mirror of
https://github.com/bytedance/deer-flow.git
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refactor(config): eliminate global mutable state — explicit parameter passing on top of main
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
This commit is contained in:
greatmengqi
@@ -3,6 +3,7 @@ import logging
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from langchain.agents import create_agent
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from langchain.agents.middleware import AgentMiddleware
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from langchain_core.runnables import RunnableConfig
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from langgraph.graph.state import CompiledStateGraph
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from deerflow.agents.lead_agent.prompt import apply_prompt_template
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from deerflow.agents.memory.summarization_hook import memory_flush_hook
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@@ -18,9 +19,8 @@ from deerflow.agents.middlewares.tool_error_handling_middleware import build_lea
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from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
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from deerflow.agents.thread_state import ThreadState
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from deerflow.config.agents_config import load_agent_config, validate_agent_name
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from deerflow.config.app_config import get_app_config
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from deerflow.config.memory_config import get_memory_config
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from deerflow.config.summarization_config import get_summarization_config
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from deerflow.config.app_config import AppConfig
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from deerflow.config.deer_flow_context import DeerFlowContext
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from deerflow.models import create_chat_model
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logger = logging.getLogger(__name__)
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@@ -35,9 +35,8 @@ def _get_runtime_config(config: RunnableConfig) -> dict:
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return cfg
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def _resolve_model_name(requested_model_name: str | None = None) -> str:
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def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None = None) -> str:
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"""Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
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app_config = get_app_config()
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default_model_name = app_config.models[0].name if app_config.models else None
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if default_model_name is None:
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raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
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@@ -50,9 +49,9 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
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return default_model_name
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def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None:
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def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummarizationMiddleware | None:
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"""Create and configure the summarization middleware from config."""
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config = get_summarization_config()
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config = app_config.summarization
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if not config.enabled:
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return None
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@@ -68,13 +67,15 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
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# Prepare keep parameter
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keep = config.keep.to_tuple()
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# Prepare model parameter
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# Prepare model parameter.
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# Bind "middleware:summarize" tag so RunJournal identifies these LLM calls
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# as middleware rather than lead_agent (SummarizationMiddleware is a
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# LangChain built-in, so we tag the model at creation time).
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if config.model_name:
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model = create_chat_model(name=config.model_name, thinking_enabled=False)
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model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
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else:
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# Use a lightweight model for summarization to save costs
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# Falls back to default model if not explicitly specified
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model = create_chat_model(thinking_enabled=False)
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model = create_chat_model(thinking_enabled=False, app_config=app_config)
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model = model.with_config(tags=["middleware:summarize"])
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# Prepare kwargs
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kwargs = {
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@@ -90,14 +91,14 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
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kwargs["summary_prompt"] = config.summary_prompt
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hooks: list[BeforeSummarizationHook] = []
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if get_memory_config().enabled:
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if app_config.memory.enabled:
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hooks.append(memory_flush_hook)
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# The logic below relies on two assumptions holding true: this factory is
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# the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
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# config is not expected to change after startup.
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try:
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skills_container_path = get_app_config().skills.container_path or "/mnt/skills"
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skills_container_path = app_config.skills.container_path or "/mnt/skills"
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except Exception:
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logger.exception("Failed to resolve skills container path; falling back to default")
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skills_container_path = "/mnt/skills"
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@@ -238,10 +239,18 @@ Being proactive with task management demonstrates thoroughness and ensures all r
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# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
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# ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
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# ClarificationMiddleware should be last to intercept clarification requests after model calls
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def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None, custom_middlewares: list[AgentMiddleware] | None = None):
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def _build_middlewares(
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app_config: AppConfig,
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config: RunnableConfig,
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*,
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model_name: str | None,
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agent_name: str | None = None,
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custom_middlewares: list[AgentMiddleware] | None = None,
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):
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"""Build middleware chain based on runtime configuration.
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Args:
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app_config: Resolved application config.
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config: Runtime configuration containing configurable options like is_plan_mode.
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agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
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custom_middlewares: Optional list of custom middlewares to inject into the chain.
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@@ -249,10 +258,10 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
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Returns:
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List of middleware instances.
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"""
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middlewares = build_lead_runtime_middlewares(lazy_init=True)
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middlewares = build_lead_runtime_middlewares(app_config=app_config, lazy_init=True)
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# Add summarization middleware if enabled
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summarization_middleware = _create_summarization_middleware()
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summarization_middleware = _create_summarization_middleware(app_config)
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if summarization_middleware is not None:
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middlewares.append(summarization_middleware)
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@@ -264,7 +273,7 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
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middlewares.append(todo_list_middleware)
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# Add TokenUsageMiddleware when token_usage tracking is enabled
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if get_app_config().token_usage.enabled:
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if app_config.token_usage.enabled:
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middlewares.append(TokenUsageMiddleware())
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# Add TitleMiddleware
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@@ -275,7 +284,6 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
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# Add ViewImageMiddleware only if the current model supports vision.
