The best AI agent builders are changing how businesses solve complex challenges in 2025. I've seen this technology grow rapidly. The AI agent market will reach $47 billion by 2030.
Your business might be falling behind if you still handle repetitive tasks manually instead of using automation. AI agents have grown beyond simple chatbots. They now offer live insights and help make smarter decisions. Tools like Moveworks Creator Studio and Microsoft Copilot Studio make AI agent creation available to anyone, even without extensive coding skills.
We tested many agent platforms to find the ones that get results. This piece cuts through the hype. You'll discover 10 AI agent builders that work well in 2025. I'll compare how they integrate with other tools, scale up operations, and perform in real life.
What is the best AI app builder?
Choosing the right AI agent builder in 2025 starts with understanding these platforms' core functions. AI agent frameworks are the foundations of creating autonomous systems that notice, reason, and act without needing explicit coding for every function. They make development faster by hiding complexities. This turns months of work into just days of prototyping.
Your choice of an AI agent builder should depend on several key factors. The platforms need strong integrations with LLMs and APIs. Your tool should blend naturally with your preferred language models (ChatGPT, Claude, Gemini) and existing software stack.
The cost structure needs careful attention. Most platforms use a credit-based system where you pay for each API call. Single calls might look cheap, but costs add up quickly during development and operation.
Each platform handles autonomous task execution differently. The best tools let complex workflows run on their own without constant human oversight. Speed makes a big difference too. Some platforms run tasks faster than others by optimizing API calls.
KumoHQ
KumoHQ stands out from other AI agent builders with its focus on relational data and predictive capabilities. The company, 2 years old, was started by PhDs from Pinterest, Airbnb, LinkedIn, and Stanford. Their innovative approach to AI agent development has quickly gained market attention.
KumoHQ key features
The KumoHQ platform builds AI agents directly on relational data without complex coding or feature engineering. Your relational data automatically converts into graph structures as the system applies Graph Neural Networks (GNNs) to create predictive models. Their flagship product, the Relational Foundation Model (KumoHQ), helps users generate accurate predictions for tasks of all types straight from relational databases.
The system handles up to 50 billion data points and excels at managing complex data relationships. KumoHQ transforms your relational data into a graph structure and uses graph-based deep learning to build predictive and embedding models once connected to your data source.
Users can deploy KumoHQ through SaaS, Snowflake Native App, or Databricks Lakehouse App. The platform offers a user-friendly web console and a Python SDK that integrates smoothly into production workflows.
KumoHQ pros and cons
Pros:
AI models come to life in minutes using SQL-like Predictive Querying Language
Accuracy jumps 30-50% higher than traditional approaches
Value delivery speeds up 20x compared to conventional methods
ML experience or data preparation needs are minimal
Results make more sense than many hand-engineered models
Neural architecture search finds the best configurations automatically
Cons:
The platform is newer than its competitors
The focus stays on predictive analytics instead of general-purpose agents
Public pricing details are hard to find
KumoHQ pricing
The company keeps their pricing details private. Like many enterprise AI platforms, they customize prices based on how much you use and what you need. You'll need to contact their sales team to learn more about specific costs.
KumoHQ best use cases
The platform shines when you need predictive insights from relational data, especially for:
Trend recommendations and individual-specific shopping experiences
Customer churn prediction and prevention
Fraud detection and transaction monitoring
Credit default risk assessment
Healthcare readmission prediction
Supply chain optimization
Relay.app
Relay.app stands out from other AI agent builders with its innovative human-in-the-loop capability. This unique feature lets companies add manual steps that bring human judgment, context, and their point of view into automated workflows.
Relay.app key features
The platform comes with a user-friendly interface that makes AI automations available to anyone with simple computer skills. Users get access to built-in AI actions that handle common tasks like summarizing text and pulling information from emails, meeting notes, and websites.
Every plan comes with reasonable usage of GPT-3.5 and GPT-4. Free plans get plenty of credits while paid plans offer 10x more. Beyond OpenAI models, you can use multiple AI providers like Google's Gemini, Anthropic's Claude, and Groq's LLaMA-based models.
The platform shines with its deep integrations that connect over 100 apps. You'll find continuous connection between Gmail, Notion, HubSpot, and many other tools. The workflow system lets you select paths, merge them, use iterators, and set up time-based and rule-based waiting steps.
