pydantic_ai.messages

The structure of ModelMessage can be shown as a graph:

graph RL
    SystemPromptPart(SystemPromptPart) --- ModelRequestPart
    UserPromptPart(UserPromptPart) --- ModelRequestPart
    ToolReturnPart(ToolReturnPart) --- ModelRequestPart
    RetryPromptPart(RetryPromptPart) --- ModelRequestPart
    TextPart(TextPart) --- ModelResponsePart
    ToolCallPart(ToolCallPart) --- ModelResponsePart
    ModelRequestPart("ModelRequestPart<br>(Union)") --- ModelRequest
    ModelRequest("ModelRequest(parts=list[...])") --- ModelMessage
    ModelResponsePart("ModelResponsePart<br>(Union)") --- ModelResponse
    ModelResponse("ModelResponse(parts=list[...])") --- ModelMessage("ModelMessage<br>(Union)")

SystemPromptPart dataclass

A system prompt, generally written by the application developer.

This gives the model context and guidance on how to respond.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class SystemPromptPart:
    """A system prompt, generally written by the application developer.

    This gives the model context and guidance on how to respond.
    """

    content: str
    """The content of the prompt."""

    part_kind: Literal['system-prompt'] = 'system-prompt'
    """Part type identifier, this is available on all parts as a discriminator."""

content instance-attribute

content: str

The content of the prompt.

part_kind class-attribute instance-attribute

part_kind: Literal['system-prompt'] = 'system-prompt'

Part type identifier, this is available on all parts as a discriminator.

UserPromptPart dataclass

A user prompt, generally written by the end user.

Content comes from the user_prompt parameter of Agent.run, Agent.run_sync, and Agent.run_stream.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class UserPromptPart:
    """A user prompt, generally written by the end user.

    Content comes from the `user_prompt` parameter of [`Agent.run`][pydantic_ai.Agent.run],
    [`Agent.run_sync`][pydantic_ai.Agent.run_sync], and [`Agent.run_stream`][pydantic_ai.Agent.run_stream].
    """

    content: str
    """The content of the prompt."""

    timestamp: datetime = field(default_factory=_now_utc)
    """The timestamp of the prompt."""

    part_kind: Literal['user-prompt'] = 'user-prompt'
    """Part type identifier, this is available on all parts as a discriminator."""

content instance-attribute

content: str

The content of the prompt.

timestamp class-attribute instance-attribute

timestamp: datetime = field(default_factory=now_utc)

The timestamp of the prompt.

part_kind class-attribute instance-attribute

part_kind: Literal['user-prompt'] = 'user-prompt'

Part type identifier, this is available on all parts as a discriminator.

ToolReturnPart dataclass

A tool return message, this encodes the result of running a tool.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class ToolReturnPart:
    """A tool return message, this encodes the result of running a tool."""

    tool_name: str
    """The name of the "tool" was called."""

    content: Any
    """The return value."""

    tool_call_id: str | None = None
    """Optional tool call identifier, this is used by some models including OpenAI."""

    timestamp: datetime = field(default_factory=_now_utc)
    """The timestamp, when the tool returned."""

    part_kind: Literal['tool-return'] = 'tool-return'
    """Part type identifier, this is available on all parts as a discriminator."""

    def model_response_str(self) -> str:
        if isinstance(self.content, str):
            return self.content
        else:
            return tool_return_ta.dump_json(self.content).decode()

    def model_response_object(self) -> dict[str, Any]:
        # gemini supports JSON dict return values, but no other JSON types, hence we wrap anything else in a dict
        if isinstance(self.content, dict):
            return tool_return_ta.dump_python(self.content, mode='json')  # pyright: ignore[reportUnknownMemberType]
        else:
            return {'return_value': tool_return_ta.dump_python(self.content, mode='json')}

tool_name instance-attribute

tool_name: str

The name of the "tool" was called.

content instance-attribute

content: Any

The return value.

tool_call_id class-attribute instance-attribute

tool_call_id: str | None = None

Optional tool call identifier, this is used by some models including OpenAI.

timestamp class-attribute instance-attribute

timestamp: datetime = field(default_factory=now_utc)

The timestamp, when the tool returned.

part_kind class-attribute instance-attribute

part_kind: Literal['tool-return'] = 'tool-return'

Part type identifier, this is available on all parts as a discriminator.

