The 10 best AI agent platforms for builders

This guide ranks AI agent platforms by how well they help teams design, ship, and operate real workflows with clear state control, typed tool calls, and evidence you can hand to security and stakeholders.

We scored each pick on builder centric criteria: multi-model support, integration depth, policy and safety features, tracing and testability, ecosystem maturity, and deployment options. The order is intentional and different from common roundups. Every entry includes who it is for, key build notes, and trade offs, plus a quick plan for a two week proof of value. The goal is simple. Help you choose a platform that accelerates building and keeps your agents reliable once they are in production.

1) LangGraph

Best for engineering teams building resilient multi-agent systems.

LangGraph is a graph-native framework from the LangChain team that lets you model agents as stateful nodes with control over loops, branches, and recovery. It pairs with LangSmith for tracing and debugging so teams can iterate quickly on complex plans. If you need production-grade orchestration with clear state control, start here. 

Builder notes

• Graph abstraction for planners and subagents
  
  

• Built-in tracing and testability with LangSmith
  
  

• Works with multiple model providers

  
  

2) OpenAI Operator and AgentKit

Best for web tasking and building agents inside ChatGPT’s app ecosystem.

OpenAI’s Operator uses its own browser to read and act on web pages. For builders, the recent developer push adds an SDK for chat-native apps and AgentKit for task-based agents. If your product strategy includes distribution inside ChatGPT or agents that must operate the web, track this closely. 

Builder notes

• App SDK and AgentKit for agent behaviors in chat
  
  

• Native web interaction with typing and clicking
  
  

• Early ecosystem momentum via the new app directory

  
  

3) CrewAI

Best for role-based multi-agent collaboration with a lean Python stack.

CrewAI is a focused framework for composing “crews” of agents with roles and goals. It is Python-first and moves fast, with active development on guardrail events and telemetry. If you want an opinionated, light dependency approach for multi-agent builds, it is a strong choice. 

Builder notes

• Role design, task routing, and crew orchestration

  
  

• Rapid release cadence and examples in the repo
  
  

• Cloud option available for teams that prefer hosted control planes

  
  

4) Gumloop

Best for no-code builders who still want real logic and subflows.

Gumloop is a visual automation framework that treats nodes and flows as first-class building blocks. Non-engineers can compose LLM steps, API calls, and data transforms, then reuse subflows inside larger workflows. It is attractive when you want to prototype quickly or empower operations teams to ship without code. 

Builder notes

• Drag-and-drop flows and reusable subflows
  
  

• Template hub for common agent tasks
  
  

• Model and API flexibility for stack fit

  
  

5) Relay.app

Best for agencies and service teams that need AI plus automation.

Relay.app combines a visual workflow builder with AI steps and dozens of integrations. For builders, the draw is practical: quick connections to everyday tools and a gentle learning curve that still supports custom prompts and policies. It is a good fit for teams that want agents to run the business glue. 

Builder notes

• Visual builder with many SaaS connectors
  
  

• Prompted steps for summarization, extraction, and routing
  
  

• Team sharing and schedules for hands-off runs
  
  

6) Postman AI Agent Builder

Best for API-first teams who need agents to test and operate services.

Postman launched an AI Agent Builder that lives where your APIs already are. Builders can evaluate APIs and models, then wire agents with Flows and the Postman ecosystem. If your agents must call internal and external services safely and you already use Postman, the integration path is short. 

Builder notes

• Access to a large API network and testing tools
  
  

• No-code Flows plus developer-grade debugging
  
  

• Clear line of sight to CI integration
  
  

7) Voiceflow

Best for support and conversation-heavy agents across channels.

Voiceflow matured from voice apps into a broad AI agent platform for customer support. Builders get conversation design, data grounding, and channel deployment in one place. If your first agent is a support assistant that must talk, type, and escalate, Voiceflow is purpose-built. 

Builder notes

• Multi-channel design and testing
  
  

• Knowledge base grounding and ticket actions
  
  

• Admin controls for operations teams
  
  

8) Devin by Cognition

Best for engineering workflows and software tasks.

Devin positions itself as an “AI software engineer.” For builders, the appeal is an agent with an IDE, shell, and browser that can follow multi-step development tasks, now with a newer Agent Preview focused on speed and accuracy. If you want an agent to help build software, this is worth piloting. 

