A fast, practical guide to the best AI agent frameworks for developers building, orchestrating, and deploying AI agents in production. We cover open-source libraries, vendor-managed platforms, and visual builders, plus a clear recommendations to help evaluate and sicover your ideal AI agent framework solution.
TL;DR
This guide ranks the top 11 AI agent frameworks.
Covers code-first, low-code, and managed approaches.
Use the evaluation criteria and comparison table to choose quickly.
Vellum leads for production-ready governance, observability, and collaboration.
LangChain: Modular, open-source framework with broad ecosystem and flexible RAG/memory.
OpenAI Agents: API-first, GPT-centric agent builder with tool calling and seamless model upgrades.
AutoGen: Open-source orchestration for agent-to-agent collaboration and self-reflection loops.
CrewAI: Role-based, team-of-agents orchestration with visual design and collaboration flows.
AI agent frameworks save weeks of developer time
I worked with a fintech customer whose their developers were struggling to stitch together multiple AI agent frameworks just to handle onboarding.
By moving into Vellum, they unified what previously required separate tools—agents for document verification, compliance checks, and escalation paths—into a single framework with built-in governance and observability.
The dev team saved weeks by cutting manual review time by more than half, and because they weren’t reinventing the plumbing, they shipped a production-ready workflow in under two weeks.
What is an AI agent framework?
An AI agent framework is software that helps teams, especially developers build, orchestrate, and deploy autonomous or semi-autonomous agents. It provides workflow automation, memory, tool integrations, and runtime controls to run reliable multi-step processes.
Why use AI agent frameworks?
AI agent frameworks quickly turn scattered prototypes into production systems. Here are the benefits you can expect from using an AI agent framework:
Accelerate time-to-market
Ship reliable, observable production workflows
Enable multi-agent collaboration and orchestration
Gain enterprise governance, versioning, and auditability
Who needs AI agent frameworks?
Any developer team moving from AI idea to AI agents with deep business impact benefits. Ideally your AI agent framework can support more teams in your org, rather than just catering to developers. Teams like FP&A, Product, Data Science, etc. should be able to collaborate with developers to make AI agents.
What makes an ideal AI agent framework?
The best frameworks are modular and observable, with governance you can take to audit and deployment options that fit your stack. Look for rich integrations and a great developer experience (SDK + visual builder + docs) so teams can ship quickly without painting themselves into a corner.
Modularity: Swap or extend components
Observability: Logs, traces, and evaluation tools
Governance: RBAC, audit logs, and compliance features
Multi-agent orchestration: Enterprises are scaling from single-agent pilots to dozens of coordinated agent systems, with initiatives like Salesforce and Google’s Agent-to-Agent (A2A) standard showing the push toward collaboration at scale [1].
Enterprise governance: Regulatory pressure is forcing enterprises to emphasize RBAC, audit trails, and compliance logging as core features of AI platforms [2].
Visual/low-code: Low and no-code platforms remain a top enterprise investment category for 2025, helping accelerate AI prototyping and delivery across teams [3].
Open-source dominance: OSS underpins most production workloads, with surveys showing 90%+ of enterprises depend on open-source software in production [4].
Vendor-managed runtimes: Vendor-managed AI platforms are gaining traction in regulated industries where compliance burden is highest, even if adoption multiples vary by sector [5].
Why these 11 Frameworks in 2025?
These platforms lead on developer adoption, feature depth, and real-world reliability. They support code-first SDKs, low-code canvases, and managed runtimes to fit different IT and compliance needs.
How to evaluate AI agent frameworks
Use these criteria to score options against your requirements:
Criterion
Description
Why It Matters
Modularity
Swappable, composable components for models, tools, memory, and routing
Enables customization and scaling without rewrites
Observability
Tracing, logs, metrics, eval harnesses, and regression alerts
Shortens MTTR; builds trust in outputs
Governance
RBAC, audit logs, change history, approvals, HITL
Mandatory for enterprise and regulated use
Deployment Options
Cloud, VPC, or on-prem; secrets and data residency controls
Fits diverse IT and compliance requirements
Integration
Connectors/SDKs for internal tools, RAG, and external APIs
Reduces glue code and maintenance
Developer Experience
Unified SDKs, clear docs, visual builder, CI hooks
Speeds onboarding and iteration
Performance
Latency, throughput, horizontal scaling patterns
Impacts UX and cost
Cost
Pricing model and total cost of ownership (infra + people)
Determines long-term feasibility
How we chose the top 11 best AI agent frameworks
We ranked frameworks by feature completeness, production readiness, governance, and developer experience. We balanced open-source flexibility against managed reliability, prioritizing solutions proven in real deployments.
