10 Best AI Agent Builders in 2025 — Reviewed & Compared

If you're building with AI agents in 2025, consider Lindy to build AI agents if you’re a small business, Relevance AI for fast internal automations, and LangChain for developers. These platforms stand out for their ability to effectively combine memory, reasoning, and app integrations across workflows.

In this article, we’ll cover:

• Top 10 AI agent builders 
• Side-by-side comparisons of these tools
• What to look for in a builder 
• How we compiled this list
• What is an AI agent builder?
• Difference between workflow automation tools and AI agent builders

Let’s first look at the list. 

The 10 best AI agent builders: TL;DR

Each of these tools has a different take on how agents should be built, deployed, and used in real workflows. Here’s a quick overview of the top AI agent builders: 

1. Lindy – Best AI agent builder for small and medium-sized businesses
2. n8n – Best for open-source automation with LLM support
3. Relevance AI – Best for no-code business ops automation
4. SmythOS – Best for enterprise-grade orchestration
5. AgentHub – Best for plug-and-play business agent templates
6. LangChain – Best for dev-first agent frameworks
7. AutoGPT – Best for open-ended, experimental autonomous agents
8. CrewAI – Best for orchestrating collaborative multi-agent systems
9. Superagent – Best for self-hosted agents with prebuilt tooling
10. Flowise – Best drag-and-drop generative AI app builder

Now, let’s take a closer look at each of these tools — what they offer, who they’re for, and where they shine.

1. Lindy – Best AI agent builder for small and medium-sized businesses

Lindy is a no-code platform that lets you build customizable AI agents for business workflows — from outbound and lead qualification to inbox triage, follow-ups, and CRM updates. Agents are context-aware and can work across tools like Gmail, Slack, and Salesforce.

If you're looking to create an AI agent that can handle workflows across your stack, with contextual memory and handoff built in, Lindy fits right in.

Features

• Drag-and-drop visual workflow builder to create AI agents
• Ready-to-use, customizable templates including SDR, email follow-up, and sales coach
• Knowledge base, file handling, and context awareness
• 7,000+ integrations across 1,600 apps via Pipedream partnership, APIs, and native connectors
• Native integrations with hundreds of everyday tools like Gmail, Slack, Salesforce, Notion, and more
• Handles inbound and outbound calls using realistic AI voices — works best with GPT-4 or GPT-4o mini for a natural tone
• SOC 2 and HIPAA-compliant for regulated teams

Lindy’s AI workflow builder gives you the flexibility to combine app actions, trigger conditions, and agent logic — all in a single flow.

Pros

• Designed for operations, sales, and marketing teams
• Context-aware agents that integrate and work across everyday business tools
• Fast onboarding with ready-made templates
• Handles both internal and client-facing workflows well

Cons

• Not built for advanced developer-level use cases
• Might need some time to set up complex workflows
• Can’t self-host, cloud-only product

Pricing

• The free plan includes 400 credits
• Paid starts at $49.99/month for 5,000 credits

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2. n8n – Best for open-source automation with LLM support

n8n is a low-code workflow automation tool that’s popular with technical users for its flexibility and open-source approach. While it’s not built specifically for AI agents, it now includes an AI Agent node, which lets users integrate language models into automated workflows. 

With custom plugins, memory nodes, and external tool connections, you can shape n8n into a capable AI agent system if you're comfortable with technical setup.

Features

• Open-source, self-hostable platform
• 1000+ app integrations including GitHub, Notion, Google Sheets
• AI nodes that support OpenAI, HuggingFace, and LangChain
• Built-in support for webhooks, triggers, and conditional logic
• Visual flow editor with JSON/code fallback for advanced control

Pros

• Developer-friendly and customizable
• Large ecosystem of prebuilt nodes and templates
• Can run fully on your infrastructure
• Good for teams already familiar with automation tools

Cons

• Workflow design can get complex fast
• Limited UI polish compared to commercial AI platforms

Pricing

• Starter plan starts at $24/month, billed monthly
• Usage-based plans scale with executions

If you're looking for an AI app builder that’s open-source, customizable, and works well with APIs, n8n is a strong option, as long as you're comfortable working with webhooks and LLM nodes.

