---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

title: "v0.4.0"
description: ""
---
NeMo Platform v0.4.0 introduces a research-preview optimization loop for
improving agents from real telemetry. Agent owners can send traces to Intake,
use agents to diagnose recurring issues, create targeted evaluation coverage,
experiment against a local code base, and review validated candidate changes.
The release also moves new agent workflows toward `agent.yaml` packages backed
by [Fabric](https://github.com/NVIDIA/nemo-fabric/) while keeping NAT workflows
available as a legacy path.

## Highlights

- **Research-preview agent optimization.** Analyst and Experimentalist
  workflows now support the observe, diagnose, experiment, and evaluate loop for
  agents under active development.
- **Trace-driven diagnosis.** NeMo Intake ingests OTLP, chat-completions, and
  ATIF telemetry, stores traces in ClickHouse, and attaches annotations and
  evaluator results so agents and humans can investigate failures from the same
  evidence.
- **Validated candidate changes.** Experimentalist can run against a local
  agent source tree or git source, evaluate candidates with Harbor, write
  optimization artifacts locally, and open a draft PR when a changed
  winner is found.
- **Fabric-first agents.** New Platform-managed agents use the
  `nemo-agents-spec-v1` `agent.yaml` contract. Fabric-backed agents can run
  through supported harnesses and route model traffic through the Inference
  Gateway; NAT remains supported for existing workflows.
- **Experiments for review.** NeMo Experiments compares evaluation runs using
  cost, latency, token, and evaluator-score rollups computed from Intake
  telemetry, with Studio leaderboard and drill-down views behind the Experiments
  feature flag.
- **Customizer DPO.** Customizer adds DPO training through the `rl` backend,
  powered by NeMo-RL and Ray on Kubernetes, with backend-specific
  hyperparameter guidance for Automodel, Unsloth, and RL jobs.

## What's included

### Insight-Driven Optimization

- `nemo agents analyst` scans Intake traces, evaluator scores, and reviewer
  annotations to produce evidence-backed Insights.
- `nemo agents experimentalist` turns an Insight, or a dataset-only objective,
  into candidate code changes, train/validation evaluations, local artifacts,
  and an optional draft PR or MR.
- `optimizer.yaml` provides the shared per-agent profile for the loop, including
  the agent name, workspace, agent source, `AGENT-SPEC.md`, datasets, task
  template, and experiment configuration.
- `AGENT-SPEC.md` remains the durable Markdown contract for intended agent
  behavior and can be consumed by the Analyst and Experimentalist when present.
- Benchmark and proof assets include Terminal-Bench 2.1, Tau3 Airline, Banking,
  Retail, and Telecom suites, plus a guided Tau3 example-agent walkthrough.

### Intake and Experiments

- Intake supports OTLP, chat-completions, and ATIF ingest paths for agent
  telemetry.
- Traces, spans, sessions, annotations, and evaluator results are queryable by
  API and reviewable in Studio.
- Local setup can automatically provision and reuse a local ClickHouse
  container for Intake unless an external ClickHouse URL is configured.
- The Helm chart includes an embedded ClickHouse option for development and
  non-critical single-node installations, plus configuration for external
  ClickHouse in production-oriented deployments.
- Experiments and Evaluations are durable entities whose leaderboard metrics are
  computed from retained Intake telemetry.
- The `nemo-experiments-upload` skill helps coding agents publish evaluation
  runs and verify rollups.

### Agents and Fabric

- New agents use Platform-managed `agent.yaml` files with the
  `nemo-agents-spec-v1` config format.
- NeMo Fabric is the preferred runtime wrapper for new agents, with support for
  harness adapters, shared model bindings, skills, MCP servers, tool policy, and
  Relay/ATIF/ATOF telemetry configuration.
- `nemo agents create`, `deploy`, `invoke`, `run`, and `package` support the
  Fabric-backed agent path.
- Fabric agent artifacts can be staged into Docker and Kubernetes deployments.
- Streaming responses are supported through Fabric-backed agents.
- Legacy NAT workflow YAMLs continue to work for existing NAT-specific
  evaluation and deployment workflows.

### Agent Evaluation

- The evaluator SDK adds Fabric runtime support, Docker Compose sandboxing,
  native agent-eval aggregation, pass-at-k scoring, typed run metadata, and 
  task/taskset revisions.
- Agent evaluations can publish results to Intake so Experiments and Insights
  can consume the same run telemetry.

### Customizer

- The `rl` customization backend supports full-weight DPO jobs through NeMo-RL.
- DPO schemas expose schedule, optimizer, batch, DPO-specific loss, gradient,
  activation-checkpointing, and parallelism controls.
- Customizer skills and references now cover backend-specific job JSON for
  Automodel, Unsloth, and RL, including dataset formats, batch sizing,
  integrations, and troubleshooting.
- Customizer adds local Optuna HPO workflows for tuning hyperparameters from a
  developer workstation.