Expect trade-offs:
Flexibility vs. ease: Code-first is powerful; visual is fast.
OSS vs. managed: Control vs. simpler ops.
Cost vs. enterprise features: Governance often raises TCO.
Ecosystem breadth vs. specialization: Broad platforms may lack vertical depth.
Top 11 best AI agent frameworks
Below are concise picks with best-fit guidance. Choose by your deployment, governance, and speed needs.
1. Vellum AI — All-in-one platform for enterprise-grade AI agents
Quick overview:Vellum AI provides developers a unified platform for building, testing, and deploying production-grade AI agents. It combines a visual builder with an SDK, built-in evaluations, and observability so teams can iterate quickly while staying enterprise-compliant. With flexible deployment (cloud, VPC, on-prem), it’s designed for engineering, product, and compliance teams to collaborate.
Best for: Developer teams needing a robust, production-ready AI agent platform to enable collaboration with the rest of their org.
Pros:
Built-in evaluations and versioning
End-to-end observability for debugging and monitoring
2. LangChain — Modular open source agent framework
Quick overview:LangChain is a open-source framework for developers building complex multi-model AI applications. It offers modular components for retrieval, memory, and orchestration, supported by a vast ecosystem of integrations. While powerful, it requires engineering resources for hosting, scaling, and ongoing maintenance.
Best for: Developers building custom multi-model agent workflows
Pros:
Modular components and broad ecosystem
Flexible RAG and memory integrations
Supports multiple LLMs and toolchains
Cons:
Steep learning curve
Requires self-hosting and maintenance
Pricing: Free tier; paid plans starting from $39/month
Quick overview:OpenAI’s SDK provides a streamlined way to build GPT-powered assistants with function calling, memory, and safety guardrails. It focuses on simplicity and rapid prototyping, with seamless upgrades as OpenAI’s models evolve. The trade-off is vendor lock-in and usage-based costs.
Best for: Fast prototyping of GPT-powered assistants with tool/function calling
Pros:
Seamless model upgrades
Easy tool/function integration
Strong guardrails and safety features
Cons:
Tied to OpenAI models
Usage-based costs can add up
Pricing: Usage-based (API metered)
4. AutoGen — Open source multi-agent orchestration
Quick overview:AutoGen is an open-source framework built for orchestrating multiple agents that can collaborate, communicate, and reflect. It’s popular in research and advanced use cases where experimentation with agent-to-agent loops is critical. However, it lacks enterprise-grade governance and requires significant engineering to productionize.
Best for: Research and advanced agent-to-agent collaboration
Pros:
Agent-to-agent communication patterns
Self-reflection and feedback loops
Open source and extensible
Cons:
Limited enterprise features
Requires engineering resources
Pricing: Free (open source)
5. CrewAI — Visual team of agents platform
Quick overview:CrewAI specializes in designing teams of role-based agents through a visual workflow interface. It helps teams prototype and deploy collaborative agent flows quickly, without heavy coding. While easy to use, advanced observability and governance features are limited.
Best for: Designing collaborative agent teams with roles
Pros:
Visual workflow builder
Role-based agent collaboration
Quick prototyping
Cons:
Limited advanced observability
Freemium model restricts some features
Pricing: Enterprise only.
6. n8n — Automation platform with AI agent plugins
Quick overview:n8n is an open-source automation platform that combines AI agents with traditional SaaS workflows. With a low-code visual builder and hundreds of integrations, it’s a versatile option for both developers and operations teams. It can run self-hosted or in the cloud, though advanced AI features often require scripting.
Best for: Workflow automation integrating AI and traditional apps
Pros:
Visual low-code interface
Large library of integrations
Self-hosting option
Cons:
Not AI-focused by default
Advanced features may need scripting
Pricing: Free (open source); cloud from $20/month
7. Zapier — No-code automation with AI integrations
Quick overview:Zapier is a no-code automation leader that connects thousands of apps, now with AI integrations. It’s designed for business users to quickly set up workflows without technical expertise. While great for simple automations, it lacks deep agent orchestration capabilities.
