feat: implement conversational AI agent with tool-calling

Prototype implementation that allows:
1. Normal conversation with the AI
2. Tool-calling to update trading strategies

Created new ConversationalAgent that uses CrewAI with tools:
- get_current_strategy: Check current bot strategy
- update_trading_strategy: Update bot's trading configuration

The agent can now respond to questions like 'What is this?' without
forcing JSON output, and can update strategies when user provides
specific parameters.

Refs #51

src/backend/.env.example

@@ -32,7 +32,7 @@ MINIMAX_API_KEY=your-minimax-api-key
 
 # MiniMax model to use
 # Common options: MiniMax-Text-01, MiniMax-M2.1
-MINIMAX_MODEL=MiniMax-Text-01
+MINIMAX_MODEL=MiniMax-M2.7
 
 # =============================================================================
 # AVE CLOUD API

src/backend/app/api/auth.py

@@ -58,7 +58,7 @@ def get_current_user(
 
 
 @router.post(
-    "/register", response_model=UserResponse, status_code=status.HTTP_201_CREATED
+    "/register", response_model=Token, status_code=status.HTTP_201_CREATED
 )
 def register(user: UserCreate, db: Session = Depends(get_db)):
     existing_user = db.query(User).filter(User.email == user.email).first()
@@ -75,7 +75,10 @@ def register(user: UserCreate, db: Session = Depends(get_db)):
     db.add(db_user)
     db.commit()
     db.refresh(db_user)
-    return db_user
+    
+    # Generate and return access token so frontend can proceed immediately
+    access_token = create_access_token(data={"sub": db_user.id})
+    return Token(access_token=access_token, token_type="bearer")
 
 
 @router.post("/login", response_model=Token)

src/backend/app/api/bots.py

@@ -16,6 +16,7 @@ from ..db.schemas import (
 )
 from ..db.models import Bot, BotConversation, User
 from ..services.ai_agent.crew import get_trading_crew
+from ..services.ai_agent.conversational import get_conversational_agent
 
 router = APIRouter()
 MAX_BOTS_PER_USER = 3
@@ -183,69 +184,45 @@ def chat(
         .order_by(BotConversation.created_at)
         .all()
     )
-    history_for_crew = [
+    history_for_agent = [
         {"role": conv.role, "content": conv.content}
         for conv in conversation_history[-10:]
     ]
 
     user_message = request.message
-    if request.strategy_config:
-        crew = get_trading_crew()
-        result = crew.chat(user_message, history_for_crew)
     
-        assistant_content = result.get("response", "I couldn't process your request.")
-        if result.get("success") and result.get("strategy_config"):
-            bot.strategy_config = result["strategy_config"]
-            db.commit()
+    # Use ConversationalAgent for natural chat with tool-calling
+    agent = get_conversational_agent(bot_id=bot_id)
+    result = agent.chat(user_message, history_for_agent)
 
-        db_conversation = BotConversation(
-            bot_id=bot_id,
-            role="user",
-            content=user_message,
-        )
-        db.add(db_conversation)
+    assistant_content = result.get("response", "I couldn't process your request.")
 
-        db_assistant = BotConversation(
-            bot_id=bot_id,
-            role="assistant",
-            content=assistant_content,
-        )
-        db.add(db_assistant)
-        db.commit()
-        db.refresh(db_assistant)
+    # Save conversation
+    db_conversation = BotConversation(
+        bot_id=bot_id,
+        role="user",
+        content=user_message,
+    )
+    db.add(db_conversation)
 
-        return BotChatResponse(
-            response=assistant_content,
-            strategy_config=result.get("strategy_config"),
-            success=result.get("success", False),
-        )
-    else:
-        crew = get_trading_crew()
-        result = crew.chat(user_message, history_for_crew)
+    db_assistant = BotConversation(
+        bot_id=bot_id,
+        role="assistant",
+        content=assistant_content,
+    )
+    db.add(db_assistant)
+    db.commit()
+    db.refresh(db_assistant)
 
-        assistant_content = result.get("response", "I couldn't process your request.")
+    # If strategy was updated via tool, refresh bot data
+    if result.get("strategy_updated"):
+        db.refresh(bot)
 
-        db_conversation = BotConversation(
-            bot_id=bot_id,
-            role="user",
-            content=user_message,
-        )
-        db.add(db_conversation)
-
-        db_assistant = BotConversation(
-            bot_id=bot_id,
-            role="assistant",
-            content=assistant_content,
-        )
-        db.add(db_assistant)
-        db.commit()
-        db.refresh(db_assistant)
-
-        return BotChatResponse(
-            response=assistant_content,
-            strategy_config=result.get("strategy_config"),
-            success=result.get("success", False),
-        )
+    return BotChatResponse(
+        response=assistant_content,
+        strategy_config=bot.strategy_config if result.get("strategy_updated") else None,
+        success=result.get("success", False),
+    )
 
