Building Agents with OpenRouter

pip install -U swarms

export OPENROUTER_API_KEY="sk-or-..."

from swarms import Agent

agent = Agent(
    agent_name="OpenRouter-Agent",
    model_name="openrouter/anthropic/claude-3.5-sonnet",
    max_loops=1,
)

print(agent.run("Summarize the case for API gateways in three paragraphs."))

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="OpenRouter-Assistant",
    model_name="openrouter/anthropic/claude-3.5-sonnet",
    tools=[get_weather],
    max_loops=3,
)

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

from swarms import Agent

agent = Agent(
    agent_name="Streaming-OpenRouter",
    model_name="openrouter/openai/gpt-4.1",
    streaming_on=True,
    max_loops=1,
)

agent.run("Walk me through how a B-tree index works in a relational database.")

from swarms import Agent, ConcurrentWorkflow

candidates = [
    "openrouter/anthropic/claude-3.5-sonnet",
    "openrouter/openai/gpt-4.1",
    "openrouter/google/gemini-2.5-pro",
    "openrouter/meta-llama/llama-3.3-70b-instruct",
]

agents = [
    Agent(
        agent_name=name.split("/")[-1],
        model_name=name,
        system_prompt="Answer in under 150 words.",
        max_loops=1,
    )
    for name in candidates
]

workflow = ConcurrentWorkflow(agents=agents)
results = workflow.run("What are the most underrated trade-offs in monolithic vs. microservice architectures?")

for name, response in results.items():
    print(f"\n=== {name} ===\n{response}")

from swarms import Agent

# Let OpenRouter route automatically
agent = Agent(
    agent_name="Auto-Routed",
    model_name="openrouter/meta-llama/llama-3.3-70b-instruct",
    max_loops=1,
)

# See https://openrouter.ai/docs#provider-routing for advanced provider preferences

from swarms import Agent

agent = Agent(
    agent_name="Production-OpenRouter",
    model_name="openrouter/anthropic/claude-3.5-sonnet",
    max_loops=1,
    persistent_memory=True,
    context_compression=True,
    context_length=128_000,
    autosave=True,
    retry_attempts=3,
    print_on=False,
)