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# Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
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app_config = get_app_config()
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model_config = app_config.get_model_config(model_name) if model_name else None
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if model_config is not None and model_config.supports_vision:
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middlewares.append(ViewImageMiddleware())
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@@ -304,11 +312,32 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
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return middlewares
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def make_lead_agent(config: RunnableConfig):
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def make_lead_agent(
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config: RunnableConfig,
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app_config: AppConfig | None = None,
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) -> CompiledStateGraph:
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"""Build the lead agent from runtime config.
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Args:
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config: LangGraph ``RunnableConfig`` carrying per-invocation options
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(``thinking_enabled``, ``model_name``, ``is_plan_mode``, etc.).
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app_config: Resolved application config. Required for in-process
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entry points (DeerFlowClient, Gateway Worker). When omitted we
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are being called via ``langgraph.json`` registration and reload
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from disk — the LangGraph Server bootstrap path has no other
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way to thread the value.
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"""
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# Lazy import to avoid circular dependency
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from deerflow.tools import get_available_tools
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from deerflow.tools.builtins import setup_agent
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if app_config is None:
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# LangGraph Server registers ``make_lead_agent`` via ``langgraph.json``
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# and hands us only a ``RunnableConfig``. Reload config from disk
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# here — it's a pure function, equivalent to the process-global the
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# old code path would have read.
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app_config = AppConfig.from_file()
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cfg = _get_runtime_config(config)
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thinking_enabled = cfg.get("thinking_enabled", True)
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@@ -325,9 +354,8 @@ def make_lead_agent(config: RunnableConfig):
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agent_model_name = agent_config.model if agent_config and agent_config.model else None
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# Final model name resolution: request → agent config → global default, with fallback for unknown names
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model_name = _resolve_model_name(requested_model_name or agent_model_name)
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model_name = _resolve_model_name(app_config, requested_model_name or agent_model_name)
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app_config = get_app_config()
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model_config = app_config.get_model_config(model_name)
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if model_config is None:
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@@ -367,20 +395,22 @@ def make_lead_agent(config: RunnableConfig):
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if is_bootstrap:
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# Special bootstrap agent with minimal prompt for initial custom agent creation flow
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return create_agent(
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model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
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tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
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middleware=_build_middlewares(config, model_name=model_name),
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system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
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model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=app_config),
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tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=app_config) + [setup_agent],
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middleware=_build_middlewares(app_config, config, model_name=model_name),
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system_prompt=apply_prompt_template(app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
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state_schema=ThreadState,
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context_schema=DeerFlowContext,
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)
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# Default lead agent (unchanged behavior)
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return create_agent(
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model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
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tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
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middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
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model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=app_config),
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tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=app_config),
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middleware=_build_middlewares(app_config, config, model_name=model_name, agent_name=agent_name),
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system_prompt=apply_prompt_template(
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subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
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app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
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),
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state_schema=ThreadState,
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context_schema=DeerFlowContext,
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)
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@@ -5,6 +5,7 @@ from datetime import datetime
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from functools import lru_cache
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from deerflow.config.agents_config import load_agent_soul
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from deerflow.config.app_config import AppConfig
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from deerflow.skills import load_skills
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from deerflow.skills.types import Skill
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from deerflow.subagents import get_available_subagent_names
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@@ -19,19 +20,20 @@ _enabled_skills_refresh_version = 0
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_enabled_skills_refresh_event = threading.Event()
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def _load_enabled_skills_sync() -> list[Skill]:
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return list(load_skills(enabled_only=True))
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def _load_enabled_skills_sync(app_config: AppConfig | None) -> list[Skill]:
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return list(load_skills(app_config, enabled_only=True))
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def _start_enabled_skills_refresh_thread() -> None:
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def _start_enabled_skills_refresh_thread(app_config: AppConfig | None) -> None:
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threading.Thread(
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target=_refresh_enabled_skills_cache_worker,
