Kagi Assistant is an AI workspace attached to a search product. Its distinctive promise is user choice among language models and the ability to control when the assistant searches the web. That makes it attractive to readers who value reproducible prompts and source access more than a single vendor’s branded model.
Product: Kagi Assistant · Developer: Kagi
At a glance
Good for: privacy-conscious search users; researchers; writers; model-comparison learners. Known application surfaces: Kagi web account and supported search entry points. Typical inputs and outputs: text questions; optional voice input; web search grounding; markdown and JSON conversation exports.
These are product-level descriptions as of the editorial cutoff, not guarantees for every geography, workspace, device or subscription. Specific pricing, usage limits, model builds and enterprise contract terms are deliberately omitted unless independently established for the applicable tier. Current price and usage assumptions require a separately dated plan comparison against the vendor’s terms.
How to use it effectively
Create a short factual question and ask it once without web access, then enable web access and compare the answer against linked primary sources. Record which model was selected; a different vendor or route may change the response. Export a thread as Markdown or JSON so the reasoning trail and follow-up questions can be preserved outside the service. Avoid using conversation sharing links for research containing personal or commercially sensitive documents; shared threads should be reviewed for unintended content before distribution.
Capabilities and deployment context
Kagi’s official Assistant documentation describes model switching, optional web access, voice input, thread sharing and Markdown/JSON export. It also says Kagi deletes threads after an inactivity period by default, with settings that can change this behavior. The vendor’s privacy documentation covers the relationship with third-party model providers and the distinction between application privacy and a provider’s separate processing practices. A model list on a documentation page is time-sensitive and should not be mistaken for a perpetual catalog.
Practical limitations
Adding web search does not automatically improve every answer, and selected models can differ in their ability to cite correctly or respect constraints. An exported thread is portable text, not a guarantee that every source link will remain accessible. The presence of a model in a menu does not confirm it is available on every Kagi subscription or account region. Kagi’s own third-party model processing table can change with provider contracts and future releases.
Privacy, access and portability
The Kagi documentation makes specific statements about not using Assistant user data for model training and describes privacy arrangements with third-party model providers. Treat these as vendor claims to validate against the current privacy policy and business contract, not as independent audit findings. Exports and shared conversation links create copies outside the ordinary thread-lifecycle settings; review both before distributing work.
Who should choose it instead of an alternative?
Kagi Assistant is worth considering when search privacy and model choice matter and a durable Markdown/JSON export is useful. Poe offers a different multi-bot interface; Perplexity emphasizes sourced web answers; Gemini Notebook is tailored to fixed source collections. For neutral performance scoring, a dated comparison guide should specify identical tests rather than inventing results.
A sensible first evaluation
- Choose one real but non-sensitive task with a checkable expected outcome.
- Record your account region, active workspace, product mode and evaluation date; do not infer a model version from the brand.
- Compare the response with the original document or primary website, paying particular attention to figures, exclusions and contradictory sources.
- Test how to edit, share, export and delete the result before building a recurring workflow around it.
- Revisit permissions and product release notes before expanding to proprietary information or wider teams.
Sources & Further Reading
Source check: 2026-10-25. Vendor documentation establishes the provider’s described functions; it is not independent performance testing. No sponsored placement or hands-on test is implied.