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Config

MemoryLayoutConfig dataclass

Filesystem layout for sandbox-backed memory generation.

Source code in src/agents/sandbox/config.py 
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@dataclass
class MemoryLayoutConfig:
    """Filesystem layout for sandbox-backed memory generation."""

    memories_dir: str = "memories"
    """Directory used for consolidated memory files."""

    sessions_dir: str = "sessions"
    """Directory used for per-rollout JSONL artifacts."""

memories_dir class-attribute instance-attribute 

memories_dir: str = 'memories'

Directory used for consolidated memory files.

sessions_dir class-attribute instance-attribute 

sessions_dir: str = 'sessions'

Directory used for per-rollout JSONL artifacts.

MemoryGenerateConfig dataclass

Configuration for sandbox-backed memory extraction and consolidation.

Run segments are appended during the sandbox session. Extraction and consolidation run when the sandbox session closes.

Source code in src/agents/sandbox/config.py 
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@dataclass
class MemoryGenerateConfig:
    """Configuration for sandbox-backed memory extraction and consolidation.

    Run segments are appended during the sandbox session. Extraction and consolidation run when
    the sandbox session closes.
    """

    max_raw_memories_for_consolidation: int = 256
    """Maximum number of recent raw memories considered during consolidation."""

    phase_one_model: str | Model = "gpt-5.4-mini"
    """Model used for phase-1 single-rollout extraction."""

    phase_one_model_settings: ModelSettings | None = field(
        default_factory=_default_memory_phase_one_model_settings
    )
    """Model settings used for phase-1 single-rollout extraction.

    Accepts a ``ModelSettings`` instance or a dictionary containing its fields.
    """

    phase_two_model: str | Model = "gpt-5.5"
    """Model used for phase-2 memory consolidation."""

    phase_two_model_settings: ModelSettings | None = field(
        default_factory=_default_memory_phase_two_model_settings
    )
    """Model settings used for phase-2 memory consolidation.

    Accepts a ``ModelSettings`` instance or a dictionary containing its fields.
    """

    extra_prompt: str | None = None
    """Optional developer-specific guidance appended to memory extraction and consolidation
    prompts.

    Use this to tell memory what extra details are important to preserve for future runs, in
    addition to the standard user preferences, failure recovery, and task summary signals.
    Prefer a few targeted bullet points or short paragraphs, not pages of extra instructions.
    Try to keep it under about 5k tokens, and usually much shorter.
    The phase-one memory generator already receives a large built-in prompt plus a truncated
    conversation in a single model context window, so oversized extra prompts can crowd out the
    evidence you actually want it to summarize.
    """

    if TYPE_CHECKING:

        def __init__(
            self,
            max_raw_memories_for_consolidation: int = 256,
            phase_one_model: str | Model = "gpt-5.4-mini",
            phase_one_model_settings: ModelSettings | dict[str, Any] | None = ...,
            phase_two_model: str | Model = "gpt-5.5",
            phase_two_model_settings: ModelSettings | dict[str, Any] | None = ...,
            extra_prompt: str | None = None,
        ) -> None: ...

    def __post_init__(self) -> None:
        if self.phase_one_model_settings is not None:
            self.phase_one_model_settings = _coerce_model_settings(
                self.phase_one_model_settings,
                parameter_name="MemoryGenerateConfig.phase_one_model_settings",
                model_settings_type=_declared_model_settings_type(
                    type(self), "phase_one_model_settings"
                ),
            )
        if self.phase_two_model_settings is not None:
            self.phase_two_model_settings = _coerce_model_settings(
                self.phase_two_model_settings,
                parameter_name="MemoryGenerateConfig.phase_two_model_settings",
                model_settings_type=_declared_model_settings_type(
                    type(self), "phase_two_model_settings"
                ),
            )

        if self.max_raw_memories_for_consolidation <= 0:
            raise ValueError(
                "MemoryGenerateConfig.max_raw_memories_for_consolidation must be greater than 0."
            )
        if self.max_raw_memories_for_consolidation > 4096:
            raise ValueError(
                "MemoryGenerateConfig.max_raw_memories_for_consolidation "
                "must be less than or equal to 4096."
            )

max_raw_memories_for_consolidation class-attribute instance-attribute 

max_raw_memories_for_consolidation: int = 256

Maximum number of recent raw memories considered during consolidation.

phase_one_model class-attribute instance-attribute 

phase_one_model: str | Model = 'gpt-5.4-mini'

Model used for phase-1 single-rollout extraction.

phase_one_model_settings class-attribute instance-attribute 

phase_one_model_settings: ModelSettings | None = field(
    default_factory=_default_memory_phase_one_model_settings
)

Model settings used for phase-1 single-rollout extraction.

Accepts a ModelSettings instance or a dictionary containing its fields.

phase_two_model class-attribute instance-attribute 

phase_two_model: str | Model = 'gpt-5.5'

Model used for phase-2 memory consolidation.

phase_two_model_settings class-attribute instance-attribute 

phase_two_model_settings: ModelSettings | None = field(
    default_factory=_default_memory_phase_two_model_settings
)

Model settings used for phase-2 memory consolidation.

Accepts a ModelSettings instance or a dictionary containing its fields.

extra_prompt class-attribute instance-attribute 

extra_prompt: str | None = None

Optional developer-specific guidance appended to memory extraction and consolidation prompts.

Use this to tell memory what extra details are important to preserve for future runs, in addition to the standard user preferences, failure recovery, and task summary signals. Prefer a few targeted bullet points or short paragraphs, not pages of extra instructions. Try to keep it under about 5k tokens, and usually much shorter. The phase-one memory generator already receives a large built-in prompt plus a truncated conversation in a single model context window, so oversized extra prompts can crowd out the evidence you actually want it to summarize.

MemoryReadConfig dataclass

Configuration for sandbox-backed memory reads.

Source code in src/agents/sandbox/config.py 
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@dataclass
class MemoryReadConfig:
    """Configuration for sandbox-backed memory reads."""

    live_update: bool = True
    """Whether the agent may update stale memory files in place during a run."""

live_update class-attribute instance-attribute 

live_update: bool = True

Whether the agent may update stale memory files in place during a run.