NemoClaw nemoclaw-user-configure-inference

Name: nemoclaw-user-configure-inference
Author: NVIDIA

Lists all inference providers offered during NemoClaw onboarding. Use when explaining which providers are available, what the onboard wizard presents, or how inference routing works. Trigger keywords - nemoclaw inference options, nemoclaw onboarding providers, nemoclaw inference routing, switch nemoclaw inference model, change inference runtime, nemoclaw local inference, ollama nemoclaw, vllm nemoclaw, local model server, openai compatible endpoint.

install

source · Clone the upstream repo

git clone https://github.com/NVIDIA/NemoClaw

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/NVIDIA/NemoClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.agents/skills/nemoclaw-user-configure-inference" ~/.claude/skills/nvidia-nemoclaw-nemoclaw-user-configure-inference && rm -rf "$T"

manifest: .agents/skills/nemoclaw-user-configure-inference/SKILL.md

NemoClaw Inference Options

Prerequisites

A running NemoClaw sandbox.
The OpenShell CLI on your
```
PATH
```
.
NemoClaw installed.
A local model server running, or Ollama installed. The NemoClaw onboard wizard can also start Ollama for you.

Change the active inference model while the sandbox is running. No restart is required.

Step 1: Switch to a Different Model

Switching happens through the OpenShell inference route. Use the provider and model that match the upstream you want to use.

NVIDIA Endpoints

$ openshell inference set --provider nvidia-prod --model nvidia/nemotron-3-super-120b-a12b

OpenAI

$ openshell inference set --provider openai-api --model gpt-5.4

Anthropic

$ openshell inference set --provider anthropic-prod --model claude-sonnet-4-6

Google Gemini

$ openshell inference set --provider gemini-api --model gemini-2.5-flash

Compatible Endpoints

If you onboarded a custom compatible endpoint, switch models with the provider created for that endpoint:

$ openshell inference set --provider compatible-endpoint --model <model-name>

$ openshell inference set --provider compatible-anthropic-endpoint --model <model-name>

If the provider itself needs to change, rerun

nemoclaw onboard

Switching from Responses API to Chat Completions

If onboarding selected

/v1/responses

but the agent fails at runtime (for example, because the backend does not emit the streaming events OpenClaw requires), re-run onboarding so the wizard re-probes the endpoint and bakes the correct API path into the image:

$ nemoclaw onboard

Select the same provider and endpoint again. The updated streaming probe will detect incomplete

/v1/responses

support and select

/v1/chat/completions

automatically.

For the compatible-endpoint provider, NemoClaw uses

/v1/chat/completions

by default, so no env var is required to keep the safe path. To opt in to

/v1/responses

for a backend you have verified end to end, set

NEMOCLAW_PREFERRED_API

before onboarding:

$ NEMOCLAW_PREFERRED_API=openai-responses nemoclaw onboard

Note:
NEMOCLAW_INFERENCE_API_OVERRIDE
patches the config at container startup but does not update the Dockerfile ARG baked into the image. If you recreate the sandbox without the override env var, the image reverts to the original API path. A fresh
nemoclaw onboard
is the reliable fix because it updates both the session and the baked image.

Step 2: Cross-Provider Switching

Switching to a different provider family (for example, from NVIDIA Endpoints to Anthropic) requires updating both the gateway route and the sandbox config.

Set the gateway route on the host:

$ openshell inference set --provider anthropic-prod --model claude-sonnet-4-6 --no-verify

Then set the override env vars and recreate the sandbox so they take effect at startup:

$ export NEMOCLAW_MODEL_OVERRIDE="anthropic/claude-sonnet-4-6"
$ export NEMOCLAW_INFERENCE_API_OVERRIDE="anthropic-messages"
$ nemoclaw onboard --resume --recreate-sandbox

The entrypoint patches

openclaw.json

at container startup with the override values. You do not need to rebuild the image. Remove the env vars and recreate the sandbox to revert to the original model.

NEMOCLAW_INFERENCE_API_OVERRIDE

accepts

openai-completions

(for NVIDIA, OpenAI, Gemini, compatible endpoints) or

anthropic-messages

(for Anthropic and Anthropic-compatible endpoints). This variable is only needed when switching between provider families.

Step 3: Tune Model Metadata

The sandbox image bakes model metadata (context window, max output tokens, and reasoning mode) into

openclaw.json

at build time. To change these values, set the corresponding environment variables before running

nemoclaw onboard

so they patch into the Dockerfile before the image builds.

Variable Values Default

NEMOCLAW_CONTEXT_WINDOW

Positive integer (tokens)

NEMOCLAW_MAX_TOKENS

Positive integer (tokens)

NEMOCLAW_REASONING

true

false

false

Invalid values are ignored, and the default bakes into the image.

$ export NEMOCLAW_CONTEXT_WINDOW=65536
$ export NEMOCLAW_MAX_TOKENS=8192
$ export NEMOCLAW_REASONING=true
$ nemoclaw onboard

These variables are build-time settings. If you change them on an existing sandbox, recreate the sandbox so the new values bake into the image:

$ nemoclaw onboard --resume --recreate-sandbox

Step 4: Verify the Active Model

Run the status command to confirm the change:

$ nemoclaw <name> status

Add the

--json

flag for machine-readable output:

$ nemoclaw <name> status --json

The output includes the active provider, model, and endpoint.

