OpenAI Swarm

Swarm is an experimental framework by OpenAI for building multi-agent systems. It showcases the handoff & routines pattern, making agent coordination and execution lightweight, highly controllable, and easily testable. Obiguard integration extends Swarm’s capabilities with production-ready features like observability, reliability, and more.

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Getting Started

pip install -U obiguard

from swarm import Swarm, Agent
from obiguard import Obiguard

obiguard_client = Obiguard(
  obiguard_api_key="vk-obg***", # Your Obiguard virtual key
)

client = Swarm(client=obiguard_client)

def get_weather(location) -> str:
  return "{'temp':67, 'unit':'F'}"

agent = Agent(
  name="Agent",
  instructions="You are a helpful agent.",
  functions=[get_weather],
)

messages = [{"role": "user", "content": "What's the weather in NYC?"}]

response = client.run(agent=agent, messages=messages)
print(response.messages[-1]["content"])

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E2E example with Function Calling in OpenAI Swarm

Here’s a complete example showing function calling and agent interaction:

from swarm import Swarm, Agent
from obiguard import Obiguard

obiguard_client = Obiguard(
    obiguard_api_key="vk-obg***",  # Your Obiguard virtual key
)

client = Swarm(client=obiguard_client)

def get_weather(location) -> str:
    return "{'temp':67, 'unit':'F'}"

agent = Agent(
    name="Agent",
    instructions="You are a helpful agent.",
    functions=[get_weather],
)

messages = [{"role": "user", "content": "What's the weather in NYC?"}]

response = client.run(agent=agent, messages=messages)
print(response.messages[-1]["content"])

The current temperature in New York City is 67°F.

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Enabling Obiguard Features

By routing your OpenAI Swarm requests through Obiguard, you get access to the following production-grade features:

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1. Interoperability - Calling Different LLMs

When building with Swarm, you might want to experiment with different LLMs or use specific providers for different agent tasks. Obiguard makes this seamless - you can switch between OpenAI, Anthropic, Gemini, Mistral, or cloud providers without changing your agent code.

Instead of managing multiple API keys and provider-specific configurations, Obiguard’s Virtual Keys give you a single point of control. Here’s how you can use different LLMs with your Swarm agents:

obiguard_client = Obiguard(
  obiguard_api_key="vk-obg***", # Your Obiguard virtual key
)

client = Swarm(client=obiguard_client)

obiguard_client = Obiguard(
  obiguard_api_key="vk-obg***", # Your Obiguard virtual key
)

client = Swarm(client=obiguard_client)

client = Obiguard(
  obiguard_api_key="vk-obg***", # Your Obiguard virtual key
)

client = Swarm(client=obiguard_client)

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2. Observability - Understand Your Agents

Building agents is the first step - but how do you know they’re working effectively? Obiguard provides comprehensive visibility into your agent operations through multiple lenses:

Metrics Dashboard: Track 40+ key performance indicators like:

• Cost per agent interaction
• Response times and latency
• Token usage and efficiency
• Success/failure rates
• Cache hit rates

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3. Logs and Traces

Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.

Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.

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4. Security & Compliance - Enterprise-Ready Controls

When deploying agents in production, security is crucial. Obiguard provides enterprise-grade security features:

Configure these settings in the Obiguard Dashboard.