Relay.app pros and cons
Pros:
More user-friendly interface than competitors like Zapier
Quick and helpful customer support team
Human-in-the-loop features help you retain control over automation
Simple, clean UI makes creating workflows easier
AI credits come built-in without needing API keys
Cons:
Integration options are growing but lag behind older competitors
Pricing could be more straightforward
Original setup might be tricky for some users
Relay.app pricing
Relay.app's pricing plans include:
Free Tier: 200 automation steps and 500 AI credits each month
Professional: USD 19.00/month with 1,500 steps and 1,000 AI credits monthly
Team: USD 69.00/month giving you 5,000 steps and 2,000 AI credits monthly
Enterprise: Custom pricing for bigger organizations
Relay.app best use cases
Relay.app works best for teams who need human oversight in their automated processes. The platform fits perfectly for:
Agencies, service providers, and freelancers who handle complex workflows
Teams dealing with lots of leads, support tickets, or customer data
Companies that want to use AI in their operations while keeping human judgment
Organizations automating Gmail, Notion, HubSpot, and other popular apps
Relay.app really shines in situations where full automation isn't the goal—at times when human judgment matters most in workflow decisions.
Stack AI
Stack AI stands out as a powerful no-code platform that helps teams create custom AI agents and workflows through an accessible interface. The platform lets non-technical teams build sophisticated AI solutions with drag-and-drop functionality, unlike competitors that need technical expertise.
Stack AI key features
The platform's visual builder shines with its no-code approach. Users can create AI agents by setting instructions, picking their LLM, and connecting knowledge bases. AI workflows appear on a visual canvas where teams can drop nodes to connect data and LLMs for complex task automation.
Stack AI works with all prominent providers like OpenAI, Anthropic, Google, Meta, and Mistral. The platform connects to many data sources including Amazon S3, Microsoft Sharepoint, Google Drive, Notion, and HubSpot. Your data stays in sync without manual updates.
Users can add tools to their agents to boost functionality. These tools enable custom knowledge base searches, pattern extraction with Regex, and API calls to external systems. Publishing an agent creates a shareable link right away. The platform shows live analytics with total runs, users, errors, and conversation history.
Stack AI pros and cons
Pros:
No-code environment available to non-technical users
Works with multiple LLM providers for flexibility
Strong security with SOC2, HIPAA, and GDPR compliance
Large template library for common use cases
Token costs built into subscription pricing
Cons:
Complex workflows might be challenging to scale
Advanced features need time to master
Some highly specialized functions might be limited
Stack AI pricing
Stack AI comes with several pricing options:
Free: 500 runs monthly, 2 projects, 1 seat, community support
Starter: USD 199.00 monthly with 5,000 runs, 3 projects, all data loaders
Team: USD 899.00 monthly with 50,000 runs, 30 projects, 5 team members
Enterprise: Custom pricing with unlimited runs/projects, dedicated infrastructure, on-premise deployment, access control, SSO, and compliance features
Stack AI best use cases
Non-technical teams can create AI-powered solutions independently with Stack AI. The platform works best for:
Creating automated assistants and chatbots for proposals
Building RFP response agents and contract analyzers
Analyzing complex reports
Setting up AI-powered knowledge bases for decisions
Streamlining customer support processes
Users say Stack AI brought "remarkable productivity gains" and proved to be "a game-changer" for their operations. The platform saved "thousands of hours" in customer support and cut down due diligence time significantly.
Stack AI combines versatility and power with its no-code approach. The platform ranks among the most comprehensive solutions in the AI agent category. Organizations looking to spread AI development across departments will find it especially useful.
Voiceflow
Voiceflow is a 5-year old no-code platform that builds conversational AI agents for channels of all types. The platform started with voice applications and grew into a detailed solution. Teams can now create sophisticated chatbots and AI assistants without technical expertise.
Voiceflow key features
The platform's core strength lies in its visual flow builder. Teams can design conversation flows through an easy-to-use drag-and-drop interface. The platform supports multiple LLM integrations such as GPT-4, Claude, Gemini, Llama, and bring-your-own LLM options.
Teams can boost their AI agents by uploading documents, articles, or URLs. This powers AI responses through the knowledge base training feature. The platform lights up blocks during conversation simulations, which makes debugging simple.
The platform shines in team settings with its multi-agent workspace support. Teams can build and manage several bots per project. Developers can use the Dialog API to deploy across channels through custom connectors.
