mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-24 00:45:57 +00:00
refactor(config): eliminate global mutable state, wire DeerFlowContext into runtime
- 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
This commit is contained in:
greatmengqi
@@ -1,6 +1,6 @@
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from .app_config import get_app_config
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from .extensions_config import ExtensionsConfig, get_extensions_config
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from .memory_config import MemoryConfig, get_memory_config
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from .app_config import AppConfig
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from .extensions_config import ExtensionsConfig
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from .memory_config import MemoryConfig
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from .paths import Paths, get_paths
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from .skill_evolution_config import SkillEvolutionConfig
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from .skills_config import SkillsConfig
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@@ -13,18 +13,16 @@ from .tracing_config import (
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)
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__all__ = [
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"get_app_config",
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"SkillEvolutionConfig",
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"Paths",
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"get_paths",
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"SkillsConfig",
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"AppConfig",
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"ExtensionsConfig",
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"get_extensions_config",
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"MemoryConfig",
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"get_memory_config",
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"get_tracing_config",
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"get_explicitly_enabled_tracing_providers",
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"Paths",
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"SkillEvolutionConfig",
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"SkillsConfig",
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"get_enabled_tracing_providers",
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"get_explicitly_enabled_tracing_providers",
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"get_paths",
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"get_tracing_config",
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"is_tracing_enabled",
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"validate_enabled_tracing_providers",
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]
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@@ -1,16 +1,13 @@
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"""ACP (Agent Client Protocol) agent configuration loaded from config.yaml."""
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import logging
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from collections.abc import Mapping
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from pydantic import BaseModel, Field
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logger = logging.getLogger(__name__)
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from pydantic import BaseModel, ConfigDict, Field
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class ACPAgentConfig(BaseModel):
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"""Configuration for a single ACP-compatible agent."""
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model_config = ConfigDict(frozen=True)
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command: str = Field(description="Command to launch the ACP agent subprocess")
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args: list[str] = Field(default_factory=list, description="Additional command arguments")
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env: dict[str, str] = Field(default_factory=dict, description="Environment variables to inject into the agent subprocess. Values starting with $ are resolved from host environment variables.")
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@@ -24,28 +21,3 @@ class ACPAgentConfig(BaseModel):
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"are denied — the agent must be configured to operate without requesting permissions."
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),
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)
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_acp_agents: dict[str, ACPAgentConfig] = {}
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def get_acp_agents() -> dict[str, ACPAgentConfig]:
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"""Get the currently configured ACP agents.
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Returns:
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Mapping of agent name -> ACPAgentConfig. Empty dict if no ACP agents are configured.
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"""
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return _acp_agents
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def load_acp_config_from_dict(config_dict: Mapping[str, Mapping[str, object]] | None) -> None:
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"""Load ACP agent configuration from a dictionary (typically from config.yaml).
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Args:
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config_dict: Mapping of agent name -> config fields.
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"""
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global _acp_agents
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if config_dict is None:
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config_dict = {}
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_acp_agents = {name: ACPAgentConfig(**cfg) for name, cfg in config_dict.items()}
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logger.info("ACP config loaded: %d agent(s): %s", len(_acp_agents), list(_acp_agents.keys()))
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@@ -5,7 +5,7 @@ import re
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from typing import Any
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import yaml
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from pydantic import BaseModel
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from pydantic import BaseModel, ConfigDict
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from deerflow.config.paths import get_paths
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@@ -18,6 +18,8 @@ AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
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class AgentConfig(BaseModel):
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"""Configuration for a custom agent."""
