After reviewing the documentation and spending several hours tinkering with these AI agent builders, we listed the best open-source frameworks and low-code/no-code platforms. To highlight AI agent builder use cases we:
- Compared the most popular AI agent frameworks—LangGraph, AutoGen, CrewAI, and OpenAI Swarm
- Provided a tutorial on building a product expert agent with CrewAI.
What are agents?
“Agent” can be defined in several ways:
- Traditional AI defines agents as systems that can perceive their environment and act upon that environment.
- Some analyst firms define agents as fully autonomous systems that operate independently over long periods, utilizing tools like functions or APIs to interact with their environment and make decisions based on context and goals.1
- Others use the term to describe more prescriptive implementations that follow predefined workflows.2
Instead of providing a strict definition, we categorize these variations as agentic systems, but make a key architectural distinction between workflows and agents:
- Workflows are systems in which LLMs and tools are organized through predefined code paths.
- Agents are systems where LLMs independently:
- Manage their processes and tool usage.
- Decide when to execute the provided tools iteratively to achieve the primary objective, determining how to complete tasks.
In this article, we listed AI agent builders that can build agents with tool usage capabilities rather than workflow automation systems.
Why use AI agent builders?
Building agents from the ground up is a complex task due to the following issues:
- Reliability: Chaining multiple AI steps can compound AI hallucinations, especially for tasks requiring exact outputs.
- Integration capability: Several use cases necessitate agents to access data stores or external applications.
- Orchestration: Agents need to operate at the right time and in the correct order to achieve a common goal, requiring complex synchronization.
- State management: It is complex to ensure that agents keep track of each other’s status and that changes in one agent’s state do not disrupt the others.
Agent builders make this easier by allowing developers to focus on the application logic, rather than dealing with AI hallucinations, tool integrations, orchestration, etc.
Builders bring the required components needed to create more reliable and capable AI agents, including:
- Frameworks defining a specialization (e.g. workflow management) of the agentic AI model.
- Data templates that help increase the likelihood of an AI model generating exact outputs, reducing hallucinations.
- Data stores that enable access to external data, SQL, and NoSQL databases for data storage and querying.
- Built-in orchestration tools (e.g. communication protocols, etc.) that coordinate multiple agents.
- State management components to enable agents to remember past interactions and adjust their behavior in dynamic environments.
Open source frameworks
Agentic frameworks are typically best for complex, AI-driven projects across development environments that require customization and coding. Some (e.g., Crew AI, AutoGen) can also offer low-code capabilities.
| AI agent builder | Primary focus | |
|---|---|---|
1. | NLP task automation | |
2. | Data and content automation | |
3. | Workflow automation | |
4. | Workflow automation | |
5. | Workflow automation | |
6. | Data and content automation | |
7. | NLP task automation | |
8. | Workflow automation | |
9. | Data and content automation | |
10. | Data and content automation | |
LangGraph is proprietary software, but it provides an open-source library for agent development.
LangGraph
LangGraph offers more control and is suitable for complex agentic workflows, particularly when using Retrieval-Augmented Generation (RAG) or orchestrating AI tasks across external APIs or databases.
AutoGen
The multi-agent coordination for automating complex workflows and research tasks, particularly in autonomous code generation. AutoGen agents focus on self-correcting, rewriting, and executing code, making it useful for tackling programming challenges without much manual intervention.
CrewAI
One of the easiest tools to start with, offering ready-made agent templates (e.g., meeting preparation agent) and a minimal learning curve with no-code options. It simplifies task design with abstractions, allowing you to focus on tasks instead of complex orchestration and state management. The trade-off is that it is more difficult to dynamically adjust roles or delegate tasks to other agents mid-workflow due to the rigid structure of predefined roles and tasks in CrewAI.
OpenAI Swarm
A lightweight solution, it is still in its experimental stage, and not yet “production-ready.” It does not provide out-of-the-box solutions for every use case and allows developers to build and customize certain aspects, such as workflow orchestration and agent interactions. It is suitable for prototyping and testing ideas, and best for simple use cases or those looking to integrate agentic processes into an existing LLM pipeline.