Step 5: Notes

The host keeps provider credentials.
The sandbox continues to use
```
inference.local
```
.
Same-provider model switches take effect immediately via the gateway route alone.
Cross-provider switches also require
```
NEMOCLAW_MODEL_OVERRIDE
```
(and
```
NEMOCLAW_INFERENCE_API_OVERRIDE
```
) plus a sandbox recreate so the entrypoint patches the config at startup.
Overrides are applied at container startup. Changing or removing env vars requires a sandbox recreate to take effect.

NemoClaw can route inference to a model server running on your machine instead of a cloud API. This page covers Ollama, compatible-endpoint paths for other servers, and two experimental options for vLLM and NVIDIA NIM.

All approaches use the same

inference.local

routing model. The agent inside the sandbox never connects to your model server directly. OpenShell intercepts inference traffic and forwards it to the local endpoint you configure.

Step 6: Ollama

Ollama is the default local inference option. The onboard wizard detects Ollama automatically when it is installed or running on the host.

If Ollama is not running, NemoClaw starts it for you. On macOS, the wizard also offers to install Ollama through Homebrew if it is not present.

Run the onboard wizard.

$ nemoclaw onboard

Select Local Ollama from the provider list. NemoClaw lists installed models or offers starter models if none are installed. It pulls the selected model, loads it into memory, and validates it before continuing.

Authenticated Reverse Proxy

NemoClaw keeps Ollama bound to

127.0.0.1:11434

and starts a token-gated reverse proxy on

0.0.0.0:11435

. Containers and other hosts on the local network reach Ollama only through the proxy, which validates a Bearer token before forwarding requests. Ollama itself is never exposed without authentication.

The onboard wizard manages the proxy automatically:

Generates a random 24-byte token on first run and stores it in
```
~/.nemoclaw/ollama-proxy-token
```
with
```
0600
```
permissions.
Starts the proxy after Ollama and verifies it before continuing.
Cleans up stale proxy processes from previous runs.
Reuses the persisted token after a host reboot so you do not need to re-run onboard.

The sandbox provider is configured to use proxy port

with the generated token as its

OPENAI_API_KEY

credential. OpenShell's L7 proxy injects the token at egress, so the agent inside the sandbox never sees the token directly.

GET /api/tags

is exempt from authentication so container health checks continue to work. All other endpoints (including

POST /api/tags

) require the Bearer token.

If Ollama is already running on a non-loopback address when you start onboard, the wizard restarts it on

127.0.0.1:11434

so the proxy is the only network path to the model server.

Non-Interactive Setup

$ NEMOCLAW_PROVIDER=ollama \
  NEMOCLAW_MODEL=qwen2.5:14b \
  nemoclaw onboard --non-interactive

NEMOCLAW_MODEL

is not set, NemoClaw selects a default model based on available memory.

Variable	Purpose
`NEMOCLAW_PROVIDER`	Set to `ollama` .
`NEMOCLAW_MODEL`	Ollama model tag to use. Optional.

Step 7: OpenAI-Compatible Server

This option works with any server that implements

/v1/chat/completions

, including vLLM, TensorRT-LLM, llama.cpp, LocalAI, and others. For compatible endpoints, NemoClaw uses

/v1/chat/completions

by default. This avoids a class of failures where local backends accept

/v1/responses

requests but silently drop the system prompt and tool definitions. To opt in to

/v1/responses

, set

NEMOCLAW_PREFERRED_API=openai-responses

before running onboard.

Start your model server. The examples below use vLLM, but any OpenAI-compatible server works.

$ vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

Run the onboard wizard.

$ nemoclaw onboard

When the wizard asks you to choose an inference provider, select Other OpenAI-compatible endpoint. Enter the base URL of your local server, for example

http://localhost:8000/v1

The wizard prompts for an API key. If your server does not require authentication, enter any non-empty string (for example,

dummy

NemoClaw validates the endpoint by sending a test inference request before continuing. The wizard probes

/v1/chat/completions

by default for the compatible-endpoint provider. If you set

NEMOCLAW_PREFERRED_API=openai-responses

, NemoClaw probes

/v1/responses

instead and only selects it when the response includes the streaming events OpenClaw requires.

Non-Interactive Setup

Set the following environment variables for scripted or CI/CD deployments.

$ NEMOCLAW_PROVIDER=custom \
  NEMOCLAW_ENDPOINT_URL=http://localhost:8000/v1 \
  NEMOCLAW_MODEL=meta-llama/Llama-3.1-8B-Instruct \
  COMPATIBLE_API_KEY=dummy \
  nemoclaw onboard --non-interactive

Variable	Purpose
`NEMOCLAW_PROVIDER`	Set to `custom` for an OpenAI-compatible endpoint.
`NEMOCLAW_ENDPOINT_URL`	Base URL of the local server.
`NEMOCLAW_MODEL`	Model ID as reported by the server.
`COMPATIBLE_API_KEY`	API key for the endpoint. Use any non-empty value if authentication is not required.

