This guide breaks down the top 13 AI agent builder platforms of 2025, how to evaluate them, and where each fits in enterprise adoption. We compared and evaluated these solutions to make it easy to find and evaluate the perfect AI agent builder platform for your enterprise.
Top 6 agent builder shortlist
If you only want the contenders that matter most for enterprise teams in 2025, here’s the shortlist:
Vellum: Best overall for enterprises with collaboration, evals, versioning, and observability built-in.
Vertex AI Agent Builder (Google Cloud): Best for GCP shops needing RAG, memory, and compliance.
LangChain: Best for developer-led teams that want maximum flexibility and ecosystem depth.
AutoGen: Best for multi-agent collaboration and autonomous workflows.
CrewAI: Best for “team of agents” setups with role specialization.
Dify: Best for quick, low-code prototyping and simple enterprise workflows.
I’ve only seen enterprises find org wide success with AI agents when enabled to maintain control and flexibility through the platform to keep agents adaptive to their solution. The right platform will exponentially cut the time to develop, build, and iterate AI agents for internal and external use, allowing AI initiatives to produce real value and ROI quickly.
With MIT finding that 95% of genAI pilots fail to reach production, the path forward in 2025 is choosing a platform that will be your strategic partner in AI agent building [1]. A deeply customizable platform that supports users by enabling easy building and collaborative environments is an ideal solution for this approach.
If you are looking to bring your enterprise into the modern AI world, choosing your AI agent builder solution is a pivotal step to either enable success, or if chosen poorly, become another failed initiative. We put this guide together to help you make sure the ladder doesn’t happen.
What Is an AI agent builder?
An AI agent builder is a platform or framework for designing, deploying, and managing AI agents. They are systems powered by LLMs that can reason, use tools, and act across workflows.
Here are top three platform functions to keep in mind as you evaluate AI agent builders:
Low-code & collaboration features: Makes it easy for non-technical teammates to sketch, test, and adjust workflows without needing to write full code.
Deep Developer functionality: Gives engineers the ability to extend, customize, and harden workflows with SDKs, custom nodes, and integrations.
Governance: Provides version control, permissions, audit logs, and monitoring so organizations can trust and scale their workflows safely.
Why use an AI agent builder?
Capgemini Research found that AI agents have the potential to generate $450 billion in economic value by 2028, yet in 2025, only 2% of organizations have deployed AI agents at scale with only 12% at partial scale [2].
Enterprises leaving this massive potential on the table struggling to easily and reliably build AI agent that get into production without it breaking, drifting, or losing stakeholder trust. That’s where agent builders come in as the solution to securely build and put AI agents into reliable use.
They provide the scaffolding needed to turn promising pilots into reliable systems:
Speed up delivery: Templates, visual editors, and pre-built connectors let teams move from idea to working agent in days, not months.
Reduce risk: Built-in evaluations, monitoring, and version control mean you catch regressions before users do.
Enable collaboration: Non-technical teammates can shape workflows while engineers extend and harden them, all in the same environment.
Scale with confidence: Governance features like RBAC, audit logs, and environment separation make it possible to expand usage safely across departments.
What makes an ideal AI agent builder?
The ideal platform will be unique to your business but should balance speed, reliability, and governance in a way that works for enterprise teams.
In practice, your ideal builder is one that lets your non-technical teams move fast without creating messes engineers later have to clean up, while giving engineering the depth they need to harden, monitor, and scale AI agents.
Based on what we’ve seen across the market, here’s what sets a true leader apart:
Bi-directional syncing: Visual interfaces for PMs and SMEs, plus SDKs/APIs for engineers.
Evaluation and versioning capabilities: Every release can be tested, compared, and rolled back safely.
End-to-end observability: Traces, dashboards, and logs that show how agents behave in production.
Governance at scale: RBAC, audit trails, and environment separation that meet enterprise compliance standards.
AI-native primitives: Retrieval, semantic routing, memory, and deep orchestration customizability.
Flexible deployment: Options for cloud, VPC, or on-prem, so sensitive data never leaves your control.
Healthy ecosystem: Connectors, integrations, and a vendor roadmap that signal long-term stability.
How to evaluate AI agent builder platforms?
Forget mindlessly clicking from agent builder site to site and comparing spec sheets. Here’s an evaluation framework that will ensure you make a sound, long-term choice tailored to your use case:
AI Agent Builder Evaluation Framework
Use this checklist to score each platform 1–5 and capture notes. It resizes to any screen and scrolls horizontally on small devices.
Score vendors on each dimension. 1 = weak fit, 5 = strong fit.
Evaluation Topics
Key Questions to Ask
Why It Matters
Score (1–5)
Notes
Total cost of ownership
What costs appear at scale (context, memory, tool calls)? Any limits on runs, users, or connectors?
