An experimental stack — MagenticLite (harness/UI), MagenticBrain (planner/coder) and the Fara1.
A new experimental agentic stack combining MagenticLite, MagenticBrain and the Fara1.5 model family has been released to demonstrate that smaller models can drive multi — step browser and local‑file workflows in a single experience. The project aims to show practical, local‑first agents that keep data on the user’s device and reduce the compute and cost normally associated with large models — a shift that could make everyday automation tasks more accessible to users and builders.
MagenticLite is the rebuilt harness and user interface for the stack, designed and tuned to run efficiently with smaller models. It unifies browser interactions and local file system access into one workflow, providing a single execution environment that preserves user data locally rather than routing it through external services. The UI also surfaces the agent’s actions so users can follow progress and intervene if needed.
MagenticBrain functions as the planner, coder and delegator within the system. It converts vague or high‑level requests into concrete plans, chooses which tools or subagents to invoke, emits code when necessary, and includes recovery behavior for mid‑task failures. The design emphasizes structured orchestration over relying solely on raw model scale to produce reliable multi‑step behavior.
Fara1.5 is the computer‑use model family built for this stack and ships in three sizes, with a flagship 9‑billion‑parameter model recommended for most use cases. According to the release, Fara1.5 sets state‑of‑the‑art results among small computer‑use models and nearly doubles the web navigation performance of Fara‑7B, including sharper handling of form completion, credentialed sites and long‑running tasks.
The team’s central research bet is that agentic performance depends more on how tools are orchestrated and actions are executed than on model scale alone. By co‑designing the application, models and execution harness, the project seeks to deliver capable agentic behavior at a fraction of the cost and compute of larger‑model approaches while still supporting a broad range of everyday tasks.
For builders the release clarifies practical tradeoffs: a 9‑billion‑parameter computer‑use model paired with a smaller planner can handle browser automation, form‑filling and local file management when combined with an orchestration layer and scenario‑tuned evaluation. The components are presented as individually useful but most effective when tightly integrated, enabling local‑first agents with clearer control over data flow. Development emphasized full‑lifecycle design: data generation, training objectives, model architecture and orchestration were iterated together, and scenario‑based evaluations were drawn from real use cases such as form completion, web research and file management. The system preserves visibility into the agent’s reasoning and supports human oversight and intervention during execution.
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