What are the main limitations of the OpenAI Agents SDK that alternatives aim to address?

Alternatives offer broader model support, more extensible architectures, and provider-agnostic workflows, which help avoid vendor lock-in and enable advanced orchestration.

How do I migrate from OpenAI Agents SDK to another agent framework?

Start by mapping your current agent workflows to the primitives of the new SDK. Update environment setup, API calls, and configuration files as per the new framework's documentation.

Which SDK alternative supports the most LLM providers?

SwarmZero and Strands both support a wide range of LLM providers, including OpenAI, Anthropic, AWS Bedrock, Mistral, and more.

Can I use these agent SDK alternatives with AWS, Azure, or Google Cloud?

Yes, most alternatives—especially Strands and SwarmZero—offer cloud provider integrations and can be deployed on AWS, Azure, and Google Cloud.

What are the best practices for managing API keys and secrets across agent SDKs?

Use environment variables and .env files for secret management. Refer to each SDK's documentation for secure handling of credentials.

Are there open-source options among OpenAI Agents SDK alternatives?

Yes, AgentOS and SwarmZero are open-source and have active communities for support and contributions.

How do handoffs and guardrails differ between these SDKs?

Each SDK implements handoffs and guardrails differently; review their documentation and code examples to ensure they meet your workflow requirements.

Top OpenAI Agents SDK Alternatives (2025): Comprehensive Comparison Guide

Compare the best OpenAI Agents SDK alternatives for AI agent orchestration in 2025. Detailed feature matrix and practical code examples for SwarmZero, Strands, AgentOS, and more.

Introduction

The rapid evolution of large language models (LLMs) and generative AI has pushed the need for robust agent orchestration SDKs. The OpenAI Agents SDK has emerged as a popular framework, offering developer-friendly APIs for building, deploying, and managing AI agents. However, as the AI landscape matures, many developers seek OpenAI Agents SDK alternatives that offer more flexibility, provider agnosticism, or advanced orchestration capabilities.

This guide provides a comprehensive comparison of the top OpenAI Agents SDK alternatives in 2025, focusing on options like SwarmZero, Strands, and AgentOS. We’ll analyze their strengths, limitations, and unique features, empowering you to choose the best agent orchestration SDK for your next AI project. Whether you require multi-agent workflows, advanced guardrails, AWS integration, or open-source flexibility, this post will help you navigate the rapidly evolving world of Python agent frameworks and LLM orchestration solutions.

What is the OpenAI Agents SDK?

The OpenAI Agents SDK is a Python-based orchestration framework designed for building intelligent agents that interact with LLMs. It provides abstractions for agents, sessions, handoffs between tools or humans, guardrails for safety, and tracing for debugging and observability. Developers can leverage the SDK to run complex agent loops, integrate with function tools, and manage the full lifecycle of AI agents.

For teams looking to add real-time communication features to their AI agents, integrating a

python video and audio calling sdk

can enable seamless video and audio interactions within agent workflows.

A typical agent implementation might look like this:

1from openai.agents import Agent, Tool
2
3def search(query):
4    # Custom tool logic
5    return "Results for: {}".format(query)
6
7search_tool = Tool(name="WebSearch", func=search)
8
9agent = Agent(
10    tools=[search_tool],
11    enable_guardrails=True,
12    enable_tracing=True,
13)
14
15session = agent.create_session()
16response = session.run("Find the latest news on quantum computing.")
17print(response)
18

Below is a simplified architectural diagram depicting the OpenAI Agents SDK:

Why Consider OpenAI Agents SDK Alternatives?

While the OpenAI Agents SDK is powerful, it does come with certain limitations that may prompt teams to seek alternatives:

Provider lock-in: The SDK is tightly integrated with OpenAI’s ecosystem, limiting native support for other LLM providers.
Feature gaps: Some use cases—such as advanced multi-agent workflows, custom tracing, or AWS-native deployment—may exceed the default capabilities.
Cost and compliance: Relying solely on OpenAI APIs may not suit all regulatory, cost, or data residency requirements.

If your project demands LLM provider agnosticism, deeper cloud integration, or open-source extensibility, exploring OpenAI Agents SDK alternatives is essential for optimal results. For example, if you’re seeking a

jitsi alternative

to add secure video conferencing to your agent applications, several SDKs now offer more customizable and scalable options.

Top OpenAI Agents SDK Alternatives

1. SwarmZero: Flexible Multi-Agent Orchestration

SwarmZero is a modern agent orchestration SDK designed for maximum flexibility and LLM provider agnosticism. It enables developers to define, configure, and deploy agent swarms that coordinate on complex tasks, integrating with various LLMs, including OpenAI, Anthropic, Cohere, and open-source models (via LiteLLM).

For those building communication features into multi-agent systems, SwarmZero can be paired with a

Video Calling API

to enable real-time video interactions between agents and users.