Camel
A low-code multi-agent role-playing agent framework that enables AI agents to communicate. Best for workflow automation and synthetic data generation. Offer 20+ integrations with model platforms.
ChatDev
Includes AI agents (such as designers, developers, testers, and documenters) that interact and work together to accomplish complex tasks. ChatDev provides a browser-based visualizer to study the interactions of each agent acting within its role and environment.
Pydantic AI
A Python agent framework, does not require learning a new domain-specific language. Useful for structured data handling and prototyping. Integrates with logging tools like Logfire for real-time data visualization.
Agent Zero
A GitHub-hosted autonomous AI agent framework. Can be used for full-stack app generation, coding, and RAG. Interacts with various tools and APIs through natural language commands.
Automatic Agents
A lightweight framework for building Agentic AI pipelines and apps. Unlike frameworks like AutoGen and Crew AI, which use high-level abstractions, Atomic Agents takes a low-level, modular approach. This gives developers direct control over components like input handling, tool integration, and memory management, making each agent more controllable.
Bee Agent Framework
An open-source no-code toolkit developed by IBM Research. Implemented in TypeScript and Python. It offers sandboxed code execution for security, flexible memory management to optimize token usage (especially for models like Llama 3.1), and workflow controls, allowing complex branching, state pausing/resuming, and seamless error handling.
Low-code/no-code platforms
Low-code/no-code platforms with prebuilt tools are best for enterprise workflow automation tasks and rapid deployment.
| AI agent builder | Primary focus | |
|---|---|---|
1. | Workflow automation | |
2. | Workflow automation | |
3. | Workflow automation | |
4. | Workflow automation | |
5. | Workflow automation | |
6. | Workflow automation | |
7. | Workflow automation | |
8. | Workflow automation | |
9. | Incident response automation | |
10. | Workflow automation | |
11. | Incident response automation | |
12. | NLP task automation | |
13. | Data and content automation | |
Low-code/no-code platforms are proprietary software.
Vertex AI Builder
A no-code agent builder for business use cases that allows you to create response templates. Supports integration with open-source frameworks like LangChain. A limitation is that the Vertex API, from authentication to endpoints, is complex to work with.
Beam AI
- Horizontal platform for creating several AI agents, such as:
- Compliance management agent
- Product return agent
- Customer service agent
- Data entry and billing agent
- Data extraction agent
- Order processing agent
Microsoft Copilot Studio Agent Builder
A low-code AI agent builder for a SaaS environment, offers over 1,200 data connectors. Best for:
- Automating tasks such as sending notifications.
- Creating internal chatbots.
- Or, business operations such as order management
Lyzr Agent Studio
Can be used by developers, enterprises, and business users. It is modular and useful for prototyping. Best for automating workflows across finance, HR, supply chain, and customer experience.
Glide
Offers no-code pre-designed themes, layouts, and components for agent creation. Best for for automating workflows across Field sales, inspections, work orders, inventory, CRM, dashboards, and portals.
Postman AI Agent Builder
Best for prototyping and building AI agents in a collaborative environment.. offers tools like the Postman client, Collection Runner, and Postman Flows to test LLM responses, prompts, and inputs.
UiPath Agent Builder
A low-code agent development tool that is part of the UiPath Studio.
Stack AI
A no-code platform that helps automate back-office tasks. AI agents are built using ready-made templates and a drag-and-drop interface. It integrates with systems such as SharePoint and Salesforce. Data security is maintained through strong compliance measures, and the platform supports both cloud and on-premises deployment.
Relevance AI
Best for ops teams looking to build AI agents for incident management without relying on developer resources. No technical background is required.
Lindy
Specializes in automating several commercial operations, including medical paperwork, customer service, human resources, and sales. With Lindy, you can build a “personalized agent” for each task, attach it to tools like Gmail or Slack, and watch it function automatically via triggers.
