Initial commit of integrated agent system

backend/agents/analyst.py

@@ -0,0 +1,139 @@
+# -*- coding: utf-8 -*-
+"""
+Analyst Agent - Based on AgentScope ReActAgent
+Performs analysis using tools and LLM
+"""
+from typing import Any, Dict, Optional
+
+from agentscope.agent import ReActAgent
+from agentscope.memory import InMemoryMemory, LongTermMemoryBase
+from agentscope.message import Msg
+
+from ..config.constants import ANALYST_TYPES
+from ..utils.progress import progress
+from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cache
+
+
+class AnalystAgent(ReActAgent):
+    """
+    Analyst Agent - Uses LLM for tool selection and analysis
+    Inherits from AgentScope's ReActAgent
+    """
+
+    def __init__(
+        self,
+        analyst_type: str,
+        toolkit: Any,
+        model: Any,
+        formatter: Any,
+        agent_id: Optional[str] = None,
+        config: Optional[Dict[str, Any]] = None,
+        long_term_memory: Optional[LongTermMemoryBase] = None,
+    ):
+        """
+        Initialize Analyst Agent
+
+        Args:
+            analyst_type: Type of analyst (e.g., "fundamentals", etc.)
+            toolkit: AgentScope Toolkit instance
+            model: LLM model instance
+            formatter: Message formatter instance
+            agent_id: Agent ID (defaults to "{analyst_type}_analyst")
+            config: Configuration dictionary
+            long_term_memory: Optional ReMeTaskLongTermMemory instance
+        """
+        if analyst_type not in ANALYST_TYPES:
+            raise ValueError(
+                f"Unknown analyst type: {analyst_type}. "
+                f"Must be one of: {list(ANALYST_TYPES.keys())}",
+            )
+
+        object.__setattr__(self, "analyst_type_key", analyst_type)
+        object.__setattr__(
+            self,
+            "analyst_persona",
+            ANALYST_TYPES[analyst_type]["display_name"],
+        )
+
+        if agent_id is None:
+            agent_id = analyst_type
+        object.__setattr__(self, "agent_id", agent_id)
+
+        object.__setattr__(self, "config", config or {})
+        object.__setattr__(self, "toolkit", toolkit)
+        sys_prompt = self._load_system_prompt()
+
+        kwargs = {
+            "name": agent_id,
+            "sys_prompt": sys_prompt,
+            "model": model,
+            "formatter": formatter,
+            "toolkit": toolkit,
+            "memory": InMemoryMemory(),
+            "max_iters": 10,
+        }
+        if long_term_memory:
+            kwargs["long_term_memory"] = long_term_memory
+            kwargs["long_term_memory_mode"] = "static_control"
+
+        super().__init__(**kwargs)
+
+    def _load_system_prompt(self) -> str:
+        """Load system prompt for analyst"""
+        return build_agent_system_prompt(
+            agent_id=self.agent_id,
+            config_name=self.config.get("config_name", "default"),
+            toolkit=self.toolkit,
+        )
+
+    async def reply(self, x: Msg = None) -> Msg:
+        """
+        Override reply method to add progress tracking
+
+        Args:
+            x: Input message (content must be str)
+
+        Returns:
+            Response message (content is str)
+        """
+        ticker = None
+        if x and hasattr(x, "metadata") and x.metadata:
+            ticker = x.metadata.get("tickers")
+
+        if ticker:
+            progress.update_status(
+                self.name,
+                ticker,
+                f"Starting {self.analyst_persona} analysis",
+            )
+
+        result = await super().reply(x)
+
+        if ticker:
+            progress.update_status(
+                self.name,
+                ticker,
+                "Analysis completed",
+            )
+
+        return result
+
+    def reload_runtime_assets(self, active_skill_dirs: Optional[list] = None) -> None:
+        """Reload toolkit and system prompt from current run assets."""
+        from .toolkit_factory import create_agent_toolkit
+
+        clear_prompt_factory_cache()
+        self.toolkit = create_agent_toolkit(
+            self.agent_id,
+            self.config.get("config_name", "default"),
+            active_skill_dirs=active_skill_dirs,
+        )
+        self._apply_runtime_sys_prompt(self._load_system_prompt())
+
+    def _apply_runtime_sys_prompt(self, sys_prompt: str) -> None:
+        """Update the prompt used by future turns and the cached system msg."""
+        self._sys_prompt = sys_prompt
+        for msg, _marks in self.memory.content:
+            if getattr(msg, "role", None) == "system":
+                msg.content = sys_prompt
+                break