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model_config = ConfigDict(frozen=True)
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name: str
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description: str = ""
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model: str | None = None
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@@ -1,29 +1,31 @@
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from __future__ import annotations
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import logging
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import os
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from contextvars import ContextVar
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from pathlib import Path
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from typing import Any, Self
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from typing import Any, ClassVar, Self
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import yaml
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from dotenv import load_dotenv
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from pydantic import BaseModel, ConfigDict, Field
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from deerflow.config.acp_config import load_acp_config_from_dict
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from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
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from deerflow.config.acp_config import ACPAgentConfig
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from deerflow.config.checkpointer_config import CheckpointerConfig
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from deerflow.config.extensions_config import ExtensionsConfig
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from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
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from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
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from deerflow.config.guardrails_config import GuardrailsConfig
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from deerflow.config.memory_config import MemoryConfig
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from deerflow.config.model_config import ModelConfig
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from deerflow.config.sandbox_config import SandboxConfig
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from deerflow.config.skill_evolution_config import SkillEvolutionConfig
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from deerflow.config.skills_config import SkillsConfig
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from deerflow.config.stream_bridge_config import StreamBridgeConfig, load_stream_bridge_config_from_dict
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from deerflow.config.subagents_config import SubagentsAppConfig, load_subagents_config_from_dict
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from deerflow.config.summarization_config import SummarizationConfig, load_summarization_config_from_dict
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from deerflow.config.title_config import TitleConfig, load_title_config_from_dict
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from deerflow.config.stream_bridge_config import StreamBridgeConfig
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from deerflow.config.subagents_config import SubagentsAppConfig
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from deerflow.config.summarization_config import SummarizationConfig
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from deerflow.config.title_config import TitleConfig
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from deerflow.config.token_usage_config import TokenUsageConfig
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from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
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from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict
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from deerflow.config.tool_search_config import ToolSearchConfig
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load_dotenv()
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@@ -55,9 +57,10 @@ class AppConfig(BaseModel):
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memory: MemoryConfig = Field(default_factory=MemoryConfig, description="Memory subsystem configuration")
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subagents: SubagentsAppConfig = Field(default_factory=SubagentsAppConfig, description="Subagent runtime configuration")
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guardrails: GuardrailsConfig = Field(default_factory=GuardrailsConfig, description="Guardrail middleware configuration")
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model_config = ConfigDict(extra="allow", frozen=False)
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model_config = ConfigDict(extra="allow", frozen=True)
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checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
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stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
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acp_agents: dict[str, ACPAgentConfig] = Field(default_factory=dict, description="ACP agent configurations keyed by agent name")
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@classmethod
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def resolve_config_path(cls, config_path: str | None = None) -> Path:
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@@ -105,41 +108,6 @@ class AppConfig(BaseModel):
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config_data = cls.resolve_env_variables(config_data)
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# Load title config if present
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if "title" in config_data:
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load_title_config_from_dict(config_data["title"])
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# Load summarization config if present
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if "summarization" in config_data:
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load_summarization_config_from_dict(config_data["summarization"])
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# Load memory config if present
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if "memory" in config_data:
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load_memory_config_from_dict(config_data["memory"])
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# Load subagents config if present
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if "subagents" in config_data:
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load_subagents_config_from_dict(config_data["subagents"])
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# Load tool_search config if present
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if "tool_search" in config_data:
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load_tool_search_config_from_dict(config_data["tool_search"])
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# Load guardrails config if present
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if "guardrails" in config_data:
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load_guardrails_config_from_dict(config_data["guardrails"])
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# Load checkpointer config if present
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if "checkpointer" in config_data:
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load_checkpointer_config_from_dict(config_data["checkpointer"])
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# Load stream bridge config if present
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if "stream_bridge" in config_data:
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load_stream_bridge_config_from_dict(config_data["stream_bridge"])
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# Always refresh ACP agent config so removed entries do not linger across reloads.
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load_acp_config_from_dict(config_data.get("acp_agents", {}))
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# Load extensions config separately (it's in a different file)
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extensions_config = ExtensionsConfig.from_file()
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config_data["extensions"] = extensions_config.model_dump()
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@@ -250,130 +218,26 @@ class AppConfig(BaseModel):
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"""
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return next((group for group in self.tool_groups if group.name == name), None)
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# -- Lifecycle (class-level singleton via ContextVar) --
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_app_config: AppConfig | None = None
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_app_config_path: Path | None = None
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_app_config_mtime: float | None = None
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_app_config_is_custom = False
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_current_app_config: ContextVar[AppConfig | None] = ContextVar("deerflow_current_app_config", default=None)
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_current_app_config_stack: ContextVar[tuple[AppConfig | None, ...]] = ContextVar("deerflow_current_app_config_stack", default=())
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_current: ClassVar[ContextVar[AppConfig]] = ContextVar("deerflow_app_config")
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@classmethod
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def init(cls, config: AppConfig) -> None:
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"""Set the AppConfig for the current context. Call once at process startup."""
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cls._current.set(config)
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def _get_config_mtime(config_path: Path) -> float | None:
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"""Get the modification time of a config file if it exists."""
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try:
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return config_path.stat().st_mtime
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except OSError:
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return None
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@classmethod
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def current(cls) -> AppConfig:
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"""Get the current AppConfig.
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def _load_and_cache_app_config(config_path: str | None = None) -> AppConfig:
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"""Load config from disk and refresh cache metadata."""