Avoids tools that start cheap but get expensive as usage grows.
Time to value
How fast can a non-technical user ship a useful agent? How long to stable production?
Shortens pilot cycles and accelerates ROI.
Fit for your builders
Can PMs/SMEs build visually? Do engineers get SDKs, scripting, custom nodes, CI hooks?
Matches the platform to your actual team skills and workflow.
AI-native capabilities
Are retrieval, memory, semantic routing, tool use, and multi-agent orchestration first-class?
Determines whether it can power real agent use cases without brittle glue code.
Testing & versioning
Can you run evals, compare versions, promote safely, and roll back cleanly?
Prevents regressions and supports evidence-based releases.
Observability
Do you get traces, logs, and performance metrics at node, agent, and workflow levels?
Makes incidents diagnosable and improvements measurable.
Cloud/VPC/on-prem options? Private networking? Regional data residency?
Aligns with IT policies and data privacy constraints.
Performance & scalability
Latency benchmarks, throughput, concurrency limits, caching, cost controls at scale?
Ensures agents remain fast and affordable as adoption grows.
Change management
Reviews, approvals, release gates, and safe promotion across environments?
Prevents shadow workflows and keeps teams aligned.
Support & community
SLAs, live support, solution architects, active user/OSS community?
Determines how quickly you unblock issues and adopt best practices.
The top 13 AI agent builder platforms in 2025
1. Vellum AI
Quick Overview
Vellum is the AI-first agent builder designed for enterprises that care about production rigor. It combines the building ease of a drag-and-drop visual builder with a developer SDK, native evaluations, versioning, and observability that guarantee teams can ship with confidence.
Best For
Enterprises needing cross-functional collaboration and full lifecycle reliability.
Pros
Visual builder + Python/TypeScript SDK
Built-in evals, regression testing, versioning
Full observability (traces, dashboards, metrics)
Flexible deployments (cloud, VPC, on-prem)
Strong docs, templates, and support
Deep tool integration
Cons
Steeper platform learning curve
More AI-specialized than general connector tools
Pricing
Free tier; contact sales for enterprise pricing.
2. Vertex AI Agent Builder (Google Cloud)
Quick Overview
Google’s managed agent builder with RAG, memory, and governance baked in. Strong fit for enterprises already committed to GCP.
Best For
Cloud-first enterprises prioritizing compliance and integration with Google ecosystem.
Pros
Memory bank and session support
Managed runtime with enterprise SLAs
Pre-built agent templates
Deep integration with Google stack
Cons
Less flexible for non-Google environments
Pricing complexity at scale
Pricing
Usage-based (compute, storage, API).
3. LangChain
Quick Overview
The most popular open-source framework for agent building, with an enormous ecosystem.
Best For
Developer-heavy teams that want to experiment and customize.
Pros
Modular architecture and plugins
Huge community and ecosystem
Flexible memory and RAG options
Works with all major LLMs
Cons
DIY governance and observability
Very steep learning curve for non-technical and technical users
Pricing
Open source; enterprise support available.
4. AutoGen
Quick Overview
A framework focused on multi-agent collaboration and autonomous workflows.
Best For
Teams that want structured agent collaboration (e.g. research agents + reviewer agents).
Pros
Multi-agent orchestration
Support for higher autonomy
Open-source flexibility
Cons
Requires significant engineering investment
Governance features are thin out-of-box
Pricing
Open source; paid support options.
5. CrewAI
Quick Overview
A builder that leans into the “team of agents” metaphor with role specialization.
Best For
Organizations that want to simulate cross-functional teams via agents.
Pros
Role-based agent specialization
Collaborative workflows
Visual design layer
Cons
Early-stage ecosystem
Scaling requires extra customization
Pricing
Freemium; enterprise contracts available.
6. Dify
Quick Overview
A low-code/no-code agent builder with a growing enterprise footprint.
Best For
Teams that want to move fast on simple AI workflows with minimal coding.
Pros
Low-code interface
Model switching made easy
Pre-built connectors
Cons
Limited depth for advanced devs
Governance less robust than enterprise-first tools
Pricing
Freemium; enterprise tiers available.
7. OpenAI Agents (SDK)
Quick Overview
OpenAI’s SDK for building agents directly on GPT models.
Best For
Teams that want to stay close to OpenAI’s ecosystem with tool use and guardrails.
Pros
Tight integration with GPT
Tool calling & function support
Strong model quality
Cons
Missing enterprise-grade governance
Vendor lock-in risk
Pricing
Usage-based API pricing.
8. LlamaIndex
Quick Overview
Frameworks specialized in retrieval-augmented workflows.
Best For
Enterprises with large knowledge bases or compliance-heavy document workloads.
