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May 12, 2025
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15
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AI Agent Makers: Top Tools and Platforms for Development
George Miloradovich
Researcher, Copywriter & Usecase Interviewer
AI agents are transforming business operations, saving time, and boosting productivity. Tools like CrewAI, AutoGen, LangChain, Vertex AI Agent Builder, and Latenode make building AI agents easier than ever. These platforms help automate tasks, analyze data, and streamline workflows. For example, CrewAI handles over 100,000 multi-agent executions daily, while Latenode offers a low-code solution with a drag-and-drop interface for non-technical users.
Want to simplify AI agent development? Start with Latenode for an affordable, user-friendly solution. Let’s dive into the details.
CrewAI is a framework designed to help developers build collaborative AI agents that can handle complex tasks through coordinated teamwork. With more than 29,400 GitHub stars and adoption by 60% of Fortune 500 companies [5], it has established itself as a key player in creating advanced AI workflows.
The framework uses a role-based architecture, assigning each agent specific functions, expertise, and objectives. This setup mirrors how human teams operate, making task delegation more intuitive and efficient [1].
| Feature | Description |
|---|---|
| Role-Based Agents | Create agents with specialized expertise and clearly defined goals. |
| Intelligent Collaboration | Agents share insights and coordinate tasks to achieve objectives. |
| Flexible Tool Integration | Connect with external services and data sources using custom tools. |
| Task Management | Manage complex workflows, resolving task dependencies automatically. |
These capabilities form the backbone of CrewAI's architecture [1].
One notable example comes from Tufts University's Doctor of Physical Therapy program. By using CrewAI, they developed personalized assessment tools and study guides, achieving better learning outcomes compared to standard chatbot solutions [2].
João Moura, the founder of CrewAI, highlights the platform's mission:
"We have made it easy for teams to build groups of AI 'agents' to perform tasks using any model, integrate with more than a thousand different applications, and to do so in a way that protects their data privacy" [6].
The platform currently handles over 100,000 multi-agent executions daily, spanning hundreds of use cases [6]. For those looking to implement AI automation, CrewAI emphasizes dedicating 80% of the effort to task design and 20% to defining agents [4].
Industry expert Ben Tossell, founder of Ben's Bites, shares his perspective:
"It's the best agent framework out there and improvements are being shipped like nothing I've ever seen before!" [5]
CrewAI's Visual Crew Builder provides a drag-and-drop interface, enabling both technical and non-technical users to design intricate workflows with ease [3]. The platform also supports various deployment options and can automatically generate user interfaces, balancing simplicity with the flexibility needed for advanced projects.
With its robust set of features and proven success in automation, CrewAI continues to lead the way in intelligent AI solutions. Next, take a look at AutoGen to discover another innovative approach to AI agent development.
AutoGen simplifies the development of multi-agent AI systems by coordinating specialized agents to tackle complex tasks. The 0.4 version focuses on improving code quality, reliability, and scalability in workflows [7].
The foundation of AutoGen lies in its advanced multi-agent architecture, which facilitates smooth collaboration among AI agents. It employs a layered design, combining a Core API and an AgentChat API. This setup not only ensures backward compatibility with earlier versions but also introduces improved features [7].
| Feature | Capability |
|---|---|
| Multi-Agent Architecture | Coordinates multiple specialized agents for collaborative problem-solving |
| LLM Integration | Connects seamlessly with large language models for enhanced data processing |
| Code Execution | Includes tools for running and testing code directly |
| Human-in-Loop | Allows for human feedback and guidance during the workflow |
| Workflow Orchestration | Manages tasks dynamically and enables effective communication between agents |
This architecture has been tested and proven effective in practical applications.
One notable example of AutoGen in action is a project to modernize legacy code. Two specialized agent teams worked together to convert COBOL files into Python. The first team focused on the initial COBOL-to-Python conversion and unit testing, while the second team transformed the Python output into a REST interface using FastAPI [9]. This case demonstrates how AutoGen facilitates seamless collaboration between agents, showcasing its potential for solving intricate challenges [8].
AutoGen incorporates a range of advanced functionalities designed to enhance its effectiveness:
These features allow for the customization of agents tailored to specific domains while maintaining modularity and type support. This flexibility enables organizations to design AI systems that handle diverse tasks without compromising performance. To maximize the benefits of AutoGen, it’s recommended to define clear roles for agents and establish structured interaction patterns. This ensures the system remains dependable and scalable, even in complex workflows [8].
LangChain is a platform that transforms AI agent development by allowing language models to dynamically choose and execute actions, moving beyond rigid, predefined workflows [10].
LangChain's architecture is built around several critical components, each playing a distinct role in creating intelligent AI agents:
| Component | Function | Key Capability |
|---|---|---|
| Agent | Decision Engine | Uses language models (LLMs) to decide next steps based on context |
| AgentExecutor | Runtime Environment | Oversees execution, manages errors, and logs activities |
| Tools | Action Functions | Offers specific functionalities that agents can utilize |
| Toolkits | Function Groups | Groups related tools for common tasks |
These elements work together to enable practical and efficient AI solutions.