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global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
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resolved_path = AppConfig.resolve_config_path(config_path)
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_app_config = AppConfig.from_file(str(resolved_path))
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_app_config_path = resolved_path
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_app_config_mtime = _get_config_mtime(resolved_path)
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_app_config_is_custom = False
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return _app_config
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def get_app_config() -> AppConfig:
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"""Get the DeerFlow config instance.
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Returns a cached singleton instance and automatically reloads it when the
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underlying config file path or modification time changes. Use
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`reload_app_config()` to force a reload, or `reset_app_config()` to clear
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the cache.
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"""
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global _app_config, _app_config_path, _app_config_mtime
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runtime_override = _current_app_config.get()
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if runtime_override is not None:
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return runtime_override
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if _app_config is not None and _app_config_is_custom:
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return _app_config
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resolved_path = AppConfig.resolve_config_path()
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current_mtime = _get_config_mtime(resolved_path)
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should_reload = _app_config is None or _app_config_path != resolved_path or _app_config_mtime != current_mtime
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if should_reload:
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if _app_config_path == resolved_path and _app_config_mtime is not None and current_mtime is not None and _app_config_mtime != current_mtime:
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logger.info(
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"Config file has been modified (mtime: %s -> %s), reloading AppConfig",
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_app_config_mtime,
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current_mtime,
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)
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_load_and_cache_app_config(str(resolved_path))
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return _app_config
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def reload_app_config(config_path: str | None = None) -> AppConfig:
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"""Reload the config from file and update the cached instance.
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This is useful when the config file has been modified and you want
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to pick up the changes without restarting the application.
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Args:
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config_path: Optional path to config file. If not provided,
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uses the default resolution strategy.
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Returns:
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The newly loaded AppConfig instance.
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"""
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return _load_and_cache_app_config(config_path)
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def reset_app_config() -> None:
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"""Reset the cached config instance.
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This clears the singleton cache, causing the next call to
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`get_app_config()` to reload from file. Useful for testing
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or when switching between different configurations.
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"""
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global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
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_app_config = None
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_app_config_path = None
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_app_config_mtime = None
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_app_config_is_custom = False
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def set_app_config(config: AppConfig) -> None:
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"""Set a custom config instance.
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This allows injecting a custom or mock config for testing purposes.
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Args:
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config: The AppConfig instance to use.
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"""
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global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
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_app_config = config
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_app_config_path = None
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_app_config_mtime = None
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_app_config_is_custom = True
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def peek_current_app_config() -> AppConfig | None:
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"""Return the runtime-scoped AppConfig override, if one is active."""
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return _current_app_config.get()
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def push_current_app_config(config: AppConfig) -> None:
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"""Push a runtime-scoped AppConfig override for the current execution context."""
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stack = _current_app_config_stack.get()
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_current_app_config_stack.set(stack + (_current_app_config.get(),))
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_current_app_config.set(config)
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def pop_current_app_config() -> None:
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"""Pop the latest runtime-scoped AppConfig override for the current execution context."""
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stack = _current_app_config_stack.get()
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if not stack:
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_current_app_config.set(None)
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return
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previous = stack[-1]
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_current_app_config_stack.set(stack[:-1])
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_current_app_config.set(previous)
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Auto-initializes from config file on first access for backward compatibility.
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Prefer calling AppConfig.init() explicitly at process startup.
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"""
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try:
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return cls._current.get()
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except LookupError:
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logger.debug("AppConfig not initialized, auto-loading from file")
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config = cls.from_file()
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cls._current.set(config)
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return config
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@@ -2,7 +2,7 @@
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from typing import Literal
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, ConfigDict, Field
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CheckpointerType = Literal["memory", "sqlite", "postgres"]
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@@ -10,6 +10,8 @@ CheckpointerType = Literal["memory", "sqlite", "postgres"]
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class CheckpointerConfig(BaseModel):
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"""Configuration for LangGraph state persistence checkpointer."""
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model_config = ConfigDict(frozen=True)
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type: CheckpointerType = Field(
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description="Checkpointer backend type. "
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"'memory' is in-process only (lost on restart). "
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@@ -23,24 +25,3 @@ class CheckpointerConfig(BaseModel):
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"For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
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"For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
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)
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# Global configuration instance — None means no checkpointer is configured.
