# {{platform_name}}

{{platform_name}} {{ release }} is an OSS, local-first platform for building,
running, optimizing, and securing AI agents. Install the Python package, start
local services, connect a model provider, then use the CLI, SDK, and Studio UI
to improve agents before you promote them.

The core workflow is agent-first: a builder agent inspects an application agent,
finds cheaper or safer configurations, validates the candidate with evaluation,
and keeps the supporting artifacts in platform files.

## What You Can Do

- **Run agents locally** with NeMo Agent Toolkit workflow YAML managed by the
  `nemo agents` command group.
- **Optimize agents** with model swaps, Switchyard routing, skill optimization,
  prompt tuning, and baseline evaluation.
- **Secure agents** by checking guardrail coverage and scanning recent telemetry
  for PII or leaked secrets.
- **Route model traffic** through Inference Gateway, virtual models, provider
  secrets, and guardrail middleware.
- **Extend the platform** with plugins and skills that add commands, jobs,
  APIs, SDK surfaces, or Studio workflows.

## Start Locally

**Prerequisites:**

- Python 3.11+
- An API key from [NVIDIA Build](https://build.nvidia.com), OpenAI, Anthropic, Google Gemini, or a local
- Node.js (≥22.18.0, <23)  and `pnpm` for Studio
  [Ollama](https://ollama.com) instance
- [System requirements](requirements.md)

Install and configure:

```bash
pip install nemo-platform
nemo setup
```

`nemo setup` starts local services, registers your model provider, configures the
CLI and SDK, and can install agent skills for your coding agent. To start the
local services directly, run:

```bash
nemo services run
```

When setup completes:

```bash
nemo models list
nemo agents list
nemo chat <model-name>
nemo --help
```

For the full setup flow, provider options, and SDK usage, see
[NeMo Setup](get-started/setup.md). For every command, see the
[CLI reference](cli/index.md).

## Agent Workflow

1. Create or import a NeMo Agent Toolkit workflow.
1. Register and deploy it with `nemo agents`.
1. Run [agent optimization](agents/optimization.md) to find model-routing,
   skill, prompt, or new-model opportunities.
1. Run [agent security checks](agents/security.md) to identify missing
   guardrails and risky telemetry.
1. Validate any candidate agent against an evaluation baseline before routing
   production traffic to it.

## Supporting Capabilities

The local agent workflow uses several platform capabilities behind the scenes:

- [Models and Inference](run-inference/about.md) for model providers, virtual
  models, and gateway calls.
- [Guardrails](guardrails/index.md) for content-safety checks and guarded model
  endpoints.
- [Files](get-started/concepts/manage-files.md), [Secrets](get-started/concepts/manage-secrets.md),
  [Entities](get-started/concepts/entities.md), and jobs for storing state and
  running local work.
- Optional capabilities such as [Data Designer](data-designer/index.md),
  [Evaluator](evaluator/index.md), and [Studio](studio/index.md) for additional
  workflows.

## Where to Go Next

- [NeMo Setup](get-started/setup.md) - install, configure providers, and run
  local services.
- [About Agents](agents/index.md) - learn the managed agent lifecycle.
- [Optimize Agents](agents/optimization.md) - improve cost, quality, and model
  routing.
- [Secure Agents](agents/security.md) - harden agents with guardrails and data
  safety checks.
- [Plugins and Skills](agents/plugins.md) - understand how the local platform is
  extended.
- [Support Matrix](support-matrix.md) - check supported Python, OS, and GPU
  combinations.
