refactor: 重构 config 目录结构，按领域分类静态配置

backend/src/core/agent/crewai/template_loader.py

@@ -12,12 +12,11 @@ class CrewAITemplate:
     agents: dict[str, Any]
     tasks: dict[str, Any]
     workflow: dict[str, Any]
-    prompts: dict[str, str]
     tools_whitelist: set[str]
 
 
 def _default_static_root() -> Path:
-    return Path(__file__).resolve().parents[3] / "config" / "static" / "agent"
+    return Path(__file__).resolve().parents[3] / "config" / "static" / "crewai"
 
 
 def _read_yaml(file_path: Path) -> dict[str, Any]:
@@ -30,12 +29,6 @@ def _read_yaml(file_path: Path) -> dict[str, Any]:
     return loaded
 
 
-def _read_prompt(file_path: Path) -> str:
-    if not file_path.is_file():
-        raise FileNotFoundError(f"Required prompt file not found: {file_path}")
-    return file_path.read_text(encoding="utf-8").strip()
-
-
 def validate_workflow_stages(stages: list[str]) -> None:
     expected = ["intent", "execution", "organization"]
     if stages != expected:
@@ -56,27 +49,19 @@ def load_tools_whitelist(static_root: Path | None = None) -> set[str]:
 
 def load_crewai_template(static_root: Path | None = None) -> CrewAITemplate:
     root = static_root or _default_static_root()
-    crewai_root = root / "crewai"
 
-    agents = _read_yaml(crewai_root / "agents.yaml")
-    tasks = _read_yaml(crewai_root / "tasks.yaml")
-    workflow = _read_yaml(crewai_root / "workflow.yaml")
+    agents = _read_yaml(root / "agents.yaml")
+    tasks = _read_yaml(root / "tasks.yaml")
+    workflow = _read_yaml(root / "workflow.yaml")
 
     stages = workflow.get("stages")
     if not isinstance(stages, list):
         raise ValueError("workflow.yaml field 'stages' must be a list")
     validate_workflow_stages([str(stage) for stage in stages])
 
-    prompts = {
-        "intent": _read_prompt(crewai_root / "prompts" / "intent.md"),
-        "execution": _read_prompt(crewai_root / "prompts" / "execution.md"),
-        "organization": _read_prompt(crewai_root / "prompts" / "organization.md"),
-    }
-
     return CrewAITemplate(
         agents=agents,
         tasks=tasks,
         workflow=workflow,
-        prompts=prompts,
         tools_whitelist=load_tools_whitelist(root),
     )

backend/src/core/config/initial/init_data.py

@@ -14,7 +14,7 @@ from core.logging import get_logger
 from models.llm import Llm
 from models.llm_factory import LlmFactory
 
-logger = get_logger("core.initialization.init_data")
+logger = get_logger("core.config.initial.init_data")
 
 
 class LlmFactorySeed(BaseModel):
@@ -35,11 +35,7 @@ class LlmCatalogSeed(BaseModel):
 
 def _default_catalog_path() -> Path:
     return (
-        Path(__file__).resolve().parents[1]
-        / "config"
-        / "static"
-        / "agent"
-        / "llm_catalog.yaml"
+        Path(__file__).resolve().parents[1] / "static" / "database" / "llm_catalog.yaml"
     )
 
 

backend/src/core/config/static/agent/crewai/agents.yaml

@@ -1,9 +0,0 @@
-intent:
-  role: Intent Agent
-  goal: Classify user intent and decide execution strategy
-execution:
-  role: Execution Agent
-  goal: Execute tasks with available tools
-organization:
-  role: Organization Agent
-  goal: Organize output for user-friendly response

backend/src/core/config/static/agent/crewai/prompts/execution.md

@@ -1,2 +0,0 @@
-你是任务执行代理。
-基于输入意图与上下文调用可用工具，并生成可验证执行结果。

backend/src/core/config/static/agent/crewai/prompts/intent.md

@@ -1,2 +0,0 @@
-你是意图识别代理。
-你的任务是识别用户输入的意图类型，并返回结构化意图标签。

backend/src/core/config/static/agent/crewai/prompts/organization.md

@@ -1,2 +0,0 @@
-你是结果整理代理。
-将执行结果组织为面向用户的清晰回复，保留关键信息与必要引用。

backend/src/core/config/static/agent/crewai/tasks.yaml

@@ -1,6 +0,0 @@
-intent:
-  description: Identify user intent and required capabilities
-execution:
-  description: Execute intent with tools and model calls
-organization:
-  description: Format final response and references

backend/src/core/config/static/crewai/agents.yaml

@@ -0,0 +1,22 @@
+intent:
+  role: Intent Agent
+  goal: Classify user intent and decide execution strategy
+  backstory: >
+    You are an expert intent classifier with deep understanding
+    of user query patterns and dialogue acts. Your role is to
+    analyze user input and determine the appropriate action.
+
+execution:
+  role: Execution Agent
+  goal: Execute tasks with available tools
+  backstory: >
+    You are a skilled task executor with expertise in tool calling,
+    API interactions, and result verification. You work systematically
+    to complete user requests.
+
+organization:
+  role: Organization Agent
+  goal: Organize output for user-friendly response
+  backstory: >
+    You specialize in presenting results in a clear, user-friendly manner.
+    You ensure responses are well-structured and actionable.

backend/src/core/config/static/crewai/tasks.yaml

@@ -0,0 +1,16 @@
+intent:
+  description: Identify user intent and required capabilities
+  expected_output: >
+    Structured intent classification with intent type, confidence score,
+    and recommended action plan
+
+execution:
+  description: Execute intent with tools and model calls
+  expected_output: >
+    Verified execution results with tool outputs, status, and any errors
+
+organization:
+  description: Format final response and references
+  expected_output: >
+    User-friendly response with structured output, citations, and
+    clear next steps if applicable

backend/src/core/config/static/database/user_agent_catalog.yaml

@@ -0,0 +1 @@
+agents: []

backend/src/core/runtime/cli.py

@@ -5,7 +5,7 @@ import subprocess
 import sys
 from pathlib import Path
 
-from core.initialization.init_data import initialize_data
+from core.config.initial.init_data import initialize_data
 from core.logging import get_logger
 
 logger = get_logger("core.runtime.cli")