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_checkpointer_config: CheckpointerConfig | None = None
|
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|
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|
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def get_checkpointer_config() -> CheckpointerConfig | None:
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"""Get the current checkpointer configuration, or None if not configured."""
|
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return _checkpointer_config
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|
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|
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def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
|
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"""Set the checkpointer configuration."""
|
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global _checkpointer_config
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_checkpointer_config = config
|
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|
||||
|
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def load_checkpointer_config_from_dict(config_dict: dict) -> None:
|
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"""Load checkpointer configuration from a dictionary."""
|
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global _checkpointer_config
|
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_checkpointer_config = CheckpointerConfig(**config_dict)
|
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|
||||
@@ -0,0 +1,59 @@
|
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"""Per-invocation context for DeerFlow agent execution.
|
||||
|
||||
Injected via LangGraph Runtime. Middleware and tools access this
|
||||
via Runtime[DeerFlowContext] parameters, through resolve_context().
|
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"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DeerFlowContext:
|
||||
"""Typed, immutable, per-invocation context injected via LangGraph Runtime.
|
||||
|
||||
Fields are all known at run start and never change during execution.
|
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Mutable runtime state (e.g. sandbox_id) flows through ThreadState, not here.
|
||||
"""
|
||||
|
||||
app_config: Any # AppConfig — typed as Any to avoid circular import at module level
|
||||
thread_id: str
|
||||
agent_name: str | None = None
|
||||
|
||||
|
||||
def resolve_context(runtime: Any) -> DeerFlowContext:
|
||||
"""Extract or construct DeerFlowContext from runtime.
|
||||
|
||||
Gateway/Client paths: runtime.context is already DeerFlowContext → return directly.
|
||||
LangGraph Server / legacy dict path: construct from dict context or configurable fallback.
|
||||
"""
|
||||
ctx = getattr(runtime, "context", None)
|
||||
if isinstance(ctx, DeerFlowContext):
|
||||
return ctx
|
||||
|
||||
from deerflow.config.app_config import AppConfig
|
||||
|
||||
# Try dict context first (legacy path, tests), then configurable
|
||||
if isinstance(ctx, dict):
|
||||
return DeerFlowContext(
|
||||
app_config=AppConfig.current(),
|
||||
thread_id=ctx.get("thread_id", ""),
|
||||
agent_name=ctx.get("agent_name"),
|
||||
)
|
||||
|
||||
# No context at all — fall back to LangGraph configurable
|
||||
try:
|
||||
from langgraph.config import get_config
|
||||
|
||||
cfg = get_config().get("configurable", {})
|
||||
except RuntimeError:
|
||||
# Outside runnable context (e.g. unit tests)
|
||||
cfg = {}
|
||||
|
||||
return DeerFlowContext(
|
||||
app_config=AppConfig.current(),
|
||||
thread_id=cfg.get("thread_id", ""),
|
||||
agent_name=cfg.get("agent_name"),
|
||||
)
|
||||
@@ -11,6 +11,8 @@ from pydantic import BaseModel, ConfigDict, Field
|
||||
class McpOAuthConfig(BaseModel):
|
||||
"""OAuth configuration for an MCP server (HTTP/SSE transports)."""
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
|
||||
token_url: str = Field(description="OAuth token endpoint URL")
|
||||
grant_type: Literal["client_credentials", "refresh_token"] = Field(
|
||||
@@ -28,12 +30,13 @@ class McpOAuthConfig(BaseModel):
|
||||
default_token_type: str = Field(default="Bearer", description="Default token type when missing in token response")
|
||||
refresh_skew_seconds: int = Field(default=60, description="Refresh token this many seconds before expiry")
|
||||
extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
|
||||
model_config = ConfigDict(extra="allow")
|
||||
|
||||
|
||||
class McpServerConfig(BaseModel):
|
||||
"""Configuration for a single MCP server."""
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
|
||||
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
|
||||
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
|
||||
@@ -43,12 +46,13 @@ class McpServerConfig(BaseModel):
|
||||
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
|
||||
oauth: McpOAuthConfig | None = Field(default=None, description="OAuth configuration (for sse or http type)")
|
||||
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
|
||||
model_config = ConfigDict(extra="allow")
|
||||
|
||||
|
||||
class SkillStateConfig(BaseModel):
|
||||
"""Configuration for a single skill's state."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether this skill is enabled")
|
||||
|
||||
|
||||
@@ -64,7 +68,7 @@ class ExtensionsConfig(BaseModel):
|
||||
default_factory=dict,
|
||||
description="Map of skill name to state configuration",
|
||||
)
|
||||
model_config = ConfigDict(extra="allow", populate_by_name=True)
|
||||
model_config = ConfigDict(extra="allow", frozen=True, populate_by_name=True)
|
||||
|
||||
@classmethod
|
||||
def resolve_config_path(cls, config_path: str | None = None) -> Path | None:
|
||||
@@ -195,62 +199,3 @@ class ExtensionsConfig(BaseModel):
|
||||
# Default to enable for public & custom skill
|
||||
return skill_category in ("public", "custom")
|
||||
return skill_config.enabled
|
||||
|
||||
|
||||
_extensions_config: ExtensionsConfig | None = None
|
||||
|
||||
|
||||
def get_extensions_config() -> ExtensionsConfig:
|
||||
"""Get the extensions config instance.