3. Relevance AI – Best for no-code business ops automation

Relevance AI lets you build agents and workflows visually — no code or prompt engineering is required. It’s aimed at business teams looking to automate internal tasks like support ticket routing, lead tagging, email classification, and other repetitive ops work.

Relevance AI is a good option if you’re looking to create an AI agent for back-office work without working with different APIs or model calls. For teams new to AI agents, the learning curve is low — and the value shows up fast.

Features

• Visual AI workflow builder with drag-and-drop interface
• Built-in support for memory, variables, and vector databases
• Integrates with Slack, Google Workspace, HubSpot, and Notion
• SOC 2 and GDPR-compliant
• Use agents to tag, route, generate summaries, and more

Pros

• Intuitive UI for non-technical users
• Great for business ops teams
• Agent templates cover many common internal workflows
• Flexible pricing tiers, including a free plan

Cons

• Agents follow predefined paths and are less autonomous than LLM-native tools
• Not ideal for multi-step goal planning or reasoning-heavy workflows

Pricing

• Free plan with limited usage
• Paid plans start at $19/month
• Credit-based model for execution volume

4. SmythOS – Best for enterprise-grade orchestration

SmythOS is built for teams who want complete visibility and control over how their AI agents behave, especially across multi-step workflows. It combines a visual builder with powerful orchestration features, making it a strong fit for internal ops, customer workflows, or productized services where structure matters.

SmythOS is great for building reliable, repeatable flows where structure and auditability matter. If you’re learning how to build an AI agent that connects tools, pulls data, and takes actions, SmythOS gives you the canvas to do it.

Features

• Drag-and-drop workflow builder with logic blocks
• Supports branching, loops, retries, and API calls
• Can scrape web content, read files, send notifications, and more
• Prebuilt templates for outbound, hiring, and knowledge tasks
• Deploy agents via HTTP or Slack
• Built-in monitoring and execution logs

Pros

• Flexible for both business and technical users
• Visual logic helps map out complex workflows
• Useful templates make it faster to deploy agents
• Good documentation and community support

Cons

• Learning curve if you're new to agent orchestration
• Not a plug-and-play solution — some setup required
• Less tone customization or memory logic than LLM-native tools

Pricing

• A free tier with $5 in credits
• Paid starts at $39/seat/month
• Pricing scales by number of API calls and features

5. AgentHub – Best for plug-and-play business agent templates

AgentHub offers a library of prebuilt AI agents that you can customize and deploy with minimal setup. From cold outreach to resume screening and admin tasks, the emphasis is on getting usable AI into your business without needing to start from scratch.

If you’re looking for AI builders that prioritize plug-and-play simplicity over customization, AgentHub is worth considering.

AgentHub lowers the barrier for teams that want fast deployment. If your use cases are common workflows like outreach or admin tasks, you can get a usable agent live in minutes without any complex setup.

Features

• Agent marketplace with templates for hiring, outbound, admin, and more
• Sandbox environment to test and tweak agents before going live
• Built-in integrations with email, CRM, and databases
• Simple UI to map out logic and outputs
• Drag-and-drop customization of agent inputs, goals, and actions

Pros

• Fastest path to a working agent
• Good for teams without technical resources
• Covers common SMB use cases well

Cons

• Templates may not fit niche use cases
• Limited flexibility for complex logic or reasoning tasks
• Agents don’t offer deep memory or tone customization

Pricing

• Paid plans start at $295/month
• No free plan currently listed
• $295 one-time setup fee

6. LangChain – Best for dev-first agent frameworks with custom control

LangChain is a developer framework for building LLM-powered agents from scratch. It’s not a platform — it’s a Python and JavaScript library that gives you fine-grained control over how your agent thinks, plans, remembers, and interacts with tools.

If you’re building custom AI infrastructure and need more control than what no-code AI builders offer, LangChain gives you the flexibility to architect it your way.

LangChain is powerful, but not plug-and-play. It’s ideal if you’re building inference workflows, multi-agent systems, or gen AI app builders from scratch, with complete control over every layer.