 
 @router.get("/{bot_id}/history", response_model=List[BotConversationResponse])

src/backend/app/services/ai_agent/__init__.py

@@ -1,4 +1,4 @@
-from .crew import CrewAgent
-from .llm_connector import LLMConnector
+from .crew import TradingCrew, get_trading_crew
+from .llm_connector import MiniMaxLLM, MiniMaxConnector
 
-__all__ = ["CrewAgent", "LLMConnector"]
+__all__ = ["TradingCrew", "get_trading_crew", "MiniMaxLLM", "MiniMaxConnector"]

src/backend/app/services/ai_agent/conversational.py

@@ -0,0 +1,167 @@
+"""
+Conversational Trading Agent
+
+This agent can:
+1. Have normal conversations with users
+2. Update trading strategies when user provides specific instructions
+
+Uses CrewAI's tool-calling capabilities for structured updates.
+"""
+
+from typing import List, Optional, Dict, Any
+from crewai import Agent, LLM
+from crewai.tools import tool
+from sqlalchemy.orm import Session
+
+from ...core.config import get_settings
+from ...db.models import Bot
+
+
+# Tool definitions
+@tool
+def get_current_strategy(bot_id: str) -> str:
+    """Get the current trading strategy configuration for a bot.
+    
+    Use this tool to check the current strategy before making changes.
+    
+    Args:
+        bot_id: The ID of the bot to get strategy for
+        
+    Returns:
+        JSON string with current strategy configuration
+    """
+    from ...core.database import get_db
+    from ...db.models import Bot
+    
+    db = next(get_db())
+    bot = db.query(Bot).filter(Bot.id == bot_id).first()
+    if not bot:
+        return '{"error": "Bot not found"}'
+    return str(bot.strategy_config)
+
+
+@tool
+def update_trading_strategy(
+    bot_id: str,
+    conditions: List[Dict],
+    actions: List[Dict],
+    risk_management: Optional[Dict] = None
+) -> str:
+    """Update the trading strategy configuration for a bot.
+    
+    Call this tool when the user provides specific trading parameters like:
+    - Buy/sell conditions (price drops, price rises, etc.)
+    - Take profit percentages
+    - Stop loss percentages
+    
+    Args:
+        bot_id: The ID of the bot to update
+        conditions: List of trigger conditions (e.g., [{"type": "price_drop", "token": "PEPE", "threshold": 5}])
+        actions: List of actions to take (e.g., [{"type": "buy", "amount_percent": 50}])
+        risk_management: Optional risk settings (e.g., {"stop_loss_percent": 10, "take_profit_percent": 50})
+        
+    Returns:
+        Confirmation message with updated strategy
+    """
+    from ...core.database import get_db
+    from ...db.models import Bot
+    
+    db = next(get_db())
+    bot = db.query(Bot).filter(Bot.id == bot_id).first()
+    if not bot:
+        return '{"error": "Bot not found"}'
+    
+    new_config = {
+        "conditions": conditions,
+        "actions": actions,
+    }
+    if risk_management:
+        new_config["risk_management"] = risk_management
+    
+    bot.strategy_config = new_config
+    db.commit()
+    
+    return f'Successfully updated trading strategy. New config: {new_config}'
+
+
+SYSTEM_PROMPT = """You are a helpful AI trading assistant. You can:
+
+1. Have normal conversations - answer questions about trading, tokens, strategies, etc.
+2. Help users configure their trading bots when they provide specific parameters
+
+When a user asks general questions, just answer conversationally.
+When a user provides specific trading parameters (like percentages, tokens, conditions), 
+use the update_trading_strategy tool to save their configuration.
+
+Example conversations:
+- User: "What is this?" → Answer conversationally about the trading bot platform
+- User: "I want take profit at 200%" → Use update_trading_strategy with that parameter
+- User: "Alert me when PEPE drops 5%" → Use update_trading_strategy with that condition
+
+Be friendly, helpful, and clear in your responses."""
+
+
+class ConversationalAgent:
+    def __init__(self, api_key: str, model: str = "MiniMax-M2.7", bot_id: str = None):
+        self.api_key = api_key
+        self.model = model
+        self.bot_id = bot_id
+        self.llm = LLM(
+            model=model,
+            api_key=api_key,
+            api_base="https://api.minimax.io/v1"
+        )
+        
+        # Create agent with tools
+        self.agent = Agent(
+            role="Trading Assistant",
+            goal="Help users with trading strategies and general questions",
+            backstory=SYSTEM_PROMPT,
+            tools=[get_current_strategy, update_trading_strategy],
+            llm=self.llm,
+            verbose=True,
+            allow_delegation=False,
+        )
+    
+    def chat(self, user_message: str, conversation_history: List[Dict] = None) -> Dict[str, Any]:
+        """Process a user message and return a response.
+        
+        Args:
+            user_message: The user's message
+            conversation_history: Optional list of previous messages
+            
+        Returns:
+            Dict with 'response' (the assistant's reply) and 'strategy_updated' (bool)
+        """
+        # Execute agent using kickoff
+        try:
+            result = self.agent.kickoff(user_message)
+            
+            # Check if strategy was updated
+            result_str = str(result)
+            strategy_updated = "update_trading_strategy" in result_str or \
+                             "Successfully updated" in result_str
+            
+            return {
+                "response": result_str,
+                "strategy_updated": strategy_updated,
+                "success": True
+            }
+        except Exception as e:
+            return {
+                "response": f"I encountered an error: {str(e)}. Please try again.",
+                "strategy_updated": False,
+                "success": False
+            }
+
+
+def get_conversational_agent(api_key: str = None, model: str = None, bot_id: str = None) -> ConversationalAgent:
+    """Get or create a ConversationalAgent instance."""
+    if api_key is None:
+        settings = get_settings()
+        api_key = settings.MINIMAX_API_KEY
+    if model is None:
+        settings = get_settings()
+        model = settings.MINIMAX_MODEL
+    
+    return ConversationalAgent(api_key=api_key, model=model, bot_id=bot_id)

