Add multi-agent hospital ecosystem with patient, nurse, and senior doctor

Backend:
- BaseAgent with Claude extended thinking support
- PatientAgent with Hindi/English (Hinglish) speech and distress levels
- NurseAgent with clinical observations, vitals reporting, and urgency alerts
- SeniorDoctorAgent with Socratic teaching methodology and progressive hints
- AgentOrchestrator coordinating all agents with session management
- New /api/agents routes (initialize, action, vitals)

Frontend:
- Animated SVG avatars (PatientAvatar, NurseAvatar, SeniorDoctorAvatar)
with state-based animations (distress, alerts, thinking bubbles)
- AgentMessage component with per-agent styling and avatar rendering
- Agent target selector (Patient/Nurse/Dr. Sharma) in chat sidebar
- Multi-agent chat replaces single AI tutor in CaseInterface

Existing functionality preserved: landing page, case browser, stage reveal,
diagnosis submission, results display, dashboard, and analytics.

https://claude.ai/code/session_01EtTBqEZVEmdWihzhSden2o

backend/app/api/agents.py

@@ -0,0 +1,65 @@
+"""API routes for the multi-agent hospital ecosystem."""
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+
+from app.core.agents.orchestrator import orchestrator
+from app.core.rag.generator import CaseGenerator
+
+router = APIRouter()
+case_generator = CaseGenerator()
+
+
+class InitializeRequest(BaseModel):
+    case_id: str
+
+
+class AgentActionRequest(BaseModel):
+    session_id: str
+    action_type: str
+    student_input: Optional[str] = None
+
+
+@router.post("/initialize")
+async def initialize_agents(request: InitializeRequest):
+    """Initialize multi-agent session for a case.
+
+    Returns initial messages from patient, nurse, and senior doctor.
+    """
+    case = case_generator.get_case(request.case_id)
+    if not case:
+        raise HTTPException(status_code=404, detail="Case not found")
+
+    result = orchestrator.initialize_session(case)
+    return result
+
+
+@router.post("/action")
+async def agent_action(request: AgentActionRequest):
+    """Process a student action through the multi-agent system.
+
+    action_type options:
+    - talk_to_patient: Talk to the patient
+    - ask_nurse: Ask the nurse
+    - consult_senior: Consult the senior doctor
+    - examine_patient: Perform examination
+    - order_investigation: Order tests
+    """
+    result = orchestrator.process_action(
+        session_id=request.session_id,
+        action_type=request.action_type,
+        student_input=request.student_input,
+    )
+    if "error" in result:
+        raise HTTPException(status_code=404, detail=result["error"])
+    return result
+
+
+@router.get("/vitals/{session_id}")
+async def get_vitals(session_id: str):
+    """Get current vital signs and urgency status for a session."""
+    vitals = orchestrator.get_session_vitals(session_id)
+    if not vitals:
+        raise HTTPException(status_code=404, detail="Session not found")
+    return vitals