Bricklayer AI
An autonomous AI system for creating agents that automate Security Operations Centers (SOCs). Can enhance various SOC tasks such as alert triage, incident response, and threat intelligence analysis. Enables SOCs to create multi-task workflows, similar to SOAR playbooks.
Vonage AI Studio
A visual agent builder in the Vonage AI Studio allows you to create automated design flows for chatbots or voice assistants across messaging and voice channels without having to write any code.
Trilex AI
A no-code agent builder that allows self-aware agents to work together as a team. It is interface-focused and not enterprise-ready.
Read more: Enterprise AI agents, open-source AI agents.
LangGraph vs AutoGen vs CrewAI vs OpenAI Swarm
| Aspect | LangGraph (Graph-Based) | AutoGen (Adaptive) | CrewAI (Role-Based) | OpenAI Swarm (Routine-Based) |
|---|---|---|---|---|
| Ease of use | 🧠 Complex – Requires understanding acyclic graph structures | 💬 Moderate – Uses conversational agent interactions to simplify usage for chat interface users | ✅ Easy – Structured, role-based design makes it easy to start | ⚡ Easy – Lightweight and routine-based, making it simple to start but requires manual history tracking |
| Setup complexity | ⚙️ Complex | ⚙️ Complex | ⚙️ Complex | 🔧 Simple |
| Multi-agent Orchestration | 🌐 Centralized: Graph-based multi-agent flows | 🔄 Adaptive orchestration | 🧑🤝🧑 Hierarchical: Role-based multi-agent flows | 🔄 No defined control flow (relies on routine-based prompting patterns) |
| Agent definition | 🔲 Nodes that maintain a state | 🤖 Agents with flexible routing | 🤖 Agents with skills and associated tasks | 🤖 Agents with routines and functions |
| Function definition | 📝 Annotations (for structured & explicit functions) | 📝 Annotations (for structured & explicit functions) | 📝 Annotations (for structured & explicit functions) | 📄 Docstrings (for general-purpose functions) |
| Memory management | 🔧 Customizable, flexible | 🧠 Contextual memory stored via context_variables | 🧠 Structured, role-based | ❌ Stateless: Requires manual history tracking via context_variables |
| Memory features | • Short-term: Customizable • Long-term: External integrations • Entity memory: Fully supported | • Short-term: Message lists • Long-term: External integrations • Entity memory: Not supported | • Short-term: RAG, Contextual • Long-term: SQLite3 DB • Entity memory: Supported via RAG | • Short-term: Context variables in routines • Long-term: External integrations • Entity memory: Not supported |
| Human-in-the-loop | ⏸️ Custom breakpoints for human input | 💬 Requests feedback after agent execution | 💬 Requests feedback after agent execution | ❌ N/A (human-as-a-tool) |
Building a CrewAI agent tutorial
In this hands-on tutorial, we will create an AI agent using CrewAI that recommends laptops tailored to the specific needs of a CTO.
Scenario: Recommend the top 3 laptops for a Chief Technology Officer (CTO) who primarily works with email and performs extensive Python-based software development.
Installation
Let’s begin by installing the required libraries:
!pip install -q crewai openaiWhy do we need the OpenAI API?
CrewAI uses an LLM, like OpenAI’s GPT models, to power agent reasoning and responses. The agent interprets tasks and generates outputs, requiring an OpenAI API key.
import os
from crewai import Agent, Task, Crew
os.environ["OPENAI_API_KEY"] = "YOUR API KEY" Note: The API key is needed to access OpenAI models like GPT-4. CrewAI can also work with open-source models, such as Llama 3.
Defining the agent
We will create a Product expert agent — an AI assistant knowledgeable about tech products. Since our scenario involves helping a technical user (a CTO), we need an agent with strong product awareness and analytical ability.
CrewAI defines an agent based on its relationship with tasks. For each agent, we must clarify its role, goal, backstory, and the tools that it can use:
product_search_agent = Agent(
role="Product Expert",
goal="Suggest best laptops for a CTO of a company with 50 employees who spends significant time on email and also develops the company’s most critical software on Python and runs compute-heavy queries.",
backstory="You're an AI assistant that helps users choose the right tech products, specifically tailored to the needs of a CTO who manages critical software development, spends significant time on email, and runs compute-heavy queries on Python.",
verbose=True
)- role: The area of expertise the agent represents — in this case, a tech-savvy product expert.
