Add GraphRun object to make use of next more ergonomic (#833)

Makefile

@@ -64,7 +64,7 @@ testcov: test ## Run tests and generate a coverage report
 
 .PHONY: update-examples
 update-examples: ## Update documentation examples
-	uv run -m pytest --update-examples
+	uv run -m pytest --update-examples tests/test_examples.py
 
 # `--no-strict` so you can build the docs without insiders packages
 .PHONY: docs

docs/agents.md

@@ -62,13 +62,14 @@ print(result.data)
 
 ## Running Agents
 
-There are three ways to run an agent:
+There are four ways to run an agent:
 
-1. [`agent.run()`][pydantic_ai.Agent.run] — a coroutine which returns a [`RunResult`][pydantic_ai.result.RunResult] containing a completed response
-2. [`agent.run_sync()`][pydantic_ai.Agent.run_sync] — a plain, synchronous function which returns a [`RunResult`][pydantic_ai.result.RunResult] containing a completed response (internally, this just calls `loop.run_until_complete(self.run())`)
-3. [`agent.run_stream()`][pydantic_ai.Agent.run_stream] — a coroutine which returns a [`StreamedRunResult`][pydantic_ai.result.StreamedRunResult], which contains methods to stream a response as an async iterable
+1. [`agent.run()`][pydantic_ai.Agent.run] — a coroutine which returns a [`RunResult`][pydantic_ai.agent.AgentRunResult] containing a completed response.
+2. [`agent.run_sync()`][pydantic_ai.Agent.run_sync] — a plain, synchronous function which returns a [`RunResult`][pydantic_ai.agent.AgentRunResult] containing a completed response (internally, this just calls `loop.run_until_complete(self.run())`).
+3. [`agent.run_stream()`][pydantic_ai.Agent.run_stream] — a coroutine which returns a [`StreamedRunResult`][pydantic_ai.result.StreamedRunResult], which contains methods to stream a response as an async iterable.
+4. [`agent.iter()`][pydantic_ai.Agent.iter] — a context manager which returns an [`AgentRun`][pydantic_ai.agent.AgentRun], an async-iterable over the nodes of the agent's underlying [`Graph`][pydantic_graph.graph.Graph].
 