Builder notes

• End-to-end coding environment
  
  

• Improvements on internal developer evals
  
  

• Early access model that is evolving
  
  

9) Zep

Best for giving agents long-term memory and context assembly.

Zep is a memory layer for agents. It engineers relevant context from chat history and business data, with APIs and open docs that make integration manageable. Use it when your agents fail because they forget user preferences or prior steps. 

Builder notes

• Agent memory, graph RAG, and context assembly
  
  

• Docs and examples for quick embedding
  
  

• Useful with LangGraph, CrewAI, and custom stacks
  
  

10) Stack AI

Best for no-code enterprise builds with templates and governance.

Stack AI is a visual platform for deploying agents with enterprise features like templates, connectors, and compliance documentation. The company’s recent funding reflects adoption in traditional industries, and the site highlights on-prem options and security commitments for regulated buyers. 

Builder notes

• Drag-and-drop agent builder with many templates
  
  

• Enterprise posture and deployment flexibility
  
  

• Connectors for databases and CRMs
  
  

Honorable mentions worth a look

AirOps: useful for SEO and growth teams that want agentic workflows tied to content operations.

HockeyStack: strong analytics plus agent features targeted at enterprise marketing, suitable when analytics and automation must live together.

(We kept these out of the top 10 to focus on core agent-building platforms.)

Comparison snapshot: which tool fits your build

• I need multi-agent orchestration with state control: LangGraph or CrewAI.
  
  

• I need web tasking or distribution inside ChatGPT: OpenAI Operator and the new app SDK or AgentKit.
  
  

• I need no-code flows for business teams: Gumloop or Relay.app.
  
  

• I need conversation agents for support: Voiceflow.
  
  

• I need an agent that helps write code: Devin.
  
  

• I need memory and context at scale: Zep.
  
  

• I need enterprise templates and governance: Stack AI.
  
  

How to run a 14-day proof of value that reflects reality

Day 1 to 2: Define one workflow with a clear user story and a pass or fail test. Document success metrics: task completion, latency budget, cost per run, and block reasons.

Day 3 to 5: Implement in two platforms from this list. Use the same prompts, tools, and datasets for fairness.

Day 6 to 8: Add guardrails referenced in OWASP LLM Top 10: prompt injection checks, tool argument validation, and DLP. Capture traces that map to NIST AI RMF evidence. 

Day 9 to 11: Run adversarial tests and failure cases. Track true blocks, false blocks, and recovery.

Day 12 to 14: Compare operations fit: alerting, trace exports, access controls, and admin policies. Pick the platform that meets the metrics and reduces operational friction.

Buying checklist for agent builders

• Orchestration supports branches, retries, and human intervention.
  
  

• Tool calls use typed schemas with argument validation.
  
  

• Supports multiple LLMs and on-prem or VPC options.
  
  

• OWASP-aligned policies for prompt injection and output handling.
  
  

• Traces include plan steps, tool inputs, outputs, and block reasons that satisfy NIST AI RMF evidence.
  
  

• Red team hooks and CI gates available.
  
  

• Admin controls for tenants, secrets, and spend caps.
  
  

• Clear roadmap on SDKs and ecosystem growth.

FAQs for builders

Are these platforms only for no-code users

No. Several are developer frameworks first, such as LangGraph and CrewAI. Others are visual and target operations teams. We prioritized tools that help teams ship faster, not a single persona.

How do I avoid lock-in

Favor platforms with multi-model support, exportable prompts and policies, and standard interfaces for tools and memory. The OpenAI app SDK and AgentKit are promising but still evolving, so evaluate with migration in mind. 

How should I think about security now

Adopt OWASP LLM Top 10 language for risks and map logs to NIST AI RMF outcomes. This keeps engineering and risk aligned and helps with audits later. 

Conclusion

Agent platforms are not equal. Some help you build with orchestration, guardrails, and evidence. Others impress in demos but stall in production. Start with one workflow, use OWASP and NIST to define success, and run a two-week proof where traces and block reasons are just as important as task completion. Your short list should come from how well a platform supports building and operating agents, not from a single benchmark.