Key Features:

Multi-agent workflow primitives
LLM provider-agnostic: seamless switching between providers
Out-of-the-box LiteLLM and AWS integration
Advanced configuration management
Python virtual environment support for isolated agent contexts
Extensive plugin ecosystem for agent tool integration

Example: Configuring a SwarmZero Agent

1from swarmzero import Agent, Swarm, LLMProvider
2
3llm = LLMProvider(
4    provider="anthropic",
5    api_key="YOUR_ANTHROPIC_KEY"
6)
7
8class ResearchAgent(Agent):
9    def act(self, query):
10        return self.llm.complete(query)
11
12swarm = Swarm(agents=[ResearchAgent(llm=llm)])
13response = swarm.run("Summarize the latest in AI agent orchestration SDKs.")
14print(response)
15

Pros:

Supports multiple LLMs and deployment targets
Plugin-based extensibility for tools and tracing
Granular configuration for each agent

Cons:

Steeper learning curve for advanced orchestration
Smaller community than OpenAI’s SDK (as of 2025)

If you’re evaluating SwarmZero and similar platforms, you may also want to explore

livekit alternatives

for scalable, real-time audio and video communication infrastructure.

2. Strands: Model-Driven, Provider-Agnostic Orchestration

Strands is a model-driven orchestration framework tailored for building robust multi-agent applications. It features primitives for agent coordination, input validation, and guardrails, with a focus on provider-agnostic design and seamless AWS integration.

Developers working with web-based agent interfaces can enhance their projects using a

javascript video and audio calling sdk

, enabling browser-based real-time communication directly within Strands-powered applications.

Core Strengths:

Model-driven agent orchestration
Built-in primitives for agent handoffs and tracing
AWS Lambda and Step Functions integration for serverless agent deployment
Configuration via YAML for reproducible workflows

Example: Strands Multi-Agent Flow

Strengths:

True provider-agnostic workflows
Robust input validation and guardrails
AWS-native deployment and scaling

Limitations:

Less Pythonic; configuration-driven approach may not suit all developers
Smaller ecosystem for custom agent tools

For teams building cross-platform mobile agent experiences, integrating a

react native video and audio calling sdk

can provide seamless audio and video capabilities on iOS and Android devices.

3. AgentOS: Open-Source API & CLI for Learning Agents

AgentOS is an open-source SDK focused on building, deploying, and monitoring learning agents. It provides a CLI, REST API, and Python SDK, making it easy to manage agent lifecycles, run experiments, and integrate with other tools.

If you need to quickly add video calling to your agent interface, you can

embed video calling sdk

components for a plug-and-play solution that works across browsers and platforms.

Highlights:

Open-source, community-driven development
CLI and REST API for agent control
Supports tracing, sessions, and configuration management
Integrates with third-party LLM providers and function tools

Typical Workflow Example

1agentos init my-agent
2agentos run --input "Analyze sentiment of recent tech news."
3

Python SDK Integration:

1from agentos import Agent
2agent = Agent(config_path="./configs/agent.yaml")
3agent.run(input_text="What are the latest trends in LLM orchestration?")
4

For agent applications that require telephony features, consider integrating a

phone call api

to enable voice calling capabilities alongside your AI workflows.

Comparison with OpenAI Agents SDK:

Open-source and highly extensible
CLI and API interfaces for automation
Requires more manual setup for advanced features

Feature Comparison Table

Below is a consolidated matrix comparing the major features of OpenAI Agents SDK and its leading alternatives:

Feature/SDK	OpenAI Agents SDK	SwarmZero	Strands	AgentOS
LLM Provider Agnostic	No	Yes	Yes	Yes
Multi-Agent Workflows	Basic	Advanced	Advanced	Intermediate
Guardrails & Input Validation	Yes	Yes	Advanced	Yes
Tracing & Monitoring	Yes	Advanced	Advanced	Yes
AWS Integration	Limited	Yes	Advanced	Via plugins
Open-Source	No	Partial	No	Yes
Python Virtual Environments	Yes	Yes	No	Yes
CLI & API	No	Partial	No	Yes
Ecosystem/Plugins	Medium	Growing	Developing	Strong OSS

How to Choose the Right SDK for Your AI Projects

When evaluating OpenAI Agents SDK alternatives, consider the following:

Provider compatibility: Ensure the SDK supports your desired LLM providers (OpenAI, Anthropic, AWS Bedrock, etc.).
Extensibility: Look for plugin or integration support for tools, tracing, and configuration management.
Ecosystem and support: Consider documentation quality, community activity, and available plugins.
Ease of use: Balance configuration-driven vs. code-driven approaches based on your team’s strengths.

Select the agent orchestration framework that best aligns with your technical requirements, deployment targets, and long-term maintenance goals. If you’re ready to experiment with these SDKs and their integrations,

Try it for free

to explore their capabilities firsthand.

Conclusion

The landscape of agent orchestration SDKs continues to expand rapidly in 2025. While the OpenAI Agents SDK remains a solid default, SwarmZero, Strands, and AgentOS offer compelling alternatives, each with unique strengths. By understanding your project’s needs and the capabilities detailed in this comparison, you can confidently choose the best SDK for your AI agent workflows.

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