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xAI’s Grok models are frontier reasoning models with strong real-time knowledge, long context windows, and good tool-use performance. Grok 4 is the right pick when you need an alternative to GPT-5.4 or Claude Opus with comparable capability.

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Installation

pip install -U swarms

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Environment Setup

export XAI_API_KEY="xai-..."
Get an API key at console.x.ai.

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Quick Start

xAI models use the xai/ prefix: 
from swarms import Agent

agent = Agent(
    agent_name="Grok-Agent",
    model_name="xai/grok-4-0709",
    max_loops=1,
)

print(agent.run("Summarize the case for first-principles thinking in three paragraphs."))

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Model Names

Modelmodel_nameBest for
Grok 4"xai/grok-4-0709" or "grok-4"Frontier reasoning, default
Grok 2"xai/grok-2-1212"Earlier generation, cheaper
Grok Beta"xai/grok-beta"Legacy

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Grok 4 — Frontier Reasoning

For your hardest analytical and planning tasks: 
from swarms import Agent

agent = Agent(
    agent_name="Grok-Strategist",
    model_name="xai/grok-4-0709",
    system_prompt="You are a senior strategy consultant. Reason first-principles and cite trade-offs.",
    max_loops=1,
)

print(agent.run(
    "Should a B2B SaaS startup with $5M ARR build an in-house data warehouse or use Snowflake?"
))

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Tool Use

Grok supports function calling: 
from swarms import Agent

def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"{city}: 21°C, partly cloudy"

agent = Agent(
    agent_name="Grok-Assistant",
    model_name="xai/grok-4-0709",
    tools=[get_weather],
    max_loops=3,
)

print(agent.run("What's the weather in Tokyo right now?"))

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Streaming

from swarms import Agent

agent = Agent(
    agent_name="Streaming-Grok",
    model_name="xai/grok-4-0709",
    streaming_on=True,
    max_loops=1,
)

agent.run("Walk me through how proof-of-stake differs from proof-of-work.")

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Multi-Provider Pipeline

Mix Grok with other models — for example, Grok for research and Claude for synthesis: 
from swarms import Agent, SequentialWorkflow

researcher = Agent(
    agent_name="Grok-Researcher",
    model_name="xai/grok-4-0709",
    system_prompt="Gather facts and quotes with citations.",
    max_loops=2,
)

writer = Agent(
    agent_name="Claude-Writer",
    model_name="claude-sonnet-4-6",
    system_prompt="Write a clear executive summary from the research.",
    max_loops=1,
)

pipeline = SequentialWorkflow(agents=[researcher, writer], max_loops=1)
print(pipeline.run("Impact of AI on the global semiconductor supply chain in 2026."))

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Production Defaults

from swarms import Agent

agent = Agent(
    agent_name="Production-Grok",
    model_name="xai/grok-4-0709",
    max_loops=1,
    persistent_memory=True,
    context_compression=True,
    context_length=128_000,
    autosave=True,
    retry_attempts=3,
    print_on=False,
)

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Next Steps