Quick verdict
These two solve different-sized problems, so most "vs" articles compare apples to a fruit bowl:
- OpenAI Agents SDK (
@openai/agents) is a minimal library: agents, tools, handoffs, guardrails, a typed context object. Best for OpenAI-centric teams shipping a scoped assistant or a triage-plus-specialists pattern with the least abstraction possible. Honest limit: persistence, memory, and evals are your problem — it hands you primitives, not a platform. - Mastra is a batteries-included TypeScript framework from the Gatsby team (Apache-2.0): graph-based workflows, database-backed memory, semantic recall, built-in evals, MCP support, a local Studio, and deployers for Vercel, Cloudflare, and Netlify. Best for product teams building a standalone agent service that needs deterministic multi-step flows and memory that survives restarts. Honest limit: you accept opinionated defaults, a bigger dependency surface, and background costs (more below) in exchange for the batteries.
Cipher's cutover rule: count your non-negotiables. If you need two or more of durable workflows, persistent memory, built-in evals, or human-in-the-loop suspension, Mastra earns its weight. If you need none of them yet, the Agents SDK plus fifty lines of your own code ships faster and reads clearer.
Last updated: August 2026. Both projects move fast — check the Mastra docs and Agents SDK docs for current APIs.
Framework matrix
| OpenAI Agents SDK | Mastra | |
|---|---|---|
| Core abstraction | Agents + handoffs + guardrails | Agents + graph workflows (.then(), .branch(), .parallel()) |
| Model support | OpenAI-native; custom model implementations possible | Unified routing across 40+ providers |
| Memory | Typed context object; persistence is DIY | Message history, working memory, semantic recall, Observational Memory |
| Human-in-the-loop | You build the pause/resume plumbing | Workflow suspend/resume with serialized state |
| Evals | External tooling | Built-in model-graded, rule-based, and statistical scorers |
| Dev tooling | OpenAI platform tracing | Local Studio playground + observability hooks |
| Deployment | Any Node runtime | Next.js/Express/Hono handler or deployers for Vercel, Cloudflare, Netlify |
| License / lock-in | MIT code, OpenAI-shaped defaults | Apache-2.0, provider-agnostic |
Where the Agents SDK wins
Handoffs are the SDK's best idea: a triage agent transfers the conversation to a specialist agent, cleanly, with typed context along for the ride. For a support bot with three departments or an intake flow feeding two specialists, that pattern plus OpenAI's built-in tracing is genuinely all you need — and every line of it is code a new hire can read in an afternoon.
It also travels light conceptually: the same primitives exist in the Python SDK, so mixed teams share a mental model. If your organisation has standardised on OpenAI models and you feel resistance to frameworks after the LangChain years, this is the low-regret choice. We said the same about its Python sibling in Agents SDK vs LangGraph: minimalism is a feature until your orchestration needs outgrow it.
Where Mastra wins
The workflow engine is the separator. Deterministic multi-step processes — content approval, onboarding, compliance review — get explicit control flow with branching and parallelism, and can suspend indefinitely while a human approves the next step, serializing state to storage and resuming later. Building that on the Agents SDK means building a workflow engine yourself.
Memory is the other separator. Mastra ships message history, Zod-validated working memory, semantic recall over past conversations, and Observational Memory that compresses long histories automatically. "What was the issue with my order last month?" works without you hand-rolling a retrieval layer.
Two production traps to respect, from real deployments rather than the README:
- Storage on serverless: Mastra's default LibSQL storage does not work with file URLs on Vercel or Cloudflare Workers — you must wire PostgreSQL, MongoDB, or Upstash before your first serverless deploy, not after it breaks.
- Observational Memory costs run in the background: compression makes its own LLM calls (Gemini Flash-class by default), and that spend does not show up in your agent's token accounting. Budget it or disable it deliberately.
The cutover in practice
| Signal | Agents SDK | Mastra |
|---|---|---|
| Scoped assistant, OpenAI models only | Yes — least code, least ceremony | Overkill |
| Deterministic multi-step business process | You'll rebuild workflows badly | Native |
| Memory across sessions | DIY Postgres + retrieval | Built-in |
| Provider flexibility / model routing | Possible, but against the grain | Native (40+ providers) |
| Evals in CI before launch | Bring LangSmith/Braintrust etc. | Built-in scorers |
For the wider framework field — Strands, LangGraph, CrewAI — see our four-way production comparison. And if your automation is honestly a workflow with one LLM step in it, n8n may beat both.
FAQ
Can I use non-OpenAI models with the Agents SDK? Yes, via custom model implementations, but you are swimming against the defaults. If multi-provider is a requirement rather than a hedge, start with Mastra.
Is Mastra production-ready? Yes — it is on a stable 1.x line, Apache-2.0 licensed, and used in production TypeScript stacks. The serverless storage and Observational Memory caveats above are configuration work, not blockers.
Can I migrate from the Agents SDK to Mastra later? Reasonably. Tool definitions and prompts port cleanly; handoff logic maps to Mastra agents or workflow branches. The expensive part to rebuild is whatever ad-hoc memory layer you wrote in the meantime — another reason to switch before writing one.
Which is cheaper to run? The SDK has no framework overhead; Mastra adds storage plus optional background memory compression. In both cases model tokens dominate the bill.
Who builds production agents on these? Cipher Projects ships production TypeScript agents for Australian and Singapore teams — framework selection, eval harnesses, and deployment on accounts you own.
Conclusion
The Agents SDK is a sharp knife; Mastra is a kitchen. Pick the knife for one clean cut — an OpenAI-native assistant with handoffs. Pick the kitchen when the job is a product: workflows that suspend for humans, memory that survives deploys, and evals that gate releases. Count your non-negotiables and the decision usually makes itself.