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args=(app_config,),
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name="deerflow-enabled-skills-loader",
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daemon=True,
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).start()
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def _refresh_enabled_skills_cache_worker() -> None:
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def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
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global _enabled_skills_cache, _enabled_skills_refresh_active
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while True:
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@@ -39,8 +41,8 @@ def _refresh_enabled_skills_cache_worker() -> None:
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target_version = _enabled_skills_refresh_version
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try:
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skills = _load_enabled_skills_sync()
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except Exception:
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skills = _load_enabled_skills_sync(app_config)
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except (OSError, ImportError):
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logger.exception("Failed to load enabled skills for prompt injection")
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skills = []
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@@ -56,7 +58,7 @@ def _refresh_enabled_skills_cache_worker() -> None:
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_enabled_skills_cache = None
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def _ensure_enabled_skills_cache() -> threading.Event:
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def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
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global _enabled_skills_refresh_active
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with _enabled_skills_lock:
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@@ -68,11 +70,11 @@ def _ensure_enabled_skills_cache() -> threading.Event:
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_enabled_skills_refresh_active = True
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_enabled_skills_refresh_event.clear()
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_start_enabled_skills_refresh_thread()
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_start_enabled_skills_refresh_thread(app_config)
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return _enabled_skills_refresh_event
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def _invalidate_enabled_skills_cache() -> threading.Event:
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def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
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global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
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_get_cached_skills_prompt_section.cache_clear()
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@@ -84,30 +86,30 @@ def _invalidate_enabled_skills_cache() -> threading.Event:
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return _enabled_skills_refresh_event
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_enabled_skills_refresh_active = True
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_start_enabled_skills_refresh_thread()
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_start_enabled_skills_refresh_thread(app_config)
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return _enabled_skills_refresh_event
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def prime_enabled_skills_cache() -> None:
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_ensure_enabled_skills_cache()
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def prime_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
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_ensure_enabled_skills_cache(app_config)
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def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
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if _ensure_enabled_skills_cache().wait(timeout=timeout_seconds):
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def warm_enabled_skills_cache(app_config: AppConfig | None = None, timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
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if _ensure_enabled_skills_cache(app_config).wait(timeout=timeout_seconds):
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return True
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logger.warning("Timed out waiting %.1fs for enabled skills cache warm-up", timeout_seconds)
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return False
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def _get_enabled_skills():
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def _get_enabled_skills(app_config: AppConfig | None = None):
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with _enabled_skills_lock:
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cached = _enabled_skills_cache
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if cached is not None:
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return list(cached)
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_ensure_enabled_skills_cache()
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_ensure_enabled_skills_cache(app_config)
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return []
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@@ -115,12 +117,12 @@ def _skill_mutability_label(category: str) -> str:
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return "[custom, editable]" if category == "custom" else "[built-in]"
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def clear_skills_system_prompt_cache() -> None:
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_invalidate_enabled_skills_cache()
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def clear_skills_system_prompt_cache(app_config: AppConfig | None = None) -> None:
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_invalidate_enabled_skills_cache(app_config)
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async def refresh_skills_system_prompt_cache_async() -> None:
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await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)
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async def refresh_skills_system_prompt_cache_async(app_config: AppConfig | None = None) -> None:
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await asyncio.to_thread(_invalidate_enabled_skills_cache(app_config).wait)
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def _reset_skills_system_prompt_cache_state() -> None:
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@@ -134,10 +136,10 @@ def _reset_skills_system_prompt_cache_state() -> None:
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_enabled_skills_refresh_event.clear()
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def _refresh_enabled_skills_cache() -> None:
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def _refresh_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
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"""Backward-compatible test helper for direct synchronous reload."""
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try:
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skills = _load_enabled_skills_sync()
|
||||
skills = _load_enabled_skills_sync(app_config)
|
||||
except Exception:
|
||||
logger.exception("Failed to load enabled skills for prompt injection")
|
||||
skills = []
|
||||
@@ -164,7 +166,7 @@ Skip simple one-off tasks.
|
||||
"""
|
||||
|
||||
|
||||
def _build_available_subagents_description(available_names: list[str], bash_available: bool) -> str:
|
||||
def _build_available_subagents_description(available_names: list[str], bash_available: bool, app_config: AppConfig) -> str:
|
||||
"""Dynamically build subagent type descriptions from registry.
|
||||
|
||||
Mirrors Codex's pattern where agent_type_description is dynamically generated
|
||||
@@ -186,7 +188,7 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
|
||||
if name in builtin_descriptions:
|
||||
lines.append(f"- **{name}**: {builtin_descriptions[name]}")
|
||||
else:
|
||||
config = get_subagent_config(name)
|
||||
config = get_subagent_config(name, app_config)
|
||||
if config is not None:
|
||||
desc = config.description.split("\n")[0].strip() # First line only for brevity
|
||||
lines.append(f"- **{name}**: {desc}")
|
||||
@@ -194,22 +196,23 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _build_subagent_section(max_concurrent: int) -> str:
|
||||
def _build_subagent_section(max_concurrent: int, app_config: AppConfig) -> str:
|
||||
"""Build the subagent system prompt section with dynamic concurrency limit.