Features

• Modular framework with support for chains, tools, memory, and agents
• Works with OpenAI, Anthropic, Cohere, and more
• Pluggable memory –– buffer, summary, vector DB
• Integrates with LangServe, LangGraph, and Retrieval tools
• An active open-source ecosystem with tons of extensions

Pros

• Fully customizable
• Community support and growing plugin ecosystem
• Great for advanced use cases –– research, summarization, autonomous tasks

Cons

• Steep learning curve
• Requires Python or JavaScript knowledge
• No built-in UI — setup and testing are manual

Pricing

• Free and open source
• Requires your infrastructure and LLM API keys

7. AutoGPT – Best for open-ended, experimental autonomous agents

AutoGPT is an open-source Python project that lets you create an agent and give it a goal. The agent then breaks that goal into subtasks, chooses tools, and attempts to complete it with minimal human input.

However, it's not built for business use. It’s a research playground, ideal if you're exploring how to build an AI agent that can plan and act recursively. 

Features

• Recursive goal-to-task planning
• Built-in memory and self-reflection loops
• Plugin ecosystem for browser, file, and API tools
• Command line interface (CLI) based setup
• Runs locally with Python and OpenAI API)

Pros

• Great for experimentation and learning
• Can simulate autonomous reasoning
• Massive open-source community
• Highly customizable for devs

Cons

• Not for real-world workflows
• High risk of hallucinations or infinite loops
• Needs setup, hosting, and monitoring
• No UI or business integrations

Pricing

• Free and open-source
• Must pay for LLM API usage –– OpenAI, Anthropic, or any other model
• Explore on GitHub

8. CrewAI – Best for orchestrating collaborative multi-agent systems

CrewAI is an open-source framework that lets you define “crews” of agents, each with a specific role and responsibility. Instead of a single agent doing everything, you can assign tasks across a team — researcher, writer, planner, executor — and have them collaborate toward a goal.

It’s a useful tool if you're experimenting with role-based delegation or building a custom AI agent builder for more structured multi-step projects.

CrewAI gives you the platform to model collaboration between autonomous agents if you're looking to create an AI agent team. It’s more abstract than most platforms, but powerful once you get the hang of it.

Features

• Define multiple agents with roles, goals, and tool access
• Assign tasks and dependencies between agents
• Use with OpenAI, LangChain, or custom toolkits
• Supports memory sharing and communication between agents
• Python-based, works locally or on the cloud

Pros

• Strong framework for multi-agent thinking
• Makes complex workflows easier to design
• Lightweight and fast to prototype
• Open-source with active development

Cons

• Requires programming knowledge
• Not designed for non-technical users
• Still evolving, documentation and stability may vary

Pricing

• Free, open-source under MIT license
• Requires external APIs –– OpenAI, Anthropic, or any other LLM model 
• Paid plans start at $99/month for features like no-code builder and advanced monitoring

9. Superagent – Best for self-hosted agents with prebuilt tooling

Superagent is a platform that lets you host and deploy AI agents using a mix of SDKs, APIs, and a hosted dashboard. It bridges the gap between developer-only frameworks like LangChain and fully managed tools by offering prebuilt integrations and cloud deployment options.

If you’re building a custom tool or need to run agents on your own infrastructure, Superagent gives you a flexible middle ground. It’s more structured than a DIY stack, but without giving up control.

There isn’t much information on the home page or on the blogs, so we recommend you check it out on its GitHub page.

Features

• Agent creation via API, dashboard, or SDK
• Prebuilt integrations with OpenAI, Pinecone, Supabase, etc.
• Offers storage, logging, scheduling, and memory out of the box
• Works with LangChain, LlamaIndex, and RAG pipelines
• Hosting options –– cloud-hosted or self-hosted 

Pros

• Developer-first, but less heavy than building from scratch
• Offers observability and monitoring
• Good fit for shipping internal AI tools
• Fast to deploy if you're already using cloud infrastructure

Cons

• Requires technical setup and configuration
• Needs you to join a waitlist to try it
• Strictly for technical teams

Pricing

• Free and open-source
• Paid tiers not listed publicly

10. Flowise – Best drag-and-drop agent builder using LLM chains

Flowise is an open-source AI agent framework that helps you prototype custom agents quickly. It offers a visual builder — a node-based interface where you can connect prompts, tools, APIs, and memory modules without writing code. It’s open-source, self-hostable, and a favorite among early-stage builders and technical teams.