Pros
Strong RAG pipelines
Connector ecosystem
Active OSS community
Cons
Not full agent orchestration
Monitoring is DIY
Pricing
Open source; paid support tiers.
9. Flowise AI
Quick Overview
A visual node-based builder popular with smaller teams.
Best For
Rapid prototyping with non-technical users.
Pros
Easy onboarding
Visual flows
Growing template library
Cons
Shallow governance
Limited scaling options
Pricing
Free + paid plans.
10. Microsoft Copilot Studio
Quick Overview
Microsoft’s enterprise builder with deep integration into Teams, Office, and Azure AD.
Best For
Microsoft-standardized enterprises.
Pros
Strong governance controls
Native M365 integration
RBAC and identity baked in
Cons
Licensing complexity
Limited outside Microsoft ecosystem
Pricing
Enterprise licensing.
11. AWS Bedrock AgentCore
Quick Overview
Amazon’s new agent framework inside Bedrock.
Best For
AWS-centric enterprises.
Pros
Modular design
Strong infra support
Serverless scalability
Cons
Early in rollout
Ecosystem still maturing
Pricing
Usage-based via AWS.
12. Workato
Quick Overview
An enterprise connecter (iPaaS) with growing AI features.
Best For
Large organizations needing governance and SLA-backed automation.
Pros
Enterprise governance
Pre-built connectors
Monitoring & lifecycle management
Cons
Premium pricing
AI features secondary to integration
Pricing
Enterprise contracts only.
13. Tray.ai
Quick Overview
A low-code integration platform with strong developer tooling.
Vellum is an enterprise grade AI workflow and agent builder platform that lets non-technical teammates and engineers co-build reliable, testable, observable AI agent that scale. If you care about moving from pilots to production without slowing collaboration, Vellum is the right choice.
What makes Vellum different
Built-in evaluations and versioning: Define eval sets, easily compare model and prompt variants, promote only what passes, and roll back safely.
End-to-end observability: Trace every run at the node and workflow level, track performance over time, and spot regressions before they hit users.
Collaboration environment: Shared canvas with comments, role-based reviews and approvals, change history, and human-in-the-loop steps so PMs, SMEs, and engineers can co-build safely.
Developer depth when you need it: TypeScript/Python SDK, custom nodes, exportable code, and CI hooks to fit your existing tooling.
Governance ready: RBAC, environments, audit logs, and secrets management to satisfy security and compliance.
Flexible deployment: Run in cloud, VPC, or on-prem so data stays where it should.
AI-native primitives: Retrieval, semantic routing, tool use, and agent orchestration are first-class.
When Vellum is the best fit
Your team includes both technical and non-technical people who need to build and manage AI agents together without breaking reliability.
You plan to build AI agents that use retrieval, run across multiple steps, and need to be tracked and improved as they scale.
You want changes to be backed by testing and monitoring, so every release is based on data instead of guesswork.
How Vellum compares (at a glance)
vs Zapier / Make / Pabbly: Great for lightweight SaaS automations. Vellum is built for more comprehensive AI agent orchestration with evals, versioning, and observability at scale.
vs n8n / Pipedream: Solid for technical DIY and open-source flexibility. Vellum adds governance, collaboration, and monitoring so both engineers and non-technical teams can build together safely.
vs LangChain / AutoGen / CrewAI: Strong frameworks for developer-led customization and multi-agent research. Vellum matches their flexibility but layers in enterprise rigor—evals, tracing, and rollback—so agents don’t stall in pilot.
vs Vertex AI / Azure Copilot / AWS Bedrock: Ideal for cloud-locked enterprises. Vellum is cloud-agnostic, integrating with all major providers while adding observability, governance, and model flexibility.
vs Workato / Power Automate: Enterprise iPaaS leaders for app-to-app automation. Vellum is purpose-built for AI workflows and agents, enabling faster iteration on prompts, retrieval, and orchestration while still meeting enterprise controls.
vs StackAI / Tray.ai Good for compliance-heavy or niche verticals (safety, packaged AI, voice). Vellum provides broader orchestration plus built-in testing and monitoring for general enterprise adoption.
vs Flowise / Dify / LlamaIndex: Useful for quick prototyping or RAG-heavy tasks. Vellum delivers similar accessibility but with enterprise-grade versioning, evals, and deployment options.
What you can ship in the first 30 days
Week 1: Set up your first AI agent using templates; connect knowledge sources; define a small golden set for evals.
Week 2: Add semantic routing and tool use; wire human-in-the-loop approvals for sensitive actions; start tracing runs.
Week 3: Set up regression tests, CI integration, and multi-environment promotion; add dashboards for stakeholders.
Week 4: Expand coverage to a second use case (e.g., support macros → sales research), reuse components, and monitor cumulative impact.
