 or newer
- An OpenAI-compatible endpoint (see Endpoint compatibility)
Installation
HACS (custom repository)
- HACS → ⋮ → Custom repositories
- Add
https://github.com/adamjs83/ha-openai-compatible, category Integration - Find OpenAI Compatible in HACS, download it, and restart Home Assistant
- Settings → Devices & services → Add integration → OpenAI Compatible
Manual
Copy custom_components/openai_compatible/ into your Home Assistant
config/custom_components/ directory and restart.
Configuration
1. Add the provider
| Field | Notes |
|---|---|
| Name | Label for this provider, e.g. LiteLLM. Leave blank to use the endpoint's host. |
| Base URL | Full URL including any version path, e.g. https://api.openai.com/v1. Passed to the OpenAI SDK as-is. |
| API key | Optional — leave blank for an endpoint that does not authenticate. |
Submitting it checks the connection with GET /v1/models and creates the entry. No
entities are created yet, deliberately: guessing them would mean pointing STT and TTS at
OpenAI model names your provider probably does not serve.
You can add several entries for the same server — one per provider-side key, say. Upstream refuses this as a duplicate; this fork does not.
2. Add endpoints for the models you want
On the entry, use Add conversation agent / Add AI task / Add speech-to-text /
Add text-to-speech. Each flow offers a model dropdown populated from that
provider's own GET /v1/models, read when you open the form, so it lists what is
actually available right then. The dropdown also accepts a typed-in name, for a provider
that routes models it does not advertise (LiteLLM wildcard routes, for instance), and
falls back to a plain text box if the endpoint will not list models at all.
Repeat per model. Name each endpoint something you will recognise in Assist — the subentry title becomes the entity name.
Base URL rules
- Only
httpandhttpsare accepted; the URL must include a host. - Whitespace and trailing slashes are stripped.
- Include the version path if your server expects one. Most do:
/v1. - Credentials embedded in the URL (
https://user:pass@host/v1) are rejected. Put the token in the API key field — it is stored as a secret and kept out of logs, while a base URL is echoed back in error and log messages. - Re-authenticating (rotating the API key) keeps your configured base URL.
Empty API keys
Local endpoints often require no auth. Leave the API key blank and the integration sends
the placeholder not-required — the OpenAI SDK refuses to build a client with no key at
all, so something has to be sent, and servers that ignore auth ignore it.
Endpoint examples
| Server | Typical base URL |
|---|---|
| OpenAI | https://api.openai.com/v1 |
| LiteLLM | http://litellm.local:4000/v1 |
| vLLM | http://vllm.local:8000/v1 |
| OpenRouter | https://openrouter.ai/api/v1 |
| Azure OpenAI (via a proxy) | your proxy's URL |
Endpoint compatibility
This is the important caveat. Upstream uses OpenAI's Responses API, not Chat
Completions, so a server that only implements /v1/chat/completions will not work
for conversation or AI Task.
| Feature | Endpoint the server must implement |
|---|---|
| Setup / connection test | GET /v1/models |
| Conversation, AI Task | POST /v1/responses |
| Text-to-speech | POST /v1/audio/speech |
| Speech-to-text | POST /v1/audio/transcriptions |
generate_image action |
POST /v1/images/generations |
You only need the endpoints for the subentries you actually add. Check your server's docs for Responses API support before filing a bug — LiteLLM and vLLM have it in recent versions; several popular local servers still do not.
Model names
Pick from the dropdown and this mostly takes care of itself. Two upstream behaviours to know about anyway:
- The fallback defaults are OpenAI's (
gpt-4o-mini,gpt-4o-mini-tts,gpt-4o-mini-transcribe), used only if you never choose a model. A new endpoint starts with Recommended settings unticked so the flow always asks. - Upstream's lists of models that don't support web search, image generation, reasoning effort and so on are keyed to OpenAI names, so they won't match a third-party model string. Options are offered based on those name patterns, and the endpoint's own error is what you'll see if you ask for something it can't do.
Relationship to Home Assistant core
custom_components/openai_compatible/ is derived from
homeassistant/components/openai_conversation/ at core tag 2026.9.2
(commit recorded in UPSTREAM_COMMIT),
with the domain renamed to openai_compatible and the base URL made configurable.
Base-URL handling lives in its own module,
url_util.py, deliberately separate
from the vendored files so re-syncing with core stays a clean merge.
Installing this alongside core's OpenAI integration is fine — different domain, separate config entries. This is not an official Home Assistant integration and is not affiliated with or endorsed by OpenAI.
Development
Requires Python 3.14+ (Home Assistant 2026.9's floor).
pip install -r requirements_test.txt
pytest tests/ -v
The suite is core's openai_conversation test suite, ported as a regression net, plus
tests for the base-URL behavior this fork adds.
License
Apache-2.0, inherited from Home Assistant core. See NOTICE for attribution.