|
||||
|
||||
Returns a cached singleton instance. Use `reload_extensions_config()` to reload
|
||||
from file, or `reset_extensions_config()` to clear the cache.
|
||||
|
||||
Returns:
|
||||
The cached ExtensionsConfig instance.
|
||||
"""
|
||||
global _extensions_config
|
||||
if _extensions_config is None:
|
||||
_extensions_config = ExtensionsConfig.from_file()
|
||||
return _extensions_config
|
||||
|
||||
|
||||
def reload_extensions_config(config_path: str | None = None) -> ExtensionsConfig:
|
||||
"""Reload the extensions config from file and update the cached instance.
|
||||
|
||||
This is useful when the config file has been modified and you want
|
||||
to pick up the changes without restarting the application.
|
||||
|
||||
Args:
|
||||
config_path: Optional path to extensions config file. If not provided,
|
||||
uses the default resolution strategy.
|
||||
|
||||
Returns:
|
||||
The newly loaded ExtensionsConfig instance.
|
||||
"""
|
||||
global _extensions_config
|
||||
_extensions_config = ExtensionsConfig.from_file(config_path)
|
||||
return _extensions_config
|
||||
|
||||
|
||||
def reset_extensions_config() -> None:
|
||||
"""Reset the cached extensions config instance.
|
||||
|
||||
This clears the singleton cache, causing the next call to
|
||||
`get_extensions_config()` to reload from file. Useful for testing
|
||||
or when switching between different configurations.
|
||||
"""
|
||||
global _extensions_config
|
||||
_extensions_config = None
|
||||
|
||||
|
||||
def set_extensions_config(config: ExtensionsConfig) -> None:
|
||||
"""Set a custom extensions config instance.
|
||||
|
||||
This allows injecting a custom or mock config for testing purposes.
|
||||
|
||||
Args:
|
||||
config: The ExtensionsConfig instance to use.
|
||||
"""
|
||||
global _extensions_config
|
||||
_extensions_config = config
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
"""Configuration for pre-tool-call authorization."""
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class GuardrailProviderConfig(BaseModel):
|
||||
"""Configuration for a guardrail provider."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
use: str = Field(description="Class path (e.g. 'deerflow.guardrails.builtin:AllowlistProvider')")
|
||||
config: dict = Field(default_factory=dict, description="Provider-specific settings passed as kwargs")
|
||||
|
||||
@@ -18,31 +20,9 @@ class GuardrailsConfig(BaseModel):
|
||||
agent's passport reference, and returns an allow/deny decision.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(default=False, description="Enable guardrail middleware")
|
||||
fail_closed: bool = Field(default=True, description="Block tool calls if provider errors")
|
||||
passport: str | None = Field(default=None, description="OAP passport path or hosted agent ID")
|
||||
provider: GuardrailProviderConfig | None = Field(default=None, description="Guardrail provider configuration")
|
||||
|
||||
|
||||
_guardrails_config: GuardrailsConfig | None = None
|
||||
|
||||
|
||||
def get_guardrails_config() -> GuardrailsConfig:
|
||||
"""Get the guardrails config, returning defaults if not loaded."""
|
||||
global _guardrails_config
|
||||
if _guardrails_config is None:
|
||||
_guardrails_config = GuardrailsConfig()
|
||||
return _guardrails_config
|
||||
|
||||
|
||||
def load_guardrails_config_from_dict(data: dict) -> GuardrailsConfig:
|
||||
"""Load guardrails config from a dict (called during AppConfig loading)."""
|
||||
global _guardrails_config
|
||||
_guardrails_config = GuardrailsConfig.model_validate(data)
|
||||
return _guardrails_config
|
||||
|
||||
|
||||
def reset_guardrails_config() -> None:
|
||||
"""Reset the cached config instance. Used in tests to prevent singleton leaks."""
|
||||
global _guardrails_config
|
||||
_guardrails_config = None
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
"""Configuration for memory mechanism."""
