Open Jarvis: making local LLMs work as agents

Open Jarvis is an open-source framework designed to improve locally run open-weight AI models by optimizing the surrounding agent harness—such as prompts, inference runtime, tool access, memory, and reasoning logic—for a specific model and hardware setup. Its research reports that a Qwen3.5-9B model’s PinchBench accuracy rose from 62.3% to 88.4% after harness retargeting, recovering much of the gap to Claude Opus 4.6 without changing the underlying model. The system separates configuration into Intelligence, Engine, Agent Logic, Tools and Memory, and Learning, allowing automated spec search guided by a frontier cloud model to identify changes that improve performance while avoiding regressions. Open Jarvis argues that this modular approach enables local models to approach cloud-model performance on many personal AI tasks with lower query costs and latency, while retaining control over data and infrastructure. On Lambda’s NVIDIA HGX B200 hardware, an evaluation found that self-hosted Kimi K2.6 scored below Claude Fable 5.1 on shared PinchBench tasks but had an estimated 34% lower per-task inference cost, illustrating the potential tradeoff between quality, cost, and operational control.

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