feat(agent): 实现 Agent Runtime LLM 配置与消息元数据结构化支持

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

@@ -9,6 +9,7 @@ from pydantic import BaseModel, ValidationError
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from core.agent.domain.system_agent_config import SystemAgentLLMConfig
 from core.db.session import AsyncSessionLocal
 from core.logging import get_logger
 from models.llm import Llm
@@ -38,7 +39,7 @@ class SystemAgentsSeed(BaseModel):
     agent_type: str
     llm_model_code: str
     status: str
-    config: dict[str, Any]
+    config: SystemAgentLLMConfig | None = None
 
 
 class SystemAgentsYaml(BaseModel):
@@ -184,7 +185,9 @@ async def initialize_system_agents() -> None:
                     agent_type=agent["agent_type"],
                     llm_id=llm.id,
                     status=agent["status"],
-                    config=agent["config"],
+                    config=SystemAgentLLMConfig.model_validate(
+                        agent.get("config") or {}
+                    ).model_dump(),
                 )
 
     logger.info("Initialized system agents")

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

@@ -4,15 +4,18 @@ agents:
     status: active
     config:
       temperature: 0.7
+      max_tokens: null
     
   - agent_type: TASK_EXECUTION
     llm_model_code: deepseek-v3.2
     status: active
     config:
       temperature: 0.7
+      max_tokens: null
     
   - agent_type: RESULT_REPORTING
     llm_model_code: deepseek-v3.2
     status: active
     config:
       temperature: 0.7
+      max_tokens: null