|
||||
|
||||
Args:
|
||||
max_concurrent: Maximum number of concurrent subagent calls allowed per response.
|
||||
app_config: Application config used to gate bash availability.
|
||||
|
||||
Returns:
|
||||
Formatted subagent section string.
|
||||
"""
|
||||
n = max_concurrent
|
||||
available_names = get_available_subagent_names()
|
||||
available_names = get_available_subagent_names(app_config)
|
||||
bash_available = "bash" in available_names
|
||||
|
||||
# Dynamically build subagent type descriptions from registry (aligned with Codex's
|
||||
# agent_type_description pattern where all registered roles are listed in the tool spec).
|
||||
available_subagents = _build_available_subagents_description(available_names, bash_available)
|
||||
available_subagents = _build_available_subagents_description(available_names, bash_available, app_config)
|
||||
direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
|
||||
direct_execution_example = (
|
||||
'# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test") # Direct execution, not task()'
|
||||
@@ -536,36 +539,34 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
|
||||
"""
|
||||
|
||||
|
||||
def _get_memory_context(agent_name: str | None = None) -> str:
|
||||
def _get_memory_context(app_config: AppConfig, agent_name: str | None = None) -> str:
|
||||
"""Get memory context for injection into system prompt.
|
||||
|
||||
Args:
|
||||
agent_name: If provided, loads per-agent memory. If None, loads global memory.
|
||||
|
||||
Returns:
|
||||
Formatted memory context string wrapped in XML tags, or empty string if disabled.
|
||||
Returns an empty string when memory is disabled or the stored memory file
|
||||
cannot be read/parsed. A corrupt memory.json degrades the prompt to
|
||||
no-memory; it never kills the agent.
|
||||
"""
|
||||
from deerflow.agents.memory import format_memory_for_injection, get_memory_data
|
||||
from deerflow.runtime.user_context import get_effective_user_id
|
||||
|
||||
memory_config = app_config.memory
|
||||
if not memory_config.enabled or not memory_config.injection_enabled:
|
||||
return ""
|
||||
|
||||
try:
|
||||
from deerflow.agents.memory import format_memory_for_injection, get_memory_data
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
memory_data = get_memory_data(memory_config, agent_name, user_id=get_effective_user_id())
|
||||
except (OSError, ValueError, UnicodeDecodeError):
|
||||
logger.exception("Failed to load memory data for prompt injection")
|
||||
return ""
|
||||
|
||||
config = get_memory_config()
|
||||
if not config.enabled or not config.injection_enabled:
|
||||
return ""
|
||||
memory_content = format_memory_for_injection(memory_data, max_tokens=memory_config.max_injection_tokens)
|
||||
if not memory_content.strip():
|
||||
return ""
|
||||
|
||||
memory_data = get_memory_data(agent_name)
|
||||
memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
|
||||
|
||||
if not memory_content.strip():
|
||||
return ""
|
||||
|
||||
return f"""<memory>
|
||||
return f"""<memory>
|
||||
{memory_content}
|
||||
</memory>
|
||||
"""
|
||||
except Exception as e:
|
||||
logger.error("Failed to load memory context: %s", e)
|
||||
return ""
|
||||
|
||||
|
||||
@lru_cache(maxsize=32)
|
||||
@@ -600,19 +601,12 @@ You have access to skills that provide optimized workflows for specific tasks. E
|
||||
</skill_system>"""
|
||||
|
||||
|
||||
def get_skills_prompt_section(available_skills: set[str] | None = None) -> str:
|
||||
def get_skills_prompt_section(app_config: AppConfig, available_skills: set[str] | None = None) -> str:
|
||||
"""Generate the skills prompt section with available skills list."""
|
||||
skills = _get_enabled_skills()
|
||||
skills = _get_enabled_skills(app_config)
|
||||
|
||||
try:
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
config = get_app_config()
|
||||
container_base_path = config.skills.container_path
|
||||
skill_evolution_enabled = config.skill_evolution.enabled
|
||||
except Exception:
|
||||
container_base_path = "/mnt/skills"
|
||||
skill_evolution_enabled = False
|
||||
container_base_path = app_config.skills.container_path
|
||||
skill_evolution_enabled = app_config.skill_evolution.enabled
|
||||
|
||||
if not skills and not skill_evolution_enabled:
|
||||
return ""
|
||||
@@ -636,7 +630,7 @@ def get_agent_soul(agent_name: str | None) -> str:
|
||||
return ""
|
||||
|
||||
|
||||
def get_deferred_tools_prompt_section() -> str:
|
||||
def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
|
||||
"""Generate <available-deferred-tools> block for the system prompt.