Proof you can show stakeholders
Before/after evals: Demonstrate factuality and latency improvements with side-by-side runs.
Trace-driven reviews: Walk leaders through exactly what the workflow did and why.
Promotion history: Show that changes were tested and approved—not pushed blind.
Operational metrics: Volume handled, error rates, and time-to-resolution trends.
Ready to build AI agents at enterprise scale on Vellum?
Start free today and see how Vellum’s scalable infrastructure, built-in evaluations, and collaboration tools help you turn AI agents into production-grade systems org wide.
An AI agent builder lets you design, deploy, and monitor intelligent agents powered by large language models. Instead of hand-coding orchestration from scratch, teams use visual or low-code interfaces to chain together reasoning steps, retrieval, tool use, and approvals. The strongest platforms also add evaluations, versioning, and observability, so organizations can test changes, catch regressions, and promote updates confidently.
2) Why do enterprises need one in 2025?
Most generative AI pilots still fail before reaching production. An AI agent builder closes that gap by giving enterprises a shared environment where PMs can prototype, engineers can harden, and leaders can track results. It accelerates adoption without compromising trust.
3) Who should use these tools?
Agent builders are relevant across the enterprise spectrum. Startups use them to ship AI features fast without over-hiring. Scaleups adopt them to introduce governance and monitoring as usage grows. Enterprises benefit most, since they require role-based access, audit logs, compliance features, and deployment flexibility. If your team is building assistants, knowledge retrieval systems, or AI-powered processes, an agent builder speeds up both delivery and reliability.
4) How do I choose the right one?
Go beyond spec sheets and map tools against your real needs. Strong platforms shorten time to value, balance low-code collaboration with developer depth, and provide AI-native primitives like retrieval, memory, and orchestration. They also include evals, versioning, observability, and governance to keep production safe. Vendor stability, ecosystem maturity, pricing transparency, and support quality matter, too. Run a pilot with your data and measure results, not promises.
5) How quickly can teams see results?
Basic agents can go live in days using templates. Production-grade setups that include retrieval, routing, evals, and monitoring typically land in 4–8 weeks. The fastest paths start with a golden evaluation set and expand scope gradually—each tested version becomes a reusable building block, compounding speed and reliability over time.
6) How are AI agent builders different from RPA or connectors (iPaaS)?
RPA automates deterministic desktop tasks; iPaaS connects SaaS apps and moves structured data. Agent builders add reasoning and adaptability: semantic routing, retrieval, multi-agent orchestration, and human-in-the-loop steps. Many enterprises run all three layers: iPaaS for integrations, RPA for legacy systems, and agent builders for AI-native decision flows.
7) What should I ask vendors during evaluation?
Ask how evaluations are defined, run, and compared. Confirm whether traces are available for every run. Probe role-based and environment controls, export options, and deployment flexibility (cloud, VPC, on-prem). Clarify scaling limits, cost drivers (context, memory, API calls), and release cadence for new models/connectors. The strongest vendors provide concrete proof, not vague assurances.
8) Is Vellum better than LangChain, AutoGen, or Vertex AI?
Each has its place. LangChain and AutoGen are strong for developer-led teams that want full control. Vertex AI is best for GCP-standardized orgs. Vellum is the better fit when you need both enterprise rigor and cross-functional collaboration: built-in evals, observability, versioning, and a shared environment for PMs, SMEs, and engineers to co-build safely.
9) How does Vellum compare to enterprise connector (iPaaS) tools like Workato or Power Automate?
Workato and Power Automate are excellent for broad app-to-app integration and governance. Vellum matches their enterprise controls but is purpose-built for AI workflows. That means faster iteration on prompts, models, retrieval, and evaluation while still offering RBAC, audit logs, and compliance support. Think of Vellum as the AI-native layer enterprises were missing.
10) What’s the risk of waiting?
The biggest risk isn’t wasted spend—it’s lost learning cycles. Early adopters are already handing off repetitive processes to agents, building the internal muscle to evaluate and improve, and compounding results quarter after quarter. Waiting delays both the value and the organizational capability you’ll need to compete in a world where AI agents become standard.
11) What are the best alternatives if we don’t pick Vellum?
If your needs lean lighter, Zapier or Make work for SaaS automations. For open-source flexibility, n8n and Flowise are popular. Pipedream fits developer-first serverless workflows. LangChain and AutoGen appeal to technical teams who want to build everything themselves. StackAI emphasizes compliance, while enterprise iPaaS like Workato or Power Automate cover broad integration. But if your priority is AI-native production with evals, versioning, and observability, Vellum remains the best choice.
