The short answer
OpenAI Agents SDK is a focused Python toolkit for applications composed from agents, tools, handoffs, guardrails, sessions, human approval steps, and tracing. Agno covers those core agent needs but extends further into teams, deterministic workflows, knowledge, memory, storage, and an AgentOS runtime and control-plane model. The practical decision is less about whether either project can call a given model and more about how much framework and operating surface the application should adopt.
Architecture and orchestration
OpenAI Agents SDK deliberately keeps the primary mental model small: an Agent carries instructions and tools, handoffs transfer control, guardrails validate boundaries, and the runner manages execution. This is attractive when a team wants orchestration primitives that remain close to normal Python code and prefers to assemble application infrastructure separately.
Agno exposes Agent, Team, and Workflow as separate first-class building blocks. That makes it easier to represent collaborative agent groups and deterministic multi-step processes inside one framework, but it also introduces a larger framework vocabulary and a broader lifecycle surface for teams to learn and govern.
Models and provider flexibility
The OpenAI SDK is not restricted to OpenAI models. Its official model layer supports additional providers through built-in integrations and custom providers, and different models can be mixed within a workflow. The documentation also warns that provider and API compatibility varies, especially around Responses-style features and structured outputs.
Agno presents provider choice as a central capability and documents a broad compatibility matrix. That breadth is useful for routing workloads across model vendors, but it is not a promise that every provider exposes identical tools, reasoning, image, audio, or structured-output behavior. Production designs should test the exact provider and modality combination.
State, knowledge, and application services
OpenAI Agents SDK includes sessions for maintaining conversation history and supports MCP and function tools, but it generally leaves broader application data architecture to the surrounding system. This can be an advantage for teams that already have opinions about persistence, retrieval, authorization, and service boundaries.
Agno integrates sessions, memory, knowledge, storage, guardrails, and deployment-facing AgentOS capabilities into the same product family. That can shorten the path from prototype to an observable service, particularly when those built-ins match the intended architecture. Teams with an established platform should still compare those abstractions with their existing data and operations standards.
Observability and data handling
OpenAI Agents SDK includes built-in tracing and enables it by default. Teams can disable tracing or replace processors, and the official documentation states that tracing is unavailable for organizations operating under Zero Data Retention. This default should be reviewed as part of a privacy and telemetry design rather than treated as a hidden implementation detail.
Agno supports observability through its platform surface and documents OpenTelemetry-oriented operation and storage choices. Its ability to keep data in a team-controlled database can be valuable, but actual privacy depends on the selected model provider, deployment topology, logging configuration, and connected services.