mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-21 07:26:50 +00:00
greatmengqi
edf345cd72
- Freeze all config models (AppConfig + 15 sub-configs) with frozen=True - Purify from_file() — remove 9 load_*_from_dict() side-effect calls - Replace mtime/reload/push/pop machinery with single ContextVar + init_app_config() - Delete 10 sub-module globals and their getters/setters/loaders - Migrate 50+ consumers from get_*_config() to get_app_config().xxx - Expand DeerFlowContext: app_config + thread_id + agent_name (frozen dataclass) - Wire into Gateway runtime (worker.py) and DeerFlowClient via context= parameter - Remove sandbox_id from runtime.context — flows through ThreadState.sandbox only - Middleware/tools access runtime.context directly via Runtime[DeerFlowContext] generic - resolve_context() retained at server entry points for LangGraph Server fallback
17 lines
527 B
Python
17 lines
527 B
Python
from pydantic import BaseModel, ConfigDict, Field
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class SkillEvolutionConfig(BaseModel):
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"""Configuration for agent-managed skill evolution."""
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model_config = ConfigDict(frozen=True)
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enabled: bool = Field(
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default=False,
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description="Whether the agent can create and modify skills under skills/custom.",
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)
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moderation_model_name: str | None = Field(
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default=None,
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description="Optional model name for skill security moderation. Defaults to the primary chat model.",
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)
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