backend/app/core/agents/base_agent.py

@@ -0,0 +1,101 @@
+"""Base agent class for the multi-agent hospital ecosystem."""
+
+import logging
+import os
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import anthropic
+
+logger = logging.getLogger(__name__)
+
+
+class BaseAgent(ABC):
+    """Abstract base class for all hospital agents."""
+
+    agent_type: str = "base"
+    display_name: str = "Agent"
+
+    def __init__(self):
+        self.conversation_history: list[dict] = []
+        self.api_key = os.environ.get("ANTHROPIC_API_KEY")
+        self.client: Optional[anthropic.Anthropic] = None
+        if self.api_key and self.api_key != "sk-ant-your-key-here":
+            try:
+                self.client = anthropic.Anthropic(api_key=self.api_key)
+            except Exception as e:
+                logger.warning(f"{self.display_name} client init failed: {e}")
+
+    @abstractmethod
+    def get_system_prompt(self, case_context: dict) -> str:
+        """Return the system prompt for this agent given case context."""
+
+    @abstractmethod
+    def get_fallback_response(self, message: str, case_context: dict) -> str:
+        """Return a fallback response when the API is unavailable."""
+
+    def respond(self, message: str, case_context: dict) -> dict:
+        """Generate a response from this agent.
+
+        Returns dict with: agent_type, display_name, content, metadata
+        """
+        self.conversation_history.append({"role": "user", "content": message})
+
+        content = ""
+        thinking = ""
+
+        if self.client:
+            result = self._respond_with_claude(message, case_context)
+            if result:
+                content, thinking = result
+
+        if not content:
+            content = self.get_fallback_response(message, case_context)
+
+        self.conversation_history.append({"role": "assistant", "content": content})
+
+        response = {
+            "agent_type": self.agent_type,
+            "display_name": self.display_name,
+            "content": content,
+        }
+        if thinking:
+            response["thinking"] = thinking
+        return response
+
+    def _respond_with_claude(
+        self, message: str, case_context: dict
+    ) -> Optional[tuple[str, str]]:
+        """Call Claude with extended thinking. Returns (content, thinking) or None."""
+        system = self.get_system_prompt(case_context)
+        messages = self.conversation_history.copy()
+
+        try:
+            response = self.client.messages.create(
+                model="claude-opus-4-6",
+                max_tokens=16000,
+                temperature=1,  # required for extended thinking
+                thinking={
+                    "type": "enabled",
+                    "budget_tokens": 10000,
+                },
+                system=system,
+                messages=messages,
+            )
+
+            content = ""
+            thinking = ""
+            for block in response.content:
+                if block.type == "thinking":
+                    thinking = block.thinking
+                elif block.type == "text":
+                    content = block.text.strip()
+
+            return (content, thinking) if content else None
+        except Exception as e:
+            logger.error(f"{self.display_name} Claude API error: {e}")
+            return None
+
+    def reset(self):
+        """Reset conversation history for a new case."""
+        self.conversation_history = []

