OpenClaw

OpenClaw is an agentic runtime that runs an AI agent with direct access to your shell, your files, and a plugin system for extending what the agent can do. Plugins install from the ClawHub marketplace with a single openclaw plugins install command, and each plugin bundles skills - Markdown instruction files the agent reads and auto-invokes whenever a request matches, or that you trigger by name. Because skills shell out through the agent’s built-in Bash tools, a plugin can wire in any local CLI without hosting a server or registering an API. Teams use OpenClaw to automate multi-step work while keeping the heavy reasoning on their primary model and offloading the cheap, repetitive steps elsewhere. ZeroGPU is an ultra-fast, compute-efficient inference provider for apps and agents. We run purpose-built small and nano language models across an edge-powered network for the high-volume, purpose-specific tasks your app or agent runs constantly. Plug in our OpenAI-compatible API and you’re live - zero GPU infrastructure, serverless, auto-scaling by default.

Overview

This guide walks through installing the zerogpu-router plugin, which turns every ZeroGPU CLI command into an OpenClaw skill your agent can call on its own. You’ll install the zerogpu CLI, authenticate once, add the plugin from ClawHub, and try both auto-invoked and manual skills. By the end your OpenClaw agent can hand off cheap, well-defined NLP work - classification, entity and PII extraction, summarization, and short chat - to ZeroGPU’s edge-optimized models, cutting token spend without changing the model that does your agent’s reasoning.

Cookbook

For a runnable, real-world example, see Build a PII-Safe Inbox Summarizer with Your OpenClaw Assistant. It walks through saving a triage workflow to your assistant once, then having it redact PII, summarize, and label every message you forward automatically, all on ZeroGPU’s nano models.

For more worked examples, browse the cookbook index for end-to-end walkthroughs of classification, extraction, and PII workflows on ZeroGPU’s small models.

Video walkthrough

Watch the redact-pii skill mask PII in-line inside an OpenClaw agent, live:

Quickstart

Prerequisites

An OpenClaw install with plugin API 2026.4.0 or newer.
Node.js 20 or newer. The zerogpu CLI that every skill shells out to runs on Node.
A ZeroGPU API key (starts with zgpu-api-).
Optional: a model ID from the model catalog if you want to call a model by name. The skills already route each task to the right model, so this is only needed for custom work.

Get your ZeroGPU API key

Sign in to the ZeroGPU dashboard.
Open API Keys and click Create key.
Copy the key (it starts with zgpu-api-). No Project ID is required - zerogpu login takes just the key.

Install the plugin

The plugin does not provision the CLI on its own, so install and log in to zerogpu first, then add the plugin from ClawHub:

# 1. Install the CLI the skills shell out to
npm install -g zerogpu-cli

# 2. Log in (prompts for your API key)
zerogpu login

# 3. Install the plugin from ClawHub
openclaw plugins install clawhub:zerogpu-router

Pin a specific release with clawhub:[email protected]. In sandboxed or Docker-based agents, make sure the zerogpu binary is present inside the container the agent actually runs in, not just on your host.

Your first request

Ask your agent in plain language. It picks the matching skill, runs the zerogpu CLI locally instead of the host model, and replies with the result:

redact the PII in this note before I share it:
"Email John Smith at [email protected] about invoice 12345."

Reply:

Email [PERSON] at [EMAIL] about invoice 12345.

model: gliner-multi-pii-v1 · 2 spans masked

The raw PII never reaches your primary reasoning model - the small model does the masking, and only the scrubbed text comes back.

Usage

Every inference skill auto-invokes when your OpenClaw agent detects a matching request, so you rarely name a skill directly: describe the task and the agent picks the route. Three skills (signin, status, and cost-savings) are manual-only and never auto-invoked - you trigger them yourself. This section covers every skill in the plugin: what it does, which ZeroGPU model it routes to, the CLI command it wraps, the arguments it accepts, and the shape of the reply you get back. Each skill returns a structured payload of { <task fields>, model, usage, savings }, which the agent renders as the result plus a trailing model: <name> · <usage> line.

