Repository: pydantic/pydantic-ai
File: pydantic_ai_slim/pydantic_ai/agent/abstract.py (Python)

Code before:
    ) -> AgentRunResult[Any]:
        """Run the agent with a user prompt in async mode.

        This method builds an internal agent graph (using system prompts, tools and output schemas) and then
        runs the graph to completion. The result of the run is returned.

        Example:
        ```python
        from pydantic_ai import Agent

        agent = Agent('openai:gpt-4o')

        async def main():
            agent_run = await agent.run('What is the capital of France?')
            print(agent_run.output)
            #> The capital of France is Paris.
        ```

        Args:
            user_prompt: User input to start/continue the conversation.
            output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no
                output validators since output validators would expect an argument that matches the agent's output type.
            message_history: History of the conversation so far.
            deferred_tool_results: Optional results for deferred tool calls in the message history.
            model: Optional model to use for this run, required if `model` was not set when creating the agent.
            instructions: Optional instructions to append to the instructions provided at the initialization of the agent and only to be used for this run.
            deps: Optional dependencies to use for this run.
            model_settings: Optional settings to use for this model's request.
            usage_limits: Optional limits on model request count or token usage.
            usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.
            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
            toolsets: Optional additional toolsets for this run.
            event_stream_handler: Optional handler for events from the model's streaming response and the agent's execution of tools to use for this run.
            builtin_tools: Optional additional builtin tools for this run.

        Returns:
            The result of the run.
        """
        if infer_name and self.name is None:
            self._infer_name(inspect.currentframe())

        event_stream_handler = event_stream_handler or self.event_stream_handler

        async with self.iter(
            user_prompt=user_prompt,
            output_type=output_type,
            message_history=message_history,
            deferred_tool_results=deferred_tool_results,
            model=model,
            instructions=instructions,
            deps=deps,

Review comment (suggestion):
Would this be clear enough, mirroring how e.g. `toolsets` is described below?

```suggestion
            instructions: Optional additional instructions to use for this run.
```



Code after:
        """Run the agent with a user prompt in async mode.

        This method builds an internal agent graph (using system prompts, tools and output schemas) and then
        runs the graph to completion. The result of the run is returned.

        Example:
        ```python
        from pydantic_ai import Agent

        agent = Agent('openai:gpt-4o')

        async def main():
            agent_run = await agent.run('What is the capital of France?')
            print(agent_run.output)
            #> The capital of France is Paris.
        ```

        Args:
            user_prompt: User input to start/continue the conversation.
            output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no
                output validators since output validators would expect an argument that matches the agent's output type.
            message_history: History of the conversation so far.
            deferred_tool_results: Optional results for deferred tool calls in the message history.
            model: Optional model to use for this run, required if `model` was not set when creating the agent.
            instructions: Optional additional instructions to use for this run.
            deps: Optional dependencies to use for this run.
            model_settings: Optional settings to use for this model's request.
            usage_limits: Optional limits on model request count or token usage.
            usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.
            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
            toolsets: Optional additional toolsets for this run.
            event_stream_handler: Optional handler for events from the model's streaming response and the agent's execution of tools to use for this run.
            builtin_tools: Optional additional builtin tools for this run.

        Returns:
            The result of the run.
        """
        if infer_name and self.name is None:
            self._infer_name(inspect.currentframe())

        event_stream_handler = event_stream_handler or self.event_stream_handler

        async with self.iter(
            user_prompt=user_prompt,
            output_type=output_type,
            message_history=message_history,
            deferred_tool_results=deferred_tool_results,
            model=model,
            instructions=instructions,
            deps=deps,
            model_settings=model_settings,