backend/app/core/agents/nurse_agent.py

@@ -0,0 +1,179 @@
+"""Nurse agent — provides clinical observations, vitals, and urgency alerts."""
+
+from app.core.agents.base_agent import BaseAgent
+
+
+NURSE_SYSTEM_PROMPT = """You are an experienced ward nurse in an Indian government hospital assisting a medical student with a patient case.
+
+CRITICAL RULES:
+1. You are professional, efficient, and supportive of the student.
+2. You provide clinical observations, vital sign readings, and nursing assessments.
+3. You alert the student about urgent/critical findings using clear urgency levels.
+4. You do NOT diagnose — you report observations and let the doctor decide.
+5. You use proper medical terminology (you're a trained nurse).
+6. You may gently prompt the student if they're missing something obvious.
+7. Keep responses concise and clinical — 2-4 sentences.
+8. You speak in English with occasional Hindi/medical terms naturally used in Indian hospitals.
+
+CASE DETAILS:
+- Patient: {age}y {gender} from {location}
+- Chief complaint: {chief_complaint}
+- Vitals: BP {bp}, HR {hr}, RR {rr}, Temp {temp}°C, SpO2 {spo2}%
+- Physical exam findings: {physical_exam}
+- Lab findings: {labs}
+
+URGENCY PROTOCOL:
+- routine: Normal observations. "Doctor, patient is stable. Vitals are within normal range."
+- attention: Something needs noting. "Doctor, I'd like to draw your attention to..."
+- urgent: Abnormal finding needs action. "Doctor, the patient's SpO2 is dropping. Should we start O2?"
+- critical: Immediate intervention needed. "Doctor! Patient's vitals are deteriorating — we need to act NOW!"
+
+Assess urgency based on the vitals and case severity. Respond ONLY as the nurse."""
+
+
+class NurseAgent(BaseAgent):
+    """Nurse agent that provides clinical observations and alerts."""
+
+    agent_type = "nurse"
+    display_name = "Nurse Priya"
+
+    def __init__(self):
+        super().__init__()
+        self.urgency_level = "routine"
+        self.case_info: dict = {}
+
+    def configure(self, case_data: dict):
+        """Configure nurse with case-specific data."""
+        vitals = case_data.get("vital_signs", {})
+        self.case_info = {
+            "age": case_data.get("patient", {}).get("age", 45),
+            "gender": case_data.get("patient", {}).get("gender", "Male"),
+            "location": case_data.get("patient", {}).get("location", "Delhi"),
+            "chief_complaint": case_data.get("chief_complaint", ""),
+            "bp": vitals.get("bp", "120/80"),
+            "hr": vitals.get("hr", 80),
+            "rr": vitals.get("rr", 16),
+            "temp": vitals.get("temp", 37.0),
+            "spo2": vitals.get("spo2", 98),
+            "physical_exam": "",
+            "labs": "",
+        }
+
+        # Extract exam and lab info from stages
+        for stage in case_data.get("stages", []):
+            if stage.get("stage") == "physical_exam":
+                self.case_info["physical_exam"] = stage.get("info", "")
+            elif stage.get("stage") == "labs":
+                self.case_info["labs"] = stage.get("info", "")
+
+        self._set_urgency_level(vitals, case_data.get("difficulty", "intermediate"))
+
+    def _set_urgency_level(self, vitals: dict, difficulty: str):
+        """Determine urgency from vitals."""
+        hr = vitals.get("hr", 80)
+        spo2 = vitals.get("spo2", 98)
+        rr = vitals.get("rr", 16)
+        temp = vitals.get("temp", 37.0)
+
+        if spo2 < 88 or hr > 140 or rr > 35 or temp > 40:
+            self.urgency_level = "critical"
+        elif spo2 < 92 or hr > 120 or rr > 28 or temp > 39:
+            self.urgency_level = "urgent"
+        elif spo2 < 95 or hr > 100 or rr > 22 or temp > 38:
+            self.urgency_level = "attention"
+        else:
+            self.urgency_level = "routine"
+
+        if difficulty == "advanced" and self.urgency_level == "routine":
+            self.urgency_level = "attention"
+
+    def get_system_prompt(self, case_context: dict) -> str:
+        info = {**self.case_info, **case_context}