How the agent picks a skill

Each skill ships with a description the OpenClaw agent reads when deciding whether to invoke it for a given message. You don’t have to remember skill names: just phrase the task. The agent keys off intent words like “summarize”, “redact”, “scrub”, “classify by”, “extract”, or “tag this as”, and off the structure of the data you supply (a flat label list implies zero-shot classification; a JSON schema with category axes implies structured classification). Arbitrary user text is passed to the CLI verbatim through a heredoc, so newlines, quotes, parentheses, and other shell metacharacters in your input are handled safely. Occasionally a reply ends with a savings line:

💰 ZeroGPU savings so far: ≈ $2 (18,730 frontier-model tokens offloaded)

This surfaces intermittently, not on every call, so a healthy total accumulates quietly in the background. To see the running figure on demand, use the cost-savings skill. If the agent picks the wrong skill for a request, rephrase to drop the ambiguous trigger word, or ask for the specific skill by name.

Chat and summarization

chat

Short, single-turn chat reply for things that don’t need host-level reasoning or prior conversation context. Use it when you’d otherwise burn primary-model tokens on a one-liner.

Model: LFM2.5-1.2B-Instruct
Wraps: zerogpu chat
Auto-invokes on: quick factual answers, one-liners, and basic rephrasings, especially when you’ve signalled “use a small model.”

Argument	Required	Description
`text`	yes	The prompt to answer.
`-i`, `--instructions <text>`	optional	System instructions to steer tone or behavior.

Example prompt

using a small model, explain WebSockets in two sentences like a concise technical writer

Illustrative reply

WebSockets keep a single TCP connection open so a client and server can send messages
to each other at any time, without repeating the HTTP request-response handshake. That
makes them a good fit for live features like chat, notifications, and streaming updates.

model: LFM2.5-1.2B-Instruct · 24 tokens in / 46 out

chat-thinking

Same shape as chat, but the model returns its reasoning trace alongside the answer.

Model: LFM2.5-1.2B-Thinking
Wraps: zerogpu chat_thinking
Auto-invokes on: short logic, math, or word-problem questions where step-by-step reasoning is useful or explicitly requested.

Example prompt

show your reasoning: if a train leaves at 3 PM going 60 mph, when does it cover 150 miles?

The reply includes the intermediate reasoning steps before the final answer (5:30 PM).

summarize

Condense a longer passage into a short summary. Use it when you need the gist of a report, ticket thread, transcript, or document without spending primary-model tokens on the read.

Model: llama-3.1-8b-instruct-fast
Wraps: zerogpu summarize
Auto-invokes on: “summarize this”, “give me the gist”, “TL;DR this”, “condense this into a few sentences.”

Example prompt

summarize this: The board met Thursday to review Q3 results. Revenue rose 18%
year-over-year to $42M, driven mainly by enterprise renewals and a strong launch in
the EU market. Operating margin slipped to 11% from 14% as headcount grew 30% ahead of
the new data-center buildout. The CFO flagged rising cloud costs as the top risk for Q4
and proposed a hiring freeze on non-engineering roles until margins recover. The board
approved the freeze and asked for a revised 2025 budget by mid-December.

Illustrative reply

Q3 revenue grew 18% YoY to $42M on enterprise renewals and EU growth, but operating
margin fell to 11% due to a 30% headcount increase for the data-center buildout. Citing
cloud costs as the main Q4 risk, the board approved a hiring freeze on non-engineering
roles and requested a revised 2025 budget by mid-December.

model: llama-3.1-8b-instruct-fast · 118 tokens in / 61 out
💰 ZeroGPU savings so far: ≈ $2 (18,730 frontier-model tokens offloaded)

For a file, run the CLI directly: zerogpu summarize "$(cat article.txt)".

Classification

classify-iab

Classify text against the IAB content and audience taxonomy (standard ad-tech category labels).

