Building Local Agents with Gemma 4's Function Calling

Google's latest Gemma 4 release is about far more than incremental performance improvements. The most significant addition is native Function Calling tokens—purpose-built primitives that make it practical to run agentic systems on local hardware without any cloud dependency.

For teams exploring on-premises AI, or developers who prioritize privacy, this shift opens new possibilities.

What Makes Gemma 4's Function Calling Different

Gemma 4 ships with four model variants:

E2B (Ultra-lightweight): 2B parameters, designed for edge devices
E4B (Lightweight): 4B parameters, runs on Raspberry Pi
26B MoE (Mixture of Experts): 26B total, but only ~4B activate per token
31B Dense (Full): 31B parameters, maximum quality

For agentic workflows, the 26B MoE is particularly compelling. It occupies 26GB in memory, but activates only 4B parameters during inference. This means tool invocation latency stays practical—critical for agent loops that need sub-second responses to feel responsive.

In previous generations, Function Calling required elaborate prompt engineering and brittle parsing. Gemma 4 introduces token-level semantics for tool invocation, making behavior more deterministic and easier to reason about.

Implementation: A Local Agent Example

Here's a working Python example for a self-hosted agent:

import json
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
 
# Load the 26B MoE variant
model_name = "google/gemma-4-26b-moe"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16
)
 
# Tool definitions
tools = [
    {
        "name": "search_web",
        "description": "Search the web for information",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query"
                }
            },
            "required": ["query"]
        }
    },
    {
        "name": "fetch_url",
        "description": "Fetch and parse the content of a URL",
        "parameters": {
            "type": "object",
            "properties": {
                "url": {
                    "type": "string",
                    "description": "The URL to fetch"
                }
            },
            "required": ["url"]
        }
    },
    {
        "name": "summarize",
        "description": "Summarize a block of text",
        "parameters": {
            "type": "object",
            "properties": {
                "text": {
                    "type": "string",
                    "description": "Text to summarize"
                },
                "max_length": {
                    "type": "integer",
                    "description": "Maximum summary length in sentences"
                }
            },
            "required": ["text"]
        }
    }
]
 
def build_system_prompt(tools):
    """Construct system prompt with tool information"""
    prompt = "You are a helpful research assistant. You have access to the following tools:\n\n"
    for tool in tools:
        prompt += f"- {tool['name']}: {tool['description']}\n"
    prompt += "\nWhen you need to use a tool, respond with JSON:\n"
    prompt += '{"tool": "tool_name", "params": {"key": "value"}}\n'
    return prompt
 
def run_agent_loop(user_query, max_iterations=5):
    """Execute the agent loop"""
    system_prompt = build_system_prompt(tools)
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_query}
    ]
    
    for iteration in range(max_iterations):
        # Generate response
        input_ids = tokenizer.apply_chat_template(
            messages,
            tokenize=True,
            add_generation_prompt=True,
            return_tensors="pt"
        ).to(model.device)
        
        with torch.no_grad():
            outputs = model.generate(
                input_ids,
                max_new_tokens=512,
                temperature=0.7,
                top_p=0.9
            )
        
        response_text = tokenizer.decode(
            outputs[0][input_ids.shape[-1]:],
            skip_special_tokens=True
        )
        
        messages.append({"role": "assistant", "content": response_text})
        
        # Check for tool invocation
        if '{"tool":' in response_text:
            try:
                # Extract JSON (simplified; production needs robust parsing)
                json_start = response_text.find('{"tool":')
                json_end = response_text.rfind('}') + 1
                tool_call = json.loads(response_text[json_start:json_end])
                
                # Simulate tool execution
                tool_result = f"Tool '{tool_call['tool']}' executed with params: {tool_call.get('params', {})}"
                messages.append({"role": "user", "content": tool_result})
                
            except (json.JSONDecodeError, ValueError):
                # If JSON parsing fails, treat as final response
                return response_text
        else:
            # No tool invocation, this is the final answer
            return response_text
    
    return response_text
 
# Usage
result = run_agent_loop(
    "Find and summarize the latest developments in open-source LLMs. "
    "Keep it to 3 sentences."
)
print(result)

Key points in this implementation:

Chat Template Integration: Gemma 4 has native chat support, so apply_chat_template handles formatting cleanly.
Streaming Output: For better UX, use streaming=True to output tokens as they arrive.
Graceful Degradation: When JSON parsing fails, fall back to treating the response as final text rather than crashing.

Local vs. Cloud: A Comparison

Here's how self-hosted Gemma 4 stacks up against using the Gemini API:

Dimension	Gemma 4 Local	Gemini API
Capital Cost	~$100K–200K GPU	Zero upfront
Operating Cost	Electricity only	Per-token billing
Latency	~1–5 seconds	~1–2 seconds
Privacy	Fully private	Data sent to Google
Fine-tuning	Supported	Not available
Scaling	Single machine	Unlimited via API
Deployment	Your infrastructure	Managed by Google

For organizations with compliance requirements or expecting high tool-call volume, the local model often wins on cost and data sovereignty. For rapid prototyping or bursty demand, the API is simpler.

Leveraging 256K Context Window

Gemma 4's 256K token context is transformative for agentic work. What does this mean in practice?

Complex multi-step workflows fit in a single request. You can:

Load entire documentation (tens of thousands of tokens)
Preserve tool invocation history across many steps
Maintain coherent decision-making even 10+ tools deep

With a local model, you can exploit this window fully. For instance, an agent researching a topic can gather context, invoke search tools, fetch documents, and synthesize—all while maintaining perfect recall of earlier decisions.

Practical Considerations for Deployment

Real-world use of local Gemma 4 reveals some constraints:

Memory Requirements: Even the 26B MoE needs 26GB of GPU VRAM. RTX 4090 (24GB) falls short; A100 or H100 recommended.

Inference Speed: CPU inference is prohibitively slow. NVIDIA GPUs are nearly essential. AMD ROCm works but with reduced optimization.

Quantization Trade-offs: 4-bit or 8-bit quantization cuts memory by 50–75%, but Function Calling accuracy can degrade. Benchmark before deploying to production.

Batch Processing vs. Real-time: If you don't need sub-second responses, quantization + batching can make 26B MoE run on more modest hardware.

Where to Go Next

Once you're ready to experiment with Gemma 4, these articles provide complementary context:

Advanced Prompting for Gemini API — techniques that apply to open models too
Choosing the Right Local LLM — hardware-specific guidance

Gemma 4's Function Calling is a watershed moment for privacy-conscious, cost-sensitive teams. If you've been waiting for a practical open-source alternative to cloud APIs, now's the time to experiment.