LangChain's impact can be seen in several prominent use cases:
LangChain supports a variety of advanced workflow patterns that expand its functionality, including:
These patterns provide a solid foundation for building reliable and efficient AI agents.
To ensure effective implementation with LangChain, several best practices should be followed:
"Agents can handle sophisticated tasks, but their implementation is often straightforward. They are typically just LLMs using tools based on environmental feedback in a loop. It is therefore crucial to design toolsets and their documentation clearly and thoughtfully." [11]
LangChain’s dynamic and flexible approach aligns with the growing demand for efficient, low-code AI workflow development, making it a valuable tool for modern automation solutions.
Vertex AI Agent Builder is a platform designed to simplify the development of AI agents while providing enterprise-grade features and flexible deployment options. Its structure revolves around two key components, enabling the creation of sophisticated AI solutions tailored to various needs.
The platform is built on two foundational elements, each serving a distinct purpose:
| Component | Purpose | Key Capabilities |
|---|---|---|
| Agent Development Kit (ADK) | Framework for building agents | Develop agents with fewer than 100 lines of code, implement deterministic guardrails, and enable seamless multi-agent transfer |
| Agent Engine | Runtime environment | Manage session contexts, utilize evaluation tools, and ensure secure deployments |
Several leading organizations have successfully implemented Vertex AI Agent Builder to optimize their operations. Renault Group provides a compelling example of its practical applications. Laurent Giraud, Chief Data (&AI) Officer at Renault Group, shares:
"We used the ADK to develop an agent that ensures we're installing EV chargers where drivers need them most. The agent assists our data analysts to leverage geographical, zoning, and traffic data to inform and prioritize critical EV infrastructure investments that maximize driver convenience with less strain on our teams." [14]
Vertex AI Agent Builder incorporates a robust security framework to support enterprise-level deployments. Key features include:
These security measures ensure smooth integration with other cloud services, making the platform a reliable choice for businesses.
The platform's strong security foundation is complemented by its seamless integration capabilities. Nippon Television Holdings exemplifies this with their video analysis project. Rina Tsuji, Senior Manager of Corporate Strategy at Nippon Television Holdings, Inc., explains:
"We've implemented the Agent Engine as the backbone of our video analysis AI agent, powered by Gemini. This setup allows us to leverage the Python Vertex AI SDK without worrying about infrastructure, saving us an estimated month of development time. Plus, the Agent Engine's API seamlessly connects with other Google Cloud products like Workflows, giving us excellent maintainability and room to grow." [14]
Vertex AI Agent Builder also offers a rich development ecosystem, including the Agent Garden, which provides pre-built samples and tools. This ecosystem facilitates collaboration with over 50 industry leaders through the Agent2Agent protocol, while Apigee API management supports over 800,000 APIs.
Aakriti Bhargava, VP of Product Engineering and AI at Revionics, highlights the platform's versatility:
"Using Agent Development Kit, Revionics is building a multi-agent system to help retailers set prices based on their business logic - such as staying competitive while maintaining margins - and accurately forecasting the impact of price changes. ADK streamlines multi-agent transfer and planning, such as knowing when to transfer between specialized agents (data retrieval) and tools (constraint application), combining Revionics' pricing AI with agentic AI to automate pricing workflows. Data is central to Revionics' process, and the development kit enables agents to efficiently reason over big data through storage artifacts rather than relying solely on the LLM context." [14]
The platform's flexibility allows developers to deploy their agents on Cloud Run, Kubernetes, or Vertex AI while ensuring security and scalability throughout the development process.
Latenode is a low-code platform designed to simplify the creation of AI agents by blending visual workflow tools with AI functionality. It caters to both technical and non-technical users, offering an intuitive interface for building advanced AI agents while supporting custom code integration for more complex needs.
Latenode's design and execution tools make it efficient to create and manage AI agents. Here’s a breakdown of its key components:
| Component | Purpose | Key Capabilities |
|---|---|---|
| Visual Workflow Builder | Agent Design | Drag-and-drop interface, conditional logic, branching paths |
| AI Code Copilot | Development Assistance | JavaScript code generation, optimization suggestions |
| Agent Runtime Engine | Execution Environment | Built-in database, NPM package support, execution monitoring |
The platform’s effectiveness is evident in practical applications. Hoang T., from Education Management, shared his experience:
"Latenode and their support team have been great and responsive in providing my team with support in creating a workflow where our data from Google Sheet Form Submissions will take the users that submitted the form and then use our custom logic for processing" [15].
This highlights how Latenode simplifies automation tasks, even for users with minimal technical expertise.
Latenode offers a range of AI-driven tools that empower businesses to automate workflows effectively:
Latenode provides a robust ecosystem for building and deploying AI agents. Its features are designed to cater to a wide range of users, from startups to large enterprises. Sri Vamshi, Founder of Wheedle.io, expressed his satisfaction with the platform:
"Latenode is a hidden gem! Super similar features but way, WAY more affordable. The free plan is generous, and it's easy to set up workflows even if you're not tech-savvy" [15].
