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limitations.md

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Known Limitations

K-Dense BYOK is in beta. The most important rough edges today are on the expert-delegation path, which runs through the Gemini CLI even when the selected expert model is routed through OpenRouter or Ollama.

Expert models and the Gemini CLI with Skills

The expert delegation system relies on the Gemini CLI to execute tasks with our scientific skills. K-Dense routes that CLI through the local LiteLLM proxy, so the expert can use any model in the OpenRouter picker that supports tool calling. Gemini 3.1 Pro Preview remains the recommended expert default because it tends to be strongest for tool-heavy work, but other supported models can be selected per chat tab. While this works well for many workflows, there are some rough edges to be aware of:

  • Skill activation is not always reliable. Models sometimes skip a relevant skill, use it partially, or misinterpret the skill's instructions. This is especially noticeable with complex multi-step skills that require strict adherence to a procedure.
  • Tool-calling consistency varies. The Gemini CLI occasionally drops tool calls mid-execution or calls tools with incorrect arguments, which can cause expert tasks to stall or produce incomplete results.
  • Long-context degradation. When a skill injects a large amount of context (detailed protocols, multiple reference databases), models may lose track of earlier instructions or produce less focused output.
  • Structured output can drift. For skills that require specific output formats (tables, JSON, citations), models sometimes deviate from the requested structure.

These are upstream limitations of the selected model and the Gemini CLI tooling, not bugs in K-Dense BYOK itself. As model tool calling and CLI support improve, the expert delegation experience will get better automatically without any changes on your end.

Workarounds:

  • If a skill isn't behaving as expected, try re-running the task - results can vary between runs.
  • Try a different expert model in the dropdown. The model list is limited to OpenRouter models that advertise tools support, but tool-calling quality still varies across providers.

Ollama / small local models

Local models served through Ollama are supported end-to-end, but they amplify the Gemini CLI caveats above:

  • Tool-calling fidelity is noticeably weaker on sub-frontier models.
  • Skills that rely on multi-tool choreography (browsing, running scripts, structured output) are the most fragile.

If a delegation loops or ignores its skill, try a larger local model (or temporarily switch back to an OpenRouter-hosted model) before assuming the workflow is broken. See Local models with Ollama.

Tabbed chats

  • Hard cap of 10 tabs per project. This keeps the browser snappy and bounds the number of parallel SSE streams to the backend. Close an existing tab before opening a new one once you hit the limit.
  • Tab list isn't persisted across reloads. Refreshing the page resets you to a single new chat tab. The underlying sessions and their message history are still on disk under the project — you just can't currently reopen them all at once into tabs from the UI. Re-opening a session by id from the UI is on the roadmap.
  • Workflows launch into the active tab. If you have a long-running turn streaming in tab A and click Launch on a workflow while tab B is active, the workflow runs in tab B. Switch to the tab you want to receive the workflow before launching.