feat: enhance agent orchestration, knowledge flow and UI refinements

backend/app/agents/state.py

@@ -1,32 +1,19 @@
-from dataclasses import dataclass
-from typing import TypedDict, Annotated
+from dataclasses import dataclass, field
+from typing import TypedDict, Annotated, Sequence
 from enum import Enum
 
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import BaseMessage
+from langgraph.graph.message import add_messages
 
 
 class AgentRole(str, Enum):
     MASTER = "master"
-    PLANNER = "planner"
+    SCHEDULE_PLANNER = "schedule_planner"
     EXECUTOR = "executor"
     LIBRARIAN = "librarian"
     ANALYST = "analyst"
 
 
-@dataclass
-class AgentInfo:
-    name: str
-    role: AgentRole
-    description: str
-
-
-@dataclass
-class ToolCall:
-    tool: str
-    args: dict
-    result: str | None = None
-
-
 @dataclass
 class ConversationTurn:
     role: str  # "user" | "assistant"
@@ -35,60 +22,41 @@ class ConversationTurn:
     model: str | None = None
 
 
-def turn_to_message(turn: ConversationTurn) -> HumanMessage:
-    return HumanMessage(content=turn.content)
-
-
-def message_to_turn(msg, agent: AgentRole | None = None) -> ConversationTurn:
-    msg_type = getattr(msg, "type", None) or getattr(msg, "role", "assistant")
-    return ConversationTurn(
-        role="user" if msg_type in ("human", "user") else "assistant",
-        content=msg.content,
-        agent=agent,
-        model=getattr(msg, "model", None),
-    )
-
-
 class AgentState(TypedDict):
-    messages: Annotated[list, None]
+    # Core message history with add_messages reducer
+    messages: Annotated[list[BaseMessage], add_messages]
+    
+    # Session identifiers
     user_id: str
     conversation_id: str
 
-    # Agent routing
-    current_agent: AgentRole
-    active_agents: list[AgentRole]
-    current_sub_commander: str | None
-    active_sub_commanders: list[str]
-    sub_commander_trace: list[dict]
-
-    # Task tracking
+    # Agent routing state
+    current_agent: str | None
+    next_step: str | None  # For explicit graph routing
+    
+    # Traceability
+    agent_trace: list[str]
+    
+    # Task & Entity Tracking (Business Logic)
     pending_tasks: list[dict]
     completed_tasks: list[dict]
+    created_entities: list[dict]
 
-    # Tool usage
-    tool_calls: list[ToolCall]
-    last_tool_result: str | None
-
-    # Knowledge context
+    # Context summaries (for long-term or cross-agent context)
     knowledge_context: str | None
-    graph_context: str | None
-
-    # Planning
-    plan: str | None
-    plan_steps: list[dict]
-
-    # Analysis
+    schedule_context_summary: str | None
     analysis_report: str | None
 
     # Output control
     final_response: str | None
-    should_respond: bool
-
-    # Memory context (injected at start of each conversation)
+    
+    # Memory & Environment
     memory_context: str | None
-
-    # User LLM config (for using user-configured models)
+    current_datetime_context: str | None
+    
+    # Configuration
     user_llm_config: dict | None
+    provider_capabilities: dict | None
 
 
 def initial_state(user_id: str, conversation_id: str) -> AgentState:
@@ -96,22 +64,18 @@ def initial_state(user_id: str, conversation_id: str) -> AgentState:
         messages=[],
         user_id=user_id,
         conversation_id=conversation_id,
-        current_agent=AgentRole.MASTER,
-        active_agents=[AgentRole.MASTER],
-        current_sub_commander=None,
-        active_sub_commanders=[],
-        sub_commander_trace=[],
+        current_agent=AgentRole.MASTER.value,
+        next_step=None,
+        agent_trace=[AgentRole.MASTER.value],
         pending_tasks=[],
         completed_tasks=[],
-        tool_calls=[],
-        last_tool_result=None,
+        created_entities=[],
         knowledge_context=None,
-        graph_context=None,
-        plan=None,
-        plan_steps=[],
+        schedule_context_summary=None,
         analysis_report=None,
         final_response=None,
-        should_respond=True,
         memory_context=None,
+        current_datetime_context=None,
         user_llm_config=None,
+        provider_capabilities=None,
     )