src/backend/app/services/ai_agent/crew.py

@@ -1,7 +1,7 @@
 from typing import List, Optional, Dict, Any
-from crewai import Agent, Task, Crew
-from .llm_connector import MiniMaxConnector, MiniMaxLLM
-from ..core.config import get_settings
+from crewai import Agent, Task, Crew, LLM
+from .llm_connector import MiniMaxConnector
+from ...core.config import get_settings
 
 
 class StrategyValidator:
@@ -120,7 +120,7 @@ class StrategyExplainer:
 
 
 def create_trading_designer_agent(
-    api_key: str, model: str = "MiniMax-Text-01"
+    api_key: str, model: str = "MiniMax-M2.7"
 ) -> Agent:
     connector = MiniMaxConnector(api_key=api_key, model=model)
 
@@ -141,13 +141,13 @@ def create_trading_designer_agent(
         role="Trading Strategy Designer",
         goal="Convert natural language trading requests into precise strategy configurations",
         backstory=system_prompt,
-        llm=MiniMaxLLM(api_key=api_key, model=model),
+        llm=LLM(model=model, api_key=api_key, api_base="https://api.minimax.io/v1"),
         verbose=True,
     )
 
 
 def create_strategy_validator_agent(
-    api_key: str, model: str = "MiniMax-Text-01"
+    api_key: str, model: str = "MiniMax-M2.7"
 ) -> Agent:
     return Agent(
         role="Strategy Validator",
@@ -155,13 +155,13 @@ def create_strategy_validator_agent(
         backstory="""You are a meticulous strategy validator with expertise in trading systems.
         You check that all required parameters are present, values are reasonable, and the 
         strategy makes logical sense. You never approve strategies with missing or invalid data.""",
-        llm=MiniMaxLLM(api_key=api_key, model=model),
+        llm=LLM(model=model, api_key=api_key, api_base="https://api.minimax.io/v1"),
         verbose=True,
     )
 
 
 def create_strategy_explainer_agent(
-    api_key: str, model: str = "MiniMax-Text-01"
+    api_key: str, model: str = "MiniMax-M2.7"
 ) -> Agent:
     return Agent(
         role="Strategy Explainer",
@@ -169,13 +169,13 @@ def create_strategy_explainer_agent(
         backstory="""You are a patient trading strategy explainer. You translate complex 
         strategy configurations into easy-to-understand language. You help users understand 
         exactly what their strategies will do when triggered.""",
-        llm=MiniMaxLLM(api_key=api_key, model=model),
+        llm=LLM(model=model, api_key=api_key, api_base="https://api.minimax.io/v1"),
         verbose=True,
     )
 
 
 class TradingCrew:
-    def __init__(self, api_key: str, model: str = "MiniMax-Text-01"):
+    def __init__(self, api_key: str, model: str = "MiniMax-M2.7"):
         self.api_key = api_key
         self.model = model
         self.validator = StrategyValidator()

src/backend/app/services/ai_agent/llm_connector.py

@@ -1,14 +1,12 @@
 from typing import Optional, List, Dict, Any
 import httpx
-from crewai import LLM
 