Best for: Non-technical users automating tasks with AI and SaaS tools
Pros:
Extensive app ecosystem
Simple no-code builder
Fast setup
Cons:
Limited agent orchestration
Usage caps on free/low tiers
Pricing: Free tier; paid plans from $19.99/month
8. Lindy AI — Personal AI assistant platform
Quick overview:Lindy AI focuses on personal and business assistants, offering customizable templates for common workflows. Its platform aims to make AI-driven productivity accessible to non-technical users. The trade-off is limited flexibility for complex multi-agent logic.
Best for: Automating personal and business workflows with AI
Pros:
Prebuilt assistant templates
Customizable workflows
Easy onboarding
Cons:
Less flexible for complex agent logic
Usage-based pricing
Pricing: Starts at $25/month
9. Gumloop — Visual LLM agent builder
Quick overview:Gumloop is a lightweight visual builder for prototyping LLM-powered agents. Its drag-and-drop interface and templates make iteration fast, appealing to startups and builders experimenting with AI. Scaling and customization options are more limited compared to enterprise frameworks.
Best for: Rapid prototyping of LLM-powered agents
Pros:
Drag-and-drop interface
Built-in templates
Fast iteration
Cons:
Limited deep customization
Scaling options limited
Pricing: Free tier; paid plans from $37/month
10. Stack AI — Low-code AI workflow platform
Quick overview:Stack AI provides a low-code platform for building AI-powered automations and workflows. It combines a visual editor with API integrations, enabling quick deployment of business-focused agents. More advanced collaboration and observability features may require custom coding.
Best for: Building AI-powered automations with minimal code
Pros:
Visual workflow editor
API integrations
Quick deployment
Cons:
Limited agent collaboration features
Some advanced features require coding
Pricing: Free tier; Enterprise plan
11. Dify — Open source visual agent builder
Quick overview:Dify is an open-source visual agent builder that emphasizes flexibility and community-driven innovation. It comes with templates and orchestration tools while giving teams the freedom to self-host and customize. Enterprise controls are limited, but it’s a strong OSS alternative for teams who want control and transparency.
Best for: Developers and teams needing open source agent orchestration
Pros:
Visual builder with templates
Open source flexibility
Community support
Cons:
Limited enterprise features
Requires self-hosting
Pricing: Free (open source); paid cloud plans available
Designing role-based teams visually: Choose CrewAI.
Connecting apps and AI in low-code workflows: Choose n8n or Zapier.
Want OSS visual builder with templates: Choose Dify.
Why choose Vellum
Vellum is the AI agent platform that lets non-technical teammates and developers 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
Regulated or security-sensitive environments: where developers need RBAC, audit logs, and compliance guardrails out of the box
Cross-functional workflows: that let engineers, product, and compliance collaborate in one shared framework instead of stitching tools together
Developer control over change management: versioning, testing sandboxes, and safe rollout pipelines
Scaling code to production: built-in observability, logs, and rollback tools that cut debugging cycles dramatically
Faster iteration for dev teams: ship agents quickly without building custom infrastructure for governance and monitoring
How Vellum compares (at a glance)
Comparison
Vellum Advantage
Vellum vs LangChain
Built-in evals, versioning, and enterprise governance out of the box
Vellum vs OpenAI SDK
Multi-model orchestration and full observability, not just GPT-centric workflows
Vellum vs AutoGen
Production-grade monitoring and safe deployment, beyond orchestration patterns
Vellum vs CrewAI
Enterprise controls and auditability, not just visual design
What you can ship on Vellum in the first 30 days
Week
Milestone
Deliverable
Week 1
Initial setup and onboarding
Vellum environment configured; team onboarded
Week 2
First model deployment and testing
Agent workflow live; initial tests run
Week 3
Evaluation framework implementation
Evals + monitoring dashboards in place
Week 4
Production rollout
Agents in production; governance enabled
FAQs
1) What’s the fastest path from prototype to production for agent workflows?
If you need versioning, evals, and rollback out of the box, a managed, enterprise-grade platform is fastest. Vellum is a strong fit here because it bundles evals, observability, and promotion workflows so devs don’t rebuild plumbing.
2) Code-first vs. visual builders—how should teams choose?