Model: zlm-v1-iab-classify-edge
Wraps: zerogpu classify_iab
Auto-invokes on: “what IAB category is this?”, “tag this article for ad targeting”, “give me the topic taxonomy.”

Example prompt

what IAB category is this: The Lakers signed a new point guard ahead of the playoffs.

Illustrative reply

{
	"categories": [{ "id": "IAB17-44", "name": "Basketball", "confidence": 0.97 }]
}

classify-iab-enriched

Enriched IAB classification that returns audience categories plus topics, keywords, and inferred user intent.

Model: zlm-v2-iab-classify-edge-enriched
Wraps: zerogpu classify_iab_enriched
Auto-invokes on: “give me topics, keywords, and intent”, any request for richer ad or audience signals than plain IAB labels.

Example prompt

give me topics, keywords, and intent for: Compare the Tesla Model Y and the Hyundai
Ioniq 5 for a family of four.

Illustrative reply

{
	"categories": [{ "id": "IAB2-1", "name": "Auto Buyers", "confidence": 0.92 }],
	"topics": ["electric vehicles", "family cars"],
	"keywords": ["Tesla Model Y", "Hyundai Ioniq 5"],
	"intent": "comparison-shopping"
}

classify-zero-shot

Zero-shot classification against an arbitrary list of candidate labels you supply at call time. The model scores every candidate and returns the best fit.

Model: deberta-v3-small
Wraps: zerogpu classify_zero_shot
Auto-invokes on: “is this positive, negative, or neutral?”, “tag this as bug, feature, or question”, any ask that names its own flat label set.

Argument	Required	Default	Description
`text`	yes	-	Text to classify.
`-l <label>` / `--labels <a,b,c>`	yes (one)	-	Candidate labels. Repeat `-l` or pass a comma-separated `--labels` list.
`-t`, `--threshold <number>`	optional	-	Confidence floor in `[0, 1]` for multi-label output.

Example prompt

classify this as billing, bug, feature-request, or account:
"I was charged twice for my October subscription, and the second charge is a different
amount than my plan. Please refund the duplicate and explain the difference."

Illustrative reply

[
	{ "label": "billing", "score": 0.94 },
	{ "label": "account", "score": 0.29 },
	{ "label": "bug", "score": 0.07 },
	{ "label": "feature-request", "score": 0.02 }
]

model: deberta-v3-small · 61 tokens in
💰 ZeroGPU savings so far: ≈ $2 (19,180 frontier-model tokens offloaded)

classify-structured

Schema-driven, multi-axis classification. You define each category and its allowed labels, and the model returns one chosen label per category.

Model: gliner2-base-v1
Wraps: zerogpu classify_structured
Auto-invokes on: “classify by sentiment and topic”, any request that names multiple classification dimensions with explicit label sets.

Argument	Required	Description
`text`	yes	Text to classify.
`-s`, `--schema <json>`	yes	Single-quoted JSON object mapping each axis to its allowed labels.

Example prompt

classify this by sentiment and topic - "Support replied quickly but the fix didn't work"

The agent synthesizes a schema such as {"sentiment":["positive","negative","neutral"],"topic":["support","billing","product"]} and calls the skill. Illustrative reply

{ "sentiment": "negative", "topic": "support" }

Extraction

extract-entities

Custom-label named-entity recognition. You define the entity labels; the model finds matching spans with confidence scores.

Model: gliner2-base-v1
Wraps: zerogpu extract_entities
Auto-invokes on: “extract all people, organizations, and locations”, “find every product mention.”

Argument	Required	Default	Description
`text`	yes	-	Source text.
`-l <label>` / `--labels <a,b,c>`	yes (one)	-	Entity labels to extract.
`-t`, `--threshold <number>`	optional	`0.3`	Minimum confidence in `[0, 1]`.

Example prompt

extract the people, organizations, and locations from:
"Apple CEO Tim Cook met with Sundar Pichai in Cupertino on Monday."