In addition to its development tools, Latenode focuses on security and scalability, making it suitable for enterprise-level applications. Key highlights include:
For businesses handling complex automation, Latenode proves to be a reliable choice. As Mike Kirshtein, Founder at Audax Group, noted:
"Our business requires us to send lots of webhooks every day and we need a reliable service that's easy on the pockets and that's Latenode" [15].
This blend of user-friendly tools, advanced AI capabilities, and enterprise-grade features makes Latenode a compelling option for organizations looking to streamline AI agent development while maintaining control over their automation processes. Up next, explore a detailed comparison of features across major platforms.
When evaluating AI agent development platforms, understanding their core features and technical capabilities is essential. Let’s explore some of the key aspects that support efficient AI agent development.
Different platforms employ unique methods for orchestrating workflows. Some rely on graph-based systems with nodes and edges to create structured workflows, while others feature visual tools like drag-and-drop builders combined with programming language support for advanced customization.
Here’s a breakdown of key workflow management features:
| Workflow Aspect | Implementation | Benefits |
|---|---|---|
| Visual Design | Drag-and-drop interfaces, conditional logic | Speeds up development; accessible for all users |
| Code Integration | JavaScript support, NPM package compatibility | Adds flexibility and enables custom logic |
| Execution Control | Real-time monitoring and debugging tools | Improves reliability and simplifies maintenance |
| Data Management | Built-in databases and structured storage | Eases data handling and state management |
Modern platforms are designed to integrate seamlessly with multiple frameworks while upholding strong privacy controls. This ensures compatibility and security as developers work across diverse ecosystems [17].
With 78% of companies planning to implement AI agents in production environments [16], security has become a critical factor. Platforms address this with several key measures:
The ability to adapt and expand AI agents is vital for enterprise use cases. Platforms enable customization through:
These options ensure platforms remain flexible as organizational needs evolve.
Monitoring tools are essential for maintaining smooth operations. Effective platforms offer features such as:
| Monitoring Feature | Purpose | Impact |
|---|---|---|
| Real-time Analytics | Tracks agent performance | Enables quick issue detection |
| Historical Data | Reviews past execution metrics | Supports optimization and planning |
| Resource Usage | Monitors computational expenses | Helps manage costs effectively |
| Error Tracking | Identifies and resolves issues | Boosts system reliability |
For large-scale deployments, platforms must provide enterprise-grade capabilities, including:
As AI agent development platforms evolve, organizations must weigh these features against their specific needs to ensure they achieve both security and scalability.
Implementing an AI agent requires thoughtful preparation and execution. Research indicates that 83% of sales teams leveraging AI have reported revenue growth [18]. Below is a structured approach to help you plan, execute, and refine your AI agent initiatives.
Begin by analyzing your organization’s specific needs and existing resources. A well-thought-out plan can ensure you maximize your platform’s potential. Consider the following key areas:
"AI agents will become commonplace as people get used to having different types of AI assistants help them in their work" [20].
To ensure a smooth rollout, follow these actionable steps:
Here’s what one user had to say:
"Latenode is a hidden gem! Super similar features but way, WAY more affordable. The free plan is generous, and it's easy to set up workflows even if you're not tech-savvy" [15].
Once your AI agent is operational, shift your focus toward continuous refinement. This involves:
Think of AI agent implementation as a long-term investment in your organization’s capabilities. Building and maintaining these systems will position your team for sustained growth and adaptability [19].
CrewAI simplifies the process of designing and deploying collaborative AI systems that work together effortlessly to handle complex tasks. Its user-friendly interface allows even those with limited technical knowledge to create multi-agent workflows with ease.
By using CrewAI, businesses can boost productivity by optimizing workflows, making better decisions, and implementing intelligent automation. This platform enables teams to develop advanced systems more quickly, helping organizations respond to evolving demands and scale their operations effectively.
AutoGen's multi-agent system streamlines the challenge of updating legacy code by leveraging a network of specialized AI agents. Each agent is tailored to perform distinct tasks, such as examining outdated code, recommending improvements, or creating updated code snippets. This collaborative setup ensures quicker and more precise updates while minimizing potential errors.
By taking over repetitive tasks and delivering clear, actionable suggestions, AutoGen allows developers to concentrate on strategic decisions. This makes the process of modernizing systems smoother, faster, and less demanding on resources.
LangChain is known for its ability to connect effortlessly with a wide range of data sources and APIs. This makes it possible for AI agents to handle real-time inputs with ease, ensuring timely and accurate responses. Its modular structure gives developers the flexibility to tailor workflows, allowing AI agents to adjust dynamically to evolving situations and user requirements.
One standout feature of LangChain is its support for memory management. This enables AI agents to maintain context throughout interactions, resulting in more natural and effective communication. Such capabilities make it well-suited for managing complex tasks and improving efficiency across both technical and non-technical environments.
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