RetryPromptPart dataclass

A message back to a model asking it to try again.

This can be sent for a number of reasons:

• Pydantic validation of tool arguments failed, here content is derived from a Pydantic ValidationError
• a tool raised a ModelRetry exception
• no tool was found for the tool name
• the model returned plain text when a structured response was expected
• Pydantic validation of a structured response failed, here content is derived from a Pydantic ValidationError
• a result validator raised a ModelRetry exception

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class RetryPromptPart:
    """A message back to a model asking it to try again.

    This can be sent for a number of reasons:

    * Pydantic validation of tool arguments failed, here content is derived from a Pydantic
      [`ValidationError`][pydantic_core.ValidationError]
    * a tool raised a [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] exception
    * no tool was found for the tool name
    * the model returned plain text when a structured response was expected
    * Pydantic validation of a structured response failed, here content is derived from a Pydantic
      [`ValidationError`][pydantic_core.ValidationError]
    * a result validator raised a [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] exception
    """

    content: list[pydantic_core.ErrorDetails] | str
    """Details of why and how the model should retry.

    If the retry was triggered by a [`ValidationError`][pydantic_core.ValidationError], this will be a list of
    error details.
    """

    tool_name: str | None = None
    """The name of the tool that was called, if any."""

    tool_call_id: str | None = None
    """Optional tool call identifier, this is used by some models including OpenAI."""

    timestamp: datetime = field(default_factory=_now_utc)
    """The timestamp, when the retry was triggered."""

    part_kind: Literal['retry-prompt'] = 'retry-prompt'
    """Part type identifier, this is available on all parts as a discriminator."""

    def model_response(self) -> str:
        if isinstance(self.content, str):
            description = self.content
        else:
            json_errors = error_details_ta.dump_json(self.content, exclude={'__all__': {'ctx'}}, indent=2)
            description = f'{len(self.content)} validation errors: {json_errors.decode()}'
        return f'{description}\n\nFix the errors and try again.'

content instance-attribute

content: list[ErrorDetails] | str

Details of why and how the model should retry.

If the retry was triggered by a ValidationError, this will be a list of error details.

tool_name class-attribute instance-attribute

tool_name: str | None = None

The name of the tool that was called, if any.

tool_call_id class-attribute instance-attribute

tool_call_id: str | None = None

Optional tool call identifier, this is used by some models including OpenAI.

timestamp class-attribute instance-attribute

timestamp: datetime = field(default_factory=now_utc)

The timestamp, when the retry was triggered.

part_kind class-attribute instance-attribute

part_kind: Literal['retry-prompt'] = 'retry-prompt'

Part type identifier, this is available on all parts as a discriminator.

ModelRequestPart module-attribute

ModelRequestPart = Annotated[
    Union[
        SystemPromptPart,
        UserPromptPart,
        ToolReturnPart,
        RetryPromptPart,
    ],
    Discriminator("part_kind"),
]

A message part sent by PydanticAI to a model.

ModelRequest dataclass

A request generated by PydanticAI and sent to a model, e.g. a message from the PydanticAI app to the model.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class ModelRequest:
    """A request generated by PydanticAI and sent to a model, e.g. a message from the PydanticAI app to the model."""

    parts: list[ModelRequestPart]
    """The parts of the user message."""

    kind: Literal['request'] = 'request'
    """Message type identifier, this is available on all parts as a discriminator."""

parts instance-attribute

parts: list[ModelRequestPart]

The parts of the user message.

kind class-attribute instance-attribute

kind: Literal['request'] = 'request'

Message type identifier, this is available on all parts as a discriminator.

TextPart dataclass

A plain text response from a model.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class TextPart:
    """A plain text response from a model."""

    content: str
    """The text content of the response."""

    part_kind: Literal['text'] = 'text'
    """Part type identifier, this is available on all parts as a discriminator."""

content instance-attribute

content: str

The text content of the response.

part_kind class-attribute instance-attribute

part_kind: Literal['text'] = 'text'

Part type identifier, this is available on all parts as a discriminator.

