Complete Guide to The Best AI Agent Builders in 2025

We’re seeing rising interest in AI agent builders, and that interest is likely to continue to grow in the coming years. Organizations are experimenting with new AI agent builders, designing new agents and workflows for all their business processes.

The rise of open-source frameworks and low-code/no-code platforms further simplified the development of AI agents. While open-source frameworks allow developers to access and customize the source code as needed, they also require technical expertise. In contrast, low-code/no-code platforms offer ready-to-use tools that enable users to build applications with minimal or no coding experience.

In this article, we’ve reviewed 20+ AI agent builders, their use cases, and demonstrated a practical video tutorial on GraphBit; a highly customizable AI agent framework.

Open-Source Agent Frameworks

Open-source agent frameworks are best for developing complex, code-driven projects requiring customization. Developers often use such frameworks to build multi-agent teams, a bespoke memory system, and support parallel task execution.

Although these frameworks remain highly customizable, some vendors offer low-code support. Below, we’ll review the ten best open-source AI agent builders like GraphBit, LangGraph, and AutoGen. Here, LangGraph is technically a proprietary platform, but it was added to our list because it offers an open-source library.

Comparison Table

Here’s a comparison of the 10 best AI agent builders, selected based on their features and capabilities.

Framework Reviews

Ten best AI agent builder platforms, hand-picked by our team.

1. GraphBit
2. LangGraph
3. AutoGen
4. CrewAI
5. Camel
6. ChatDev
7. Pydantic AI
8. Agent Zero
9. Automatic Agents
10. Bee Agent Framework

1. GraphBit

GraphBit is an enterprise‑grade Agentic AI framework built on the Rust core with a Python API wrapper. GraphBit allows you to create high‑performance multi‑agent workflows with a low overhead (CPU/memory).

Key capabilities

• Allows you to design the concurrent and parallel execution of agent tasks.
• Best for workflow orchestration, memory persistence across steps, failure recovery, and integration with services/data.
• Offers built‑in benchmarking and performance optimisation tools for measuring throughput (tasks/min), CPU/memory usage, etc.

Ideal use cases

Great for orchestrating production AI agent workflows, multi-step or branching workflows, and large-scale deployments where many agents must efficiently work in parallel using the resource.

Limitations

• Developers need to have a deeper understanding of agent orchestration and Rust/Python interop.
• A steeper learning curve compared to no‑code/low‑code frameworks.

2. LangGraph

LangGraph is part of the LangChain ecosystem and offers an open-source library for graph‑based workflow modelling.

Key capabilities

• Allows you to model agents, memory, flows, and tasks with its graph (nodes & edges).
• Lets you create long‑running, stateful agents such as persistent memory, multi‑agent orchestration, and hierarchical flows.
• Offers pre‑built components and visual/IDE support

Ideal use cases

Best for building non-linear workflows and coordinating across tasks. Also suitable for developers seeking high flexibility and control of agent architecture.

Limitations

• Not suitable for simple projects
• The learning curve might be steeper than that of template-based agent frameworks.

3. CrewAI

CrewAI is a Python-based framework that allows orchestrating multiple agents and collaboration between them.

Key capabilities

• Lets you design event‑driven task orchestration
• Allows precise low-level control while maintaining high‑level abstractions

Ideal use cases

CrewAI is well-suited to creating multiple specialised agents that coordinate and collaborate, such as a validation agent, an investigative research agent, and a writing summary agent.

Limitations

• May lead to Higher memory/CPU usage
• Not for building simple agents

4 AutoGen

AutoGen, developed by Microsoft Research, is an open-source framework for creating multi‑agent AI applications.

Key capabilities

• Offers event‑driven programming for agent behaviours and multi‑agent workflows
• Allows multi‑agent collaboration

Ideal use cases

Best for building sophisticated workflows that require multi-agent collaboration, particularly suited for research-driven or controlled environments.

Limitations

• Requires developer expertise for mult-agent orchestration
• Not suitable for basic single-agent tasks

5. CAMEL‑AI

CAMEL AI is designed for creating large‑scale agent systems, simulations, data generation, and task automation that support “scaling laws” for agents.

Key capabilities

• Allows multiple agents to interact in simulated environments (“societies”) for research/automation
• Can be scaled to support a wide range of agents, evolving behaviours, environment simulation, and even RL‑style loops
• Allows agents to use tools, retrieve documents, maintain memory, load storage, etc

Ideal use cases

Typically used in research where behaviour analysis, simulation, and emergent multi‑agent interactions are required.

Limitations

• Not suitable for simple agent tasks and geared towards research and large-scale simulation
• Might have higher infrastructure demands
• Sleep learning curve

6. ChatDev

ChatDev allows organisations to create a software development team of agents and interact with each other in a simulation. Each agent can be assigned roles like developer, CEO, tester, etc which can design, code, test, and document software, mimicking how real-world software development teams work together.