Flowise isn’t built to handle full business workflows like lead outreach or enrichment. But if you want full control and fast experimentation, it’s one of the most flexible ways to build an AI agent visually.

Features

• Drag-and-drop interface for chaining tools and memory
• Built on LangChain — supports OpenAI, Pinecone, Weaviate, and more
• Connects to APIs, webhooks, and external services
• Can be deployed locally or in the cloud
• Export workflows as APIs or embed them into other tools

Pros

• Fast and easy to prototype complex agents
• Great for technical founders or AI teams
• Self-hostable and open-source
• Can be extended with custom nodes

Cons

• UI can get messy with larger workflows
• Not ideal for business users or teams without technical resources
• Requires LangChain knowledge to fully benefit from the advanced features

Pricing

• Starts free with 100 predictions/month
• Paid versions start from $35/month

If you're searching for an AI builder that's visual-first and LLM-native, Flowise is worth a look.

Next, let’s put these tools side-by-side for a quick-glance comparison.

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Top AI agent builders: at a glance

Whether you’re building internal ops agents or experimenting with multi-agent systems, this table gives you a quick feel for where each one fits. Here’s how each tool compares — from pricing and setup style to what they’re best suited for: 

Now that we’ve covered these tools, let’s see how and why they ended up on this list.

How we tested the best AI agent builders

Only a few AI agents deliver the value they promise across workflows. To cut through the noise, we evaluated each platform using the same four questions most operators or builders would ask. What makes a great AI agent builder in 2025: 

• Can it handle goal-based, multi-step tasks without needing a human in the loop for every step?
• Does it support memory, context, and tool usage — not just one-off prompts?
• Can it maintain a human-level tone and consistency, especially in client-facing roles?
• Will it scale without breaking, or needing to be rewritten every time the use case changes?

What we looked at while testing

We look at how well the tool nailed the basics. Here’s the criteria we used to evaluate each tool:

• Ease of setup: How long does it take to build a usable agent? Are there templates? Support docs? A UI you’d want to use?
• Reliability across tasks: Can the agent handle edge cases without failing silently or hallucinating responses? Does it recover gracefully?
• Integration with real tools: Does it play well with CRMs, inboxes, calendars, and other systems where the real work happens?
• Customization (with or without code): Can I edit logic, reroute steps, or add fallback conditions without rewriting the whole thing?

A tool like Lindy with a visual AI workflow builder makes it easy to deploy agents that work across apps with drag-and-drop logic. On the other end, something like LangChain gives you the canvas to build it, but you need to have the knowledge and expertise to do it yourself.

We covered 10 of the best AI agent builders, but never covered what these tools are, how they benefit businesses, and where they’re used. So, let’s explore that next.

What is an AI agent builder?

An AI agent builder is a tool or framework for creating software agents. These agents can reason, remember, and take action — without human oversight or needing to program every step manually.

Agents built on these platforms understand goals, interpret context, and use tools (like email, CRMs, or APIs) to get work done. The best ones are consistent, customizable, and capable of adapting to new inputs.

If you’ve ever wished you had an assistant that could just handle the tasks like replying to leads, scheduling calls, triaging emails, that's what AI agents are built to do.

They’ll work on the workflow you configure, use available tools, ask follow-up questions if needed, and loop you in when the job's done or if they’re stuck. The more they work, the better you understand their workflow. That helps you fine-tune it.

Common use cases

AI agents are already being used across ops, sales, and support teams. They can help you with tasks like:

• Lead routing and meeting scheduling
• Inbox triage and drafting replies
• CRM updates and data entry
• Internal reporting and summaries
• Document parsing and info extraction
• Research and contextual recommendations

Some platforms like Lindy or Relevance AI combine memory, integrations, and business context out of the box. Others — like LangChain or AutoGPT — give you more flexibility but require custom development.

