Providers
Swival supports LM Studio for local inference, HuggingFace Inference API for hosted inference, OpenRouter for multi-provider access through a single API, Google Gemini API for Google's models, a ChatGPT Plus/Pro provider for using OpenAI models through your existing subscription via OAuth, a generic provider for any OpenAI-compatible server, and a command provider for shelling out to an external program. 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"
HuggingFace Inference Endpoints
HuggingFace Inference Endpoints let you deploy any supported model on dedicated infrastructure. Create an endpoint from the HuggingFace UI, then point Swival at it with --base-url.
swival --provider huggingface \
--model Qwen/Qwen3.5-35B-A3B \
--base-url https://tfg1ghx03o7xuv5p.us-east-1.aws.endpoints.huggingface.cloud \
--repl
Most inference endpoints use vLLM as the serving backend. For tool calling to work, you must add the following to the Container Arguments field in your endpoint's configuration on HuggingFace:
--enable-auto-tool-choice --tool-call-parser qwen3_xml
The --tool-call-parser value depends on the model you deploy. For Qwen models use qwen3_xml, for other model families check the vLLM tool calling documentation for the correct parser name. Without these arguments, the endpoint will not return structured tool calls and Swival will not be able to use its tools.
For recently released models, the default vLLM version configured in Inference Endpoints may not support them yet. If you hit errors during model loading, set the Engine URI in your endpoint configuration to vllm/vllm-openai:latest to use a more recent build.
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.
Some models served through vLLM leak internal reasoning markers like <think> tags into their responses. If you see these in the output, enable --sanitize-thinking to strip them. See Thinking Tag Sanitization for details.
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. Pass bare model identifiers like z-ai/glm-5. If you pass a model ID that already includes the full LiteLLM prefix (e.g. openrouter/z-ai/glm-5), Swival detects and corrects the double prefix.
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, DeepSeek API, 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"
# DeepSeek API
export DEEPSEEK_API_KEY=sk-...
swival --provider generic \
--base-url https://api.deepseek.com \
--model deepseek-chat \
--api-key "$DEEPSEEK_API_KEY" \
--max-output-tokens 8192 \
"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.
Google Gemini API
The google provider connects to Google's Gemini API through its OpenAI-compatible endpoint (/v1beta/openai).
--model is required. Authentication comes from --api-key, GEMINI_API_KEY, or OPENAI_API_KEY.
export GEMINI_API_KEY=...
swival --provider google \
--model gemini-2.5-flash \
"task"
When --max-context-tokens is not set, Swival auto-detects the context window via litellm.get_model_info(). If detection fails, context length is unknown and compaction may not trigger at the right time — set --max-context-tokens explicitly if you hit issues.
Internally, the model is routed through Google's OpenAI-compatible endpoint at https://generativelanguage.googleapis.com/v1beta/openai. --base-url overrides this if you need a custom endpoint.
ChatGPT Plus/Pro
The chatgpt provider lets you use OpenAI models through your existing ChatGPT Plus or ChatGPT Pro subscription, without needing a separate API key.
Authentication uses an OAuth device-code flow handled by LiteLLM -- on first use, LiteLLM prints a device code and a verification URL to your terminal. Open the URL, enter the code, and authorize with your ChatGPT account. The resulting tokens are cached locally and refreshed automatically on subsequent runs.
If you need to pass an API key explicitly (for example, when using --self-review which passes credentials via environment variables), set CHATGPT_API_KEY or use --api-key.
--model is required. There is no default model.
swival --provider chatgpt --model gpt-5.4 "task"
On the first run, you will see a device-code prompt with a URL and a code to enter in your browser. Once you complete the flow, the OAuth tokens are stored at ~/.config/litellm/chatgpt/auth.json and refreshed automatically.
Supported model names come from LiteLLM's ChatGPT provider and may change over time. See the LiteLLM ChatGPT provider docs for the current model list and naming conventions.
Two environment variables are available for advanced use. CHATGPT_TOKEN_DIR overrides the default token storage directory. CHATGPT_API_BASE overrides the API base URL.
export CHATGPT_TOKEN_DIR=/path/to/tokens
swival --provider chatgpt --model gpt-5.4 "task"
The --top-p, --seed, and tool_choice parameters are not supported by the ChatGPT Plus/Pro backend. Swival drops them automatically when using this provider.
Models like gpt-5.4 support tunable reasoning effort. Use --reasoning-effort to control how much the model thinks before responding:
swival --provider chatgpt --model gpt-5.4 --reasoning-effort high "task"
All OAuth handling happens inside LiteLLM. Swival normalizes the model to chatgpt/<model_id> and passes it through. No other configuration is needed.
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.
When using vLLM as the inference backend, models may leak internal reasoning markers like <think> tags into their output even with thinking disabled. Use --sanitize-thinking to strip them. See Thinking Tag Sanitization for details.
For reasoning effort specifically, Swival provides a dedicated --reasoning-effort flag instead of requiring extra_body. See Customization for details.
Command (External Program)
The command provider shells out to an external program instead of calling an API. This is useful when you want to wrap an existing CLI tool — such as codex exec --skip-git-repo-check --full-auto --ephemeral, ollama run, or a custom script — as Swival's backend.
The conversation transcript is written to the program's stdin, and the program's stdout is read back as the model response. This is text-only: tool calling is not available, so Swival runs without tools.
--model holds the command string, which is split with shlex. --base-url and --api-key are ignored.
swival --provider command --model "codex exec --skip-git-repo-check --full-auto --ephemeral" "task"
Or in config:
provider = "command"
model = "codex exec --skip-git-repo-check --full-auto --ephemeral"
Because the external program handles all model routing, there is no auto-discovery and no LiteLLM involvement. If you set --max-context-tokens, Swival will apply output clamping and graduated compaction as usual; otherwise context management is left to the command itself.
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.