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class MemoryConfig(BaseModel):
|
||||
"""Configuration for global memory mechanism."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(
|
||||
default=True,
|
||||
description="Whether to enable memory mechanism",
|
||||
@@ -59,24 +61,3 @@ class MemoryConfig(BaseModel):
|
||||
le=8000,
|
||||
description="Maximum tokens to use for memory injection",
|
||||
)
|
||||
|
||||
|
||||
# Global configuration instance
|
||||
_memory_config: MemoryConfig = MemoryConfig()
|
||||
|
||||
|
||||
def get_memory_config() -> MemoryConfig:
|
||||
"""Get the current memory configuration."""
|
||||
return _memory_config
|
||||
|
||||
|
||||
def set_memory_config(config: MemoryConfig) -> None:
|
||||
"""Set the memory configuration."""
|
||||
global _memory_config
|
||||
_memory_config = config
|
||||
|
||||
|
||||
def load_memory_config_from_dict(config_dict: dict) -> None:
|
||||
"""Load memory configuration from a dictionary."""
|
||||
global _memory_config
|
||||
_memory_config = MemoryConfig(**config_dict)
|
||||
|
||||
@@ -12,7 +12,7 @@ class ModelConfig(BaseModel):
|
||||
description="Class path of the model provider(e.g. langchain_openai.ChatOpenAI)",
|
||||
)
|
||||
model: str = Field(..., description="Model name")
|
||||
model_config = ConfigDict(extra="allow")
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
use_responses_api: bool | None = Field(
|
||||
default=None,
|
||||
description="Whether to route OpenAI ChatOpenAI calls through the /v1/responses API",
|
||||
|
||||
@@ -4,6 +4,8 @@ from pydantic import BaseModel, ConfigDict, Field
|
||||
class VolumeMountConfig(BaseModel):
|
||||
"""Configuration for a volume mount."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
host_path: str = Field(..., description="Path on the host machine")
|
||||
container_path: str = Field(..., description="Path inside the container")
|
||||
read_only: bool = Field(default=False, description="Whether the mount is read-only")
|
||||
@@ -80,4 +82,4 @@ class SandboxConfig(BaseModel):
|
||||
description="Maximum characters to keep from ls tool output. Output exceeding this limit is head-truncated. Set to 0 to disable truncation.",
|
||||
)
|
||||
|
||||
model_config = ConfigDict(extra="allow")
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
@@ -1,9 +1,11 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class SkillEvolutionConfig(BaseModel):
|
||||
"""Configuration for agent-managed skill evolution."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(
|
||||
default=False,
|
||||
description="Whether the agent can create and modify skills under skills/custom.",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from pathlib import Path
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
def _default_repo_root() -> Path:
|
||||
@@ -11,6 +11,8 @@ def _default_repo_root() -> Path:
|
||||
class SkillsConfig(BaseModel):
|
||||
"""Configuration for skills system"""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
path: str | None = Field(
|
||||
default=None,
|
||||
description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
StreamBridgeType = Literal["memory", "redis"]
|
||||
|
||||
@@ -10,6 +10,8 @@ StreamBridgeType = Literal["memory", "redis"]
|
||||
class StreamBridgeConfig(BaseModel):
|
||||
"""Configuration for the stream bridge that connects agent workers to SSE endpoints."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
type: StreamBridgeType = Field(
|
||||
default="memory",
|
||||
description="Stream bridge backend type. 'memory' uses in-process asyncio.Queue (single-process only). 'redis' uses Redis Streams (planned for Phase 2, not yet implemented).",
|
||||
@@ -22,25 +24,3 @@ class StreamBridgeConfig(BaseModel):
|
||||
default=256,
|
||||
description="Maximum number of events buffered per run in the memory bridge.",
|
||||
)
|
||||
|
||||
|
||||
# Global configuration instance — None means no stream bridge is configured
|
||||
# (falls back to memory with defaults).
|
||||
_stream_bridge_config: StreamBridgeConfig | None = None
|
||||
|
||||
|
||||
def get_stream_bridge_config() -> StreamBridgeConfig | None:
|
||||
"""Get the current stream bridge configuration, or None if not configured."""
|
||||
return _stream_bridge_config
|
||||
|
||||
|
||||
def set_stream_bridge_config(config: StreamBridgeConfig | None) -> None:
|
||||
"""Set the stream bridge configuration."""
