Add mem-layer integration for persistent cross-agent memory

requirements.txt

@@ -1,47 +1,50 @@
-# 🌙 Moon Dev's Compliance Agent Requirements 🌙
-# Created with ❤️ by Moon Dev's AI Assistant
-
-# Core dependencies
-numpy>=1.24.0
-opencv-python>=4.8.0
-whisper>=1.1.10
-openai>=1.51.0
-anthropic>=0.5.0
-groq>=0.4.0
-# google-generativeai>=0.3.0  # Temporarily disabled due to protobuf conflict
-# To re-enable Gemini, uncomment above and ensure compatible versions:
-# google-generativeai==0.8.3
-# protobuf==4.25.1  
-# proto-plus==1.25.0
-termcolor>=2.3.0
-python-dotenv>=1.0.0
-pathlib>=1.0.1
-
-# For transcription
-openai-whisper>=20231117
-ffmpeg-python>=0.2.0
-
-# For image processing
-pillow>=10.0.0
-
-# For API access
-requests>=2.31.0
-
-# For data handling
-pandas>=2.0.0
-
-# For progress bars and arXiv paper downloader
-# Updated by Moon Dev's AI Assistant on arXiv downloader project
-tqdm>=4.66.0
-
-backtesting>=0.3.3
-ta-lib>=0.4.0
-PyPDF2>=3.0.0
-youtube-transcript-api>=0.6.2
-pandas-ta>=0.3.14b0
-
-# MetaTrader 5 Integration
-MetaTrader5>=5.0.45
-
-# Production Utilities
-psutil>=5.9.0  # System monitoring
+annotated-types==0.7.0
+argcomplete==3.1.4
+blinker==1.7.0
+certifi==2025.10.5
+charset-normalizer==3.4.4
+click==8.3.0
+click-default-group==1.2.4
+colorama==0.4.6
+conan==2.22.1
+cryptography==41.0.7
+dbus-python==1.3.2
+distro==1.8.0
+fasteners==0.20
+httplib2==0.20.4
+idna==3.11
+Jinja2==3.1.6
+launchpadlib==1.11.0
+lazr.restfulclient==0.14.6
+lazr.uri==1.0.6
+markdown-it-py==4.0.0
+MarkupSafe==3.0.3
+mdurl==0.1.2
+mem-layer @ git+https://github.com/icojerrel/mem-layer.git@d5573c9f7600b485c79051065bf66b8f76d6acb4
+networkx==3.5
+oauthlib==3.2.2
+packaging==24.0
+patch-ng==1.18.1
+pluggy==1.6.0
+pydantic==2.12.4
+pydantic_core==2.41.5
+Pygments==2.19.2
+PyGObject==3.48.2
+PyJWT==2.7.0
+pyparsing==3.1.1
+python-apt==2.7.7+ubuntu5
+python-dateutil==2.9.0.post0
+PyYAML==6.0.1
+requests==2.32.5
+rich==14.2.0
+six==1.16.0
+sqlite-fts4==1.0.3
+sqlite-utils==3.38
+tabulate==0.9.0
+toml==0.10.2
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+urllib3==2.5.0
+wadllib==1.3.6
+xmltodict==0.13.0
+yq==3.1.0

src/agents/risk_agent.py

@@ -57,6 +57,14 @@
 from src.agents.base_agent import BaseAgent
 import traceback
 
+# Memory integration
+try:
+    from src.memory import AgentMemory, MemoryScope
+    MEMORY_AVAILABLE = True
+except ImportError:
+    MEMORY_AVAILABLE = False
+    print("⚠️ Memory module not available - running without persistent memory")
+
 # Load environment variables
 load_dotenv()
 