Code-first (e.g., LangChain) maximizes control; visual builders (e.g., CrewAI, Dify) speed collaboration. Many teams do both: code for custom logic + a visual canvas for orchestration and reviews. Vellum supports this hybrid with SDKs and a shared canvas.
3) How do we prevent prompt or model changes from breaking production?
Version every artifact (prompts, tools, retrieval, models), run gated evals, and ship via canary. Vellum bakes this in (versioning + eval gates + instant rollback), which reduces blast radius for developer changes.
4) Which frameworks are best for multi-agent collaboration?
For research-heavy agent-to-agent patterns: AutoGen and CrewAI are popular. If you need multi-agent plus enterprise guardrails and run-time observability, Vellum is typically the safer production choice.
5) We’re in a regulated environment—what should we prioritize?
RBAC, audit logs, environment separation, data residency, and HITL. Vellum covers these natively (RBAC, audit trails, VPC/on-prem options), while OSS stacks can do it with more engineering work.
6) What observability signals matter most for debugging agents?
Trace spans per step, input/output snapshots (with redaction), tool-call results, latency, token usage, and eval outcomes tied to KPIs. Neutral options: OpenTelemetry-compatible traces, custom log sinks, and eval harnesses; Vellum bundles these for quicker setup.
7) How do I control LLM spend as usage grows?
Use routing (cheap models for easy tasks), caching, batch retrieval, tool-time limits, and token budgets per workflow. Any platform can implement this; Vellum helps by exposing per-route metrics and guardrails without custom dashboards.
8) When is open source the better starting point?
If you need deep customization, self-hosting, or want to experiment cheaply, OSS (e.g., LangChain, Dify, n8n) is great. As reliability and compliance needs increase, teams often graduate to a managed platform like Vellum for governance and ops.
9) What’s a pragmatic 30-day plan to show value?
Week 1: pick one measurable use case + set evals/tracing. Week 2: internal pilot behind flags. Week 3: add guardrails + regression alerts. Week 4: canary to a subset, monitor, then expand. This works on any stack; Vellum accelerates it by giving you evals, traces, and promotion gates day one.
A fast, practical guide to the best AI agent frameworks for developers building, orchestrating, and deploying AI agents in production. We cover open-source libraries, vendor-managed platforms, and visual builders, plus a clear recommendations to help evaluate and sicover your ideal AI agent framework solution.
TL;DR
This guide ranks the top 11 AI agent frameworks.
Covers code-first, low-code, and managed approaches.
Use the evaluation criteria and comparison table to choose quickly.
Vellum leads for production-ready governance, observability, and collaboration.
LangChain: Modular, open-source framework with broad ecosystem and flexible RAG/memory.
OpenAI Agents: API-first, GPT-centric agent builder with tool calling and seamless model upgrades.
AutoGen: Open-source orchestration for agent-to-agent collaboration and self-reflection loops.
CrewAI: Role-based, team-of-agents orchestration with visual design and collaboration flows.
AI agent frameworks save weeks of developer time
I worked with a fintech customer whose their developers were struggling to stitch together multiple AI agent frameworks just to handle onboarding.
By moving into Vellum, they unified what previously required separate tools—agents for document verification, compliance checks, and escalation paths—into a single framework with built-in governance and observability.
The dev team saved weeks by cutting manual review time by more than half, and because they weren’t reinventing the plumbing, they shipped a production-ready workflow in under two weeks.
What is an AI agent framework?
An AI agent framework is software that helps teams, especially developers build, orchestrate, and deploy autonomous or semi-autonomous agents. It provides workflow automation, memory, tool integrations, and runtime controls to run reliable multi-step processes.
Why use AI agent frameworks?
AI agent frameworks quickly turn scattered prototypes into production systems. Here are the benefits you can expect from using an AI agent framework:
Accelerate time-to-market
Ship reliable, observable production workflows
Enable multi-agent collaboration and orchestration
Gain enterprise governance, versioning, and auditability
Who needs AI agent frameworks?
Any developer team moving from AI idea to AI agents with deep business impact benefits. Ideally your AI agent framework can support more teams in your org, rather than just catering to developers. Teams like FP&A, Product, Data Science, etc. should be able to collaborate with developers to make AI agents.
What makes an ideal AI agent framework?
The best frameworks are modular and observable, with governance you can take to audit and deployment options that fit your stack. Look for rich integrations and a great developer experience (SDK + visual builder + docs) so teams can ship quickly without painting themselves into a corner.
