The NemoClaw architecture merges LangChain Deep Agents code with the NVIDIA Nemotron 3 Ultra model and the NVIDIA OpenShell runtime. This stack allows development teams to customize agent behavior for specific business workflows while maintaining strict governance and security. According to recent evaluation benchmarks, the Nemotron 3 Ultra model achieved an aggregate score of 0.86 at a cost of $4.48, significantly outperforming comparable models that reached similar benchmarks at costs exceeding $43.
LangChain and NVIDIA Unveil NemoClaw Blueprint for Enterprise Agents
LangChain and NVIDIA have launched the NemoClaw blueprint, a reference architecture designed to help enterprises build, evaluate, and deploy open agent systems. By integrating specific model tuning with optimized runtimes, the collaboration aims to provide businesses with proprietary control over their AI while slashing inference costs by tenfold.
Scaling Production AI
Beyond raw performance, the initiative addresses the necessity for enterprises to retain ownership of their intellectual property, including agent memory, model weights, and tuning data. Harrison Chase, CEO of LangChain, noted that performance compounds when teams tune models, tools, and context management together. This shift toward open stacks allows companies to move beyond one-size-fits-all solutions, enabling them to build specialized agents that reflect unique operational data. The blueprint is currently supported by a broad ecosystem of infrastructure partners, including EY, Baseten, Fireworks, and Nebius, facilitating deployment in regulated industries where auditability and risk management are primary concerns.
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