When to Use Agent SDK, LangChain, or CrewAI to Build Your AI Agents

AI agents are transforming automation and enhancing decision-making across various industries. However, choosing the right framework is crucial. Agent SDK, LangChain, and CrewAI each offer unique capabilities for building intelligent agents. Agent SDK focuses on seamless AI automation, LangChain excels in agent workflows with LLMs, and CrewAI enables multi-agent collaboration. Understanding their differences helps in selecting the best tool for your needs, whether it’s AI workflow automation, agent integration, or custom AI development. This Agent SDK, vs LangChain vs CrewAI guide explores when to use each framework to maximize efficiency and performance in AI-driven applications.

What is OpenAI’s Agent SDK?

The OpenAI Agents SDK is a powerful, lightweight framework for building intelligent, agent-based AI applications with minimal abstractions. It is a production-ready upgrade from OpenAI’s previous Swarm experimentation, designed to streamline AI development while providing flexibility and customizability.

With just a few core primitives, the Agents SDK makes it easy to build real-world applications that can reason, take action, and delegate tasks efficiently. It empowers developers to build AI-driven assistants, automation tools, and intelligent systems without the complexity of traditional AI orchestration frameworks. It also includes built-in tracing and evaluation tools, enabling developers to monitor, debug, and fine-tune their agent workflows effortlessly.

Key Features of Agent SDK

• Agent Loop: Automates agent interactions, tool calling, and response handling.
• Handoffs: Enable agents to delegate tasks to specialized sub-agents.
• Guardrails: Validate inputs and enforce safety measures before execution.

Learn More: How to Use OpenAI Responses API & Agent SDK?

Overview of LangChain Framework

LangChain is an open-source framework that emphasizes the use of language models in a modular fashion. It allows developers to build applications by chaining together various components, such as prompt templates, memory modules, and agent executors. This modularity facilitates the creation of complex workflows and the integration of external tools, making it a versatile choice for developing AI applications.

Key Features of LangChain

• Graph-based Architecture: Enables visualization and control over complex workflows.
• Modular Design: Allows seamless integration of memory, prompts, and external APIs.
• Multi-Tool Integration: Connects to databases, APIs, and third-party tools for enhanced functionality.

Learn More: Understanding LangChain Agent Framework

Overview of CrewAI Framework

CrewAI focuses on orchestrating role-playing autonomous AI agents. By assigning specific roles, backgrounds, goals, and memories to each agent, CrewAI enables collaborative interactions among agents to achieve complex objectives. Its design emphasizes ease of use, allowing developers to define and manage multi-agent systems with minimal complexity.

Key Features of CrewAI

• Role-Based Autonomous Agents: Assign distinct roles, expertise, and objectives to agents, enabling structured collaboration.
• Goal-Oriented & Memory-Aware Workflow: Agents retain context and work towards predefined goals efficiently.
• Seamless Multi-Agent Orchestration: Supports both independent and collaborative task execution with easy management.

Learn More: Building Collaborative AI Agents With CrewAI

Designing an AI Agent System

Now that we’ve learned about all three frameworks, let’s apply them to build agentic systems. In this section, I will introduce the problem statement and explain the steps involved in developing a multi-agent AI system to address it.

Problem Statement

We need to develop an AI-powered Virtual Travel Guide that assists users with travel-related queries. The system must provide real-time, accurate, and structured travel assistance by leveraging multiple specialized agents working collaboratively.

Steps to Build the AI System

To develop an efficient AI-powered travel assistant, we’ll follow these 3 steps:

1. Define Agents & Tasks: We’ll first identify key travel needs and create specialized agents for specific tasks such as itinerary planning, budget estimation, and local guide recommendations.
2. Integrate Real-Time Information Retrieval: We will then use APIs like Responses API, SerpAPI, and Tavily to fetch up-to-date travel data.
3. Implement Multi-Agent Collaboration: Finally, we’ll leverage LangChain, CrewAI, and Agent SDK to enable seamless communication between agents. This will ensure efficient task execution and coordination.

Let’s get started.

Step 1: Define the Agents & Tasks

Here are the key areas where travelers need assistance, for which we need to create specialized agents:

• Destination Advisor: Recommends places based on user preferences.
• Trip Budget Estimator: Calculates estimated costs for the trip.
• Local Guide Finder: Helps find tour guides or local experiences.
• Travel Itinerary Planner: Creates structured itineraries.
• Flight & Hotel Finder: Fetches real-time flight/hotel information.
• Triage Travel Agent: Routes queries to the correct agent.

Step 2: Integrate Real-Time Information Retrieval

To ensure accurate and up-to-date travel details, we need to incorporate different web search APIs across frameworks, as follows:

• Agent SDK: Use Responses API for retrieving relevant travel data.
• CrewAI: Integrate SerpAPI to fetch live search results.
• LangChain: Utilize Tavily for real-time information on flights, hotels, and attractions.