@@ -113,6 +121,15 @@ def __init__(self):
         print(f"🏦 Initial Portfolio Balance: ${self.start_balance:.2f}")
 
         self.current_value = self.start_balance
+
+        # Initialize memory
+        if MEMORY_AVAILABLE:
+            self.memory = AgentMemory(agent_name="risk_agent")
+            if self.memory.enabled:
+                cprint("🧠 Memory layer initialized", "cyan")
+        else:
+            self.memory = None
+
         cprint("🛡️ Risk Agent initialized!", "white", "on_blue")
 
     def get_portfolio_value(self):
@@ -453,6 +470,19 @@ def check_risk_limits(self):
     def handle_limit_breach(self, breach_type, current_value):
         """Handle breached risk limits with AI consultation if enabled"""
         try:
+            # Store breach event in memory
+            if self.memory and self.memory.enabled:
+                self.memory.broadcast(
+                    f"RISK LIMIT BREACH: {breach_type} - Current value: {current_value}",
+                    scope=MemoryScope.ALERTS,
+                    priority="critical",
+                    metadata={
+                        "breach_type": breach_type,
+                        "current_value": current_value,
+                        "timestamp": datetime.now().isoformat()
+                    }
+                )
+
             # If AI confirmation is disabled, close positions immediately
             if not USE_AI_CONFIRMATION:
                 print(f"\n🚨 {breach_type} limit breached! Closing all positions immediately...")
@@ -537,7 +567,21 @@ def handle_limit_breach(self, breach_type, current_value):
 
             # Parse decision
             decision = response_text.split('\n')[0].strip()
-
+
+            # Store AI decision in memory
+            if self.memory and self.memory.enabled:
+                self.memory.store(
+                    f"AI Risk Decision: {decision} - {response_text[:200]}",
+                    scope=MemoryScope.RISK,
+                    priority="high",
+                    metadata={
+                        "breach_type": breach_type,
+                        "decision": decision,
+                        "reasoning": response_text,
+                        "model": "DeepSeek" if self.deepseek_client else "Claude"
+                    }
+                )
+
             if decision == "CLOSE_ALL":
                 print("🚨 AI recommends closing all positions!")
                 self.close_all_positions()
@@ -571,11 +615,24 @@ def run(self):
             # Get current PnL
             current_pnl = self.get_current_pnl()
             current_balance = self.get_portfolio_value()
-            
+
             print(f"\n💰 Current PnL: ${current_pnl:.2f}")
             print(f"💼 Current Balance: ${current_balance:.2f}")
             print(f"📉 Minimum Balance Limit: ${MINIMUM_BALANCE_USD:.2f}")
-
+
+            # Store portfolio status in memory
+            if self.memory and self.memory.enabled:
+                self.memory.store(
+                    f"Portfolio status: PnL ${current_pnl:.2f}, Balance ${current_balance:.2f}",
+                    scope=MemoryScope.RISK,
+                    priority="medium",
+                    metadata={
+                        "pnl": current_pnl,
+                        "balance": current_balance,
+                        "start_balance": self.start_balance
+                    }
+                )
+
             # Check minimum balance limit
             if current_balance < MINIMUM_BALANCE_USD:
                 print(f"⚠️ ALERT: Current balance ${current_balance:.2f} is below minimum ${MINIMUM_BALANCE_USD:.2f}")

src/agents/sentiment_agent.py

@@ -91,9 +91,17 @@ def patched_client(*args, **kwargs):
 
 httpx.Client = patched_client
 
-# imports 
+# imports
 from twikit import Client, TooManyRequests, BadRequest
 
+# Memory integration
+try:
+    from src.memory import AgentMemory, MemoryScope
+    MEMORY_AVAILABLE = True
+except ImportError:
+    MEMORY_AVAILABLE = False
+    print("⚠️ Memory module not available - running without persistent memory")
+
 class SentimentAgent:
     def __init__(self):
         """Initialize the Sentiment Agent"""
@@ -102,15 +110,23 @@ def __init__(self):
         self.model = None
         self.audio_dir = Path("src/audio")
         self.audio_dir.mkdir(parents=True, exist_ok=True)
-        
+
         # Initialize sentiment history file
         if not os.path.exists(SENTIMENT_HISTORY_FILE):
             pd.DataFrame(columns=['timestamp', 'sentiment_score', 'num_tweets']).to_csv(SENTIMENT_HISTORY_FILE, index=False)
-        
+
         # Load the sentiment model at initialization
         cprint("🤖 Loading sentiment model...", "cyan")
         self.init_sentiment_model()
-
+
+        # Initialize memory
+        if MEMORY_AVAILABLE:
+            self.memory = AgentMemory(agent_name="sentiment_agent")
+            if self.memory.enabled:
+                cprint("🧠 Memory layer initialized", "cyan")
+        else:
+            self.memory = None
+
         cprint("🌙 Moon Dev's Sentiment Agent initialized!", "green")
 
