LangChain has officially announced the launch of the NemoClaw for LangChain Deep Agents blueprint. Developed in strategic collaboration with NVIDIA, this newly unveiled framework provides organizations with a robust reference architecture designed to build, assess, and deploy sophisticated, open enterprise AI agents with industry-leading efficiency.
As corporate teams transition AI agents into production environments, the infrastructure constructed around the primary model increasingly represents critical intellectual property. Core elements such as proprietary workflows, memory structures, system traces, model weights, and hyper-tuning data serve as invaluable, business-specific intelligence. Consequently, enterprise developers require agile methodologies to retain ownership of their innovations, continuously iterate on performance, and manage agent execution under rigorous corporate governance and cost frameworks.
The NemoClaw blueprint addresses these precise needs by merging LangChain Deep Agents Code, the NVIDIA Nemotron 3 Ultra model, and the NVIDIA OpenShell runtime. This integrated stack empowers technical teams to secure, customize, and execute enterprise-grade agents at a substantially minimized operational cost.
Landmark Benchmarks and Exponential Cost Reductions
Performance evaluation suites managed by LangChain demonstrate that an open-source agent architecture can now rival or outperform closed ecosystems.
The NVIDIA Nemotron 3 Ultra, when assessed using the LangChain Deep Agents framework for the benchmark test, attained an outstanding cumulative score of 0.86. Interestingly enough, this optimal result was accomplished at a mere expense of $4.48. On the other hand, the nearest alternative solution to this challenge only attained comparable results at a price of $43.48.
These metrics stem directly from precise harness modifications engineered for Nemotron 3 Ultra. Leveraging Deep Agents, LangChain fine-tuned critical variables including tool utilization, contextual management, and intermediate execution step evaluations. From the perspective of the enterprise decision-maker, it follows that the larger significance of AI agent efficiency can be summarized by saying that AI agent efficiency is significantly enhanced when the model as well as the harness for orchestrating it is tailored together.
“The way to build better agents is to keep improving the system around the model,” said Harrison Chase, Co-founder and CEO of LangChain. “Memory, tool use, evaluation, and model behavior compound when teams can tune them together. Our work with NVIDIA shows that enterprises can get strong performance from an open stack while keeping control over the agent systems they’re building.”
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Furthermore, driving down inference costs unlocks new possibilities for deploying highly specialized agents across production environments. Organizations can easily build niche agents for specialized industry domains, utilize detailed tracing and evaluation systems to quantify efficiency, and seamlessly modify the underlying harness as organizational requirements pivot.
“Super agents have arrived,” said Jensen Huang, Founder and CEO of NVIDIA. “With an open model like NVIDIA Nemotron, a LangChain harness, the NVIDIA OpenShell runtime, and a company’s own data, every enterprise can build custom agents that understand its business, use its tools, and turn knowledge into action. The future of AI won’t be one-size-fits-all companies will use AI cloud services and build their own AI, shaped by their proprietary data, know-how, and workflows, and run it safely and securely wherever they operate.”
In tandem with the launch, a recorded fireside conversation between Harrison Chase and Jensen Huang has been released today, offering deeper insights into open agent frameworks and the strategic roadmap toward affordable corporate AI implementation.
Deconstructing the NemoClaw Blueprint
The NemoClaw for LangChain Deep Agents blueprint is built upon three foundational, enterprise-grade pillars:
- NVIDIA Nemotron 3 Ultra: Serves as an advanced, open-weight model layer tailored for organizations aiming to modify core AI behaviors for domain-specific tasks while maintaining low costs.
- LangChain Deep Agents: Establishes the core harness layer required for managing persistent, long-running agents covering task planning, memory retention, tool interaction, and sequence execution. This blueprint includes a profile uniquely tuned for the Nemotron 3 Ultra model.
- NVIDIA OpenShell: Provisions a secure, regulated runtime environment, giving enterprise IT teams full governance over how autonomous agents access databases, enterprise systems, and operational tools.
Combined, this unified ecosystem delivers an expert-tuned framework equipped for deployment, continuous measurement, and live iteration in production.
Widespread Ecosystem and Industry Support
This landmark announcement is backed by extensive collaboration across the global AI infrastructure and professional services landscape. Global consultancy EY is establishing a specialized implementation practice dedicated to this software stack, alongside a robust cohort of foundational tech partners including Baseten, Fireworks, Nebius, Crusoe, DeepInfra, and Together AI. These partners collectively empower enterprises to run Nemotron models at scale and integrate the blueprint into mission-critical applications.
“EY clients in regulated industries are ready to move agentic AI out of isolated pilots and into production and are often constrained by governance, security, and the ability to prove control to a regulator or a board. Open agent architectures matter because they give enterprises transparency into how agents operate, control over where data and inference run, and the freedom to deploy on their own terms without committing to a closed stack. By delivering the NVIDIA NemoClaw blueprint, which incorporates together with LangChain technology, EY teams help give clients a secure, sandboxed foundation for always-on agents that can meet enterprise standards for auditability and risk from the first deployment.” – Geoff Vickrey, Global Chief Commercial Officer, NVIDIA, EY
“Production agents need inference that is fast, reliable, and cost-efficient at scale. We have optimized NVIDIA Nemotron models on Baseten to deliver high throughput and low latency on NVIDIA hardware, so teams get strong price-performance without operating the infrastructure themselves. Delivering Nemotron through the NemoClaw blueprint with LangChain gives enterprises a clear path to run open agentic models in production with the performance and economics these workloads demand.” – Philip Kiely, Head of Developer Relations, Baseten.
“Agentic workloads make many model calls per task, so inference speed and cost directly determine whether an agent is viable in production. Fireworks serves NVIDIA Nemotron models with the throughput and price-performance that high-volume agent systems require, tuned for the tool calling and reasoning patterns these workloads depend on. Offering Nemotron through the NemoClaw blueprint with LangChain gives enterprises an efficient, open foundation they can scale with confidence.” – Lin Qiao, CEO and Cofounder, Fireworks AI.
“The next challenge for enterprise AI is running complex agentic workloads economically at production scale. Nebius was built for that challenge. Our AI-native cloud gives customers dedicated infrastructure optimized for high-performance inference and cost-efficient scaling. By offering NVIDIA Nemotron models through the NemoClaw blueprint with LangChain, we’re making it easier for organizations to deploy and scale open agentic AI across their business.” – Roman Chernin, Chief Business Officer, Nebius