Modularity: Swap or extend components
Observability: Logs, traces, and evaluation tools
Governance: RBAC, audit logs, and compliance features
Multi-agent orchestration: Enterprises are scaling from single-agent pilots to dozens of coordinated agent systems, with initiatives like Salesforce and Google’s Agent-to-Agent (A2A) standard showing the push toward collaboration at scale [1].
Enterprise governance: Regulatory pressure is forcing enterprises to emphasize RBAC, audit trails, and compliance logging as core features of AI platforms [2].
Visual/low-code: Low and no-code platforms remain a top enterprise investment category for 2025, helping accelerate AI prototyping and delivery across teams [3].
Open-source dominance: OSS underpins most production workloads, with surveys showing 90%+ of enterprises depend on open-source software in production [4].
Vendor-managed runtimes: Vendor-managed AI platforms are gaining traction in regulated industries where compliance burden is highest, even if adoption multiples vary by sector [5].
Why these 11 Frameworks in 2025?
These platforms lead on developer adoption, feature depth, and real-world reliability. They support code-first SDKs, low-code canvases, and managed runtimes to fit different IT and compliance needs.
How to evaluate AI agent frameworks
Use these criteria to score options against your requirements:
Criterion
Description
Why It Matters
Modularity
Swappable, composable components for models, tools, memory, and routing
Enables customization and scaling without rewrites
Observability
Tracing, logs, metrics, eval harnesses, and regression alerts
Shortens MTTR; builds trust in outputs
Governance
RBAC, audit logs, change history, approvals, HITL
Mandatory for enterprise and regulated use
Deployment Options
Cloud, VPC, or on-prem; secrets and data residency controls
Fits diverse IT and compliance requirements
Integration
Connectors/SDKs for internal tools, RAG, and external APIs
Reduces glue code and maintenance
Developer Experience
Unified SDKs, clear docs, visual builder, CI hooks
Speeds onboarding and iteration
Performance
Latency, throughput, horizontal scaling patterns
Impacts UX and cost
Cost
Pricing model and total cost of ownership (infra + people)
Determines long-term feasibility
How we chose the top 11 best AI agent frameworks
We ranked frameworks by feature completeness, production readiness, governance, and developer experience. We balanced open-source flexibility against managed reliability, prioritizing solutions proven in real deployments.
Expect trade-offs:
Flexibility vs. ease: Code-first is powerful; visual is fast.
OSS vs. managed: Control vs. simpler ops.
Cost vs. enterprise features: Governance often raises TCO.
Ecosystem breadth vs. specialization: Broad platforms may lack vertical depth.
Top 11 best AI agent frameworks
Below are concise picks with best-fit guidance. Choose by your deployment, governance, and speed needs.
1. Vellum AI — All-in-one platform for enterprise-grade AI agents
Quick overview:Vellum AI provides developers a unified platform for building, testing, and deploying production-grade AI agents. It combines a visual builder with an SDK, built-in evaluations, and observability so teams can iterate quickly while staying enterprise-compliant. With flexible deployment (cloud, VPC, on-prem), it’s designed for engineering, product, and compliance teams to collaborate.
Best for: Developer teams needing a robust, production-ready AI agent platform to enable collaboration with the rest of their org.
Pros:
Built-in evaluations and versioning
End-to-end observability for debugging and monitoring
2. LangChain — Modular open source agent framework
Quick overview:LangChain is a open-source framework for developers building complex multi-model AI applications. It offers modular components for retrieval, memory, and orchestration, supported by a vast ecosystem of integrations. While powerful, it requires engineering resources for hosting, scaling, and ongoing maintenance.
Best for: Developers building custom multi-model agent workflows
Pros:
Modular components and broad ecosystem
Flexible RAG and memory integrations
Supports multiple LLMs and toolchains
Cons:
Steep learning curve
Requires self-hosting and maintenance
Pricing: Free tier; paid plans starting from $39/month
Quick overview:OpenAI’s SDK provides a streamlined way to build GPT-powered assistants with function calling, memory, and safety guardrails. It focuses on simplicity and rapid prototyping, with seamless upgrades as OpenAI’s models evolve. The trade-off is vendor lock-in and usage-based costs.