+        return NURSE_SYSTEM_PROMPT.format(
+            age=info.get("age", 45),
+            gender=info.get("gender", "Male"),
+            location=info.get("location", "Delhi"),
+            chief_complaint=info.get("chief_complaint", "unknown"),
+            bp=info.get("bp", "120/80"),
+            hr=info.get("hr", 80),
+            rr=info.get("rr", 16),
+            temp=info.get("temp", 37.0),
+            spo2=info.get("spo2", 98),
+            physical_exam=info.get("physical_exam", "Not yet examined"),
+            labs=info.get("labs", "Pending"),
+        )
+
+    def get_fallback_response(self, message: str, case_context: dict) -> str:
+        msg = message.lower()
+        vitals = self.case_info
+
+        if self.urgency_level == "critical":
+            return (
+                f"Doctor! Patient's vitals are concerning — "
+                f"HR {vitals['hr']}, SpO2 {vitals['spo2']}%, RR {vitals['rr']}. "
+                f"We need to act quickly. What do you want me to start?"
+            )
+
+        if any(w in msg for w in ["vitals", "vital signs", "bp", "pulse"]):
+            return (
+                f"Doctor, latest vitals: BP {vitals['bp']}, HR {vitals['hr']} bpm, "
+                f"RR {vitals['rr']}/min, Temp {vitals['temp']}°C, SpO2 {vitals['spo2']}%. "
+                f"{'I notice the SpO2 is on the lower side.' if vitals['spo2'] < 95 else 'Vitals are noted.'}"
+            )
+
+        if any(w in msg for w in ["oxygen", "o2", "spo2"]):
+            return (
+                f"SpO2 is currently {vitals['spo2']}%. "
+                f"{'Shall I start supplemental O2 via nasal cannula?' if vitals['spo2'] < 94 else 'Saturation is being maintained.'}"
+            )
+
+        if any(w in msg for w in ["iv", "line", "access", "cannula"]):
+            return "Doctor, shall I get an IV line set up? I have 18G and 20G cannulas ready. Which access do you want?"
+
+        if any(w in msg for w in ["monitor", "ecg", "cardiac"]):
+            return "I'll put the patient on continuous cardiac monitoring right away. ECG machine is on its way."
+
+        if any(w in msg for w in ["lab", "blood", "test", "investigation"]):
+            return "Doctor, I can send the samples to lab right away. What tests do you want me to order — CBC, RFT, LFT, or anything specific?"
+
+        if self.urgency_level == "urgent":
+            return (
+                f"Doctor, just to update you — the patient's HR is {vitals['hr']} and SpO2 {vitals['spo2']}%. "
+                f"I'd recommend we keep a close watch. Want me to prepare any emergency medications?"
+            )
+
+        return "Yes doctor, I'm here. What do you need me to do for the patient?"
+
+    def get_initial_report(self) -> dict:
+        """Generate nurse's initial patient handoff report."""
+        vitals = self.case_info
+        alerts = []
+
+        if vitals["spo2"] < 94:
+            alerts.append(f"SpO2 is low at {vitals['spo2']}%")
+        if vitals["hr"] > 110:
+            alerts.append(f"tachycardic at {vitals['hr']} bpm")
+        if vitals["rr"] > 24:
+            alerts.append(f"tachypneic with RR {vitals['rr']}")
+        if vitals["temp"] > 38.5:
+            alerts.append(f"febrile at {vitals['temp']}°C")
+
+        base = (
+            f"Doctor, we have a {vitals['age']}-year-old {vitals['gender'].lower()} patient "
+            f"presenting with {vitals['chief_complaint'].lower()}. "
+            f"Vitals — BP {vitals['bp']}, HR {vitals['hr']}, RR {vitals['rr']}, "
+            f"Temp {vitals['temp']}°C, SpO2 {vitals['spo2']}%."
+        )
+
+        if alerts:
+            base += f" Please note: patient is {', '.join(alerts)}."
+
+        if self.urgency_level in ("urgent", "critical"):
+            base += " I'd recommend we prioritize this case."
+
+        return {
+            "agent_type": self.agent_type,
+            "display_name": self.display_name,
+            "content": base,
+            "urgency_level": self.urgency_level,
+        }