     def init_sentiment_model(self):
@@ -197,7 +213,7 @@ def save_sentiment_score(self, sentiment_score, num_tweets):
                 'sentiment_score': sentiment_score,
                 'num_tweets': num_tweets
             }])
-            
+
             # Load existing data
             if os.path.exists(SENTIMENT_HISTORY_FILE):
                 history_df = pd.read_csv(SENTIMENT_HISTORY_FILE)
@@ -212,9 +228,33 @@ def save_sentiment_score(self, sentiment_score, num_tweets):
                 history_df = pd.concat([history_df, new_data], ignore_index=True)
             else:
                 history_df = new_data
-                
+
             history_df.to_csv(SENTIMENT_HISTORY_FILE, index=False)
-
+
+            # Store in memory layer
+            if self.memory and self.memory.enabled:
+                # Convert score to readable label
+                if sentiment_score > 0.3:
+                    label = "very positive"
+                elif sentiment_score > 0:
+                    label = "slightly positive"
+                elif sentiment_score > -0.3:
+                    label = "slightly negative"
+                else:
+                    label = "very negative"
+
+                self.memory.store(
+                    f"Sentiment: {label} (score: {sentiment_score:.2f}) from {num_tweets} tweets",
+                    scope=MemoryScope.SENTIMENT,
+                    priority="medium",
+                    metadata={
+                        "sentiment_score": sentiment_score,
+                        "num_tweets": num_tweets,
+                        "score_percent": (sentiment_score + 1) * 50,
+                        "label": label
+                    }
+                )
+
         except Exception as e:
             cprint(f"❌ Error saving sentiment history: {str(e)}", "red")
 
@@ -300,11 +340,37 @@ def analyze_and_announce_sentiment(self, tweets):
                 message += f" - a small {abs(percent_change):.1f}% change"
 
         message += "."
-        
+
         # Announce with voice if sentiment is significant or if there's a big change
         is_important = abs(sentiment_score) > SENTIMENT_ANNOUNCE_THRESHOLD or (percent_change is not None and abs(percent_change) > 5)
         self._announce(message, is_important)
-
+
+        # Broadcast extreme sentiment or significant changes to all agents
+        if self.memory and self.memory.enabled:
+            if abs(sentiment_score) > 0.5:  # Very extreme sentiment
+                self.memory.broadcast(
+                    f"EXTREME sentiment detected: {sentiment} ({score_percent:.1f}/100)",
+                    scope=MemoryScope.ALERTS,
+                    priority="high",
+                    metadata={
+                        "sentiment_score": sentiment_score,
+                        "label": sentiment,
+                        "num_tweets": len(texts)
+                    }
+                )
+            elif percent_change is not None and abs(percent_change) > 10:  # Big sentiment shift
+                direction = "up" if percent_change > 0 else "down"
+                self.memory.broadcast(
+                    f"Large sentiment shift {direction}: {abs(percent_change):.1f} points in {int(time_diff)} min",
+                    scope=MemoryScope.ALERTS,
+                    priority="high",
+                    metadata={
+                        "percent_change": percent_change,
+                        "time_minutes": int(time_diff),
+                        "current_score": sentiment_score
+                    }
+                )
+
         # If not announcing vocally, print the raw score for debugging
         if not is_important:
             cprint(f"📊 Raw sentiment score: {sentiment_score:.2f} (on scale of -1 to 1)", "cyan")