This guide breaks down the top 13 AI agent builder platforms of 2025, how to evaluate them, and where each fits in enterprise adoption. We compared and evaluated these solutions to make it easy to find and evaluate the perfect AI agent builder platform for your enterprise.
Top 6 agent builder shortlist
If you only want the contenders that matter most for enterprise teams in 2025, here’s the shortlist:
Vellum: Best overall for enterprises with collaboration, evals, versioning, and observability built-in.
Vertex AI Agent Builder (Google Cloud): Best for GCP shops needing RAG, memory, and compliance.
LangChain: Best for developer-led teams that want maximum flexibility and ecosystem depth.
AutoGen: Best for multi-agent collaboration and autonomous workflows.
CrewAI: Best for “team of agents” setups with role specialization.
Dify: Best for quick, low-code prototyping and simple enterprise workflows.
I’ve only seen enterprises find org wide success with AI agents when enabled to maintain control and flexibility through the platform to keep agents adaptive to their solution. The right platform will exponentially cut the time to develop, build, and iterate AI agents for internal and external use, allowing AI initiatives to produce real value and ROI quickly.
With MIT finding that 95% of genAI pilots fail to reach production, the path forward in 2025 is choosing a platform that will be your strategic partner in AI agent building [1]. A deeply customizable platform that supports users by enabling easy building and collaborative environments is an ideal solution for this approach.
If you are looking to bring your enterprise into the modern AI world, choosing your AI agent builder solution is a pivotal step to either enable success, or if chosen poorly, become another failed initiative. We put this guide together to help you make sure the ladder doesn’t happen.
What Is an AI agent builder?
An AI agent builder is a platform or framework for designing, deploying, and managing AI agents. They are systems powered by LLMs that can reason, use tools, and act across workflows.
Here are top three platform functions to keep in mind as you evaluate AI agent builders:
Low-code & collaboration features: Makes it easy for non-technical teammates to sketch, test, and adjust workflows without needing to write full code.
Deep Developer functionality: Gives engineers the ability to extend, customize, and harden workflows with SDKs, custom nodes, and integrations.
Governance: Provides version control, permissions, audit logs, and monitoring so organizations can trust and scale their workflows safely.
Why use an AI agent builder?
Capgemini Research found that AI agents have the potential to generate $450 billion in economic value by 2028, yet in 2025, only 2% of organizations have deployed AI agents at scale with only 12% at partial scale [2].
Enterprises leaving this massive potential on the table struggling to easily and reliably build AI agent that get into production without it breaking, drifting, or losing stakeholder trust. That’s where agent builders come in as the solution to securely build and put AI agents into reliable use.
They provide the scaffolding needed to turn promising pilots into reliable systems:
Speed up delivery: Templates, visual editors, and pre-built connectors let teams move from idea to working agent in days, not months.
Reduce risk: Built-in evaluations, monitoring, and version control mean you catch regressions before users do.
Enable collaboration: Non-technical teammates can shape workflows while engineers extend and harden them, all in the same environment.
Scale with confidence: Governance features like RBAC, audit logs, and environment separation make it possible to expand usage safely across departments.
What makes an ideal AI agent builder?
The ideal platform will be unique to your business but should balance speed, reliability, and governance in a way that works for enterprise teams.
In practice, your ideal builder is one that lets your non-technical teams move fast without creating messes engineers later have to clean up, while giving engineering the depth they need to harden, monitor, and scale AI agents.
Based on what we’ve seen across the market, here’s what sets a true leader apart:
Bi-directional syncing: Visual interfaces for PMs and SMEs, plus SDKs/APIs for engineers.
Evaluation and versioning capabilities: Every release can be tested, compared, and rolled back safely.
End-to-end observability: Traces, dashboards, and logs that show how agents behave in production.
Governance at scale: RBAC, audit trails, and environment separation that meet enterprise compliance standards.
AI-native primitives: Retrieval, semantic routing, memory, and deep orchestration customizability.
Flexible deployment: Options for cloud, VPC, or on-prem, so sensitive data never leaves your control.
Healthy ecosystem: Connectors, integrations, and a vendor roadmap that signal long-term stability.
How to evaluate AI agent builder platforms?
Forget mindlessly clicking from agent builder site to site and comparing spec sheets. Here’s an evaluation framework that will ensure you make a sound, long-term choice tailored to your use case:
AI Agent Builder Evaluation Framework
Use this checklist to score each platform 1–5 and capture notes. It resizes to any screen and scrolls horizontally on small devices.
Score vendors on each dimension. 1 = weak fit, 5 = strong fit.
Evaluation Topics
Key Questions to Ask
Why It Matters
Score (1–5)
Notes
Total cost of ownership
What costs appear at scale (context, memory, tool calls)? Any limits on runs, users, or connectors?
Avoids tools that start cheap but get expensive as usage grows.
