ha-openai-compatible/README.md

OpenAI Compatible for Home Assistant

Fork of adamjs83/ha-openai-compatible v0.2.1 with fixes for running it against a proxy that serves Claude models. Drop it once upstream has fixed these issues:

• #3: a tool call with an empty argument string (tools without parameters) is treated as {} instead of raising JSONDecodeError.
• #4: earlier web searches are not replayed in the history; they carry no results, and the model then disowns its own answers and searches again.
• #5: the home location lookup for web search uses the subentry's model instead of gpt-4o-mini and asks as a user message (Anthropic needs one); a failed lookup no longer blocks saving the options, and empty location fields are left out of the web search tool.
• Requests that Home Assistant's local intents answered (prefer_local_intents) are described to the model in a note instead of being replayed as its own calls of intents like HassGetCurrentTime, which are not among its tools; replayed, they make it apologise for using tools it does not have. Not reported upstream yet.

HACS only installs from GitHub, so this fork is installed by copying custom_components/openai_compatible into the Home Assistant config.

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)

1. HACS → ⋮ → Custom repositories
2. Add https://github.com/adamjs83/ha-openai-compatible, category Integration
3. Find OpenAI Compatible in HACS, download it, and restart Home Assistant
4. 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 http and https are 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.

Known OpenAI-specific behaviour

The base URL is configurable, but much of the rest is still upstream's OpenAI-shaped logic: some request parameters are sent to every provider, and which options the config flow offers is inferred from OpenAI model-name prefixes. That means options can appear for endpoints that cannot honour them, and be withheld from endpoints that could.

docs/provider-genericity.md has the full audit, what each item costs, and the design decision that has to be made first.

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.

The manifest requires openai>=2.45.0 rather than core's exact pin: Home Assistant installs custom-integration requirements against its own package constraints, so an exact pin stops the integration loading whenever core bumps openai.

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.

Releases

HACS falls back to serving the default branch when a repository has no releases, so every download looks identical and there is no way to tell what is installed. Tagged releases fix that, and Home Assistant shows manifest.json's version on the integration page.

Versions are 0.x while the config entry shape is still changing; 1.0.0 when it settles.

To cut one:

python scripts/release.py 0.2.0
git push origin main --follow-tags

The script bumps manifest.json, commits, and creates the annotated tag v0.2.0. It deliberately does not push — pushing the tag is what publishes. That triggers release.yaml, which refuses to publish if the tag and the manifest version disagree (HACS reads the tag, HA reads the manifest; when they drift you install "v0.2.0" and it reports 0.1.0) and then creates the GitHub release.

License

Apache-2.0, inherited from Home Assistant core. See NOTICE for attribution.