Illustrative reply

[
	{ "label": "organization", "text": "Apple", "score": 0.98 },
	{ "label": "person", "text": "Tim Cook", "score": 0.97 },
	{ "label": "person", "text": "Sundar Pichai", "score": 0.96 },
	{ "label": "location", "text": "Cupertino", "score": 0.91 }
]

extract-json

Pull specific named fields out of free text into a structured JSON object, defined by a per-field schema. Each field is declared as name::type::description.

Model: gliner2-base-v1
Wraps: zerogpu extract_json
Auto-invokes on: “extract the contact info as JSON”, “parse this invoice”, “pull these fields out.”

Argument	Required	Description
`text`	yes	Source text.
`-s`, `--schema <json>`	yes	Single-quoted JSON. Each field is `name::type::description`, grouped under a key.

Example prompt

pull the name, email, and phone out of this as JSON:
"Reach Maria Lopez at [email protected] or 415-555-0188."

The agent builds a schema like {"contact":["name::str::Full name","email::str::Email address","phone::str::Phone number"]}. Illustrative reply

{
	"contact": {
		"name": "Maria Lopez",
		"email": "[email protected]",
		"phone": "415-555-0188"
	}
}

PII handling

extract-pii

Extract personally identifiable information entities, grouped by category, without modifying the source text. Use it when you need structured data about PII (for redaction policies, audits, or downstream tooling) rather than a masked version.

Model: gliner-multi-pii-v1
Wraps: zerogpu extract_pii
Auto-invokes on: “find all PII”, “what personal info is in this?”, “list emails, phones, and names.”

Argument	Required	Default	Description
`text`	yes	-	Source text.
`-t`, `--threshold <number>`	optional	`0.5`	Minimum confidence.
`-c`, `--categories <list>`	optional	`identity,contact`	Comma-separated. Other values: `financial`, `medical`, `credentials`.

Example prompt

find the PII in this, including financial:
"Contact Jane Doe at [email protected] or +1 (415) 555-1212."

Illustrative reply

[
	{
		"category": "identity",
		"label": "person",
		"text": "Jane Doe",
		"score": 0.96
	},
	{
		"category": "contact",
		"label": "email",
		"text": "[email protected]",
		"score": 0.99
	},
	{
		"category": "contact",
		"label": "phone",
		"text": "+1 (415) 555-1212",
		"score": 0.95
	}
]

To mask PII in-line rather than extract it, use redact-pii.

redact-pii

Detect PII and replace each span in-line with a [LABEL] placeholder. Use it before sharing or logging sensitive text, or before forwarding user input to another model you don’t want to expose raw PII to.

Model: gliner-multi-pii-v1
Wraps: zerogpu redact_pii
Auto-invokes on: “redact”, “scrub”, “mask”, “anonymize”, or “sanitize this for sharing.”

Example prompt

redact the PII in this CRM note before I share it with the vendor -
"Call with Daniel Okafor ([email protected], +1 206-555-0147) on Tue.
Send the enterprise quote to their AP team at 400 Pine St, Seattle, WA 98101.
Card on file ends 4412 - do not reference it in email."

Illustrative reply

Call with [PERSON] ([EMAIL], [PHONE_NUMBER]) on Tue. Send the enterprise quote to their
AP team at [ADDRESS]. Card on file ends 4412 - do not reference it in email.

model: gliner-multi-pii-v1 · 5 spans masked

The card last-four is left untouched: only spans the model recognizes as PII are replaced. Project-specific identifiers (account numbers, internal ticket IDs) should be handled by your own redaction layer or by extract-entities with custom labels.

Account and savings

These three skills are manual-only. The agent never auto-invokes them - trigger them yourself when you need them. Sign in to ZeroGPU and persist your API key so every subsequent skill call works without re-prompting.

Wraps: zerogpu login

Flag	Required	Description
`--api-key <key>`	optional	API key (must start with `zgpu-api-`). If omitted, you’re prompted interactively (masked).

