BotPsy: On-Device Personal Assistant with a Sub-Billion-Parameter Agentic Language Model

Frontier cloud LLMs carry high operational costs that can fluctuate unexpectedly, and relying on such third-party models introduces vulnerabilities, as providers can modify, deny, or deprecate access at any time. Furthermore, proprietary models offer limited customization for specific workflows, while posing risks to privacy, data control, and transparency. Besides, not every task demands frontier-level intelligence: small, specialized agents can be equally capable on specific tasks and significantly cheaper to run, since many routine or smaller workloads can be processed efficiently on-device. As intelligence per parameter continues to improve rapidly, we aim to stand at the forefront of this shift toward performant, privacy-focused, self-hosted models.

Tether AI Research introduces BotPsy, a 0.8B-parameter model introduce BotPsy, a 0.8B-parameter model designed for function calling and agentic workflows, built on top of Qwen3.5-0.8B and suitable for on-device deployment. BotPsy is a "personal assistant" that uses tools to perform a variety of simple tasks for the user, in domains such as web search & research, travel & lifestyle, commerce & finance, business & customer operations, device & vehicle control, files & documents, calendar, and email & messaging. Due to its small parameter count, it is best suited to tasks of low to medium difficulty. BotPsy achieves state-of-the-art (SOTA) tool-use and agentic performance for its size, outperforming every model of up to 1.2B parameters that we tested (Figures 1 and 2). Designed for secure on-device workflows, it provides exceptional instruction-following reliability and efficiency while keeping user data private. As a small model, BotPsy still makes mistakes and is not intended for high-stakes use; please read the Intended Use and Limitations for more information.

🤗 Model (safetensors)  |  📦 GGUFs (bf16, Q8, Q4)