Best for: Fast prototyping of GPT-powered assistants with tool/function calling
Pros:
Seamless model upgrades
Easy tool/function integration
Strong guardrails and safety features
Cons:
Tied to OpenAI models
Usage-based costs can add up
Pricing: Usage-based (API metered)
4. AutoGen — Open source multi-agent orchestration
Quick overview:AutoGen is an open-source framework built for orchestrating multiple agents that can collaborate, communicate, and reflect. It’s popular in research and advanced use cases where experimentation with agent-to-agent loops is critical. However, it lacks enterprise-grade governance and requires significant engineering to productionize.
Best for: Research and advanced agent-to-agent collaboration
Pros:
Agent-to-agent communication patterns
Self-reflection and feedback loops
Open source and extensible
Cons:
Limited enterprise features
Requires engineering resources
Pricing: Free (open source)
5. CrewAI — Visual team of agents platform
Quick overview:CrewAI specializes in designing teams of role-based agents through a visual workflow interface. It helps teams prototype and deploy collaborative agent flows quickly, without heavy coding. While easy to use, advanced observability and governance features are limited.
Best for: Designing collaborative agent teams with roles
Pros:
Visual workflow builder
Role-based agent collaboration
Quick prototyping
Cons:
Limited advanced observability
Freemium model restricts some features
Pricing: Enterprise only.
6. n8n — Automation platform with AI agent plugins
Quick overview:n8n is an open-source automation platform that combines AI agents with traditional SaaS workflows. With a low-code visual builder and hundreds of integrations, it’s a versatile option for both developers and operations teams. It can run self-hosted or in the cloud, though advanced AI features often require scripting.
Best for: Workflow automation integrating AI and traditional apps
Pros:
Visual low-code interface
Large library of integrations
Self-hosting option
Cons:
Not AI-focused by default
Advanced features may need scripting
Pricing: Free (open source); cloud from $20/month
7. Zapier — No-code automation with AI integrations
Quick overview:Zapier is a no-code automation leader that connects thousands of apps, now with AI integrations. It’s designed for business users to quickly set up workflows without technical expertise. While great for simple automations, it lacks deep agent orchestration capabilities.
Best for: Non-technical users automating tasks with AI and SaaS tools
Pros:
Extensive app ecosystem
Simple no-code builder
Fast setup
Cons:
Limited agent orchestration
Usage caps on free/low tiers
Pricing: Free tier; paid plans from $19.99/month
8. Lindy AI — Personal AI assistant platform
Quick overview:Lindy AI focuses on personal and business assistants, offering customizable templates for common workflows. Its platform aims to make AI-driven productivity accessible to non-technical users. The trade-off is limited flexibility for complex multi-agent logic.
Best for: Automating personal and business workflows with AI
Pros:
Prebuilt assistant templates
Customizable workflows
Easy onboarding
Cons:
Less flexible for complex agent logic
Usage-based pricing
Pricing: Starts at $25/month
9. Gumloop — Visual LLM agent builder
Quick overview:Gumloop is a lightweight visual builder for prototyping LLM-powered agents. Its drag-and-drop interface and templates make iteration fast, appealing to startups and builders experimenting with AI. Scaling and customization options are more limited compared to enterprise frameworks.
Best for: Rapid prototyping of LLM-powered agents
Pros:
Drag-and-drop interface
Built-in templates
Fast iteration
Cons:
Limited deep customization
Scaling options limited
Pricing: Free tier; paid plans from $37/month
10. Stack AI — Low-code AI workflow platform
Quick overview:Stack AI provides a low-code platform for building AI-powered automations and workflows. It combines a visual editor with API integrations, enabling quick deployment of business-focused agents. More advanced collaboration and observability features may require custom coding.
Best for: Building AI-powered automations with minimal code
Pros:
Visual workflow editor
API integrations
Quick deployment
Cons:
Limited agent collaboration features
Some advanced features require coding
Pricing: Free tier; Enterprise plan
11. Dify — Open source visual agent builder
Quick overview:Dify is an open-source visual agent builder that emphasizes flexibility and community-driven innovation. It comes with templates and orchestration tools while giving teams the freedom to self-host and customize. Enterprise controls are limited, but it’s a strong OSS alternative for teams who want control and transparency.
Best for: Developers and teams needing open source agent orchestration
Pros:
Visual builder with templates
Open source flexibility
Community support
Cons:
Limited enterprise features
Requires self-hosting
Pricing: Free (open source); paid cloud plans available
Designing role-based teams visually: Choose CrewAI.