|
||||
global _stream_bridge_config
|
||||
_stream_bridge_config = config
|
||||
|
||||
|
||||
def load_stream_bridge_config_from_dict(config_dict: dict) -> None:
|
||||
"""Load stream bridge configuration from a dictionary."""
|
||||
global _stream_bridge_config
|
||||
_stream_bridge_config = StreamBridgeConfig(**config_dict)
|
||||
|
||||
@@ -1,15 +1,13 @@
|
||||
"""Configuration for the subagent system loaded from config.yaml."""
|
||||
|
||||
import logging
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class SubagentOverrideConfig(BaseModel):
|
||||
"""Per-agent configuration overrides."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
timeout_seconds: int | None = Field(
|
||||
default=None,
|
||||
ge=1,
|
||||
@@ -25,6 +23,8 @@ class SubagentOverrideConfig(BaseModel):
|
||||
class SubagentsAppConfig(BaseModel):
|
||||
"""Configuration for the subagent system."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
timeout_seconds: int = Field(
|
||||
default=900,
|
||||
ge=1,
|
||||
@@ -62,41 +62,3 @@ class SubagentsAppConfig(BaseModel):
|
||||
if self.max_turns is not None:
|
||||
return self.max_turns
|
||||
return builtin_default
|
||||
|
||||
|
||||
_subagents_config: SubagentsAppConfig = SubagentsAppConfig()
|
||||
|
||||
|
||||
def get_subagents_app_config() -> SubagentsAppConfig:
|
||||
"""Get the current subagents configuration."""
|
||||
return _subagents_config
|
||||
|
||||
|
||||
def load_subagents_config_from_dict(config_dict: dict) -> None:
|
||||
"""Load subagents configuration from a dictionary."""
|
||||
global _subagents_config
|
||||
_subagents_config = SubagentsAppConfig(**config_dict)
|
||||
|
||||
overrides_summary = {}
|
||||
for name, override in _subagents_config.agents.items():
|
||||
parts = []
|
||||
if override.timeout_seconds is not None:
|
||||
parts.append(f"timeout={override.timeout_seconds}s")
|
||||
if override.max_turns is not None:
|
||||
parts.append(f"max_turns={override.max_turns}")
|
||||
if parts:
|
||||
overrides_summary[name] = ", ".join(parts)
|
||||
|
||||
if overrides_summary:
|
||||
logger.info(
|
||||
"Subagents config loaded: default timeout=%ss, default max_turns=%s, per-agent overrides=%s",
|
||||
_subagents_config.timeout_seconds,
|
||||
_subagents_config.max_turns,
|
||||
overrides_summary,
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
"Subagents config loaded: default timeout=%ss, default max_turns=%s, no per-agent overrides",
|
||||
_subagents_config.timeout_seconds,
|
||||
_subagents_config.max_turns,
|
||||
)
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
ContextSizeType = Literal["fraction", "tokens", "messages"]
|
||||
|
||||
@@ -10,6 +10,8 @@ ContextSizeType = Literal["fraction", "tokens", "messages"]
|
||||
class ContextSize(BaseModel):
|
||||
"""Context size specification for trigger or keep parameters."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
type: ContextSizeType = Field(description="Type of context size specification")
|
||||
value: int | float = Field(description="Value for the context size specification")
|
||||
|
||||
@@ -21,6 +23,8 @@ class ContextSize(BaseModel):
|
||||
class SummarizationConfig(BaseModel):
|
||||
"""Configuration for automatic conversation summarization."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(
|
||||
default=False,
|
||||
description="Whether to enable automatic conversation summarization",
|
||||
@@ -51,24 +55,3 @@ class SummarizationConfig(BaseModel):
|
||||
default=None,
|
||||
description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
|
||||
)
|
||||
|
||||
|
||||
# Global configuration instance
|
||||
_summarization_config: SummarizationConfig = SummarizationConfig()
|
||||
|
||||
|
||||
def get_summarization_config() -> SummarizationConfig:
|
||||
"""Get the current summarization configuration."""
|
||||
return _summarization_config
|
||||
|
||||
|
||||
def set_summarization_config(config: SummarizationConfig) -> None:
|
||||
"""Set the summarization configuration."""
|
||||
global _summarization_config
|
||||
_summarization_config = config
|
||||
|
||||
|
||||
def load_summarization_config_from_dict(config_dict: dict) -> None:
|
||||
"""Load summarization configuration from a dictionary."""
|
||||
global _summarization_config
|
||||
_summarization_config = SummarizationConfig(**config_dict)
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
"""Configuration for automatic thread title generation."""