Voiceflow pros and cons
Pros:
Easy-to-use visual editor with no coding needed
Multi-model support with bring-your-own LLM options
Powerful collaboration tools for teams
Flexible knowledge base setup with context control
Component system that reuses elements across agents
Cons:
Higher starting price at USD 60.00/editor/month
Missing native live chat or social channel support
Basic analytics for performance tracking
Code requirements for deployment to real-life channels
Voiceflow pricing
The platform uses a per-editor pricing model instead of usage-based billing:
Free: 2 agents, 100K AI tokens, USD 0.00
Pro: 20 agents, 10M AI tokens, USD 60.00/editor/month
Team: Unlimited agents, 30M AI tokens, USD 125.00/editor/month
Enterprise: Unlimited agents, custom tokens, custom pricing
Voiceflow best use cases
Voiceflow excels at creating conversational AI for websites and telephony applications. The platform works best for:
Customer service automation (one company cut ticket costs from USD 31.00 to USD 2.00)
AI agents that combine smoothly with knowledge bases and CRM systems
Creating individual-specific experiences with tailored offers
Automating L1 and L2 support tickets (Trilogy automated 60% of their support tickets)
Cutting manual workload (Roam Auto saved 30 hours per week)
Startups and enterprise teams can build, test, launch, and manage conversational AI at scale without extensive technical resources.
OpenAI Operator
OpenAI launched Operator in January 2025, marking its entry into the AI agent market. This browser-based tool works autonomously and stands as one of the most advanced AI agents you can get commercially, though it has some limitations.
OpenAI Operator key features
The Computer-Using Agent (CUA) model powers Operator, built on GPT-4o's vision capabilities and reinforcement learning. The agent views everything through screenshots and interacts with graphical user interfaces. It types, clicks, and scrolls in a remote browser on OpenAI's servers.
The platform comes with Watch mode to monitor sensitive operations and Takeover mode to handle login credentials or payment information. Users can save their commonly used prompts on the homepage, which works great for tasks they do often, like restocking groceries.
OpenAI Operator pros and cons
Pros:
No API integrations needed to guide through websites
Confirms before completing important actions
Runs multiple tasks in parallel
Offers clear data management options
Shows strong results on browser-based measurements
Cons:
Response time lags on certain tasks
Available only to Pro subscribers in the US
Doesn't deal very well with complex interfaces like calendars
Cannot access certain websites including Reddit
Remains in research preview phase with growing capabilities
OpenAI Operator pricing
ChatGPT Pro subscribers can access Operator at USD 200.00 per month. OpenAI plans to make these features accessible to Plus, Team, and Enterprise users by integrating them directly into ChatGPT.
OpenAI Operator best use cases
Operator shines at handling repetitive browser-based tasks. The agent works together with companies like DoorDash, Instacart, OpenTable, Priceline, StubHub, and Thumbtack.
The tool helps with online shopping, travel bookings, form completion, and setting priorities for reservations. OpenAI also learns about public sector uses by working with organizations like the City of Stockton to make city service enrollment easier.
Operator takes a big step toward fully autonomous AI agents that can traverse the web on their own, even with its current limitations.
Devin AI
Cognition's Devin AI has emerged as a revolutionary autonomous coding assistant that created waves in the developer community. This platform, known as "the world's first AI software engineer," wants to make development processes smoother through advanced automation.
Devin AI key features
The cloud-based IDE environment in Devin 2.0 lets users run multiple instances at once and handle various tasks side by side. Interactive Planning analyzes codebases quickly. It finds relevant files and suggests development plans in seconds. Devin Search helps developers explore their codebases by answering specific questions with detailed responses that point to relevant code snippets. The Devin Wiki indexes repositories every few hours automatically. It creates complete documentation with architecture diagrams and source links.
Devin AI pros and cons
Pros:
Cuts engineering hours by 12x for migration tasks
Saves 20 times more money compared to human engineers
Finishes 83% more junior-level development tasks per Agent Compute Unit than its earlier version
Builds self-improving scripts for tasks that repeat
Makes fewer errors by taking a consistent approach to migrations
Cons:
Only finished 3 out of 20 tasks in one evaluation
Creates complex code with unnecessary abstractions sometimes
Shows uneven performance, especially with complex interfaces
Pay-as-you-go costs can add up with heavy usage
Devin AI pricing
The platform started at USD 500.00 monthly. Now it has a starter option at USD 20.00 that moves to pay-as-you-go. Each "Agent Compute Unit" (ACU) costs USD 2.25. You get about 15 minutes of active work per ACU, so your first USD 20.00 buys roughly 2.25 hours of development help.
Devin AI best use cases
Devin shines at migration and large refactoring projects where it spots repetitive patterns. The platform works great as a coding co-pilot. Developers can tackle complex problems while Devin handles routine coding tasks. Companies working on large-scale migrations find Devin valuable because it cuts down engineering hours in enterprise settings.