### Studio

- Studio includes primary surfaces for Agents, Data Designer, Guardrails,
  Safe Synthesizer, Intake traces, Optimizer Insights, Experiments, and Jobs,
  with Customizer and Anonymizer surfaces available behind their feature flags.
- Guardrails in Studio can create and delete configurations and manage
  guardrail test cases through the checks surface.
- Data Designer adds more seed sources, AI-assisted job configuration, preview,
  and file transform workflows.
- Safe Synthesizer reports include score-driven gauges.
- Studio can load plugin web bundles through `/apis/plugins` and render them
  inside the Studio React tree with trusted bundle URL checks.
- NeMo Studio Assistant adds a packaged agent, chat history, tool-call rendering,
  and reasoning display for Studio-assisted workflows.

### Platform, CLI, and Deployment

- Early access: Agent deployment to an OpenShell Gateway with customizable
  network policies (default-deny).
- Deployment readiness now gates on workload reachability for container-backed
  deployments.
- Optional scoped access keys are available for authenticated deployments.
- The CLI adds machine-readable output controls, Intake and Experiments command
  exposure, anonymous usage telemetry, and a Telemetry and Privacy reference
  page.
- Python 3.12 is now the minimum supported Python version for the source
  checkout and `nemo-platform` distribution.
- Auditor adds a blocking submit option and an aggregated artifacts endpoint.

## Install

For a fresh local checkout:

```bash
git clone https://github.com/NVIDIA-NeMo/nemo-platform.git
cd nemo-platform
make bootstrap
source .venv/bin/activate
nemo setup
```

See [Setup](/documentation/get-started) for prerequisites and provider
configuration.

For self-managed Kubernetes, start with
[Install NeMo Platform Helm Chart](/documentation/self-managed-deployment/setup/helm/install).

## Upgrade from v0.3.x

From an existing local checkout:

```bash
git fetch
git checkout main
make bootstrap
source .venv/bin/activate
nemo setup
```

After setup, restart local services before using CLI, SDK, Studio, or plugin
workflows against the upgraded checkout.

## Compatibility

- Python 3.12-3.13
- macOS and Linux for local CLI, SDK, Studio, and hosted-provider workflows
- Linux x86_64 for local NVIDIA GPU workloads
- CUDA 13-capable NVIDIA drivers for local GPU workloads
- Self-managed Kubernetes clusters deployed with Helm
- Docker for local services, Docker-backed platform jobs, local ClickHouse,
  Docker Sandboxes, and local model-serving workflows
- ClickHouse for Intake trace storage and Experiments rollups
- NVIDIA GPU access for local training, model serving, and GPU-backed
  synthetic data workflows
- Node 22.18.0+ for Studio assets
- Platform API and `nemo-platform` Python SDK `0.4.0`

## Current constraints

- **Research-preview optimizer.** Analyst and Experimentalist are early agentic
  workflows. They are useful for guided optimization, but quality and autonomy
  are still research-preview and require developer review.
- **Local-first issue-driven loop.** The full Insight to Experimentalist loop
  runs from a local developer environment against local paths or a git source.
  A remote platform can provide Intake and entity APIs, but the issue-driven
  optimization agents do not yet run as a horizontally scaled cluster service.
- **Evaluation backend scope.** The Experimentalist loop validates candidates
  with Harbor-compatible train and validation datasets. Other evaluator
  backends are not the validated path for this loop yet.
- **Optuna HPO runtime.** Optuna-based hyperparameter optimization runs locally
  from a developer workstation. It is not available as a horizontally scaled
  cluster service in this release.
- **DPO runtime.** Customizer `rl` DPO jobs run on Kubernetes/Ray and do not
  have a local Docker fallback. DPO is full-weight only; PEFT/adapter output is
  not supported for RL jobs.
- **Experiments feature flag.** The Experiments Studio surface is gated by
  `VITE_FF_EXPERIMENT`, which is off by default.
- **Embedded ClickHouse scope.** Helm's embedded ClickHouse is intended for
  development, evaluation, and non-critical single-node deployments. Use an
  externally managed ClickHouse for production deployments that require high
  availability, backups, or larger scale.
- **Python support.** Python 3.11 is no longer part of the supported source
  checkout or `nemo-platform` distribution matrix.

## Known issues

- **Deep Agents trials can be scored as failures.** Agent-eval trials run
  through the NeMo Fabric Deep Agents adapter can be recorded as failures even
  when the agent completed successfully. This will be fixed in the next release.

## Links

- Repository: [https://github.com/NVIDIA-NeMo/nemo-platform](https://github.com/NVIDIA-NeMo/nemo-platform)
- Issues: [https://github.com/NVIDIA-NeMo/nemo-platform/issues](https://github.com/NVIDIA-NeMo/nemo-platform/issues)
- NeMo Fabric: [https://docs.nvidia.com/nemo/fabric/about-nemo-fabric/overview/](https://docs.nvidia.com/nemo/fabric/about-nemo-fabric/overview/)
- NeMo Agent Toolkit: [https://docs.nvidia.com/nemo/agent-toolkit/latest/](https://docs.nvidia.com/nemo/agent-toolkit/latest/)
