Jul 17, 2025 · Jul 13, 2025 · Jul 13, 2025 · Jul 13, 2025 · Jul 13, 2025 · Jul 13, 2025
diff --git a/docs/agents.md b/docs/agents.md

 ### Iterating Over an Agent's Graph

 Under the hood, each `Agent` in Pydantic AI uses **Pydantic Graph** to manage its execution flow. **Pydantic Graph** is a generic, type-centric library for building and running finite state machines in Python. It doesn't actually depend on Pydantic AI — you can use it standalone for workflows that have nothing to do with GenAI — but Pydantic AI makes use of it to orchestrate the handling of model requests and model responses in an agent's run.
 Under the hood, each `Agent` in Pydantic AI uses **pydantic-graph** to manage its execution flow. **pydantic-graph** is a generic, type-centric library for building and running finite state machines in Python. It doesn't actually depend on Pydantic AI — you can use it standalone for workflows that have nothing to do with GenAI — but Pydantic AI makes use of it to orchestrate the handling of model requests and model responses in an agent's run.

 In many scenarios, you don't need to worry about pydantic-graph at all; calling `agent.run(...)` simply traverses the underlying graph from start to finish. However, if you need deeper insight or control — for example to capture each tool invocation, or to inject your own logic at specific stages — Pydantic AI exposes the lower-level iteration process via [`Agent.iter`][pydantic_ai.Agent.iter]. This method returns an [`AgentRun`][pydantic_ai.agent.AgentRun], which you can async-iterate over, or manually drive node-by-node via the [`next`][pydantic_ai.agent.AgentRun.next] method. Once the agent's graph returns an [`End`][pydantic_graph.nodes.End], you have the final result along with a detailed history of all steps.
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		### Iterating Over an Agent's Graph

		Under the hood, each `Agent` in Pydantic AI uses Pydantic Graph to manage its execution flow. Pydantic Graph is a generic, type-centric library for building and running finite state machines in Python. It doesn't actually depend on Pydantic AI — you can use it standalone for workflows that have nothing to do with GenAI — but Pydantic AI makes use of it to orchestrate the handling of model requests and model responses in an agent's run.
		Under the hood, each `Agent` in Pydantic AI uses pydantic-graph to manage its execution flow. pydantic-graph is a generic, type-centric library for building and running finite state machines in Python. It doesn't actually depend on Pydantic AI — you can use it standalone for workflows that have nothing to do with GenAI — but Pydantic AI makes use of it to orchestrate the handling of model requests and model responses in an agent's run.

		In many scenarios, you don't need to worry about pydantic-graph at all; calling `agent.run(...)` simply traverses the underlying graph from start to finish. However, if you need deeper insight or control — for example to capture each tool invocation, or to inject your own logic at specific stages — Pydantic AI exposes the lower-level iteration process via [`Agent.iter`][pydantic_ai.Agent.iter]. This method returns an [`AgentRun`][pydantic_ai.agent.AgentRun], which you can async-iterate over, or manually drive node-by-node via the [`next`][pydantic_ai.agent.AgentRun.next] method. Once the agent's graph returns an [`End`][pydantic_graph.nodes.End], you have the final result along with a detailed history of all steps.

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