Providers

Swival supports LM Studio for local inference, HuggingFace Inference API for hosted inference, OpenRouter for multi-provider access through a single API, and a generic provider for any OpenAI-compatible server. All provider calls are normalized through LiteLLM, so the runtime loop stays consistent while credential and model routing change per provider.

LM Studio

LM Studio is the default provider and usually requires no flags when the local server is already running with a loaded model.

At startup, Swival calls http://127.0.0.1:1234/api/v1/models unless you override --base-url. It looks for the first model entry with type: "llm" and a non-empty loaded_instances array, then extracts the model identifier and current context length from that payload. If no loaded model is found, Swival exits and asks you to load a model or pass --model explicitly.

If LM Studio is running on another host or port, set --base-url.

swival --base-url http://192.168.1.100:1234 "task"

If you want to bypass auto-discovery, pass --model.

swival --model "qwen3-coder-next" "task"

If you pass --max-context-tokens, Swival may reload the model through LM Studio's /api/v1/models/load endpoint.

swival --max-context-tokens 131072 "task"

If the requested value already matches the loaded context length, no reload happens. When a reload is required, it can take noticeable time depending on model size and hardware.

Internally, LM Studio calls are routed through LiteLLM as an OpenAI-compatible endpoint. Swival sends the model as openai/<model_id>, sets api_base to <base_url>/v1, and uses the placeholder API key lm-studio.

HuggingFace Inference API

For HuggingFace, --model is required and must be in org/model format. Authentication comes from HF_TOKEN by default or --api-key if you pass one explicitly.

export HF_TOKEN=hf_your_token_here
swival --provider huggingface --model zai-org/GLM-5 "task"

Serverless HuggingFace endpoints often expose smaller context windows than local deployments, so long multi-turn coding sessions can hit context pressure sooner.

For dedicated endpoints, keep the same model identifier and pass your endpoint URL and key.

swival --provider huggingface \
    --model zai-org/GLM-5 \
    --base-url https://xyz.endpoints.huggingface.cloud \
    --api-key hf_your_key \
    "task"

Internally, Swival normalizes the model to huggingface/<model_id> for LiteLLM and strips an existing huggingface/ prefix if you already included it. If --base-url is set, it is forwarded as api_base.

Dedicated endpoints usually let you use the full deployed model context window rather than tighter serverless limits.

OpenRouter

For OpenRouter, --model is required and authentication comes from OPENROUTER_API_KEY or --api-key.

export OPENROUTER_API_KEY=sk_or_your_token_here
swival --provider openrouter --model z-ai/glm-5 "task"

If you use an OpenRouter-compatible custom endpoint, set --base-url.

swival --provider openrouter \
    --model z-ai/glm-5 \
    --base-url https://custom.openrouter.endpoint \
    --api-key sk_or_key \
    "task"

OpenRouter models vary widely in context limits, so you should set --max-context-tokens to match the model you chose.

swival --provider openrouter --model z-ai/glm-5 \
    --max-context-tokens 131072 "task"

Internally, Swival normalizes OpenRouter models to LiteLLM's openrouter/... format. If the model identifier starts with the literal double prefix openrouter/openrouter/, Swival strips the redundant prefix so LiteLLM does not see a stutter. Any other openrouter/ prefix (e.g. openrouter/z-ai/glm-5) is treated as part of the model path and gets prefixed normally, so you should pass bare identifiers like z-ai/glm-5.

Generic (OpenAI-compatible)

The generic provider works with any server that exposes an OpenAI-compatible chat completions endpoint. This covers mlx_lm.server, ollama, llama.cpp, vLLM, LocalAI, text-generation-webui, and similar tools.

Both --model and --base-url are required. Pass the server's root URL without /v1 — Swival appends it automatically. If your URL already ends in /v1, that's fine too.

# mlx_lm.server
swival --provider generic \
    --base-url http://127.0.0.1:8080 \
    --model mlx-community/Qwen3-Coder-480B-A35B-4bit \
    "task"

# ollama
swival --provider generic \
    --base-url http://127.0.0.1:11434 \
    --model qwen3:32b \
    "task"

# llama.cpp server
swival --provider generic \
    --base-url http://127.0.0.1:8080 \
    --model default \
    "task"

No API key is required for most local servers. If your server needs one, pass --api-key or set OPENAI_API_KEY.

export OPENAI_API_KEY=sk-...
swival --provider generic \
    --base-url https://my-server.example.com \
    --model my-model \
    "task"

There is no model auto-discovery and no context window reload. Set --max-context-tokens manually if you need Swival to know the window size.

Internally, generic calls are routed through LiteLLM as openai/<model_id> with api_base pointing at your server's /v1 path.

Extra Provider Parameters

Some models and servers accept parameters that go beyond the standard OpenAI API. Use --extra-body to pass them through. The value is a JSON object that gets forwarded directly to the API call.

For example, Qwen models served through vLLM can disable internal thinking mode:

swival --provider generic \
    --base-url http://127.0.0.1:8000 \
    --model Qwen/Qwen3.5-35B-A3B \
    --extra-body '{"chat_template_kwargs": {"enable_thinking": false}}' \
    "task"

You can also set this in config so you don't repeat it every time:

provider = "generic"
base_url = "http://127.0.0.1:8000"
model = "Qwen/Qwen3.5-35B-A3B"
extra_body = { chat_template_kwargs = { enable_thinking = false } }

The dictionary is forwarded as extra_body to LiteLLM, which passes it through to the server. Refer to your model or server documentation for supported parameters.

Adding More Providers Later

Because API calls are already abstracted behind LiteLLM, adding a provider is mostly a matter of argument validation, model normalization, and credential wiring. The provider-specific branch in call_llm() is intentionally compact so new providers can be added without changing the rest of the agent loop.

In practice, each provider branch is only about ten lines of routing logic.