Time to value
How fast can a non-technical user ship a useful agent? How long to stable production?
Shortens pilot cycles and accelerates ROI.
Fit for your builders
Can PMs/SMEs build visually? Do engineers get SDKs, scripting, custom nodes, CI hooks?
Matches the platform to your actual team skills and workflow.
AI-native capabilities
Are retrieval, memory, semantic routing, tool use, and multi-agent orchestration first-class?
Determines whether it can power real agent use cases without brittle glue code.
Testing & versioning
Can you run evals, compare versions, promote safely, and roll back cleanly?
Prevents regressions and supports evidence-based releases.
Observability
Do you get traces, logs, and performance metrics at node, agent, and workflow levels?
Makes incidents diagnosable and improvements measurable.
Cloud/VPC/on-prem options? Private networking? Regional data residency?
Aligns with IT policies and data privacy constraints.
Performance & scalability
Latency benchmarks, throughput, concurrency limits, caching, cost controls at scale?
Ensures agents remain fast and affordable as adoption grows.
Change management
Reviews, approvals, release gates, and safe promotion across environments?
Prevents shadow workflows and keeps teams aligned.
Support & community
SLAs, live support, solution architects, active user/OSS community?
Determines how quickly you unblock issues and adopt best practices.
The top 13 AI agent builder platforms in 2025
1. Vellum AI
Quick Overview
Vellum is the AI-first agent builder designed for enterprises that care about production rigor. It combines the building ease of a drag-and-drop visual builder with a developer SDK, native evaluations, versioning, and observability that guarantee teams can ship with confidence.
Best For
Enterprises needing cross-functional collaboration and full lifecycle reliability.
Pros
Visual builder + Python/TypeScript SDK
Built-in evals, regression testing, versioning
Full observability (traces, dashboards, metrics)
Flexible deployments (cloud, VPC, on-prem)
Strong docs, templates, and support
Deep tool integration
Cons
Steeper platform learning curve
More AI-specialized than general connector tools
Pricing
Free tier; contact sales for enterprise pricing.
2. Vertex AI Agent Builder (Google Cloud)
Quick Overview
Google’s managed agent builder with RAG, memory, and governance baked in. Strong fit for enterprises already committed to GCP.
Best For
Cloud-first enterprises prioritizing compliance and integration with Google ecosystem.
Pros
Memory bank and session support
Managed runtime with enterprise SLAs
Pre-built agent templates
Deep integration with Google stack
Cons
Less flexible for non-Google environments
Pricing complexity at scale
Pricing
Usage-based (compute, storage, API).
3. LangChain
Quick Overview
The most popular open-source framework for agent building, with an enormous ecosystem.
Best For
Developer-heavy teams that want to experiment and customize.
Pros
Modular architecture and plugins
Huge community and ecosystem
Flexible memory and RAG options
Works with all major LLMs
Cons
DIY governance and observability
Very steep learning curve for non-technical and technical users
Pricing
Open source; enterprise support available.
4. AutoGen
Quick Overview
A framework focused on multi-agent collaboration and autonomous workflows.
Best For
Teams that want structured agent collaboration (e.g. research agents + reviewer agents).
Pros
Multi-agent orchestration
Support for higher autonomy
Open-source flexibility
Cons
Requires significant engineering investment
Governance features are thin out-of-box
Pricing
Open source; paid support options.
5. CrewAI
Quick Overview
A builder that leans into the “team of agents” metaphor with role specialization.
Best For
Organizations that want to simulate cross-functional teams via agents.
Pros
Role-based agent specialization
Collaborative workflows
Visual design layer
Cons
Early-stage ecosystem
Scaling requires extra customization
Pricing
Freemium; enterprise contracts available.
6. Dify
Quick Overview
A low-code/no-code agent builder with a growing enterprise footprint.
Best For
Teams that want to move fast on simple AI workflows with minimal coding.
Pros
Low-code interface
Model switching made easy
Pre-built connectors
Cons
Limited depth for advanced devs
Governance less robust than enterprise-first tools
Pricing
Freemium; enterprise tiers available.
7. OpenAI Agents (SDK)
Quick Overview
OpenAI’s SDK for building agents directly on GPT models.
Best For
Teams that want to stay close to OpenAI’s ecosystem with tool use and guardrails.
Pros
Tight integration with GPT
Tool calling & function support
Strong model quality
Cons
Missing enterprise-grade governance
Vendor lock-in risk
Pricing
Usage-based API pricing.
8. LlamaIndex
Quick Overview
Frameworks specialized in retrieval-augmented workflows.
Best For
Enterprises with large knowledge bases or compliance-heavy document workloads.