Key capabilities

• Offers multiple communication protocols for agent interaction
• Can be used for rapid prototyping, learning, and automating software development pipelines

Idea use cases

ChatDev is particularly used to stimulate software development projects and to train AI for development tasks. It can also be an educational tool for software engineering students.

Limitations

• ChatDev is a specialized framework only for software‑development organizations and not for other industries

7. Pydantic AI

Pydantic AI is a Python agent tool that is designed to help you build production‑grade generative AI applications with fast API support.

Key capabilities

• Agents can be defined with typed inputs/outputs using Pydantic models
• Offers multi-agent support
• Comes with production features like instrumented logging, retries, and deferred tools

Ideal use cases

Pydantic AI is best for developers who want to build production‑grade generative AI applications with Python and Pydantic.

Limitations

• Not the best option for non-code/drag-and-drop workflows
• Not suitable for unconstrained conversational open‑ended agents

8. Agent Zero

Agent Zero allows you to develop AI agents with high transparency, user control, and dynamic behavior.

Key capabilities

• Allows tool execution and code orchestration
• Easily customizable and fully transparent
• Supports multi‑agent cooperation

Ideal use cases

Agent Zero is best for teams that want full control over their projects, including agent internals, tool invocation, code execution, and automation.

Limitations

• Risk and complexity can arise regarding security, deployment, and sandboring.
• Not the best framework for simpler conversational tasks

9. Atomic Agents

Atomic Agents lets you create modular agents, including tools and content providers, that can be merged into pipelines.

Key capabilities

• Offers explicit input/output schemas for agents
• Designed to be lightweight and maintainable

Ideal use cases

Best for developers seeking simple, clear architecture for agent workflows, where each agent has a specific goal. Atomic Agents can be used to create AI pipelines, tool chaining, and micro-agent orchestration.

Limitations

• Not for large agent orchestration

10 Bee Agent Framework

Bee Agent Framework is for creating production‑grade multi-agent systems in Python or TypeScript.

Key capabilities

• Supports cross-languages; Python and TypeScript implementations.
• Comes with production-ready features such as built‑in memory strategies, caching, workflow orchestration, and support for multi‑agent systems.
• Integrates with multiple LLM providers and tools

Ideal use cases

Best for enterprises that want to build scalable, agent-based systems with multi-agent collaboration, observability, and production controls. It’s also ideal for teams working in TypeScript/Node.js environments or Python.

Limitations

• Complexity could arise while managing multiple toolchains and languages

Low-Code / No-Code AI Agent Builders

Not all AI agents require you to code. Some frameworks allow you to build AI agents without coding or with minimal coding by customizing the built-in templates or drag-and-drop interfaces; these are known as low-code/no-code AI agent builders. Low-code / no-code AI agent builders are typically proprietary and best for:

• Enterprise workflow automation
• Rapid deployment
• Prebuilt components

Here’s the expert-selected top 14 no-code/low-code AI agent builders:

1. Vertex AI Builder

2. Beam AI

3. Microsoft Copilot Studio Agent Builder

4. Lyzr Agent Studio

5. Glide

6. Postman AI Agent Builder

7. UiPath Agent Builder

8. Stack AI

9. String

10. Relevance AI

11. Lindy

12. Bricklayer AI

13. Vonage AI Studio

14. Trilex AI

Comparison Table

1. Vertex AI Builder

Vertex AI Builder is a no-code agent builder from Google Cloud that helps you create and deploy machine learning models and AI agents quickly.

Key capabilities

• Offers customizable pre-configured templates
• Integrates with LangChain and Google Cloud

Best use cases

• Customer service automation
• Data extraction and automation
• Internal process automation

Limitations

• API setup could be complex for non-tech users.

2. Beam AI

• Horizontal agent builder

• Supported agent types (compliance, returns, CS, billing, data extraction, order processing)

2. Beam AI

Beam AI is a horizontal agent builder that allows you to create AI agents to automate various operations across multiple industries.

Key capabilities

• Supports various agent types like compliance, returns, CS, billing, data extraction, and order processing.
• Offers prebuilt integrations with existing enterprise systems like CRM and ERP.

Best use cases

• Enterprise workflow automation
• Customer support and billing

Limitations

• Limited customization options.

3. Microsoft Copilot Studio Agent Builder

Microsoft Copilot Studio allows you to automate and create AI-powered assistants that integrate with Microsoft’s suite of business tools.

Key capabilities

• 1,200+ data connectors
• Offers AI-powered insights

Best use cases

• Internal chatbots
• Task automation

Limitations

• Highly dependent on the Microsoft ecosystem

4. Lyzr Agent Studio

Lyzr Agent Studio is a modular agent builder that can be used across industries such as finance, HR, and supply chain.

Key capabilities

• Enables modular design
• Allows rapid prototyping
• Offers Prebuilt use case templates

Best use cases

• Finance automation
• HR process automation
• Customer experience automation
• Supply chain management

Limitations

• Not for enterprise-level fully-scaled production systems.