With no arguments the CLI prompts for the key. For non-interactive setups (CI), pass --api-key zgpu-api-…. On success the key is written to the local config file and ZEROGPU_API_KEY is upserted into your shell profile so other tools can pick it up. Ask your agent to sign you in, or run zerogpu login directly in your terminal.

status

Show your current ZeroGPU sign-in status and the masked API key.

Wraps: zerogpu status

Exit code is 0 when signed in, 1 when not - handy as a hard fail in scripts before running any inference skill. Ask “am I signed in to ZeroGPU?” or run zerogpu status.

cost-savings

Show how much you’ve saved by routing tasks to ZeroGPU instead of the host model - cumulative dollars and tokens offloaded.

Wraps: zerogpu cost_savings

Token counts are actual (from the API); the dollar figure estimates what the same work would have cost on the host model. The baseline defaults to claude-opus-4-8 and is overridable with the ZEROGPU_SAVINGS_MODEL env var. Pass --json for raw data or --reset to clear the history. Ask “how much have I saved with ZeroGPU?” to see the summary.

Skills reference

Every skill at a glance.

Skill	Workload	Backing model	Invocation
`chat`	Short small-model chat replies	`LFM2.5-1.2B-Instruct`	auto
`chat-thinking`	Short chat with a visible reasoning trace	`LFM2.5-1.2B-Thinking`	auto
`summarize`	TL;DRs, abstracts, meeting summaries	`llama-3.1-8b-instruct-fast`	auto
`classify-iab`	IAB topic classification	`zlm-v1-iab-classify-edge`	auto
`classify-iab-enriched`	IAB categories plus topics, keywords, intent	`zlm-v2-iab-classify-edge-enriched`	auto
`classify-zero-shot`	Classify against a flat label list	`deberta-v3-small`	auto
`classify-structured`	Multi-axis schema classification	`gliner2-base-v1`	auto
`extract-entities`	Custom-label named-entity recognition	`gliner2-base-v1`	auto
`extract-json`	Pull structured fields into grouped JSON	`gliner2-base-v1`	auto
`extract-pii`	Extract PII grouped by category	`gliner-multi-pii-v1`	auto
`redact-pii`	Mask PII in-line with `[LABEL]` placeholders	`gliner-multi-pii-v1`	auto
`signin`	Sign in and persist your API key	-	manual
`status`	Show current sign-in status	-	manual
`cost-savings`	Cumulative dollars and tokens offloaded	-	manual

For the full flag reference on any command, run zerogpu <command> --help in your terminal outside the agent session.

Patterns and recipes

Sanitize before the host model sees raw input. Ask the agent to redact-pii untrusted text first when you don’t want personal data captured in your primary model’s transcript or forwarded to a downstream LLM. Pair it with extract-pii when you also need an audit log of exactly what was masked. Cheap router in front of your reasoning model. Use classify-zero-shot or classify-structured to triage an incoming message (bug / feature / question, urgent / normal, in-scope / out-of-scope) and only escalate the hard cases to your primary model. The classifier call costs orders of magnitude less than a full reasoning turn. Structured extraction over free-form parsing. For semi-structured text (signatures, invoices, contact blocks), prefer extract-json over asking your host model to “parse this into JSON.” It’s deterministic on the schema, faster, and cheaper. Keep field descriptions short and specific - the description is what the model uses to locate the span. Tune thresholds for the job. For NER and PII extraction the defaults (0.3 and 0.5) favor recall. Raise -t to 0.6 or higher when you need precision (compliance-grade redaction lists); lower it when you’d rather over-extract and filter downstream.