- goal: A clear and specific objective for the agent.
- backstory: Gives the agent character, depth, and domain knowledge.
Defining the task
In this part, we assign the agent the task of recommending three suitable laptops for the CTO, including their pricing and a brief one-sentence summary for each.
CrewAI handles the reasoning and formatting based on your constraints via description, expected_output, and agent parameters.
search_task = Task(
description="List top 3 laptops for a CTO of a company with 50 employees who spends significant time on email and also develops the company’s most critical software on Python and runs compute-heavy queries.",
expected_output="3 laptop names with prices and 1-sentence descriptions focusing on performance for coding, running compute-heavy queries, and email management. Include links for each laptop with US-based retailers.",
agent=product_search_agent
)- description: Explains what the agent should do.
- expected_output: Defines the output structure — this ensures clarity and quality.
- agent: Assigns the task to the agent we created.
Building the Crew & running the workflow
Next, we create the crew, a system in which agents are created, assigned tasks, and interact to complete their objectives.
crew = Crew(
agents=[product_search_agent],
tasks=[search_task],
verbose=True
)
def main():
result = crew.kickoff()
print("\n✅ Final Result:\n", result)
main()CrewAI in action: Agent execution output
Once the crew.kickoff() method is called, CrewAI executes the task using the defined agent. Below is a sample output from the terminal, showing how the task is assigned, executed, and the final answer returned by the Product Expert agent:
╭────────── Crew Execution Started ─────────────────────────────────────────────╮
│ │
│ Crew Execution Started │
│ Name: crew │
│ ID: e88eb914-5033-4d38-aaed-765f74c0c97d │
│ │
│ │
╰────────────────────────────────────────────────────────────────────────────
🚀 Crew: crew
└── 📋 Task: 6dc39e8a-a28f-4ba1-8f8b-3a4e616fda6b
Status: Executing Task...
🚀 Crew: crew
└── 📋 Task: 6dc39e8a-a28f-4ba1-8f8b-3a4e616fda6b
Status: Executing Task...
└── 🤖 Agent: Product Expert
Status: In ProgressThen the agent provides its output as follows:
# Agent: Product Expert
## Task: List top 3 laptops for a CTO of a company with 50 employees who spends significant time on email and also develops the company’s most critical software on Python and runs compute-heavy queries.
# Agent: Product Expert
## Final Answer:
1. **Apple MacBook Pro 16-inch (M2 Pro, 16GB RAM, 512GB SSD)** - Priced around **$2,499**. Known for its exceptional performance, battery life, and Retina display, this laptop excels in running compute-heavy Python queries and managing emails efficiently.
[Link to purchase](https://www.apple.com/shop/buy-mac/macbook-pro/16-inch)
2. **Dell XPS 15 (11th Gen Intel Core i7, 16GB RAM, 512GB SSD)** - Priced around **$1,999**. This machine combines powerful Intel processing capabilities with superb graphics, making it ideal for software development and multitasking on emails.
[Link to purchase](https://www.dell.com/en-us/shop/dell-laptops/xps-15-laptop/spd/xps-15-9500-laptop)
3. **Lenovo ThinkPad X1 Carbon Gen 10 (Intel Core i7, 16GB RAM, 512GB SSD)** - Priced around **$2,049**. Renowned for its durability and exceptional keyboard, the X1 Carbon provides reliable performance for Python development and is well-suited for handling a high volume of emails.
[Link to purchase](https://www.lenovo.com/us/en/laptops/thinkpad/thinkpad-x/ThinkPad-X1-Carbon-Gen-10/p/20XW000BUS)
These laptops are optimized for the performance needs of a CTO who balances heavy software development work with efficient email management.
This output showcases how an agent, when properly defined, can deliver structured, relevant, and high-quality answers for integrating into real-world tools or workflows.
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