|
||||
|
||||
Lists only deferred tool names so the agent knows what exists
|
||||
@@ -645,12 +639,7 @@ def get_deferred_tools_prompt_section() -> str:
|
||||
"""
|
||||
from deerflow.tools.builtins.tool_search import get_deferred_registry
|
||||
|
||||
try:
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
if not get_app_config().tool_search.enabled:
|
||||
return ""
|
||||
except Exception:
|
||||
if not app_config.tool_search.enabled:
|
||||
return ""
|
||||
|
||||
registry = get_deferred_registry()
|
||||
@@ -661,15 +650,9 @@ def get_deferred_tools_prompt_section() -> str:
|
||||
return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
|
||||
|
||||
|
||||
def _build_acp_section() -> str:
|
||||
def _build_acp_section(app_config: AppConfig) -> str:
|
||||
"""Build the ACP agent prompt section, only if ACP agents are configured."""
|
||||
try:
|
||||
from deerflow.config.acp_config import get_acp_agents
|
||||
|
||||
agents = get_acp_agents()
|
||||
if not agents:
|
||||
return ""
|
||||
except Exception:
|
||||
if not app_config.acp_agents:
|
||||
return ""
|
||||
|
||||
return (
|
||||
@@ -681,15 +664,9 @@ def _build_acp_section() -> str:
|
||||
)
|
||||
|
||||
|
||||
def _build_custom_mounts_section() -> str:
|
||||
def _build_custom_mounts_section(app_config: AppConfig) -> str:
|
||||
"""Build a prompt section for explicitly configured sandbox mounts."""
|
||||
try:
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
mounts = get_app_config().sandbox.mounts or []
|
||||
except Exception:
|
||||
logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
|
||||
return ""
|
||||
mounts = app_config.sandbox.mounts or []
|
||||
|
||||
if not mounts:
|
||||
return ""
|
||||
@@ -703,13 +680,20 @@ def _build_custom_mounts_section() -> str:
|
||||
return f"\n**Custom Mounted Directories:**\n{mounts_list}\n- If the user needs files outside `/mnt/user-data`, use these absolute container paths directly when they match the requested directory"
|
||||
|
||||
|
||||
def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3, *, agent_name: str | None = None, available_skills: set[str] | None = None) -> str:
|
||||
def apply_prompt_template(
|
||||
app_config: AppConfig,
|
||||
subagent_enabled: bool = False,
|
||||
max_concurrent_subagents: int = 3,
|
||||
*,
|
||||
agent_name: str | None = None,
|
||||
available_skills: set[str] | None = None,
|
||||
) -> str:
|
||||
# Get memory context
|
||||
memory_context = _get_memory_context(agent_name)
|
||||
memory_context = _get_memory_context(app_config, agent_name)
|
||||
|
||||
# Include subagent section only if enabled (from runtime parameter)
|
||||
n = max_concurrent_subagents
|
||||
subagent_section = _build_subagent_section(n) if subagent_enabled else ""
|
||||
subagent_section = _build_subagent_section(n, app_config) if subagent_enabled else ""
|
||||
|
||||
# Add subagent reminder to critical_reminders if enabled
|
||||
subagent_reminder = (
|
||||
@@ -730,14 +714,14 @@ def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagen
|
||||
)
|
||||
|
||||
# Get skills section
|
||||
skills_section = get_skills_prompt_section(available_skills)
|
||||
skills_section = get_skills_prompt_section(app_config, available_skills)
|
||||
|
||||
# Get deferred tools section (tool_search)
|
||||
deferred_tools_section = get_deferred_tools_prompt_section()
|
||||
deferred_tools_section = get_deferred_tools_prompt_section(app_config)
|
||||
|
||||
# Build ACP agent section only if ACP agents are configured
|
||||
acp_section = _build_acp_section()
|
||||
custom_mounts_section = _build_custom_mounts_section()
|
||||
acp_section = _build_acp_section(app_config)
|
||||
custom_mounts_section = _build_custom_mounts_section(app_config)
|
||||
acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)
|
||||
|
||||
# Format the prompt with dynamic skills and memory
|
||||
|
||||
Reference in New Issue
Block a user