AirOps
AirOps launched in 2021 as a detailed platform that helps SEO and content teams combine human expertise with AI automation. The platform stands out among best AI agent builders because it focuses on propelling organic development through adaptable content operations.
AirOps key features
AirOps has a user-friendly Grid Workflow system that lets users build automations through a drag-and-drop interface to scale complex processes precisely. The platform combines over 40 AI models with custom data and human oversight steps. Its AI Copilot helps turn ideas into working workflows without coding. The system also works smoothly with popular tools like Webflow, Shopify, and Semrush, among data providers like DataForSEO and People Data Labs.
AirOps pros and cons
Pros:
Saves time and effort when building and deploying AI applications
The drag-and-drop interface makes it available to non-technical users
Case studies show up to 24x increase in organic traffic
Brand Kit feature will give a consistent voice and tone to AI content
Human review options help maintain accuracy and quality
Cons:
New users face a learning curve
The pricing structure needs more clarity
Users need time to discover the platform's full potential
AirOps pricing
AirOps offers different pricing tiers based on task volume:
Solo (Free): Has 1,000 tasks/month, 5 Knowledge Base Sources, simple templates
Solo (Paid): $199/month for 10,000 credits, 3 users, analytics
Scale: Custom pricing with advanced features like Semrush SEO Data and Getty Images Library
Agency: Custom pricing with multi-account CMS integrations and workspace cloning
AirOps best use cases
Content-focused teams that want to automate repetitive tasks will find AirOps valuable. The platform serves:
Product teams that launch AI features, content creators who need high-quality material at scale, and e-commerce businesses that automate product descriptions. Organizations like Deepgram have seen big improvements, with one reporting a 24-fold increase in organic traffic after using the platform.
Zep
Zep stands out among best ai agent builders by focusing on boosting existing AI systems instead of building new ones from scratch. This open-source platform gives AI agents long-term memory through an innovative temporal knowledge graph.
Zep key features
The platform's temporal knowledge graph (Graphiti) combines chat messages and business data into a unified memory system. AI agents can remember important details without loading entire chat histories in prompts. The system quickly recognizes user intentions, builds semantic pathways, and responds within milliseconds.
Zep extracts emails, phone numbers, and other significant data while categorizing intent and recognizing emotions from conversations. Recent tests show Zep achieves up to 18.5% higher accuracy and runs 90% faster than tools like MemGPT.
Zep pros and cons
Pros:
Builds a knowledge graph that learns from every interaction
Updates facts automatically and tracks changes over time
Cuts down token usage and costs for memory recall
Maintains SOC 2 Type II certification with CCPA and GDPR compliance options
Supports Python, TypeScript, and Go with full open-source access
Cons:
Works best with existing agents rather than new builds
Developers new to AI face a learning challenge
Sales team consultation needed for some pricing options
Zep pricing
The platform starts with a free tier offering 10,000 monthly messages before switching to $1.25 per thousand messages. Business storage begins free at 5MB per month, with additional space at $2.50 per MB. Enterprise clients get custom packages with dedicated account managers and single tenancy options.
Zep best use cases
Zep shines when AI assistants need context for extended conversations. The platform creates tailored experiences based on past interactions. Teams in healthcare, finance, and customer support rely on Zep to track their users' changing needs and priorities over time.
Postman
Postman has expanded its renowned API platform with powerful agentic AI capabilities. This expansion makes it a serious contender among the best AI agent builders in 2025. The platform, which started as an API testing tool, now has tools to create and manage AI agents at scale.
Postman key features
Postman's AI Agent Builder works with the Model Context Protocol (MCP). Users can transform APIs into callable agent tools instantly. The platform has an MCP Server Network - the first curated network for agent-compatible endpoints. Postman Insights delivers up-to-the-minute data analysis for APIs and tracks usage across human and agent interactions while detecting failure patterns. Developers can reproduce API failures with real-life headers and payloads through the Repro Mode in Insights. This feature proves valuable when debugging agent interactions in production.
