---
title: "Why OpenAI Agents SDK should be the main framework for agentic development for most use cases?"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

### Why OpenAI Agents SDK should be the main framework for agentic development for most use cases?

**Comparison of Abstraction Levels in AI Agent Frameworks**

| **Framework**         | **Abstraction Level** | **Key Characteristics**                                                                 | **Learning Curve** | **Control Level** | **Simplicity** |
|-----------------------|-----------------------|-----------------------------------------------------------------------------------------|--------------------|-------------------|----------------|
| **OpenAI Agents SDK** | Minimal              | Python-first, core primitives (Agents, Handoffs, Guardrails), direct control           | Low               | High             | High           |
| **CrewAI**            | Moderate             | Role-based agents, crews, tasks, focus on collaboration                                | Low-Medium        | Medium           | Medium         |
| **AutoGen**           | High                 | Conversational agents, flexible conversation patterns, human-in-the-loop support       | Medium            | Medium           | Medium         |
| **Google ADK**        | Moderate             | Multi-agent hierarchies, Google Cloud integration (Gemini, Vertex AI), rich tool ecosystem, bidirectional streaming | Medium            | Medium-High      | Medium         |
| **LangGraph**         | Low-Moderate         | Graph-based workflows, nodes, edges, explicit state management                        | Very High         | Very High        | Low            |
| **Dapr Agents**       | Moderate             | Stateful virtual actors, event-driven multi-agent workflows, Kubernetes integration, 50+ data connectors, built-in resiliency | Medium            | Medium-High      | Medium         |

#### Analysis of OpenAI Agents SDK’s Suitability for Agentic Development

Agentic development involves creating AI agents that can reason, act, and collaborate autonomously or with human input. Key considerations for selecting a framework include ease of use (simplicity, learning curve), flexibility (control level), and how much complexity the framework hides (abstraction level). Let’s break down OpenAI Agents SDK’s strengths based on the table:


#### Why OpenAI Agents SDK Stands Out for Agentic Development
The table highlights OpenAI Agents SDK as the optimal choice for agentic development for the following reasons:
1. **Ease of Use (High Simplicity, Low Learning Curve)**: OpenAI Agents SDK’s high simplicity and low learning curve make it the most accessible framework. This is critical for agentic development, where teams need to quickly prototype, test, and deploy agents. Unlike LangGraph, which demands a “Very High” learning curve and offers “Low” simplicity, OpenAI Agents SDK allows developers to get started fast without a steep onboarding process.
2. **Flexibility (High Control)**: With “High” control, OpenAI Agents SDK provides the flexibility needed to build tailored agents, surpassing CrewAI, AutoGen, Google ADK, and Dapr Agents. While LangGraph offers “Very High” control, its complexity makes it overkill for many projects, whereas OpenAI Agents SDK strikes a balance between power and usability.
3. **Minimal Abstraction**: The “Minimal” abstraction level ensures developers can work directly with agent primitives, avoiding the limitations of higher-abstraction frameworks like AutoGen (“High”) or CrewAI (“Moderate”). This aligns with agentic development’s need for experimentation and customization, as developers can easily adjust agent behavior without fighting the framework’s abstractions.
4. **Practicality for Broad Use Cases**: OpenAI Agents SDK’s combination of simplicity, control, and minimal abstraction makes it versatile for a wide range of agentic development scenarios, from simple single-agent tasks to more complex multi-agent systems. Frameworks like Google ADK and Dapr Agents, while powerful, introduce ecosystem-specific complexities (e.g., Google Cloud, distributed systems) that may not be necessary for all projects [previous analysis].

#### Potential Drawbacks of OpenAI Agents SDK
While the table strongly supports OpenAI Agents SDK, there are potential considerations:
- **Scalability and Ecosystem Features**: Google ADK and Dapr Agents offer advanced features like bidirectional streaming, Kubernetes integration, and 50+ data connectors, which are valuable for enterprise-scale agentic systems. OpenAI Agents SDK, while simpler, may lack such built-in scalability features, requiring more manual effort for large-scale deployments.
- **Maximum Control**: LangGraph’s “Very High” control might be preferable for highly complex, custom workflows where fine-grained control is non-negotiable, despite its complexity.

#### Comparison to Alternatives
- **CrewAI**: Better for collaborative, role-based agent systems but lacks the control and simplicity of OpenAI Agents SDK.
- **AutoGen**: Suited for conversational agents with human-in-the-loop support, but its “High” abstraction reduces control.
- **Google ADK**: Strong for Google Cloud integration and multi-agent systems, but its “Medium” simplicity and learning curve make it less accessible.
- **LangGraph**: Ideal for developers needing maximum control and willing to invest in a steep learning curve, but impractical for most due to “Low” simplicity and “Very High” learning curve.
- **Dapr Agents**: Excellent for distributed, scalable systems, but its distributed system concepts add complexity not present in OpenAI Agents SDK.

### Conclusion: Why OpenAI Agents SDK Should Be Used?
The table clearly identifies why OpenAI Agents SDK should be the main framework for agentic development for most use cases:
- It excels in **simplicity** and **ease of use**, making it the best choice for rapid development and broad accessibility.
- It offers **high control** with **minimal abstraction**, providing the flexibility needed for agentic development without the complexity of frameworks like LangGraph.
- It outperforms most alternatives (CrewAI, AutoGen, Google ADK, Dapr Agents) in balancing usability and power, and while LangGraph offers more control, its complexity makes it less practical for general use.

If your priority is ease of use, flexibility, and quick iteration in agentic development, OpenAI Agents SDK is the clear winner based on the table. However, if your project requires enterprise-scale features (e.g., Dapr Agents) or maximum control for complex workflows (e.g., LangGraph), you might consider those alternatives despite their added complexity.