backend/app/core/agents/orchestrator.py

@@ -0,0 +1,195 @@
+"""Agent orchestrator — coordinates patient, nurse, and senior doctor agents."""
+
+import logging
+import uuid
+from typing import Optional
+
+from app.core.agents.patient_agent import PatientAgent
+from app.core.agents.nurse_agent import NurseAgent
+from app.core.agents.senior_agent import SeniorDoctorAgent
+
+logger = logging.getLogger(__name__)
+
+
+class AgentSession:
+    """Holds the agent instances and state for a single case session."""
+
+    def __init__(self, session_id: str, case_data: dict):
+        self.session_id = session_id
+        self.case_data = case_data
+
+        # Initialize agents
+        self.patient = PatientAgent()
+        self.nurse = NurseAgent()
+        self.senior = SeniorDoctorAgent()
+
+        # Configure with case data
+        self.patient.configure(case_data)
+        self.nurse.configure(case_data)
+        self.senior.configure(case_data)
+
+        # Track conversation state
+        self.message_history: list[dict] = []
+        self.stages_revealed: set[int] = set()
+        self.diagnosis_submitted = False
+
+    def get_vitals(self) -> dict:
+        """Return current vital signs with nurse's urgency assessment."""
+        vitals = self.case_data.get("vital_signs", {})
+        return {
+            "vitals": vitals,
+            "urgency_level": self.nurse.urgency_level,
+            "patient_distress": self.patient.distress_level,
+        }
+
+
+class AgentOrchestrator:
+    """Coordinates all hospital agents for multi-agent case interactions."""
+
+    def __init__(self):
+        self.sessions: dict[str, AgentSession] = {}
+
+    def initialize_session(self, case_data: dict) -> dict:
+        """Create a new agent session for a case.
+
+        Returns initial messages from all agents (patient greeting, nurse report,
+        senior guidance).
+        """
+        session_id = str(uuid.uuid4())[:8]
+        session = AgentSession(session_id, case_data)
+        self.sessions[session_id] = session
+
+        # Gather initial messages from all agents
+        initial_messages = []
+
+        # 1. Nurse gives initial report
+        nurse_report = session.nurse.get_initial_report()
+        initial_messages.append(nurse_report)
+
+        # 2. Patient greets
+        patient_greeting = session.patient.get_initial_greeting()
+        initial_messages.append(patient_greeting)
+
+        # 3. Senior doctor gives initial guidance
+        senior_guidance = session.senior.get_initial_guidance()
+        initial_messages.append(senior_guidance)
+
+        # Store in history
+        session.message_history.extend(initial_messages)
+
+        return {
+            "session_id": session_id,
+            "messages": initial_messages,
+            "vitals": session.get_vitals(),
+        }
+
+    def process_action(
+        self,
+        session_id: str,
+        action_type: str,
+        student_input: Optional[str] = None,
+    ) -> dict:
+        """Process a student action and get multi-agent responses.
+
+        action_type can be:
+        - 'talk_to_patient': Student talks to the patient
+        - 'ask_nurse': Student asks the nurse something
+        - 'consult_senior': Student discusses with senior doctor
+        - 'examine_patient': Student performs examination (triggers nurse + patient)
+        - 'order_investigation': Student orders tests (triggers nurse response)
+        """
+        session = self.sessions.get(session_id)
+        if not session:
+            return {"error": "Session not found", "messages": []}
+
+        case_context = {
+            "chief_complaint": session.case_data.get("chief_complaint", ""),
+            "specialty": session.case_data.get("specialty", ""),
+            "difficulty": session.case_data.get("difficulty", ""),
+        }
+
+        messages = []
+
+        if action_type == "talk_to_patient":
+            # Student talks to patient, patient responds
+            patient_response = session.patient.respond(
+                student_input or "Tell me about your problem",
+                case_context,
+            )
+            messages.append(patient_response)
+
+        elif action_type == "ask_nurse":
+            # Student asks nurse
+            nurse_response = session.nurse.respond(
+                student_input or "What are the current vitals?",
+                case_context,
+            )
+            messages.append(nurse_response)
+
+        elif action_type == "consult_senior":
+            # Student discusses with senior doctor
+            senior_response = session.senior.respond(
+                student_input or "What do you think about this case?",
+                case_context,
+            )
+            messages.append(senior_response)
+
+        elif action_type == "examine_patient":
+            # Physical examination — patient reacts, nurse assists
+            patient_response = session.patient.respond(
+                "The doctor is examining you now. How do you feel?",
+                case_context,
+            )
+            messages.append(patient_response)
+
+            nurse_response = session.nurse.respond(
+                f"Student is performing physical examination. Key findings to report: {student_input or 'general exam'}",
+                case_context,
+            )
+            messages.append(nurse_response)
+
+        elif action_type == "order_investigation":
+            # Order tests — nurse acknowledges and reports
+            nurse_response = session.nurse.respond(
+                f"Student has ordered: {student_input or 'routine investigations'}. Report the findings.",
+                case_context,
+            )
+            messages.append(nurse_response)
+
+        else:
+            # Default: route to senior doctor
+            senior_response = session.senior.respond(
+                student_input or "I need guidance",
+                case_context,
+            )
+            messages.append(senior_response)
+
+        # Store in history
+        if student_input:
+            session.message_history.append({
+                "agent_type": "student",
+                "display_name": "You",
+                "content": student_input,
+            })
+        session.message_history.extend(messages)
+
+        return {
+            "session_id": session_id,
+            "messages": messages,
+            "vitals": session.get_vitals(),
+        }
+
+    def get_session_vitals(self, session_id: str) -> Optional[dict]:
+        """Get current vitals for a session."""
+        session = self.sessions.get(session_id)
+        if not session:
+            return None
+        return session.get_vitals()
+
+    def get_session(self, session_id: str) -> Optional[AgentSession]:
+        """Get an agent session by ID."""
+        return self.sessions.get(session_id)
+
+
+# Singleton orchestrator shared across the app
+orchestrator = AgentOrchestrator()