5. Glide

Glide is a no-code AI agent builder that lets you automate field-based workflows for processes like sales, inspections, work orders, inventory, and CRM.

Key capabilities

• Pre-designed templates
• Mobile and cloud integration

Best use cases

• Field sales automation
• Inventory management
• Work order automation

Limitations

• Not for highly specified workflows

6. Postman AI Agent Builder

Postman AI Agent Builder is built on Postman’s API testing environment, enabling you to create AI agents that automate API workflows.

Key capabilities

• Postman Client
• Collection Runner
• Flows

Best use cases

• API testing and automation
• Prototyping API integrations

Limitations

• Even as a no-code builder, it might not be effortless for non-technical teams.

7. UiPath Agent Builder

UiPath Agent Builder is a low-code builder built within the UiPath automation suite to create AI agents and automate robotic processes within enterprises.

Key capabilities

• RPA integration
• Task automation

Best use cases

• Robotic process automation (RPA)
• Task automation at scale

Limitations

• Not for AI-driven decision-making tasks.

8. Stack AI

Stack AI is a no-code AI agent builder with ready-made templates that let you automate business workflows.

Key capabilities

• Integration with SharePoint & Salesforce
• Cloud + on-prem deployment
• Strong compliance

Best use cases

• Compliance-heavy industries like healthcare, finance, and the legal sector.
• CRM and data automation

Limitations

• May not scale well for massive organizations.

9. String

String is a no-code, drag-and-drop platform that allows you to build and deploy AI agents for enterprise workflow automation.

Key capabilities

• Drag-and-drop interface
• Pre-configured templates for workflow automation
• Integrations with enterprise tools
• Supports enterprise compliance standards

Best use cases

• HR, finance, and customer service automation
• Compliance-driven businesses

Limitations

• Does not offer enough customization options for building complex workflows.

10. Relevance AI

Relevance AI allows non-technical operations teams to automate decision-making processes.

Key capabilities

• Incident management
• Real-time data and alerts.

Best use cases

• Incident response workflow automation, particularly for IT and customer support.

Limitations

• Limited AI use cases.

11. Lindy

Lindy is a no-code platform for automating business tasks by connecting personalized agents to email and Slack.

Key capabilities

• Trigger-based automation
• Personalized agents

Best use cases

• Customer service automation
• HR automation
• Sales automation
• Paperwork automation

Limitations

• Suitable only for communication-based workflows

12. Bricklayer AI

Bricklayer AI is a SOC-focused autonomous agent builder typically used to automate security operations.

Key capabilities

• SOAR-like workflows
• Enhances triage and incident response
• Threat intelligence

Best use cases

• Security operations
• Threat monitoring

Limitations

• Not suited for general business process automation.

13. Vonage AI Studio

Vonage AI Studio is a visual flow builder that allows you to create chatbots and voice bots to automate customer service interactions.

Key capabilities

• Visual flow builder with a Drag-and-drop interface

• No coding required

Best use cases

• Customer service chatbots
• Voice bots

Limitations

• Only suited for basic conversational bots

14. Trilex AI

Trilex AI is a no-code builder that allows you to create self-aware multi-agent teams that collaborate autonomously.

Key capabilities

• Multi-agent systems
• No-code interface

Best use cases

• Autonomous systems development
• Organizational automation

Limitations

• Not yet enterprise-ready

Why Use AI Agent Builders?

Building AI agents comes with a variety of challenges. When multiple agents work collaboratively, they can produce multi-step hallucinations, which can compromise their effectiveness in complex workflows. Moreover, integrating with external systems, orchestrating synchronization, and managing state across processes further increases complexity.

Here are a few solutions agent builders provide to help mitigate these challenges.

• Frameworks - A structured environment equipped with standardized components, libraries, and methodologies for developing AI agents.

• Data templates- These are pre-defined structures that standardize the way data is formatted and handled, particularly helpful for multi-step agent collaboration. The data templates ensure data remains consistent, reducing the risk of hallucinations and other errors.

• Data stores (SQL/NoSQL) - Stores SQL (relational) and NoSQL (non-relational), enabling AI agents to store and retrieve data reliably.

• Orchestration protocols - These protocols ensure each task within a larger system is executed in the correct order.

• State management components - Effective state management ensures AI agents maintain context throughout their processes, reducing hallucinations and incorrect decisions. This feature is particularly required for multi-step tasks or long-running processes.

Practical Tutorial: Building a GraphBit

GraphBit is one of the best platforms for building AI agents. If you’re wondering how to create your own AI agents, you can go through this tutorial for comprehensive guidelines.

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Conclusion

In the enterprise space, the importance of agent builders will grow by the day due to their scalability and automation capabilities. While some AI agent builders are fully customizable and codable, some builders are no-code/low-code platforms that even non-tech people can use.

For fast deployment and ease of use, low-code platforms are better suited. On the other hand, open-source frameworks are best for accessing high-level customization options and advanced functionalities.