# Agno vs OpenAI Agents SDK

URL: https://agno-com-nine.vercel.app/compare/agno-vs-openai-agents-sdk

OpenAI Agents SDK facts checked against their own docs on 2026-08-10.

The OpenAI Agents SDK is a small, well-designed library, and inside the OpenAI platform it is the natural choice. A library, though, hands you the loop and leaves the service around it to you. Agno covers the same primitives, remains indifferent to your choice of model and supplies the service: a runtime, crash recovery, approvals and tracing that never leaves your system.

| Aspect | OpenAI Agents SDK | Agno |
|---|---|---|
| What you write | Agents with handoffs, guardrails and sessions. A minimal, code-first loop. | Agents, teams and workflows in plain Python. |
| Models | OpenAI models are the paved road. Other providers go through beta adapters with documented gaps. | Model-agnostic by design. Switch providers without rewriting your agents. |
| Tracing | Traces go to OpenAI servers by default. Zero Data Retention orgs get no tracing at all. | Tracing lives in your AgentOS, on your infrastructure. Nothing leaves. |
| Durability | It is a library. Crash recovery and long-running work come from third parties like Temporal. | Checkpointing, crash recovery and a durable job queue are built in. Runs resume in place, and background jobs survive restarts and deploys. |
| Streaming | A dropped connection ends the stream. | Streams are resumable. Reconnect with the last event index and catch up where you left off. |
| Approvals | Human-in-the-loop hooks exist. The inbox, the approver identity and the access rules are yours to build. | Included by default. Pending approvals persist, admins resolve them by scope, and the requester cannot approve their own run. |
| Runtime | You build and host the service yourself. | AgentOS is the service. One command, your cloud, your database, your auth. |
| Maturity | Pre-1.0 and moving fast, with releases most weeks. | Stable 2.x, with every release on our releases page. |
| License | MIT. | Apache 2.0 for the SDK and AgentOS. |

## Where OpenAI Agents SDK is strong

- The loop is small and well documented. You can read all of it in an afternoon.
- Handoffs between agents are simple and work well.
- Inside the OpenAI platform the integration is deep: hosted tools, traces, realtime and voice agents.
- Session storage has many backends, from SQLite to Redis to Dapr.

## Choose OpenAI Agents SDK when

- You are committed to OpenAI models and want the deepest platform integration.
- You want the smallest possible library and are prepared to build the service around it.
- You need realtime or voice agents on the OpenAI stack today.

## Choose Agno when

- You want to pick your model provider freely, now and later.
- You want crash recovery, a durable job queue and approvals without wiring third parties together.
- You want a runtime and a control plane without building them yourself.

## Sources

- OpenAI Agents SDK docs: models: https://openai.github.io/openai-agents-python/models/
- OpenAI Agents SDK docs: tracing: https://openai.github.io/openai-agents-python/tracing/
- Agno docs: checkpointing examples: https://docs.agno.com/examples/agents/checkpointing/crash-recovery
- Agno docs: human in the loop: https://docs.agno.com/hitl/overview
- The benchmark harness: https://github.com/agno-agi/agno/tree/main/cookbook/09_evals/performance/comparison