backend/app/core/agents/patient_agent.py

@@ -0,0 +1,148 @@
+"""Patient agent — speaks in Hindi/English mix with realistic distress levels."""
+
+from app.core.agents.base_agent import BaseAgent
+
+
+PATIENT_SYSTEM_PROMPT = """You are a patient in an Indian hospital. You are being examined by a medical student.
+
+CRITICAL RULES:
+1. You speak in Hindi-English mix (Hinglish) naturally — like a real Indian patient would.
+   Examples: "Doctor sahab, mujhe bahut zyada dard ho raha hai chest mein", "Haan doctor, breathing mein problem hai"
+2. You do NOT know medical terminology. Describe symptoms in simple, lay terms.
+3. You have a specific distress level based on your condition severity.
+4. You may be anxious, scared, in pain, or confused — act accordingly.
+5. You can only share information you would realistically know (symptoms, history, lifestyle).
+6. You do NOT know your own diagnosis. You are the patient, not the doctor.
+7. If asked about something you don't know (like lab values), say "Yeh toh doctor aapko pata hoga"
+8. Keep responses realistic — 1-3 sentences typically, more if telling your history.
+
+PATIENT DETAILS:
+- Age: {age}, Gender: {gender}, Location: {location}
+- Chief complaint: {chief_complaint}
+- Presentation: {presentation}
+- History: {history}
+- Distress level: {distress_level} (low=calm, moderate=worried, high=distressed/crying, critical=severe pain/panic)
+
+DISTRESS BEHAVIOR:
+- low: Calm, answers questions clearly. "Haan doctor, yeh problem 2 hafte se hai."
+- moderate: Worried but cooperative. "Doctor, mujhe dar lag raha hai... kuch serious toh nahi?"
+- high: In visible distress, may cry or groan. "Aaahhh... bahut dard ho raha hai doctor... please kuch karo!"
+- critical: Severe pain/panic, short responses. "Doctor... saans... nahi aa rahi... please..."
+
+Respond ONLY as the patient. Stay in character completely."""
+
+
+class PatientAgent(BaseAgent):
+    """Patient agent that speaks in Hinglish with realistic distress."""
+
+    agent_type = "patient"
+    display_name = "Patient"
+
+    def __init__(self):
+        super().__init__()
+        self.distress_level = "moderate"
+        self.patient_info: dict = {}
+
+    def configure(self, case_data: dict):
+        """Configure patient with case-specific data."""
+        self.patient_info = {
+            "age": case_data.get("patient", {}).get("age", 45),
+            "gender": case_data.get("patient", {}).get("gender", "Male"),
+            "location": case_data.get("patient", {}).get("location", "Delhi"),
+            "chief_complaint": case_data.get("chief_complaint", ""),
+            "presentation": case_data.get("initial_presentation", ""),
+            "history": "",
+        }
+
+        # Extract history from stages
+        for stage in case_data.get("stages", []):
+            if stage.get("stage") == "history":
+                self.patient_info["history"] = stage.get("info", "")
+                break
+
+        # Set distress based on vital signs and difficulty
+        self._set_distress_level(case_data)
+
+    def _set_distress_level(self, case_data: dict):
+        """Determine distress level from vitals and difficulty."""
+        difficulty = case_data.get("difficulty", "intermediate")
+        vitals = case_data.get("vital_signs", {})
+
+        hr = vitals.get("hr", 80)
+        spo2 = vitals.get("spo2", 98)
+        rr = vitals.get("rr", 16)
+
+        if difficulty == "advanced" or spo2 < 90 or hr > 130 or rr > 30:
+            self.distress_level = "critical"
+        elif difficulty == "intermediate" or spo2 < 94 or hr > 110 or rr > 24:
+            self.distress_level = "high"
+        elif hr > 100 or rr > 20:
+            self.distress_level = "moderate"
+        else:
+            self.distress_level = "low"
+
+    def get_system_prompt(self, case_context: dict) -> str:
+        info = {**self.patient_info, **case_context}
