Verdict: choose Mastra if your team wants a TypeScript-first agent application framework with workflows, memory, MCP, tracing, and evals already woven into one build surface. Choose OpenAI Agents SDK if your team wants a code-first runtime closer to the model layer, more direct control over orchestration and approvals, and growing native support for sandboxed long-running agent work. If you are comparing frameworks before you have even defined the workflow, review loop, and business owner, stop framework shopping and run an implementation audit first.
The real decision is application framework versus model-near runtime
These products overlap, but they do not start from the same design center. Mastra presents itself as a framework for building stateful agents with memory, tools, MCP, logging, tracing, evals, workflows, and agent networks built into one TypeScript-oriented stack. That makes it attractive when the surrounding agent application is the project, not just the model loop.
OpenAI Agents SDK is closer to a runtime layer for teams that want their own server to own orchestration, tool execution, approvals, and state. OpenAI now positions the SDK as the code-first path, while separating it from the hosted Agent Builder and ChatKit path. That is a meaningful distinction: if you want to write and own the workflow logic in code, the SDK is the product; if you want OpenAI-hosted visual workflow creation, that is a different surface.
Mastra vs OpenAI Agents SDK at a glance
| Decision area | Mastra | OpenAI Agents SDK |
|---|---|---|
| Best fit | Teams shipping a full agent application in TypeScript | Teams owning orchestration in code close to the OpenAI runtime |
| What stands out | Built-in memory, workflows, tracing, evals, guardrails, and MCP | Handoffs, guardrails, hosted tools, observability, and growing sandbox runtime |
| Workflow philosophy | Build the surrounding app and agent system together | Build the runtime loop and own the surrounding app yourself |
| Language center | TypeScript-first | TypeScript and Python SDKs, with some newer runtime capabilities rolling out in Python first |
| Who usually buys it | Product teams standardizing agent app development | Engineering teams that want tighter runtime control and OpenAI-native patterns |
| Main risk | More framework surface than you need for a narrow workflow | You may still need to assemble more of the surrounding app stack yourself |
Choose Mastra when shipping the full agent application is the job
Mastra is usually the better pick when your team is not just wiring a single agent loop, but building an application surface around agents that needs durable workflows, memory, evaluation, and observability from the start.
Mastra makes more of the surrounding stack a default
Mastra highlights memory, tool calling, MCP, logging, tracing, evals, workflows, and agent networks as built-in primitives. It also pushes a clear workflow story: when a process needs to be repeatable, predictable, and auditable, you define execution graphs with sequential, parallel, branching, or looping steps instead of hoping one agent reasons through everything correctly. That is a strong fit for teams building internal agent products, operational copilots, or multi-step business automations where the workflow shape matters as much as the model choice.
Mastra is the stronger default for TypeScript-led teams
Mastra is explicit about its TypeScript orientation. If your team already builds backend logic, APIs, and product surfaces in TypeScript, Mastra gives you a more unified mental model for the application layer around the agent. That matters when the real work is not “call a model” but “ship a reliable service with review points, memory, monitoring, and repeatable business logic.”
Mastra also gives evaluation a bigger seat earlier
Its scorers framework is not a side note. Mastra treats automated scoring as part of the development loop, including cloud execution and CI/CD use. If your team expects to iterate on prompt behavior, workflow steps, or output quality with structured scoring from the beginning, Mastra feels more opinionated in a useful way.
Choose OpenAI Agents SDK when your server should own the runtime loop
OpenAI Agents SDK is usually the better choice when your team wants more direct control over how agent runs execute, how specialists hand off work, how tools and approvals are handled, and how the runtime should interact with OpenAI’s own evolving agent infrastructure.
The SDK is best when you want code-first orchestration, not an all-in-one framework
OpenAI’s documentation is clear that the SDK track is for teams whose server owns orchestration, tool execution, approvals, and state. That makes it attractive when you want a thinner framework layer and more authority over how your application behaves. If you already have your own storage, business logic, permission model, and service boundaries, the SDK can fit more naturally than a heavier all-in-one framework.
OpenAI’s newer sandbox story changes the buying decision
The April 15, 2026 update matters. OpenAI added a more capable harness, native sandbox execution, manifest-based workspace configuration, and durable execution patterns such as snapshotting and rehydration. That makes the SDK more compelling for long-running agents that need files, commands, packages, and isolated environments. If your evaluation centers on agents that inspect documents, edit code, run commands, and continue across failures, this is a real buying signal, not a cosmetic release note.
The hosted path is a bonus, not the main reason to choose it
Another advantage is optionality. OpenAI separates the code-first SDK from Agent Builder and ChatKit, but the products connect. If your team wants to start code-first and later take advantage of OpenAI-hosted workflow creation or embedding, that path exists. Still, if the hosted visual path is the main thing you want, you are really evaluating Agent Builder as much as the SDK.
The workflow differences buyers actually feel
- Mastra is better when you want one framework to carry agents, workflows, memory, evals, and observability together.
- OpenAI Agents SDK is better when you want to stay close to the runtime, own orchestration decisions in your own codebase, and benefit from OpenAI’s model-native agent primitives.
- Mastra is better when process shape is already clear and the app around the agent is becoming the main engineering surface.
- OpenAI Agents SDK is better when your organization already centers on OpenAI infrastructure and wants tighter control over tools, handoffs, results, and run state.
- Mastra usually feels more complete out of the box for a TypeScript product team.
- OpenAI Agents SDK usually feels better when the agent runtime itself is the architecture decision.
Cost, risk, and tradeoffs most teams miss
The sticker price is usually not the real issue here. The real cost is how much framework work, runtime assembly, testing, and review-loop design your team must own after the first demo.
Mastra’s risk is overbuying framework surface. If you only need a narrow agent with a few tools and a clear approval path, you may not need a fuller framework abstraction around workflows, memory, and evaluation from day one.
OpenAI Agents SDK’s risk is the opposite. You can get excellent control, but you may still need to stitch together more of the surrounding application layer yourself. That can be the right tradeoff for strong engineering teams and the wrong one for product or operations teams that mostly want a business workflow deployed reliably.
There is also one practical wrinkle buyers should not ignore: OpenAI’s April 2026 runtime expansion makes the SDK stronger for sandboxed long-horizon work, but OpenAI said those new harness and sandbox capabilities launch first in Python, with TypeScript support planned later. If your team is deeply TypeScript-first and wants that exact runtime surface immediately, check current implementation gaps before committing.
When Nerova is the better path than either framework
If your company is comparing Mastra and OpenAI Agents SDK because you need a support agent, outbound worker, internal knowledge assistant, or operations workflow, you may be solving the wrong problem at the wrong layer. Framework selection makes sense when you are building a reusable internal platform or product. It is a poor first move when what you actually need is one business outcome deployed fast.
Choose a Nerova-generated agent when you already know the job and want one AI worker for it. Choose a Nerova AI team when the workflow spans multiple coordinated steps or handoffs. Choose a Nerova audit when you are still deciding what should be automated, what needs human review, and which workflow deserves engineering time first.
Final recommendation
Pick Mastra if your team wants the most cohesive TypeScript-first framework for building the whole agent application layer: workflows, memory, traces, evals, and guardrails included.
Pick OpenAI Agents SDK if your team wants to own the runtime in code, stay closer to OpenAI’s agent stack, and take advantage of the platform’s expanding support for orchestration, review, observability, and sandboxed execution.
Pick neither first if you are still vague on the business workflow, the approval model, or the success metric. In that situation, the highest-leverage move is not a framework decision. It is scoping the workflow correctly before you build.