Conversation state and context windows

Once an agent is running under the OpenAI Agents SDK, it stops being a series of isolated requests. Each interaction builds on what came before. This lesson exists to orient us to how the SDK tracks conversation state and how that state shapes an agent’s behavior over time, which is essential when building agents that feel coherent, responsive, and grounded in prior exchanges.

Conversation state in an SDK-managed agent

In an SDK-managed agent, conversation state is the accumulated record of interactions between the user, the agent, and any tools the agent has used. This includes user inputs, agent responses, and tool results that the SDK chooses to retain.

We do not manually pass this state back and forth on every call. The SDK maintains it for us, attaching new interactions to the existing conversation automatically. From our perspective, each agent invocation happens “in context” rather than starting fresh.

How the SDK maintains conversational context

The SDK maintains conversational context by appending new messages to the existing conversation state behind the scenes. When the agent reasons or responds, the SDK supplies the relevant portion of that conversation to the model.

This means the agent can refer back to earlier questions, clarifications, or decisions without us explicitly restating them. The continuity comes from the SDK managing what information is carried forward and how it is presented to the model.

Context windows and their limits

A context window is the amount of past conversation the model can consider at once. It is finite. When the conversation grows too large, older parts of the state eventually fall outside that window.

The SDK handles this constraint for us, but the limit still matters. Not everything that ever happened can influence the agent forever. At any given moment, only a slice of the full conversation state is actively shaping the model’s reasoning.

How past interactions influence agent responses

Within the active context window, past interactions directly influence how the agent responds. Earlier instructions, decisions, and clarifications act as signals that shape tone, assumptions, and reasoning.

If an agent previously agreed on a plan or adopted a specific role, that information remains influential as long as it stays within the context window. From the agent’s point of view, this is what gives conversations a sense of memory and continuity.

Inspecting and reasoning about current conversation state

Even though the SDK manages conversation state automatically, we still need to reason about what the agent currently “knows.” This means thinking about which interactions are likely still in context and which may have fallen away.

When an agent behaves unexpectedly, inspecting recent inputs, outputs, and tool results is often enough to explain why. The key is to treat conversation state as a real, evolving input to the system, not as invisible magic.

Conclusion

At this point, we are oriented to how conversation state works inside an SDK-managed agent. We understand that the SDK maintains context for us, that this context lives within a bounded window, and that recent interactions actively shape agent behavior. With this mental model, we can design agents that rely on continuity while staying aware of the limits that context windows impose.