+        info["distress_level"] = self.distress_level
+        return PATIENT_SYSTEM_PROMPT.format(
+            age=info.get("age", 45),
+            gender=info.get("gender", "Male"),
+            location=info.get("location", "Delhi"),
+            chief_complaint=info.get("chief_complaint", "unknown"),
+            presentation=info.get("presentation", ""),
+            history=info.get("history", ""),
+            distress_level=self.distress_level,
+        )
+
+    def get_fallback_response(self, message: str, case_context: dict) -> str:
+        msg = message.lower()
+
+        if self.distress_level == "critical":
+            if any(w in msg for w in ["pain", "dard", "hurt"]):
+                return "Doctor... bahut... zyada dard... please kuch karo... saans nahi aa rahi..."
+            return "Doctor... please... jaldi..."
+
+        if self.distress_level == "high":
+            if any(w in msg for w in ["how long", "kab se", "when"]):
+                return "Doctor sahab, yeh 2-3 din se bahut zyada ho gaya hai... pehle thoda thoda hota tha, ab toh sehen nahi hota!"
+            if any(w in msg for w in ["pain", "dard", "hurt"]):
+                return "Haan doctor, bahut dard hai... yahan pe... aaahhh... please dawai de do!"
+            return "Doctor, mujhe bahut takleef ho rahi hai... kuch serious toh nahi na?"
+
+        if self.distress_level == "moderate":
+            if any(w in msg for w in ["history", "pehle", "before", "past"]):
+                return "Doctor, pehle aisa kabhi nahi hua tha. Bas 1-2 baar thoda sa hua tha lekin itna nahi tha."
+            if any(w in msg for w in ["medicine", "dawai", "medication"]):
+                return "Haan doctor, mein BP ki dawai leta hoon... naam yaad nahi aa raha... chhoti wali goli hai."
+            if any(w in msg for w in ["family", "gharwale", "parents"]):
+                return "Ji doctor, mere father ko bhi sugar tha... aur unko heart ka bhi problem tha."
+            return "Ji doctor, bataiye kya karna hai? Mujhe thoda dar lag raha hai."
+
+        # low distress
+        if any(w in msg for w in ["how", "kaise"]):
+            return "Doctor sahab, yeh problem thode dinon se hai. Pehle chalta tha lekin ab zyada ho gaya."
+        if any(w in msg for w in ["smoke", "drink", "sharab", "cigarette"]):
+            return "Nahi doctor, mein na pita hoon na cigarette peeta hoon. Bas kabhi kabhi chai peeta hoon."
+        return f"Ji doctor, main {self.patient_info.get('chief_complaint', 'problem').lower()} ki wajah se aaya hoon. Aap bataiye kya karna chahiye?"
+
+    def get_initial_greeting(self) -> dict:
+        """Generate the patient's initial complaint on arrival."""
+        cc = self.patient_info.get("chief_complaint", "problem")
+        age = self.patient_info.get("age", 45)
+        gender = self.patient_info.get("gender", "Male")
+        honorific = "beti" if gender == "Female" and age < 30 else "bhai" if gender == "Male" and age < 40 else "uncle" if gender == "Male" else "aunty"
+
+        greetings = {
+            "critical": f"Doctor sahab... please... {cc.lower()}... bahut... zyada hai... saans nahi aa rahi...",
+            "high": f"Doctor sahab, namaste... mujhe bahut zyada problem ho rahi hai... {cc.lower()}... please jaldi check karo!",
+            "moderate": f"Namaste doctor sahab. Mein aapke paas aaya hoon kyunki mujhe {cc.lower()} ki problem hai. 2-3 din se ho raha hai, ab zyada ho gaya.",
+            "low": f"Namaste doctor sahab. Mujhe {cc.lower()} ki thodi si problem hai, isliye aaya hoon. Dekhiye na please.",
+        }
+
+        content = greetings.get(self.distress_level, greetings["moderate"])
+        return {
+            "agent_type": self.agent_type,
+            "display_name": self.display_name,
+            "content": content,
+            "distress_level": self.distress_level,
+        }