ArgsJson dataclass

Tool arguments as a JSON string.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class ArgsJson:
    """Tool arguments as a JSON string."""

    args_json: str
    """A JSON string of arguments."""

args_json instance-attribute

args_json: str

A JSON string of arguments.

ArgsDict dataclass

Tool arguments as a Python dictionary.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class ArgsDict:
    """Tool arguments as a Python dictionary."""

    args_dict: dict[str, Any]
    """A python dictionary of arguments."""

args_dict instance-attribute

args_dict: dict[str, Any]

A python dictionary of arguments.

ToolCallPart dataclass

A tool call from a model.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class ToolCallPart:
    """A tool call from a model."""

    tool_name: str
    """The name of the tool to call."""

    args: ArgsJson | ArgsDict
    """The arguments to pass to the tool.

    Either as JSON or a Python dictionary depending on how data was returned.
    """

    tool_call_id: str | None = None
    """Optional tool call identifier, this is used by some models including OpenAI."""

    part_kind: Literal['tool-call'] = 'tool-call'
    """Part type identifier, this is available on all parts as a discriminator."""

    @classmethod
    def from_json(cls, tool_name: str, args_json: str, tool_call_id: str | None = None) -> Self:
        return cls(tool_name, ArgsJson(args_json), tool_call_id)

    @classmethod
    def from_dict(cls, tool_name: str, args_dict: dict[str, Any], tool_call_id: str | None = None) -> Self:
        return cls(tool_name, ArgsDict(args_dict), tool_call_id)

    def has_content(self) -> bool:
        if isinstance(self.args, ArgsDict):
            return any(self.args.args_dict.values())
        else:
            return bool(self.args.args_json)

tool_name instance-attribute

tool_name: str

The name of the tool to call.

args instance-attribute

args: ArgsJson | ArgsDict

The arguments to pass to the tool.

Either as JSON or a Python dictionary depending on how data was returned.

tool_call_id class-attribute instance-attribute

tool_call_id: str | None = None

Optional tool call identifier, this is used by some models including OpenAI.

part_kind class-attribute instance-attribute

part_kind: Literal['tool-call'] = 'tool-call'

Part type identifier, this is available on all parts as a discriminator.

ModelResponsePart module-attribute

ModelResponsePart = Annotated[
    Union[TextPart, ToolCallPart],
    Discriminator("part_kind"),
]

A message part returned by a model.

ModelResponse dataclass

A response from a model, e.g. a message from the model to the PydanticAI app.

Source code in pydantic_ai_slim/pydantic_ai/messages.py

@dataclass
class ModelResponse:
    """A response from a model, e.g. a message from the model to the PydanticAI app."""

    parts: list[ModelResponsePart]
    """The parts of the model message."""

    timestamp: datetime = field(default_factory=_now_utc)
    """The timestamp of the response.

    If the model provides a timestamp in the response (as OpenAI does) that will be used.
    """

    kind: Literal['response'] = 'response'
    """Message type identifier, this is available on all parts as a discriminator."""

    @classmethod
    def from_text(cls, content: str, timestamp: datetime | None = None) -> Self:
        return cls([TextPart(content)], timestamp=timestamp or _now_utc())

    @classmethod
    def from_tool_call(cls, tool_call: ToolCallPart) -> Self:
        return cls([tool_call])

parts instance-attribute

parts: list[ModelResponsePart]

The parts of the model message.

timestamp class-attribute instance-attribute

timestamp: datetime = field(default_factory=now_utc)

The timestamp of the response.

If the model provides a timestamp in the response (as OpenAI does) that will be used.

kind class-attribute instance-attribute

kind: Literal['response'] = 'response'

Message type identifier, this is available on all parts as a discriminator.

ModelMessage module-attribute

ModelMessage = Union[ModelRequest, ModelResponse]

Any message send to or returned by a model.

ModelMessagesTypeAdapter module-attribute

ModelMessagesTypeAdapter = TypeAdapter(
    list[Annotated[ModelMessage, Discriminator("kind")]],
    config=ConfigDict(defer_build=True),
)

Pydantic TypeAdapter for (de)serializing messages.