LangGraphFreemium
A low-level orchestration framework for building stateful, multi-step agent workflows with explicit control over state, branching, and human-in-the-loop steps.
Agent Development comparison
LangGraph vs OpenAI AgentKit
Pricing, pros, cons, and ideal use cases — side by side.
OpenAI AgentKitFreemium
OpenAI first-party agent toolkit — a visual Agent Builder, embeddable ChatKit UI, a connector registry, and evals, billed at plain API rates.
At a glance
| LangGraph | OpenAI AgentKit | |
|---|---|---|
| Pricing | FreemiumOpen-source framework, free to use. Paid tiers apply to the surrounding LangSmith platform, not the framework itself. | FreemiumNo separate platform fee. Agent Builder design is free; you pay standard OpenAI API model and storage rates for execution. As a reference point, GPT-5.2 runs roughly $1.75 per million input tokens and $14 per million output tokens, with GPT-5 mini at about $0.25 per million input tokens. |
| Category | Agent Development | Agent Development |
| Ideal for | Engineering teams building multi-step agentsEnterprise platform teamsWorkflows needing human-in-the-loop control | Product teams embedding agents into their own softwareEngineering teams already building on the OpenAI APIStartups that want agent scaffolding without a platform feeTeams standardizing evals alongside agent development |
Pros & cons
LangGraph
Pros
- Explicit control over agent state and branching
- First-class human-in-the-loop and checkpointing
- Strong fit for durable, long-running workflows
- Large ecosystem and active development
Cons
- Requires real engineering investment
- Lower-level than no-code agent builders
- You own deployment and observability
OpenAI AgentKit
Pros
- No platform fee on top of API usage
- Visual workflow builder with versioning
- ChatKit ships a production-ready chat UI
- Evals are first-class rather than an afterthought
- Cost model is the API cost model you already forecast
Cons
- Single-provider lock-in at the orchestration layer
- Agent Builder and Connector Registry have been in beta rather than fully GA
- Fewer enterprise governance controls than Copilot Studio or Agentforce
- No managed hosting story comparable to Bedrock AgentCore
Which should you choose?
LangGraph is built around engineering teams building multi-step agents; OpenAI AgentKit leans more toward product teams embedding agents into their own software. Shortlist the one whose strengths line up with your biggest constraint.
Still deciding between LangGraph and OpenAI AgentKit?
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