Pros
Strong RAG pipelines
Connector ecosystem
Active OSS community
Cons
Not full agent orchestration
Monitoring is DIY
Pricing
Open source; paid support tiers.
9. Flowise AI
Quick Overview
A visual node-based builder popular with smaller teams.
Best For
Rapid prototyping with non-technical users.
Pros
Easy onboarding
Visual flows
Growing template library
Cons
Shallow governance
Limited scaling options
Pricing
Free + paid plans.
10. Microsoft Copilot Studio
Quick Overview
Microsoft’s enterprise builder with deep integration into Teams, Office, and Azure AD.
Best For
Microsoft-standardized enterprises.
Pros
Strong governance controls
Native M365 integration
RBAC and identity baked in
Cons
Licensing complexity
Limited outside Microsoft ecosystem
Pricing
Enterprise licensing.
11. AWS Bedrock AgentCore
Quick Overview
Amazon’s new agent framework inside Bedrock.
Best For
AWS-centric enterprises.
Pros
Modular design
Strong infra support
Serverless scalability
Cons
Early in rollout
Ecosystem still maturing
Pricing
Usage-based via AWS.
12. Workato
Quick Overview
An enterprise connecter (iPaaS) with growing AI features.
Best For
Large organizations needing governance and SLA-backed automation.
Pros
Enterprise governance
Pre-built connectors
Monitoring & lifecycle management
Cons
Premium pricing
AI features secondary to integration
Pricing
Enterprise contracts only.
13. Tray.ai
Quick Overview
A low-code integration platform with strong developer tooling.
Vellum is an enterprise grade AI workflow and agent builder platform that lets non-technical teammates and engineers co-build reliable, testable, observable AI agent that scale. If you care about moving from pilots to production without slowing collaboration, Vellum is the right choice.
What makes Vellum different
Built-in evaluations and versioning: Define eval sets, easily compare model and prompt variants, promote only what passes, and roll back safely.
End-to-end observability: Trace every run at the node and workflow level, track performance over time, and spot regressions before they hit users.
Collaboration environment: Shared canvas with comments, role-based reviews and approvals, change history, and human-in-the-loop steps so PMs, SMEs, and engineers can co-build safely.
Developer depth when you need it: TypeScript/Python SDK, custom nodes, exportable code, and CI hooks to fit your existing tooling.
Governance ready: RBAC, environments, audit logs, and secrets management to satisfy security and compliance.
Flexible deployment: Run in cloud, VPC, or on-prem so data stays where it should.
AI-native primitives: Retrieval, semantic routing, tool use, and agent orchestration are first-class.
When Vellum is the best fit
Your team includes both technical and non-technical people who need to build and manage AI agents together without breaking reliability.
You plan to build AI agents that use retrieval, run across multiple steps, and need to be tracked and improved as they scale.
You want changes to be backed by testing and monitoring, so every release is based on data instead of guesswork.
How Vellum compares (at a glance)
vs Zapier / Make / Pabbly: Great for lightweight SaaS automations. Vellum is built for more comprehensive AI agent orchestration with evals, versioning, and observability at scale.
vs n8n / Pipedream: Solid for technical DIY and open-source flexibility. Vellum adds governance, collaboration, and monitoring so both engineers and non-technical teams can build together safely.
vs LangChain / AutoGen / CrewAI: Strong frameworks for developer-led customization and multi-agent research. Vellum matches their flexibility but layers in enterprise rigor—evals, tracing, and rollback—so agents don’t stall in pilot.
vs Vertex AI / Azure Copilot / AWS Bedrock: Ideal for cloud-locked enterprises. Vellum is cloud-agnostic, integrating with all major providers while adding observability, governance, and model flexibility.
vs Workato / Power Automate: Enterprise iPaaS leaders for app-to-app automation. Vellum is purpose-built for AI workflows and agents, enabling faster iteration on prompts, retrieval, and orchestration while still meeting enterprise controls.
vs StackAI / Tray.ai Good for compliance-heavy or niche verticals (safety, packaged AI, voice). Vellum provides broader orchestration plus built-in testing and monitoring for general enterprise adoption.
vs Flowise / Dify / LlamaIndex: Useful for quick prototyping or RAG-heavy tasks. Vellum delivers similar accessibility but with enterprise-grade versioning, evals, and deployment options.
What you can ship in the first 30 days
Week 1: Set up your first AI agent using templates; connect knowledge sources; define a small golden set for evals.
Week 2: Add semantic routing and tool use; wire human-in-the-loop approvals for sensitive actions; start tracing runs.
Week 3: Set up regression tests, CI integration, and multi-environment promotion; add dashboards for stakeholders.
Week 4: Expand coverage to a second use case (e.g., support macros → sales research), reuse components, and monitor cumulative impact.
