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RikkaHub Agent test: Android phone agent compiles whisper.cpp on its own

Adam Conway at XDA has tested RikkaHub Agent, a fork by ExTV of the open-source Android LLM client RikkaHub, which adds an agent layer with more than 80 device tools. The phone runs the agent loop and executes the tools; the model itself runs elsewhere on the local network over an OpenAI-compatible endpoint.

The test device was an Oppo Find N5 with a Snapdragon 8 Elite and 16 GB of RAM. Inference ran on a Radeon RX 7900 XTX with Qwen 3.6 27B at Q4_K_M, roughly 16 GB, at 40 to 50 tokens per second. A second configuration used Qwen 3.6 35B on a Lenovo ThinkStation PGX with Nvidia’s GB10 Grace Blackwell.

RikkaHub Agent interface showing a whisper_status tool call returning that the Whisper CLI and model are missing, followed by a termux_run_command call
Screenshot: XDA.

The tool set covers tapping, swiping, scrolling and typing on screen, reading notifications, sending SMS, file management, browser control, SSH, scheduled jobs, a JavaScript runtime and Telegram. All tools are off by default and enabled per assistant, actions with side effects require approval, and a deterministic blocklist rejects destructive commands. The default context is 7,000 input tokens, rising to 32,000 with every tool enabled.

Asked whether whisper.cpp was installed, the agent determined that neither the command-line tool nor a model was present, requested permission, then installed the dependencies, cloned the repository and compiled it inside Termux. The build took seven minutes, and the agent chose the 75 MB small model on speed and accuracy grounds. It then transcribed a Telegram voice message, installing ffmpeg, converting the OGG/Opus file to 16 kHz mono WAV and returning a transcript in about two seconds.

Running the model on the phone itself was slower. With Gemma 4 E2B, a 2.6 GB build accelerated by LiteRT-LM, Conway reports chat worked but agentic tasks were painful, and tool calls were emitted as plain text until an update fixed the behaviour.

Frontier intelligence is never what I’d want from a phone agent, which is why a local LLM works perfectly.

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