Connecting apps and AI in low-code workflows: Choose n8n or Zapier.
Want OSS visual builder with templates: Choose Dify.
Why choose Vellum
Vellum is the AI agent platform that lets non-technical teammates and developers 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
Regulated or security-sensitive environments: where developers need RBAC, audit logs, and compliance guardrails out of the box
Cross-functional workflows: that let engineers, product, and compliance collaborate in one shared framework instead of stitching tools together
Developer control over change management: versioning, testing sandboxes, and safe rollout pipelines
Scaling code to production: built-in observability, logs, and rollback tools that cut debugging cycles dramatically
Faster iteration for dev teams: ship agents quickly without building custom infrastructure for governance and monitoring
How Vellum compares (at a glance)
Comparison
Vellum Advantage
Vellum vs LangChain
Built-in evals, versioning, and enterprise governance out of the box
Vellum vs OpenAI SDK
Multi-model orchestration and full observability, not just GPT-centric workflows
Vellum vs AutoGen
Production-grade monitoring and safe deployment, beyond orchestration patterns
Vellum vs CrewAI
Enterprise controls and auditability, not just visual design
What you can ship on Vellum in the first 30 days
Week
Milestone
Deliverable
Week 1
Initial setup and onboarding
Vellum environment configured; team onboarded
Week 2
First model deployment and testing
Agent workflow live; initial tests run
Week 3
Evaluation framework implementation
Evals + monitoring dashboards in place
Week 4
Production rollout
Agents in production; governance enabled
FAQs
1) What’s the fastest path from prototype to production for agent workflows?
If you need versioning, evals, and rollback out of the box, a managed, enterprise-grade platform is fastest. Vellum is a strong fit here because it bundles evals, observability, and promotion workflows so devs don’t rebuild plumbing.
2) Code-first vs. visual builders—how should teams choose?
Code-first (e.g., LangChain) maximizes control; visual builders (e.g., CrewAI, Dify) speed collaboration. Many teams do both: code for custom logic + a visual canvas for orchestration and reviews. Vellum supports this hybrid with SDKs and a shared canvas.
3) How do we prevent prompt or model changes from breaking production?
Version every artifact (prompts, tools, retrieval, models), run gated evals, and ship via canary. Vellum bakes this in (versioning + eval gates + instant rollback), which reduces blast radius for developer changes.
4) Which frameworks are best for multi-agent collaboration?
For research-heavy agent-to-agent patterns: AutoGen and CrewAI are popular. If you need multi-agent plus enterprise guardrails and run-time observability, Vellum is typically the safer production choice.
5) We’re in a regulated environment—what should we prioritize?
RBAC, audit logs, environment separation, data residency, and HITL. Vellum covers these natively (RBAC, audit trails, VPC/on-prem options), while OSS stacks can do it with more engineering work.
6) What observability signals matter most for debugging agents?
Trace spans per step, input/output snapshots (with redaction), tool-call results, latency, token usage, and eval outcomes tied to KPIs. Neutral options: OpenTelemetry-compatible traces, custom log sinks, and eval harnesses; Vellum bundles these for quicker setup.
7) How do I control LLM spend as usage grows?
Use routing (cheap models for easy tasks), caching, batch retrieval, tool-time limits, and token budgets per workflow. Any platform can implement this; Vellum helps by exposing per-route metrics and guardrails without custom dashboards.
8) When is open source the better starting point?
If you need deep customization, self-hosting, or want to experiment cheaply, OSS (e.g., LangChain, Dify, n8n) is great. As reliability and compliance needs increase, teams often graduate to a managed platform like Vellum for governance and ops.
9) What’s a pragmatic 30-day plan to show value?
Week 1: pick one measurable use case + set evals/tracing. Week 2: internal pilot behind flags. Week 3: add guardrails + regression alerts. Week 4: canary to a subset, monitor, then expand. This works on any stack; Vellum accelerates it by giving you evals, traces, and promotion gates day one.
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.
ABOUT THE reviewer
David Vargas
Full Stack Founding Engineer
A Full-Stack Founding Engineer at Vellum, David Vargas is an MIT graduate (2017) with experience at a Series C startup and as an independent open-source engineer. He built tools for thought through his company, SamePage, and now focuses on shaping the next era of AI-driven tools for thought at Vellum.
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