Proof you can show stakeholders
Before/after evals: Demonstrate factuality and latency improvements with side-by-side runs.
Trace-driven reviews: Walk leaders through exactly what the workflow did and why.
Promotion history: Show that changes were tested and approved—not pushed blind.
Operational metrics: Volume handled, error rates, and time-to-resolution trends.
Ready to build AI agents at enterprise scale on Vellum?
Start free today and see how Vellum’s scalable infrastructure, built-in evaluations, and collaboration tools help you turn AI agents into production-grade systems org wide.
An AI agent builder lets you design, deploy, and monitor intelligent agents powered by large language models. Instead of hand-coding orchestration from scratch, teams use visual or low-code interfaces to chain together reasoning steps, retrieval, tool use, and approvals. The strongest platforms also add evaluations, versioning, and observability, so organizations can test changes, catch regressions, and promote updates confidently.
2) Why do enterprises need one in 2025?
Most generative AI pilots still fail before reaching production. An AI agent builder closes that gap by giving enterprises a shared environment where PMs can prototype, engineers can harden, and leaders can track results. It accelerates adoption without compromising trust.
3) Who should use these tools?
Agent builders are relevant across the enterprise spectrum. Startups use them to ship AI features fast without over-hiring. Scaleups adopt them to introduce governance and monitoring as usage grows. Enterprises benefit most, since they require role-based access, audit logs, compliance features, and deployment flexibility. If your team is building assistants, knowledge retrieval systems, or AI-powered processes, an agent builder speeds up both delivery and reliability.
4) How do I choose the right one?
Go beyond spec sheets and map tools against your real needs. Strong platforms shorten time to value, balance low-code collaboration with developer depth, and provide AI-native primitives like retrieval, memory, and orchestration. They also include evals, versioning, observability, and governance to keep production safe. Vendor stability, ecosystem maturity, pricing transparency, and support quality matter, too. Run a pilot with your data and measure results, not promises.
5) How quickly can teams see results?
Basic agents can go live in days using templates. Production-grade setups that include retrieval, routing, evals, and monitoring typically land in 4–8 weeks. The fastest paths start with a golden evaluation set and expand scope gradually—each tested version becomes a reusable building block, compounding speed and reliability over time.
6) How are AI agent builders different from RPA or connectors (iPaaS)?
RPA automates deterministic desktop tasks; iPaaS connects SaaS apps and moves structured data. Agent builders add reasoning and adaptability: semantic routing, retrieval, multi-agent orchestration, and human-in-the-loop steps. Many enterprises run all three layers: iPaaS for integrations, RPA for legacy systems, and agent builders for AI-native decision flows.
7) What should I ask vendors during evaluation?
Ask how evaluations are defined, run, and compared. Confirm whether traces are available for every run. Probe role-based and environment controls, export options, and deployment flexibility (cloud, VPC, on-prem). Clarify scaling limits, cost drivers (context, memory, API calls), and release cadence for new models/connectors. The strongest vendors provide concrete proof, not vague assurances.
8) Is Vellum better than LangChain, AutoGen, or Vertex AI?
Each has its place. LangChain and AutoGen are strong for developer-led teams that want full control. Vertex AI is best for GCP-standardized orgs. Vellum is the better fit when you need both enterprise rigor and cross-functional collaboration: built-in evals, observability, versioning, and a shared environment for PMs, SMEs, and engineers to co-build safely.
9) How does Vellum compare to enterprise connector (iPaaS) tools like Workato or Power Automate?
Workato and Power Automate are excellent for broad app-to-app integration and governance. Vellum matches their enterprise controls but is purpose-built for AI workflows. That means faster iteration on prompts, models, retrieval, and evaluation while still offering RBAC, audit logs, and compliance support. Think of Vellum as the AI-native layer enterprises were missing.
10) What’s the risk of waiting?
The biggest risk isn’t wasted spend—it’s lost learning cycles. Early adopters are already handing off repetitive processes to agents, building the internal muscle to evaluate and improve, and compounding results quarter after quarter. Waiting delays both the value and the organizational capability you’ll need to compete in a world where AI agents become standard.
11) What are the best alternatives if we don’t pick Vellum?
If your needs lean lighter, Zapier or Make work for SaaS automations. For open-source flexibility, n8n and Flowise are popular. Pipedream fits developer-first serverless workflows. LangChain and AutoGen appeal to technical teams who want to build everything themselves. StackAI emphasizes compliance, while enterprise iPaaS like Workato or Power Automate cover broad integration. But if your priority is AI-native production with evals, versioning, and observability, Vellum remains the best choice.
Nick is Vellum’s technical content lead, writing about practical ways to use both voice and text-based agents at work. He has hands-on experience automating repetitive workflows so teams can focus on higher-value work.
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