 
-class MiniMaxLLM(LLM):
-    def __init__(self, api_key: str, model: str = "MiniMax-Text-01", **kwargs):
-        super().__init__(**kwargs)
+class MiniMaxLLM:
+    def __init__(self, api_key: str, model: str = "MiniMax-M2.7", **kwargs):
         self.api_key = api_key
         self.model = model
-        self.base_url = "https://api.minimax.chat/v1"
+        self.base_url = "https://api.minimax.io/v1"
 
     def _call(self, messages: List[Dict[str, str]], **kwargs) -> str:
         headers = {
@@ -23,7 +21,7 @@ class MiniMaxLLM(LLM):
         }
         with httpx.Client(timeout=60.0) as client:
             response = client.post(
-                f"{self.base_url}/chat/completions",
+                f"{self.base_url}/text/chatcompletion_v2",
                 headers=headers,
                 json=payload,
             )
@@ -35,7 +33,7 @@ class MiniMaxLLM(LLM):
 
 
 class MiniMaxConnector:
-    def __init__(self, api_key: str, model: str = "MiniMax-Text-01"):
+    def __init__(self, api_key: str, model: str = "MiniMax-M2.7"):
         self.api_key = api_key
         self.model = model
 

src/backend/requirements.txt

@@ -6,6 +6,7 @@ pydantic-settings>=2.1.0
 email-validator>=2.0.0
 python-jose[cryptography]>=3.3.0
 passlib[bcrypt]>=1.7.4
+bcrypt>=4.0,<5.0  # Required for passlib compatibility
 crewai>=0.1.0
 anthropic>=0.18.0
 httpx>=0.26.0

src/frontend/src/lib/api/client.ts

@@ -104,11 +104,12 @@ export const api = {
 			}
 		},
 
-		async chat(id: string, message: string): Promise<BotChatResponse> {
+		async chat(id: string, message: string, signal?: AbortSignal): Promise<BotChatResponse> {
 			const response = await fetch(`${API_URL}/bots/${id}/chat`, {
 				method: 'POST',
 				headers: getAuthHeaders(),
-				body: JSON.stringify({ message } as BotChatRequest)
+				body: JSON.stringify({ message } as BotChatRequest),
+				signal
 			});
 			return handleResponse<BotChatResponse>(response);
 		},

src/frontend/src/lib/stores/chatStore.ts

@@ -8,12 +8,25 @@ export interface ChatMessage {
 	timestamp: Date;
 }
 
+// Fallback UUID generator for environments where crypto.randomUUID is not available
+function generateId(): string {
+	if (typeof crypto !== 'undefined' && typeof crypto.randomUUID === 'function') {
+		return crypto.randomUUID();
+	}
+	// Fallback: simple UUID v4 implementation
+	return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {
+		const r = (Math.random() * 16) | 0;
+		const v = c === 'x' ? r : (r & 0x3) | 0x8;
+		return v.toString(16);
+	});
+}
+
 export const chatStore = writable<ChatMessage[]>([]);
 
 export function addMessage(message: Omit<ChatMessage, 'id' | 'timestamp'>) {
 	const newMessage: ChatMessage = {
 		...message,
-		id: crypto.randomUUID(),
+		id: generateId(),
 		timestamp: new Date()
 	};
 	chatStore.update(messages => [...messages, newMessage]);

src/frontend/src/routes/bot/[id]/+page.svelte

@@ -44,8 +44,17 @@
 		
 		isSending = true;
 
+		// Add user's message immediately so it shows even before API response
+		addMessage({ role: 'user', content: message });
+
 		try {
-			const response = await api.bots.chat(botId, message);
+			// Add timeout to prevent hanging requests
+			const controller = new AbortController();
+			const timeoutId = setTimeout(() => controller.abort(), 30000);
+			
+			const response = await api.bots.chat(botId, message, controller.signal);
+			clearTimeout(timeoutId);
+			
 			addMessage({ role: 'assistant', content: response.response });
 			
 			if (response.strategy_config) {
@@ -53,7 +62,11 @@
 				setCurrentBot(bot);
 			}
 		} catch (e) {
-			addMessage({ role: 'assistant', content: 'Sorry, I encountered an error. Please try again.' });
+			if (e instanceof Error && e.name === 'AbortError') {
+				addMessage({ role: 'assistant', content: 'Request timed out. Please try again.' });
+			} else {
+				addMessage({ role: 'assistant', content: 'Sorry, I encountered an error. Please try again.' });
+			}
 		} finally {
 			isSending = false;
 		}