Troubleshooting

zerogpu: command not found - the CLI isn’t installed or isn’t on your PATH. Run npm install -g zerogpu-cli and restart your shell. If you use a Node version manager (nvm, fnm, volta), make sure the shell that launched OpenClaw has the same Node version active. A sandboxed or Docker agent can’t find zerogpu - the CLI has to exist inside the container the agent runs in, not just on your host. Add npm install -g zerogpu-cli to your image build, or mount the binary into the sandbox. Skill returns “You’re not signed in yet.” - no credentials on disk. Run the signin skill, or zerogpu login in your terminal, then confirm with the status skill (zerogpu status). The agent won’t use the plugin’s skills - the plugin may not be installed or enabled. Re-run openclaw plugins install clawhub:zerogpu-router and confirm it’s enabled in your OpenClaw plugin settings. Pin a known-good release with clawhub:[email protected] if a newer build misbehaves. Request failed with status 401 - your API key is missing, revoked, or mistyped. Rotate the key in the dashboard and re-run the signin skill. Keys must start with zgpu-api-. Request failed with status 403 - the key is valid but doesn’t have access to the requested project or model. Confirm the key belongs to a project that owns the model you’re routing to. Request failed with status 429 - you’re being rate-limited. Back off and retry with exponential delay, or move heavy workloads to the Batch API, which has separate quotas tuned for bulk jobs. The wrong skill was auto-invoked - the agent picks based on phrasing. Rephrase to drop the ambiguous trigger word (“redact”, “classify”, “extract”), or ask for the specific skill by name. Schema quoting errors on classify-structured / extract-json - the -s flag expects a single-quoted JSON string. On Windows PowerShell, escape inner double quotes or use a here-string. Run the CLI directly (zerogpu classify_structured … -s '…') to isolate quoting issues from the agent. Empty or low-confidence results - lower -t to surface more candidates, or check that the labels match the language of the source text (the underlying models are English-tuned for most label sets). Very short inputs return lower confidence across the board. No savings line appears - it’s intentionally occasional, not shown on every call. Trigger the cost-savings skill (zerogpu cost_savings) any time to see the cumulative dollars and tokens offloaded.

Conclusion

The zerogpu-router plugin turns ZeroGPU’s nano language models into first-class OpenClaw skills, so your agent can hand off classification, extraction, and short chat to a cheaper, faster model without changing the model that does its reasoning. It’s a fast way to keep raw PII out of your primary model’s context, cut token spend on well-defined NLP work, and watch the savings add up per call.

Model Catalog

Browse every model the plugin can route to.

API Reference

Explore the full OpenAI-compatible API surface.

Cookbook

Worked examples for classification, extraction, and batch jobs.

Join Discord

Ask questions and share what you’re building.

Integrations

Official SDKs

Overview

Cookbook

Video walkthrough

Quickstart

Prerequisites

Get your ZeroGPU API key

Install the plugin

Your first request

Usage

How the agent picks a skill

Chat and summarization

chat

chat-thinking

summarize

Classification

classify-iab

classify-iab-enriched

classify-zero-shot

classify-structured

Extraction

extract-entities

extract-json

PII handling

extract-pii

redact-pii

Account and savings

status

cost-savings

Skills reference

Patterns and recipes

Troubleshooting

Conclusion

Model Catalog

API Reference

Cookbook

Join Discord

​Overview

​Cookbook

​Video walkthrough

​Quickstart

​Prerequisites

​Get your ZeroGPU API key

​Install the plugin

​Your first request

​Usage

​How the agent picks a skill

​Chat and summarization

​chat

​chat-thinking

​summarize

​Classification

​classify-iab

​classify-iab-enriched

​classify-zero-shot

​classify-structured

​Extraction

​extract-entities

​extract-json

​PII handling

​extract-pii

​redact-pii

​Account and savings

​signin

​status

​cost-savings

​Skills reference

​Patterns and recipes

​Troubleshooting

​Conclusion

Model Catalog

API Reference

Cookbook

Join Discord

Overview

Cookbook

Video walkthrough

Quickstart

Prerequisites

Get your ZeroGPU API key

Install the plugin

Your first request

Usage

How the agent picks a skill

Chat and summarization

chat

chat-thinking

summarize

Classification

classify-iab

classify-iab-enriched

classify-zero-shot

classify-structured

Extraction

extract-entities

extract-json

PII handling

extract-pii

redact-pii

Account and savings

signin

status

cost-savings

Skills reference

Patterns and recipes

Troubleshooting

Conclusion