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class TitleConfig(BaseModel):
|
||||
"""Configuration for automatic thread title generation."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(
|
||||
default=True,
|
||||
description="Whether to enable automatic title generation",
|
||||
@@ -30,24 +32,3 @@ class TitleConfig(BaseModel):
|
||||
default=("Generate a concise title (max {max_words} words) for this conversation.\nUser: {user_msg}\nAssistant: {assistant_msg}\n\nReturn ONLY the title, no quotes, no explanation."),
|
||||
description="Prompt template for title generation",
|
||||
)
|
||||
|
||||
|
||||
# Global configuration instance
|
||||
_title_config: TitleConfig = TitleConfig()
|
||||
|
||||
|
||||
def get_title_config() -> TitleConfig:
|
||||
"""Get the current title configuration."""
|
||||
return _title_config
|
||||
|
||||
|
||||
def set_title_config(config: TitleConfig) -> None:
|
||||
"""Set the title configuration."""
|
||||
global _title_config
|
||||
_title_config = config
|
||||
|
||||
|
||||
def load_title_config_from_dict(config_dict: dict) -> None:
|
||||
"""Load title configuration from a dictionary."""
|
||||
global _title_config
|
||||
_title_config = TitleConfig(**config_dict)
|
||||
|
||||
@@ -1,7 +1,9 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class TokenUsageConfig(BaseModel):
|
||||
"""Configuration for token usage tracking."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(default=False, description="Enable token usage tracking middleware")
|
||||
|
||||
@@ -5,7 +5,7 @@ class ToolGroupConfig(BaseModel):
|
||||
"""Config section for a tool group"""
|
||||
|
||||
name: str = Field(..., description="Unique name for the tool group")
|
||||
model_config = ConfigDict(extra="allow")
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
||||
class ToolConfig(BaseModel):
|
||||
@@ -17,4 +17,4 @@ class ToolConfig(BaseModel):
|
||||
...,
|
||||
description="Variable name of the tool provider(e.g. deerflow.sandbox.tools:bash_tool)",
|
||||
)
|
||||
model_config = ConfigDict(extra="allow")
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
"""Configuration for deferred tool loading via tool_search."""
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class ToolSearchConfig(BaseModel):
|
||||
@@ -11,25 +11,9 @@ class ToolSearchConfig(BaseModel):
|
||||
via the tool_search tool at runtime.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(
|
||||
default=False,
|
||||
description="Defer tools and enable tool_search",
|
||||
)
|
||||
|
||||
|
||||
_tool_search_config: ToolSearchConfig | None = None
|
||||
|
||||
|
||||
def get_tool_search_config() -> ToolSearchConfig:
|
||||
"""Get the tool search config, loading from AppConfig if needed."""
|
||||
global _tool_search_config
|
||||
if _tool_search_config is None:
|
||||
_tool_search_config = ToolSearchConfig()
|
||||
return _tool_search_config
|
||||
|
||||
|
||||
def load_tool_search_config_from_dict(data: dict) -> ToolSearchConfig:
|
||||
"""Load tool search config from a dict (called during AppConfig loading)."""
|
||||
global _tool_search_config
|
||||
_tool_search_config = ToolSearchConfig.model_validate(data)
|
||||
return _tool_search_config
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import os
|
||||
import threading
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
_config_lock = threading.Lock()
|
||||
|
||||
@@ -9,6 +9,8 @@ _config_lock = threading.Lock()
|
||||
class LangSmithTracingConfig(BaseModel):
|
||||
"""Configuration for LangSmith tracing."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(...)
|
||||
api_key: str | None = Field(...)
|
||||
project: str = Field(...)
|
||||
@@ -26,6 +28,8 @@ class LangSmithTracingConfig(BaseModel):
|
||||
class LangfuseTracingConfig(BaseModel):
|
||||
"""Configuration for Langfuse tracing."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
enabled: bool = Field(...)
|
||||
public_key: str | None = Field(...)
|
||||
secret_key: str | None = Field(...)
|
||||
@@ -50,6 +54,8 @@ class LangfuseTracingConfig(BaseModel):
|
||||
class TracingConfig(BaseModel):
|
||||
"""Tracing configuration for supported providers."""
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
langsmith: LangSmithTracingConfig = Field(...)
|
||||
langfuse: LangfuseTracingConfig = Field(...)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user