Run OpenClaw Agents with Gemma 4 Locally (2026)

What is Gemma 4?

Gemma 4 is Google DeepMind's latest open model family, released April 2, 2026. It comes in four sizes, supports text, image, and audio inputs, and is trained on 140+ languages. The key properties that make it interesting for OpenClaw agents:

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Native tool calling — All Gemma 4 variants support function calling with structured JSON output — essential for OpenClaw skills like web browsing and file management.

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256K context window — The 26B and 31B models handle 256K tokens of context. That is entire codebases, long research documents, or extended agent conversations without truncation.

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Zero API cost — Apache 2.0 license. Run it locally via Ollama and pay nothing per token. No rate limits, no monthly bill from a model provider.

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Full privacy — Everything runs on your machine. No conversation data sent to any cloud service. Ideal for agents handling sensitive business or personal data.

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MoE efficiency — The 26B model uses Mixture of Experts architecture, activating only 3.8B parameters during inference. It runs significantly faster than a 26B dense model would.

Gemma 4 Model Variants: Which One to Use

Model	Size	RAM needed	Context	Best for
gemma4:e2b	2B	4GB+	128K	Simple triage, fast routing
gemma4:e4b	4B	6GB+	128K	Light agents, low-RAM machines
gemma4:26b	MoE (3.8B active)	16GB+	256K	Best balance — recommended
gemma4:31b	31B dense	24GB+	256K	Highest quality, high-end hardware

Recommendation: gemma4:26b. The MoE architecture means it runs at the speed of a ~4B model while delivering the reasoning quality of a much larger one. If you have an M2 Pro, M3, M4 Mac or equivalent PC, this is your best local agent model in 2026.

Setup: OpenClaw + Gemma 4 via Ollama

The cleanest way to run Gemma 4 with OpenClaw is through Ollama. Three steps:

Step 1: Install Ollama and pull Gemma 4

# Install Ollama (macOS)
brew install ollama

# Or download from ollama.ai for Windows/Linux

# Pull Gemma 4 26B (recommended)
ollama pull gemma4:26b

# Or pull a smaller variant if RAM is limited
ollama pull gemma4:e4b    # 6GB RAM

# Verify it works
ollama run gemma4:26b "Hello, who are you?"

# Start Ollama server (runs on port 11434)
ollama serve

Step 2: Configure OpenClaw to use Ollama

OpenClaw needs to know about the Ollama provider. Add this to your ~/.openclaw/openclaw.json:

~/.openclaw/openclaw.json

{
  "models": {
    "providers": {
      "ollama": {
        "baseUrl": "http://127.0.0.1:11434",
        "apiKey": "ollama-local",
        "api": "ollama",
        "models": [
          {
            "id": "gemma4:26b",
            "name": "Gemma 4 26B",
            "contextWindow": 256000,
            "maxOutput": 8192,
            "toolCalling": true
          },
          {
            "id": "gemma4:e4b",
            "name": "Gemma 4 4B",
            "contextWindow": 128000,
            "maxOutput": 4096,
            "toolCalling": true
          }
        ]
      }
    }
  }
}

Important: Use "api": "ollama" (not "openai-responses") and no /v1 suffix in the baseUrl. Ollama's native API gives better tool calling support than OpenAI compatibility mode.

Step 3: Configure your agent's SOUL.md

agents/researcher/SOUL.md

# Market Research Analyst

## Identity
- Name: Radar
- Role: Market Research Analyst
- Model: ollama/gemma4:26b    ← Use this format
- Timezone: UTC

## Personality
- Data-driven and precise
- Cites sources for every factual claim
- Structures output with headers and bullet points

## Rules
- Always search the web before answering factual questions
- Include publication date and URL for every source
- Flag when data is older than 6 months
- Output format: Summary → Key Findings → Sources

## Skills
- browser: Search and read web pages
- files: Read and write local files

## Channels
- Telegram:
    token: ${TELEGRAM_BOT_TOKEN}
    allowed_users: [${ALLOWED_USER_ID}]

Step 4: Register and start

# Make sure Ollama is running first
ollama serve &

# Register your agent
openclaw agents add radar --workspace ./agents/researcher

# Start the gateway
openclaw gateway start

# Test it
openclaw agent --agent radar --message "What are the top AI agent frameworks in 2026?"

Alternative: Gemma 4 via Google AI Studio API

If you do not have the hardware for local inference, Gemma 4 is available via Google AI Studio with a free API tier. This gives you Gemma 4's capabilities without needing 16GB+ RAM.

Using Gemma 4 via Google AI Studio API

# 1. Get a free API key at: aistudio.google.com

# 2. Set environment variable
export GEMINI_API_KEY="your-api-key-here"

# 3. In your SOUL.md, use the Gemini provider
## Identity
- Name: Radar
- Role: Research Analyst
- Model: gemma-4-27b-it    ← Google AI Studio model ID

# Note: Google AI Studio uses "gemma-4-27b-it" (instruction-tuned)
# The free tier has generous rate limits for agent use

The Google AI Studio free tier handles moderate agent usage comfortably. If you need higher throughput or enterprise-grade SLAs, Google Cloud Vertex AI offers Gemma 4 with dedicated compute resources.

