A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness-1, a 20-billion parameter open-source search agent built atop OpenAI’s gpt-oss-20B open source model that fundamentally redesigns how AI executes complex retrieval tasks. Harness-1 achieves a massive leap in performance, scoring 73% average on its ability to recall relevant information correctly from a curated dataset, outperforming even GPT-5.4 (70.9%) and the next, most accurate open source search agent, Tongyi DeepResearch 30B, by 11.4 percentage points. (While GPT-5.5 has also been out for more than a month, the researchers didn’t test against this model as it wasn’t available when they were building theirs.) Harness-1 accuracy benchmark performance compared to other leading AI search agents and models. Credit: University of Illinois at Urbana-Champaign, UC Berkeley, Chroma Crucially for developers, the model and its environment are available immediately under the highly permissive Apache 2.0 license and model code/weights on Hugging Face. Harness-1 also serves as proof-of-efficacy of another effort, Tinker, the distributed, …