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
SAN FRANCISCO, July 8, 2026 /PRNewswire/ — LangChain today 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.
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.
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.
Ecosystem support
The announcement is supported by partners across the AI infrastructure and enterprise ecosystem, including EY, who is building an implementation practice around the software stack, and Baseten, Fireworks, Nebius, Crusoe, DeepInfra, and Together AI. These partners help enterprises serve Nemotron models in production and adapt the blueprint for business 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
Availability
The NemoClaw for LangChain Deep Agents Blueprint is available now. Enterprises can access the blueprint to evaluate the stack for their own workloads.
About LangChain
LangChain powers the full agent development lifecycle — building, testing, deploying, and monitoring — so AI teams can improve their agents systematically. LangSmith Engine accelerates this cycle, automatically surfacing and fixing issues to improve agents over time. LangSmith is neutral by design, so teams can customize their own stack to optimize on cost and performance as the landscape evolves. More than 7,000 customers, including NVIDIA, Bridgewater, LinkedIn, Workday, Harvey, and Rippling trust LangSmith to build and manage their agents.
SOURCE LangChain