LangChain and NVIDIA Launch NemoClaw Deep Agents Blueprint for Enterprise Agents

The NemoClaw for LangChain Deep Agents blueprint gives enterprises a reference architecture for building open agent systems with benchmark-leading performance and more than 10x lower inference costs

LangChain announced the NemoClaw for LangChain Deep Agents blueprint, developed with NVIDIA to help enterprises build, evaluate, and deploy advanced open agent systems.

As enterprises move agents into production, the systems they build around the model become valuable IP. Agent memory, workflows, traces, model weights, and tuning data are proprietary intelligence specific to the business. Teams need a way to own that work, improve it over time, and run agents with the performance, cost control, and governance their organizations require.

This new blueprint combines LangChain Deep Agents Code, NVIDIA Nemotron 3 Ultra, and NVIDIA OpenShell runtime so teams can customize agents for their workloads, deploy them securely, and run them at lower cost.

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Benchmark-leading performance with 10x lower cost

LangChain’s evaluation benchmarks show that enterprises can now get top-performing agents from an open agent stack.

In LangChain’s agent eval suite, NVIDIA Nemotron 3 Ultra evaluated with LangChain Deep Agents achieved an aggregate score of 0.86 at a cost of $4.48. The next closest performing model cost $43.48, making Nemotron 3 Ultra roughly 10x lower inference cost on this benchmark.

The results reflect harness customizations made for Nemotron 3 Ultra. LangChain tuned how the agent uses tools, manages context, and evaluates intermediate steps with Deep Agents. For enterprises, the key takeaway is that agent performance improves when teams tune the model and harness together around the tool-use patterns, context requirements, and workflows specific to their business.

“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.”

Lower inference costs also make it practical to run and evaluate more specialized agents in production. Teams can create agents for specific domains, use evals and traces to measure performance, and adapt the harness as their workflows change.

“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.”

Harrison Chase and Jensen Huang discuss the blueprint, open agent systems, and the path to lower-cost enterprise AI agents in a fireside chat released today.

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How the blueprint works

The NemoClaw for LangChain Deep Agents blueprint brings together three components essential for building agents for the enterprise:

• NVIDIA Nemotron 3 Ultra provides the open-weight model layer for teams that want to customize model behavior for their domains while improving agent performance and lowering cost.
• LangChain Deep Agents provides the harness layer for long-running agents, including planning, tool use, memory, and task execution. The Blueprint includes a Deep Agents harness profile is tuned for Nemotron 3 Ultra.
• NVIDIA OpenShell provides the runtime layer for secure, governed deployment, helping teams control how agents interact with tools, systems, and data.

Together, these components give teams a tuned agent system that can be deployed, measured, governed, and improved in production.