src/app/agents/pipeline.py

@@ -1,8 +1,5 @@
-from agno.run.agent import RunOutput
 from app.agents.models import AppModels
-from app.agents.team import create_team_with
+from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorOutput, PredictorStyle
-from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
-from app.base.markets import ProductInfo
 
 
 class Pipeline:
@@ -17,7 +14,6 @@ class Pipeline:
         self.all_styles = list(PredictorStyle)
 
         self.style = self.all_styles[0]
-        self.team = create_team_with(AppModels.OLLAMA_QWEN_1B)
         self.choose_predictor(0)  # Modello di default
 
     # ======================
@@ -65,41 +61,10 @@ class Pipeline:
         4. Restituisce la strategia finale
         """
         # Step 1: raccolta output dai membri del Team
-        team_outputs = self.team.run(query) # type: ignore
+        from app.agents import AppTeam
-
+        from agno.agent import RunEvent
-        # Step 2: aggregazione output strutturati
+        team = AppTeam(AppModels.OLLAMA_QWEN_1B) # TODO rendere dinamico
-        all_products: list[ProductInfo] = []
+        team.add_listener(RunEvent.tool_call_started, lambda e: print(f"Team tool call started: {e.agent_name}")) # type: ignore
-        sentiments: list[str] = []
+        team.add_listener(RunEvent.tool_call_completed, lambda e: print(f"Team tool call completed: {e.agent_name}")) # type: ignore
-
+        result = team.run_team(query)
-        for agent_output in team_outputs.member_responses:
+        return result
-            if isinstance(agent_output, RunOutput) and agent_output.metadata is not None:
-                keys = agent_output.metadata.keys()
-                if "products" in keys:
-                    all_products.extend(agent_output.metadata["products"])
-                if "sentiment_news" in keys:
-                    sentiments.append(agent_output.metadata["sentiment_news"])
-                if "sentiment_social" in keys:
-                    sentiments.append(agent_output.metadata["sentiment_social"])
-
-        aggregated_sentiment = "\n".join(sentiments)
-
-        # Step 3: invocazione Predictor
-        predictor_input = PredictorInput(
-            data=all_products,
-            style=self.style,
-            sentiment=aggregated_sentiment
-        )
-
-        result = self.predictor.run(predictor_input) # type: ignore
-        if not isinstance(result.content, PredictorOutput):
-            return "❌ Errore: il modello non ha restituito un output valido."
-        prediction: PredictorOutput = result.content
-
-        # Step 4: restituzione strategia finale
-        portfolio_lines = "\n".join(
-            [f"{item.asset} ({item.percentage}%): {item.motivation}" for item in prediction.portfolio]
-        )
-        return (
-            f"📊 Strategia ({self.style.value}): {prediction.strategy}\n\n"
-            f"💼 Portafoglio consigliato:\n{portfolio_lines}"
-        )

src/app/agents/team.py

@@ -1,32 +1,87 @@
-from agno.team import Team
+import asyncio
+import logging
+from typing import Callable, Self
+from agno.run.agent import RunOutputEvent
+from agno.team import Team, TeamRunEvent, TeamRunOutputEvent
+from agno.tools.reasoning import ReasoningTools
 from app.agents import AppModels
 from app.markets import MarketAPIsTool
 from app.news import NewsAPIsTool
 from app.social import SocialAPIsTool
 
+logging = logging.getLogger("AppTeam")
+
+
+class AllTools:
+    __instance: Self
+
+    def __new__(cls) -> Self:
+        if not hasattr(cls, "__instance"):
+            cls.__instance = super(AllTools, cls).__new__(cls)
+        return cls.__instance
+
+    # TODO scegliere un modo migliore per inizializzare gli strumenti
+    # TODO magari usare un config file o una classe apposta per i configs
+    def __init__(self):
+        self.market = MarketAPIsTool("EUR")
+        self.news = NewsAPIsTool()
+        self.social = SocialAPIsTool()
+
+
+class AppTeam:
+    def __init__(self, team_models: AppModels, coordinator: AppModels | None = None):
+        self.team_models = team_models
+        self.coordinator = coordinator or team_models
+        self.listeners: dict[str, Callable[[RunOutputEvent | TeamRunOutputEvent], None]] = {}
+
+    def add_listener(self, event: str, listener: Callable[[RunOutputEvent | TeamRunOutputEvent], None]) -> None:
+        self.listeners[event] = listener
+
+    def run_team(self, query: str) -> str:
+        return asyncio.run(self.run_team_async(query))
+
+    async def run_team_async(self, query: str) -> str:
+        logging.info(f"Running team q='{query}'")
+        team = AppTeam.create_team_with(self.team_models, self.coordinator)
+        result = "No output from team"
+
+        async for run_event in team.arun(query, stream=True, stream_intermediate_steps=True): # type: ignore
+            if run_event.event in self.listeners:
+                self.listeners[run_event.event](run_event)
+            if run_event.event in [TeamRunEvent.run_completed]:
+                if isinstance(run_event.content, str):
+                    result = run_event.content
+                    thinking = result.rfind("</think>")
+                    if thinking != -1: result = result[thinking:]
+
+        logging.info(f"Team finished")
+        return result
+
+    @staticmethod
+    def create_team_with(models: AppModels, coordinator: AppModels) -> Team:
+        tools = AllTools()
 
-def create_team_with(models: AppModels, coordinator: AppModels | None = None) -> Team:
         market_agent = models.get_agent(
             instructions=MARKET_INSTRUCTIONS,
             name="MarketAgent",
-        tools=[MarketAPIsTool()]
+            tools=[tools.market]
         )
         news_agent = models.get_agent(
             instructions=NEWS_INSTRUCTIONS,
             name="NewsAgent",
-        tools=[NewsAPIsTool()]
+            tools=[tools.news]
         )
         social_agent = models.get_agent(
             instructions=SOCIAL_INSTRUCTIONS,
             name="SocialAgent",
-        tools=[SocialAPIsTool()]
+            tools=[tools.social]
         )
 
-    coordinator = coordinator or models
         return Team(
             model=coordinator.get_model(COORDINATOR_INSTRUCTIONS),
             name="CryptoAnalysisTeam",
             members=[market_agent, news_agent, social_agent],
+            tools=[ReasoningTools()]
         )
 
 COORDINATOR_INSTRUCTIONS = """