Step 3: Develop the Agent Framework

Now that our agents and integrated tools are all in place, we need to implement multi-agent collaboration to get our system running. For this, we’ll be using LangChain, CrewAI, and OpenAI’s Agent SDK to enable efficient coordination between the agents. On each of these frameworks, we’ll design the agents to perform specific tasks, while sharing relevant data with other agents when needed.

Expected Output

The system generates a structured itinerary based on the given prompt, considering user preferences like travel duration, interests, and budget.

Building the AI System on Agent SDK, LangChain, and CrewAI

Following the above steps, I’ve built the multi-agent system on all three frameworks – Agent SDK, LangChain, and CrewAI.

Here are the outputs of each of the systems, for the following prompt:

“I want to visit India for 10 days. I love history and food. My budget is ₹2,00,000. Can you suggest an itinerary?”

By analyzing the outputs from each framework, we will compare their response quality, accuracy, and efficiency to determine the best framework for different use cases.

OpenAI’s Agent SDK

You can access the Colab notebook containing the code that demonstrates OpenAI’s Agent SDK in action by clicking the link. Here’s how the system responded to the given prompt.

Output:

Understanding the Output

The code structure in OpenAI’s Agent SDK is centered around defining agents with specific capabilities and integrating them into applications. Developers can utilize pre-built tools and APIs to extend the functionality of these agents, allowing for tasks like real-time information retrieval and document processing. The output is typically a seamless execution of tasks delegated to the agent, leveraging OpenAI’s models for tasks such as web searches and file operations.

LangChain

You can access the Colab notebook containing the LangChain code that generates the output below by clicking the link. Here is the output obtained by the LangChain framework.

Output:

Understanding the Output

LangChain’s code structure is modular, enabling developers to chain together various components to build complex workflows. For instance, a developer can define a prompt template, integrate a memory module for context retention, and set up an agent executor to handle task execution. This modularity allows for flexible and customizable agent behaviors. The output is highly dependent on the specific configuration of components, offering versatility in application design.

CrewAI

You can access the Colab notebook containing the CrewAI code that generates the output below by clicking the link. Here is the output obtained by the CrewAI framework.

Output:

Understanding the Output

CrewAI’s approach involves defining agents with distinct roles and goals, facilitating collaborative problem-solving. The code structure allows for the assignment of specific tasks to each agent, promoting a division of labor within the system. This role-based design simplifies the management of complex tasks by distributing responsibilities among agents. The output is a coordinated effort from multiple agents working towards a shared objective, enhancing efficiency and effectiveness.

Agent SDK vs CrewAI vs LangChain: A Comparative Analysis

Now, let’s compare Agent SDK, LangChain, and CrewAI in terms of their focus, strengths, key features, and use cases to help you choose the best framework for your AI agent development needs. Below is a table comparing these aspects of each framework.

My Take on Using Agent SDK vs CrewAI, and LangChain

Selecting the appropriate AI agent framework depends on the specific requirements and goals of the project:

• OpenAI’s Agent SDK is ideal for developers seeking to integrate robust AI capabilities with minimal setup, leveraging OpenAI’s powerful models for tasks like web searches and file operations.
• LangChain offers a modular approach, providing flexibility to build customized workflows by chaining together various components suitable for applications requiring intricate configurations.
• CrewAI emphasizes role-based agent collaboration, making it a compelling choice for projects that benefit from distributed problem-solving and team dynamics among agents.

The choice of an AI agent framework should align with the project’s objectives, complexity, and desired level of customization. Each of the discussed frameworks offers unique features and advantages, catering to diverse development needs in the realm of AI agents.

Conclusion

Choosing the right AI agent framework comes down to your project’s complexity, customization needs, and how agents collaborate. OpenAI’s Agent SDK is a straightforward, production-ready option with built-in tools, perfect for developers who want seamless AI integration without extra hassle. LangChain offers a more flexible, modular approach, making it ideal for applications that require intricate workflows and external tool connections. Meanwhile, CrewAI shines in multi-agent collaboration, providing a structured, role-based system for projects that rely on distributed problem-solving. Each framework has its own strengths, and understanding these differences helps developers pick the right one to build efficient and effective AI-driven solutions.

Frequently Asked Questions

Soumil Jain

Data Scientist | AWS Certified Solutions Architect | AI & ML Innovator

As a Data Scientist at Analytics Vidhya, I specialize in Machine Learning, Deep Learning, and AI-driven solutions, leveraging NLP, computer vision, and cloud technologies to build scalable applications.

With a B.Tech in Computer Science (Data Science) from VIT and certifications like AWS Certified Solutions Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Fake News Detection, and Emotion Recognition. Passionate about innovation, I strive to develop intelligent systems that shape the future of AI.