Postman pros and cons
Pros:
Accessible interface that speeds up learning
Powerful features that make shared work easier
Works on Windows, Mac OS, and Linux
Scales well for small teams and enterprise environments
Smart collection and environment management
Cons:
Support for non-REST APIs is limited
Large API collections need substantial resources
You need licenses for advanced features
Data management capabilities are restricted
Teams face version control challenges
Postman pricing
Postman comes in four pricing tiers:
Free: USD 0.00 if you have small teams (up to 3 collaborators)
Basic: USD 14.00 per user/month (billed annually) with unlimited collaborators
Professional: USD 29.00 per user/month (billed annually) with internal workspaces
Enterprise: USD 49.00 per user/month with advanced security and control
Postman best use cases
Postman shines in API-driven agent development scenarios. Teams building intelligent systems that need production-grade observability find the platform valuable. Organizations moving from prototype to production benefit from Postman's detailed testing and debugging capabilities. The platform's MCP integration streamlines the process for companies developing AI agents that interact with multiple APIs.
CrewAI
CrewAI leads the multi-agent collaboration space as an open-source framework that organizes AI agents into specialized teams. The platform was built from scratch without LangChain or other agent frameworks. It gained attention with over 29,400 GitHub stars and powers automation for all but one of the Fortune 500 companies.
CrewAI key features
The platform's role-based agent model lets users create specialized AI team members with defined responsibilities, objectives, and backstories. These agents make decisions on their own based on their assigned duties. The platform supports both sequential and hierarchical processes. Sequential processes handle linear task execution while hierarchical ones enable manager-led orchestration.
CrewAI gives users flexible deployment options through cloud, self-hosted, or local installations. The system naturally connects with any LLM provider and over 700 applications. Enterprise users can access monitoring tools to track their AI agents' quality, efficiency, and ROI.
CrewAI pros and cons
Pros:
Well-laid-out, modular agent collaboration
Simple extension and composition of agent teams
YAML/Python configuration makes setup straightforward
Production-oriented with focus on usable code
10 million agents run monthly with proven reliability
Cons:
Support for long-running agents needs improvement
Ecosystem is still growing compared to LangChain
Privacy concerns arise from collected anonymized usage data
Non-technical teams face a steep learning curve
Users need solid Python skills
CrewAI pricing
CrewAI's pricing tiers depend on execution volume:
Free: 50 monthly executions, 1 deployed crew, 1 seat
Basic: USD 99.00/month with 100 monthly executions
Standard: USD 500.00/month with 1,000 monthly executions
Pro: USD 1000.00/month with 2,000 monthly executions
Enterprise & Ultra: Custom pricing for high-volume needs
CrewAI best use cases
CrewAI shines at structured workflows that need multiple specialist agents. The platform works well for report generation (researcher → writer → reviewer workflows), code refactoring (architect → coder → QA collaboration), and business process automation.
The platform's collaborative intelligence brings value to customer service, marketing, fintech, and content generation. CrewAI serves enterprises that build custom, multi-agent systems and development teams with strong Python expertise.
Conclusion
AI agent builders have reshaped the scene of business automation and decision-making in 2025. This piece explores ten powerful platforms that deliver measurable results for businesses of all sizes. Each solution brings something special to the table - from KumoHQ's remarkable predictive analytics to Relay.app's human-in-the-loop functionality and CrewAI's multi-agent collaboration framework.
Your specific requirements will determine the best AI agent builder for your needs. Technical teams might prefer open-source options like Zep or CrewAI. Stack AI or Voiceflow are more available to non-technical users. Budget plays a vital role too, with options that range from generous free tiers to enterprise-level pricing structures.
Our comparison table gives you a complete overview of features, pricing, advantages, and limitations for all platforms. This side-by-side look should help you pick solutions that match your organization's needs.
Companies can get customized recommendations about AI agent solutions through KumoHQ's contact page.
FAQ
What is the best tool to build AI agents?
Microsoft Copilot Studio and Azure AI Foundry Agent Service lead the pack as the best AI agent builders in 2025. These solutions pack a serious punch - more than 230,000 organizations, including 90% of Fortune 500 companies, have built AI agents and automations with Copilot Studio.
The "best" tool really comes down to what you need. Stack AI's visual builder works great for teams that want a no-code approach. CrewAI might be your best bet if you need multiple agents working together, and it's already powering automation for many Fortune 500 companies. Teams focused on customer service often find Voiceflow's conversation features hit the mark perfectly.
Which AI agent framework is best?
Looking at frameworks instead of complete platforms, a few heavy hitters dominate the 2025 market. LangChain remains the go-to choice for LLM-based agent development. It provides the foundations for reasoning, tool use, and memory retention. CrewAI shines when you need multiple agents that work together through defined roles and shared goals.
Microsoft's Semantic Kernel fits like a glove for companies already deep into Microsoft tech. AutoGen takes a different approach that works great for complex tasks where specialization matters.