So, how are these AI agent builders different from automation tools? We answer that next.

AI agent builders vs workflow automation tools

Workflow automation tools are built around fixed triggers and actions. AI agent builders, on the other hand, create goal-driven workers. 

With rule-based automation, you set up a rule –– when a form is submitted, add it to a sheet — and the system runs that rule over and over. It’s reliable, but not flexible. 

But with AI agent builders, you create an AI agent and configure its workflow in advance based on the end goal. You give the agent a goal, and using memory, logic, and tool integrations, it’ll complete the task.

A lot of people confuse AI agents with traditional automation. Both save time — but they function differently and suit different use cases.

Comparing AI agent builders with workflow automation tools

Workflow automation tools are limited in capabilities. Here’s how:

If your goal is to automate something simple, like syncing form data or posting a Slack message, workflow tools are still great. 

But if you're trying to build an AI agent that can manage outreach, route leads, or handle client conversations, workflow automation tools won’t help. You’ll need AI agent building platforms. Lindy combines workflow automation with AI agents and gives you the best of both worlds.

For example, Lindy’s AI sales assistant can personalize outreach, book meetings, and log updates automatically — things that would take a lot of manual effort or complex logic in a traditional workflow tool.

Frequently asked questions

Can I build an AI agent without code?

Yes, you can. Platforms like Lindy, Relevance AI, and SmythOS let you create an AI agent using visual workflows and templates. These tools are built for non-technical people, not engineers.

Which platform is best for internal workflow agents?

Lindy and Relevance AI both stand out if you're automating inboxes, routing tasks, or syncing data between tools. Lindy offers strong context handling, while Relevance shines in data classification workflows.

How are AI agents different from chatbots or Zaps?

Chatbots respond to messages using scripts. Zaps follow strict triggers and actions. On the other hand, agents are task-focused. They understand goals, hold memory, and interact across multiple systems.

What’s the easiest AI agent tool for teams?

Lindy is the fastest to deploy thanks to its prebuilt templates, especially with its lead outreacher template and human-like agent tone for voice calling.

Can I self-host an AI agent builder?

Yes, you can self-host an AI agent builder if the tool is open-source, like Flowise, LangChain, AutoGPT, Superagent, and CrewAI. Just know that you must have strong technical skills to set them up and pay for their maintenance.

Is n8n good for building autonomous agents?

Out of the box, n8n isn’t an autonomous AI agent builder. It’s great for building automated flows, especially with LLM plugins but needs LangChain or similar add-ons for agent logic.

How much does it cost to run an AI agent?

Hosted tools like Lindy start free, with paid tiers from $49.99/month. Open-source frameworks are free, but you'll pay for API usage and infrastructure.

Which builder is best for customer support workflows?

Lindy is built for this with agents that can triage emails and pull in context. It can draft tone-aware replies using prompts or templates — and if needed, you can review them before they go out. Plus, it integrates directly with your inbox and CRM.

What’s the difference between LangChain and Lindy?

LangChain is a framework and is great if you want to build an AI agent from scratch. Lindy is a platform and it works better if you want agents working in your business right away, no developer time required.

Try Lindy, your versatile AI agent builder

If you’re looking for an easy-to-use AI solution that provides automations around emails, meetings, and sales, go with Lindy. 

Out of all the AI agent builders, here’s why we believe Lindy is your best bet:

• Simple no-code interface: You won’t need coding, programming, or technical skills to create your automations with Lindy — it offers a drag-and-drop visual workflow builder. 

• AI agents customized to your needs: You can make versatile AI agents that understand plain English and accelerate your productivity in many ways. For instance, create an assistant that bolsters your sales funnel by finding leads from websites and business intelligence sources like People Data Labs. Create another agent that sends out emails to each lead and schedules meetings with members of your sales team. 

• Affordability: Build your first few automations with Lindy’s free version and get up to 400 tasks. With the Pro plan, you can automate up to 5,000 tasks, which offers much more value than Lindy’s competitors.  

Try Lindy today for free.