Selecting the API Path

For the compatible-endpoint provider,

/v1/chat/completions

is the default. NemoClaw tests streaming events during onboarding and uses chat completions without probing the Responses API.

To opt in to

/v1/responses

, set

NEMOCLAW_PREFERRED_API

before running onboard:

$ NEMOCLAW_PREFERRED_API=openai-responses nemoclaw onboard

The wizard then probes

/v1/responses

and only selects it when streaming support is complete. If the probe fails, the wizard falls back to

/v1/chat/completions

automatically. You can use this variable in both interactive and non-interactive mode.

Variable Values Default

NEMOCLAW_PREFERRED_API

openai-completions

openai-responses

openai-completions

for compatible endpoints

If you already onboarded and the sandbox is failing at runtime, re-run

nemoclaw onboard

to re-probe the endpoint and bake the correct API path into the image. Refer to Switch Inference Models (use the

nemoclaw-user-configure-inference

skill) for details.

Step 8: Anthropic-Compatible Server

If your local server implements the Anthropic Messages API (

/v1/messages

), choose Other Anthropic-compatible endpoint during onboarding instead.

$ nemoclaw onboard

For non-interactive setup, use

NEMOCLAW_PROVIDER=anthropicCompatible

and set

COMPATIBLE_ANTHROPIC_API_KEY

$ NEMOCLAW_PROVIDER=anthropicCompatible \
  NEMOCLAW_ENDPOINT_URL=http://localhost:8080 \
  NEMOCLAW_MODEL=my-model \
  COMPATIBLE_ANTHROPIC_API_KEY=dummy \
  nemoclaw onboard --non-interactive

Step 9: vLLM Auto-Detection (Experimental)

When vLLM is already running on

localhost:8000

, NemoClaw can detect it automatically and query the

/v1/models

endpoint to determine the loaded model.

Set the experimental flag and run onboard.

$ NEMOCLAW_EXPERIMENTAL=1 nemoclaw onboard

Select Local vLLM [experimental] from the provider list. NemoClaw detects the running model and validates the endpoint.

Note: NemoClaw forces the
chat/completions
API path for vLLM. The vLLM
/v1/responses
endpoint does not run the
--tool-call-parser
, so tool calls arrive as raw text.

Non-Interactive Setup

$ NEMOCLAW_EXPERIMENTAL=1 \
  NEMOCLAW_PROVIDER=vllm \
  nemoclaw onboard --non-interactive

NemoClaw auto-detects the model from the running vLLM instance. To override the model, set

NEMOCLAW_MODEL

Step 10: NVIDIA NIM (Experimental)

NemoClaw can pull, start, and manage a NIM container on hosts with a NIM-capable NVIDIA GPU.

Set the experimental flag and run onboard.

$ NEMOCLAW_EXPERIMENTAL=1 nemoclaw onboard

Select Local NVIDIA NIM [experimental] from the provider list. NemoClaw filters available models by GPU VRAM, pulls the NIM container image, starts it, and waits for it to become healthy before continuing.

Note: NIM uses vLLM internally. The same
chat/completions
API path restriction applies.

Non-Interactive Setup

$ NEMOCLAW_EXPERIMENTAL=1 \
  NEMOCLAW_PROVIDER=nim \
  nemoclaw onboard --non-interactive

To select a specific model, set

NEMOCLAW_MODEL

Step 11: Timeout Configuration

Local inference requests use a default timeout of 180 seconds. Large prompts on hardware such as DGX Spark can exceed shorter timeouts, so NemoClaw sets a higher default for local providers (Ollama, vLLM, NIM).

To override the timeout, set the

NEMOCLAW_LOCAL_INFERENCE_TIMEOUT

environment variable before onboarding:

$ export NEMOCLAW_LOCAL_INFERENCE_TIMEOUT=300
$ nemoclaw onboard

The value is in seconds. This setting is baked into the sandbox at build time. Changing it after onboarding requires re-running

nemoclaw onboard

Step 12: Verify the Configuration

After onboarding completes, confirm the active provider and model.

$ nemoclaw <name> status

The output shows the provider label (for example, "Local vLLM" or "Other OpenAI-compatible endpoint") and the active model.

Step 13: Switch Models at Runtime

You can change the model without re-running onboard. Refer to Switch Inference Models (use the

nemoclaw-user-configure-inference

skill) for the full procedure.

For compatible endpoints, the command is:

$ openshell inference set --provider compatible-endpoint --model <model-name>

If the provider itself needs to change (for example, switching from vLLM to a cloud API), rerun

nemoclaw onboard

References

Load references/inference-options.md when explaining which providers are available, what the onboard wizard presents, or how inference routing works. Lists all inference providers offered during NemoClaw onboarding.

Related Skills

```
nemoclaw-user-get-started
```
— Quickstart (use the
```
nemoclaw-user-get-started
```
skill) for first-time installation