-Here's a simple example demonstrating all three:
+Here's a simple example demonstrating the first three:
 
 ```python {title="run_agent.py"}
 from pydantic_ai import Agent
@@ -94,6 +95,131 @@ _(This example is complete, it can be run "as is" — you'll need to add `asynci
 You can also pass messages from previous runs to continue a conversation or provide context, as described in [Messages and Chat History](message-history.md).
 
 
+### Iterating Over an Agent's Graph
+
+Under the hood, each `Agent` in PydanticAI 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 PydanticAI — you can use it standalone for workflows that have nothing to do with GenAI — but PydanticAI 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 — PydanticAI 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.
+
+#### `async for` iteration
+
+Here's an example of using `async for` with `iter` to record each node the agent executes:
+
+```python {title="agent_iter_async_for.py"}
+from pydantic_ai import Agent
+
+agent = Agent('openai:gpt-4o')
+
+
+async def main():
+    nodes = []
+    # Begin an AgentRun, which is an async-iterable over the nodes of the agent's graph
+    with agent.iter('What is the capital of France?') as agent_run:
+        async for node in agent_run:
+            # Each node represents a step in the agent's execution
+            nodes.append(node)
+    print(nodes)
+    """
+    [
+        ModelRequestNode(
+            request=ModelRequest(
+                parts=[
+                    UserPromptPart(
+                        content='What is the capital of France?',
+                        timestamp=datetime.datetime(...),
+                        part_kind='user-prompt',
+                    )
+                ],
+                kind='request',
+            )
+        ),
+        HandleResponseNode(
+            model_response=ModelResponse(
+                parts=[TextPart(content='Paris', part_kind='text')],
+                model_name='function:model_logic',
+                timestamp=datetime.datetime(...),
+                kind='response',
+            )
+        ),
+        End(data=FinalResult(data='Paris', tool_name=None)),
+    ]
+    """
+    print(agent_run.result.data)
+    #> Paris
+```
+
+- The `AgentRun` is an async iterator that yields each node (`BaseNode` or `End`) in the flow.
+- The run ends when an `End` node is returned.
+
+#### Using `.next(...)` manually
+
+You can also drive the iteration manually by passing the node you want to run next to the `AgentRun.next(...)` method. This allows you to inspect or modify the node before it executes or skip nodes based on your own logic, and to catch errors in `next()` more easily:
+
+```python {title="agent_iter_next.py"}
+from pydantic_ai import Agent
+from pydantic_graph import End
+
+agent = Agent('openai:gpt-4o')
+
+
+async def main():
+    with agent.iter('What is the capital of France?') as agent_run:
+        node = agent_run.next_node  # (1)!
+
+        all_nodes = [node]
+
+        # Drive the iteration manually:
+        while not isinstance(node, End):  # (2)!
+            node = await agent_run.next(node)  # (3)!
+            all_nodes.append(node)  # (4)!
+
+        print(all_nodes)
+        """
+        [
+            UserPromptNode(
+                user_prompt='What is the capital of France?',
+                system_prompts=(),
+                system_prompt_functions=[],
+                system_prompt_dynamic_functions={},
+            ),
+            ModelRequestNode(
+                request=ModelRequest(
+                    parts=[
+                        UserPromptPart(
+                            content='What is the capital of France?',
+                            timestamp=datetime.datetime(...),
+                            part_kind='user-prompt',
+                        )
+                    ],
+                    kind='request',
+                )
+            ),
+            HandleResponseNode(
+                model_response=ModelResponse(
+                    parts=[TextPart(content='Paris', part_kind='text')],
+                    model_name='function:model_logic',
+                    timestamp=datetime.datetime(...),
+                    kind='response',
+                )
+            ),
+            End(data=FinalResult(data='Paris', tool_name=None)),
+        ]
+        """
+```
+
+1. We start by grabbing the first node that will be run in the agent's graph.
+2. The agent run is finished once an `End` node has been produced; instances of `End` cannot be passed to `next`.
+3. When you call `await agent_run.next(node)`, it executes that node in the agent's graph, updates the run's history, and returns the *next* node to run.
+4. You could also inspect or mutate the new `node` here as needed.
+
+#### Accessing usage and the final result
+
+You can retrieve usage statistics (tokens, requests, etc.) at any time from the [`AgentRun`][pydantic_ai.agent.AgentRun] object via `agent_run.usage()`. This method returns a [`Usage`][pydantic_ai.usage.Usage] object containing the usage data.
+
+Once the run finishes, `agent_run.final_result` becomes a [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] object containing the final output (and related metadata).
+
+---
+
 ### Additional Configuration
 
 #### Usage Limits
@@ -177,7 +303,7 @@ except UsageLimitExceeded as e:
 2. This run will error after 3 requests, preventing the infinite tool calling.
 
 !!! note
-    This is especially relevant if you're registered a lot of tools, `request_limit` can be used to prevent the model from choosing to make too many of these calls.
+    This is especially relevant if you've registered many tools. The `request_limit` can be used to prevent the model from calling them in a loop too many times.
 
 #### Model (Run) Settings
 
@@ -441,7 +567,7 @@ If models behave unexpectedly (e.g., the retry limit is exceeded, or their API r
 
 In these cases, [`capture_run_messages`][pydantic_ai.capture_run_messages] can be used to access the messages exchanged during the run to help diagnose the issue.
 
-```python
+```python {title="agent_model_errors.py"}
 from pydantic_ai import Agent, ModelRetry, UnexpectedModelBehavior, capture_run_messages
 
 agent = Agent('openai:gpt-4o')

docs/api/agent.md

@@ -4,6 +4,8 @@
     options:
         members:
             - Agent
+            - AgentRun
+            - AgentRunResult
             - EndStrategy
-            - RunResultData
+            - RunResultDataT
             - capture_run_messages

docs/api/result.md

@@ -2,4 +2,7 @@
 
 ::: pydantic_ai.result
     options:
-      inherited_members: true
+        inherited_members: true
+        members:
+            - ResultDataT
+            - StreamedRunResult