Gemma 4 vs Other Local Models for OpenClaw

Model	Tool Calling	Context	RAM (recommended)	Agent Quality
Gemma 4 26B	Native	256K	16GB	Excellent
Llama 3.1 8B	Yes	128K	8GB	Good
Mistral 7B	Partial	32K	8GB	Good (limited context)
Qwen2.5 14B	Yes	128K	10GB	Very good
Phi-4 14B	Yes	16K	10GB	Good (small context)

Gemma 4 26B stands out primarily for its context window (256K is class-leading for a locally runnable model) and its native tool calling reliability. If your agents process long documents or need extended conversation memory, Gemma 4 26B is the clear choice.

Best Agent Types for Gemma 4

Research & Analysis Agents

Best fit

The 256K context window lets your agent ingest entire research papers, long web pages, or comprehensive reports without truncation. Gemma 4's instruction following ensures it extracts and structures the information correctly.

Private Data Agents

Best fit

Agents that process emails, financial data, health records, or confidential business documents. Everything stays on your machine — no conversation data ever reaches Google, Anthropic, or any other provider.

Code Review & Documentation Agents

Strong fit

256K context means the agent can hold an entire codebase in context. Gemma 4 performs well on code-related tasks and can review, explain, or document code without needing to truncate large files.

Long-Running Conversation Agents

Strong fit

Agents that maintain context across many messages benefit from the extended context window. A customer support agent or personal assistant can remember far more conversation history before needing a context reset.

Simple Triage / Routing Agents

Use E4B variant

The E2B or E4B variants (2B-4B parameters) are fast enough for high-volume message routing. Cost is zero, and latency on modern hardware is acceptable for non-time-critical classification tasks.

Known Limitations

⚠ Hardware requirements for 26B

You need 16GB RAM minimum for the recommended 26B variant. The E4B (4B) works with 6GB but has lower reasoning quality. If your machine has less than 16GB RAM, Gemma 4 E4B or a cloud API is the better path.

⚠ No real-time internet access

Like all local models, Gemma 4 has no built-in internet access. Add the browser skill to your SOUL.md to give the agent web search capabilities. Without it, the model can only reason over its training data.

⚠ Hallucination risk without grounding

Gemma 4 can confidently state incorrect facts, especially for recent events. For research agents, always pair it with the browser skill or a document retrieval system. Validate outputs for mission-critical information.

⚠ Slower than cloud APIs

On consumer hardware, Gemma 4 26B generates tokens at ~20-40 tokens/second. Cloud APIs like Claude Haiku return results faster. For interactive agents where response latency matters, cloud APIs still have an edge.

Gemma 4 vs Cloud Models: When to Use Each

Use Gemma 4 locally when:

✓ You process sensitive or private data
✓ You want zero ongoing API cost
✓ You need 256K+ context window
✓ You are building 24/7 always-on agents
✓ You have 16GB+ RAM available
✓ Internet connectivity is unreliable

Use cloud model when:

→ Low latency response is critical
→ You have less than 16GB RAM
→ Complex multi-step tool use needed
→ Multiple agents running concurrently
→ Usage is sporadic (not 24/7)
→ Data privacy is not a concern

Related Guides

OpenClaw + Ollama Setup

Full guide to running local models

Best Cheap API Models for OpenClaw

Cloud model cost comparison

Frequently Asked Questions

What hardware do I need to run Gemma 4 with OpenClaw?

For Gemma 4 E4B (4B parameters), you need at least 8GB RAM — any modern Mac, Windows, or Linux machine works. For Gemma 4 26B (the MoE variant, recommended), you need 16GB RAM minimum — Apple Silicon M2 Pro or better, or a PC with a mid-range GPU. For Gemma 4 31B, you need 24GB+ RAM or a high-end GPU. Start with the 26B MoE model if your machine supports it; it activates only 3.8B parameters during inference so it runs faster than its size suggests.

Does Gemma 4 support tool calling for OpenClaw agents?

Yes. Gemma 4 includes native function calling support across all model sizes. This means your OpenClaw agent can use skills like web browsing, file management, and API calls reliably. Use Ollama's native API mode (not the OpenAI compatibility mode) for the best tool calling support with Gemma 4.

Is Gemma 4 free to use commercially?

Yes. Gemma 4 is released under the Apache 2.0 license, which allows commercial use without fees. Running it locally via Ollama costs nothing beyond your electricity bill. This makes it an attractive option for production agent deployments where API costs would otherwise add up.

How does Gemma 4 compare to Claude Haiku for OpenClaw agents?

Gemma 4 26B is competitive with Claude Haiku 4.5 on instruction following and tool use, with the advantage of zero API cost. Claude Haiku tends to be more reliable on complex multi-step instructions and outputs cleaner structured data. Gemma 4's advantages are cost (free), privacy (fully local), and its 256K context window which is larger than Haiku's. For routine agent tasks, Gemma 4 26B is a strong Haiku alternative if you have the hardware.

Can I use Gemma 4 via API without running it locally?

Yes. Gemma 4 is available through Google AI Studio (free tier) and Google Cloud Vertex AI (paid, enterprise scale). For Google AI Studio, you get a free API key and can use the model identifier in your OpenClaw SOUL.md. This gives you Gemma 4's capabilities without needing local hardware. See the API section of this guide for setup instructions.

Pre-configured OpenClaw agent templates for Gemma 4

CrewClaw templates come with the right model and skills already configured. Download, point at your local Gemma 4 instance, and your agent is live.

Browse Agent Templates Create Your Agent

Run OpenClaw Agents with Gemma 4 Locally