Liquid, an AI startup founded by former MIT computer scientists, has released LFM2.5-2.6B, an open-weight language model optimized for agentic tasks. This model is designed to run entirely on local hardware, from smartphones to Raspberry Pis, eliminating the need for cloud inference or GPUs. This capability unlocks edge AI applications and offers enhanced privacy for sensitive data. LFM2.5-2.6B excels at well-defined, high-volume agentic tasks like tool calling, document management, and workflow automation. It is also suited for environments with limited connectivity, such as vehicles and robotics. The model has 2.6 billion parameters and a substantial 128,000-token context window. It features native tool calling and is available on Hugging Face with support for major inference stacks. Liquid positions this model not as a competitor to large frontier models, but as a solution where latency, privacy, and cost are paramount. The LFM architecture prioritizes real-world CPU performance, demonstrating impressive speeds even on devices like a Raspberry Pi. Liquid trained LFM2.5-2.6B specifically for agent frameworks, focusing on its ability to use tools effectively rather than just conversational tasks. The model's training pipeline includes a dedicated reinforcement learning phase within production agent harnesses. Liquid also developed its own proactive agent harness, aiming for assistants that operate autonomously in the background. The license for LFM2.5-2.6B permits commercial use for organizations under $10 million in annual revenue, with larger companies requiring a separate commercial agreement. In benchmarks, LFM2.5-2.6B shows strong performance on instruction-following and tool-use tasks, often outperforming larger models in its specialized areas.