Development

This page collects contributor-facing notes for extending nanobot. User-facing setup and runtime options live in configuration.md.

Adding an LLM Provider

nanobot uses the provider registry in nanobot/providers/registry.py as the source of truth for LLM provider metadata. Most OpenAI-compatible providers need only two changes.

Add a ProviderSpec entry to PROVIDERS:

python

ProviderSpec(
    name="myprovider",
    keywords=("myprovider", "mymodel"),
    env_key="MYPROVIDER_API_KEY",
    display_name="My Provider",
    default_api_base="https://api.myprovider.com/v1",
)

Add a field to ProvidersConfig in nanobot/config/schema.py:

python

class ProvidersConfig(BaseModel):
    ...
    myprovider: ProviderConfig = Field(default_factory=ProviderConfig)

Environment variables, config matching, provider status, and WebUI credential display derive from those two entries.

Useful ProviderSpec options:

Field	Description
`default_api_base`	Default OpenAI-compatible base URL.
`env_extras`	Additional environment variables derived from the provider config.
`model_overrides`	Per-model request parameter overrides.
`is_gateway`	Provider can route many model families, like OpenRouter.
`detect_by_key_prefix`	Match configured gateways by API-key prefix.
`detect_by_base_keyword`	Match configured gateways by API base URL.
`strip_model_prefix`	Strip `provider/` before sending the model to the upstream API.
`supports_max_completion_tokens`	Use `max_completion_tokens` instead of `max_tokens`.
`is_transcription_only`	Provider has credentials but cannot serve chat completions.

Adding a Transcription Provider

Transcription is intentionally split into two layers:

nanobot/audio/transcription_registry.py owns provider names, aliases, default models, and adapter loading.
nanobot/providers/transcription.py owns provider-specific HTTP behavior.

Credentials still live under providers.<provider> so chat channels and WebUI resolve API keys and API bases the same way.

Add provider credentials to ProvidersConfig.

python

class ProvidersConfig(BaseModel):
    ...
    my_stt: ProviderConfig = Field(default_factory=ProviderConfig)

Add a ProviderSpec in nanobot/providers/registry.py.

For transcription-only providers, set is_transcription_only=True so they show up in credential/settings surfaces but stay out of chat model selection.

python

ProviderSpec(
    name="my_stt",
    keywords=("my_stt",),
    env_key="MY_STT_API_KEY",
    display_name="My STT",
    default_api_base="https://api.example.com/v1",
    is_transcription_only=True,
)

Add an adapter class in nanobot/providers/transcription.py.

Adapters receive resolved credentials and settings. They return an empty string for provider errors so channel voice messages fail quietly instead of crashing the agent loop.

python

class MySTTTranscriptionProvider:
    def __init__(
        self,
        api_key: str | None = None,
        api_base: str | None = None,
        language: str | None = None,
        model: str | None = None,
    ):
        self.api_key = api_key or os.environ.get("MY_STT_API_KEY")
        self.api_base = api_base or "https://api.example.com/v1"
        self.language = language or None
        self.model = model or "my-default-stt-model"

    async def transcribe(self, file_path: str | Path) -> str:
        ...

python

TranscriptionProviderSpec(
    name="my_stt",
    default_model="my-default-stt-model",
    adapter="nanobot.providers.transcription:MySTTTranscriptionProvider",
    aliases=("mystt",),
)

Add tests.

At minimum, cover:

config resolution in tests/providers/test_transcription.py
adapter request/response behavior and retry/error handling
WebUI settings payload/update behavior in tests/webui/test_settings_api.py
provider brand mapping if the provider appears in Settings

Update user-facing docs.

Add the provider to configuration.md where users choose transcription.provider, but keep implementation details in this development guide.