LFM2.5 Edge Agent
I built a research agent that runs entirely in the browser and puts its full think, plan, and act loop on display. Give it a question and it: - Creates an explicit action plan - Reasons about the next step - Chooses and calls tools - Inspects each result - Updates the plan and repeats It does not stop after one tool call. The agent keeps looping until every step in the plan is complete. Along the way, it can ask clarifying questions, use local context, delegate focused research to an isolated Wikipedia subagent, and collect sources. Once the plan is done, it turns the evidence into a cited response and downloadable research paper. The incredible part is the size: just 2.6B parameters. That makes LFM2.5 seriously fast on-device with WebGPU, while still being capable enough to plan, reason, use tools, and finish multi-step objectives. The model, agent loop, and inference pipeline all run locally. No inference server, no account, and prompts and outputs stay in the browser. Small models are not just autocomplete. They can be agents at the edge.
