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getbased supports OpenRouter, PPQ, Routstr, Venice, Custom API, Local AI, and CLI agents for chat and supported AI features. Some providers also expose private or encrypted modes, such as PPQ Private TEE and Venice Encrypted TEE Mode. You can switch in Settings → AI without losing your data. Direct-provider requests go from the browser to the selected endpoint without a getbased relay fallback. CLI routes go through your local Companion to the selected agent or personal gateway. Supported browser credentials are wrapped with a device key when passphrase encryption is off and with your passphrase-derived key when it is on. Already use an installed agent? Connect it through Companion. Local means a compatible model server; CLI agents means an agent harness with its own configuration and account. Neither label guarantees on-device inference.
Voice uses a separate service chosen in Settings → Voice. Same as chat can reuse a connected OpenRouter, PPQ, or Venice account and otherwise falls back on-device. You can also select on-device voice, a compatible voice server, one of those AI accounts, xAI, or ElevenLabs independently from the provider that generates chat answers. See Use voice in AI chat.
Before the first request to a cloud AI or cloud voice recipient, getbased shows a recipient-specific consent prompt. Nothing is sent if you decline. An approval also covers later requests you choose to initiate with that recipient; review or withdraw approvals at any time in Settings → Privacy.

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Which features need AI?

All non-AI features work fully without any provider configured. You can load a demo profile, enter results manually, and explore charts and trends before setting up AI.
The built-in demo profiles ship with precomputed context-card insights. Local AI can recalculate edited demo cards automatically, while cloud and other potentially paid providers stay off for demo-card recalculation until you explicitly enable live AI for the selected provider and model. The consent is session-only, so a reload or model/provider change requires a new confirmation. See Health context.

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Set up a provider

Open Settings → AI → CLI agents, run the displayed Copy connection command on the same computer as your browser, and choose Check connection. Enable an available Codex, OpenCode, Hermes, Grok Build, or OpenClaw agent. Node.js 20+ and an installed, configured agent are required; the hosted app can use this connection without moving your data to localhost.The Companion reuses the agent’s existing sign-in. Models and supported reasoning choices are available in Settings and beside the chat microphone. Images and non-chat features require declared adapter/model capabilities; personal gateways are text-chat only. Read CLI setup, automatic startup, privacy, and troubleshooting before connecting.
The Settings and chat model pickers scope choices to the active provider and privacy mode. Search is available for long catalogs. Supported catalogs show curated groups:
  • Recommended — the latest, most capable models for lab interpretation, sorted first
  • Other — all remaining available models
Recommendations are starting points, not a guarantee of clinical accuracy, entitlement, or suitability for a particular report. Check results and compare models on your own task. Recommendation families and exact IDs change with the catalog. Use the in-app list rather than a fixed list in this guide. CLI catalogs are scoped further to the selected agent and execution target; personal gateways identify their current/default model instead of importing recommendations from unrelated providers. Explicitly unavailable entries are excluded, but an agent may only discover an account restriction when it sends a request. When the active model exposes reasoning controls, adjust them in Settings or in the chat picker. Unsupported controls stay unavailable rather than guessing from a model name. A model can reason internally without offering a separate effort setting. Supported settings also pass through the selected private/TEE transport; a reasoning slider does not add privacy protection. Meal-photo selectors are separate: they show only models reported as image-capable and can follow the chat assistant or use a dedicated route. Following a CLI assistant uses its actual selected model and capability, not the last direct-provider model. If the route cannot process images, select a compatible feature route explicitly. Use Meal Benchmarks to compare those models on the same images.
Use the same model for all your imports. When you import a lab PDF, the AI generates marker keys (like biochemistry.glucose) to map results. Different models may generate slightly different keys for the same marker, which can cause the same biomarker to appear as two separate entries in your charts. Pick a model and stick with it. If you do switch, getbased runs a pre-flight check before each import and warns you if your model has changed since the last import.

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Test before you choose

Open Settings → Data → Test models on lab reports to run the same verified 68-result report through different setups. You can compare raw accuracy, getbased’s corrected result, speed, tokens, and runtime details side by side. The comparison works across local models, hosted compatible endpoints, and built-in cloud providers. You can also import the same personal report with different setups and compare the review effort each one required. See Test and compare AI models.

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How much does it cost?

AI providers usually charge for input and output tokens, and image-capable models may also price image input. Catalogs and prices change frequently. getbased uses the current pricing metadata returned by supported providers when available and shows tracked tokens and estimated cost on chat, meal analysis, and benchmark results. A provider can still bill a canceled or failed request even when it returns no usage count.
Review the model’s current provider price before a large import or multi-model meal benchmark. When the provider returns usage, getbased records it per result so you can compare cost with review quality.
Run a downloaded model on your own hardware with Ollama, LM Studio, Unsloth Studio, or Jan and pay no provider fee. Hardware needs depend on the model and quantization; the Model Advisor shows what the connected server reports and what may fit.