LangChain and NVIDIA Launch NemoClaw: Open Agent Stack Achieves 10× Lower Cost Than Competitors

LangChain and NVIDIA did not launch a simple API wrapper, but a complete agent blueprint that can be assembled and run on your own infrastructure in just a few minutes. NemoClaw has been available since July 8, 2026 as an open-source project that can be launched with a single command.

Three Layers of the Open Agent Stack

NemoClaw’s architecture rests on three functional layers that together form a coherent system:

Nemotron 3 Ultra — NVIDIA’s open model post-trained specifically for multi-turn agentic workflows. Unlike models optimized for static prompts, Nemotron 3 Ultra is oriented toward the longer reasoning and planning sequences that agents require.

LangChain Deep Agents Code — the harness that orchestrates the agent’s actions: system prompts, tool descriptions, and the middleware coordinating between the model and the runtime environment. As will become clear, this layer is critical to the results achieved.

NVIDIA OpenShell — a secure sandbox for code execution with defined access policies. The agent executes code within an isolated environment with explicitly configured boundaries.

Benchmark: 10× Lower Cost, Nearly Identical Result

The aggregate score of the NemoClaw combination is 0.86 at a cost of just $4.48 per evaluation. The next-best competitor achieves a comparable level of performance at a cost of $43.48 — NemoClaw is nearly 10 times cheaper at nearly identical results.

For broader context: Claude Opus 4.8 achieves an aggregate score of 0.87 — a difference of 0.01 compared to NemoClaw, but at significantly higher cost. NemoClaw thus enters the conversation about the boundary of parity between open and closed models.

Lower costs carry strategic significance that extends beyond simple savings: when inference is 10× cheaper, an enterprise can afford to systematically run evaluation suites, test multiple harness and agent variants, and deploy specialized agent teams for projects that would otherwise be covered exclusively by expensive closed APIs.

Is the Harness More Important Than the Model?

Particularly interesting is how the jump from 0.80 to 0.86 in aggregate score was achieved: not by retraining the model, but by optimizing the harness. LangChain adjusted the system prompt, tool descriptions, and middleware coordination — and achieved an improvement of +0.06 without a single change to the Nemotron 3 Ultra model’s parameters.

This is not a trivial technical anecdote. It supports the thesis that the value of agentic systems is increasingly shifting from the LLM itself to the layer surrounding it: how tasks are posed to it, how tools are defined, how multiple steps are coordinated. The model becomes the engine, and the harness — the vehicle. If this is a repeatable pattern (and LangChain claims it is), a new category of engineering work opens up: harness optimization as a discipline as important as fine-tuning the model itself.

Security Framework and Data Control

For enterprises that cannot or do not want to send data to external cloud providers, NemoClaw offers a fully self-hosted deployment — data remains on-premises and the infrastructure is under full organizational control.

The OpenShell runtime uses a deny-by-default approach to network access: the agent’s code cannot by default communicate with external services without explicit authorization. In addition, the architecture includes human approval gates for critical operations and a full audit trail of all agent actions — particularly important for regulated industries such as finance and healthcare.

Target Applications and Business Context

EY is named as a key implementation partner for regulated industries. Infrastructure hosting partners include Baseten, Fireworks AI, Nebius, Crusoe Energy, DeepInfra, and Together AI — ensuring availability across different geographies and compute platforms.

Primary target applications are legacy code migration projects: COBOL-to-Java conversions and .NET application migrations. These migrations have remained a bottleneck in large-enterprise digitalization for decades, partly because previous AI tools were too expensive for systematic application across large codebases. NemoClaw’s cost profile opens up new economics for such projects and makes AI-assisted modernization of legacy systems economically viable.