backend/app/core/agents/senior_agent.py

@@ -0,0 +1,156 @@
+"""Senior doctor agent — Socratic teaching mentor who guides without giving answers."""
+
+from app.core.agents.base_agent import BaseAgent
+
+
+SENIOR_SYSTEM_PROMPT = """You are a senior consultant doctor (professor) in an Indian teaching hospital. You are supervising a final-year MBBS student who is handling a case.
+
+CRITICAL RULES:
+1. Use the SOCRATIC METHOD — ask probing questions, never give the answer directly.
+2. Guide the student's clinical reasoning through structured questioning.
+3. You are supportive but academically rigorous.
+4. You reference Indian clinical guidelines (ICMR, API, NHM) when relevant.
+5. You know the correct diagnosis but must NOT reveal it unless the student has already diagnosed correctly.
+6. If the student is on the wrong track, gently redirect with questions.
+7. If the student is stuck, provide progressive hints (never the answer).
+8. Occasionally reference exam relevance: "This is a common NEET-PG question pattern."
+9. Keep responses concise — 2-4 sentences with 1-2 Socratic questions.
+10. You speak in professional English with occasional Hindi phrases natural in Indian hospitals.
+
+CASE DETAILS:
+- Patient: {age}y {gender}, {chief_complaint}
+- Specialty: {specialty}
+- Difficulty: {difficulty}
+- Correct diagnosis: {diagnosis}
+- Key differentials: {differentials}
+- Critical learning points: {learning_points}
+
+TEACHING APPROACH:
+1. If student hasn't started: "Let's approach this systematically. What are your initial impressions?"
+2. If student has a hypothesis: Challenge it. "Good thinking, but what else could present similarly?"
+3. If student is stuck: Hint progressively. "Think about the vital signs... what pattern do you see?"
+4. If student is close: Encourage. "You're on the right track. Now, what investigation would confirm?"
+5. If student is wrong: Redirect gently. "That's a reasonable thought, but consider — would that explain ALL the findings?"
+6. After diagnosis: Teach the deeper lesson. "Excellent. Now for the exam — what's the pathophysiology here?"
+
+Respond ONLY as the senior doctor. Be a great teacher."""
+
+
+class SeniorDoctorAgent(BaseAgent):
+    """Senior doctor agent that teaches using Socratic method."""
+
+    agent_type = "senior_doctor"
+    display_name = "Dr. Sharma"
+
+    def __init__(self):
+        super().__init__()
+        self.case_info: dict = {}
+        self.hints_given = 0
+        self.student_on_track = False
+
+    def configure(self, case_data: dict):
+        """Configure senior doctor with full case knowledge."""
+        self.case_info = {
+            "age": case_data.get("patient", {}).get("age", 45),
+            "gender": case_data.get("patient", {}).get("gender", "Male"),
+            "chief_complaint": case_data.get("chief_complaint", ""),
+            "specialty": case_data.get("specialty", ""),
+            "difficulty": case_data.get("difficulty", "intermediate"),
+            "diagnosis": case_data.get("diagnosis", ""),
+            "differentials": ", ".join(case_data.get("differentials", [])[:5]),
+            "learning_points": "; ".join(case_data.get("learning_points", [])[:3]),
+        }
+        self.hints_given = 0
+        self.student_on_track = False
+
+    def get_system_prompt(self, case_context: dict) -> str:
+        info = {**self.case_info, **case_context}
+        return SENIOR_SYSTEM_PROMPT.format(
+            age=info.get("age", 45),
+            gender=info.get("gender", "Male"),
+            chief_complaint=info.get("chief_complaint", "unknown"),
+            specialty=info.get("specialty", "general"),
+            difficulty=info.get("difficulty", "intermediate"),
+            diagnosis=info.get("diagnosis", "unknown"),
+            differentials=info.get("differentials", ""),
+            learning_points=info.get("learning_points", ""),
+        )
+
+    def get_fallback_response(self, message: str, case_context: dict) -> str:
+        msg = message.lower()
+        self.hints_given += 1
+
+        # Check if student mentions the correct diagnosis
+        diagnosis = self.case_info.get("diagnosis", "").lower()
+        if diagnosis and any(
+            word in msg for word in diagnosis.split() if len(word) > 3
+        ):
+            self.student_on_track = True
+            return (
+                "Excellent clinical reasoning! You've identified the key diagnosis. "
+                "Now tell me — what is the pathophysiological mechanism here? "
+                "And what would be your first-line management according to current guidelines?"
+            )
+
+        if self.hints_given <= 1:
+            return (
+                "Let's think about this systematically. "
+                f"You have a {self.case_info.get('age', 45)}-year-old presenting with "
+                f"{self.case_info.get('chief_complaint', 'these symptoms')}. "
+                "What are the most dangerous diagnoses you need to rule out first? "
+                "Start with your differential — what's at the top of your list?"
+            )
+
+        if self.hints_given == 2:
+            return (
+                "Good effort. Now look at the vital signs carefully — do you see a pattern? "
+                f"This is a {self.case_info.get('specialty', 'clinical')} case. "
+                "What investigation would help you narrow down your differential? "
+                "Remember — systematic approach is key for NEET-PG as well."
+            )
+
+        if self.hints_given == 3:
+            specialty = self.case_info.get("specialty", "")
+            return (
+                f"Let me give you a hint — think about the classic {specialty} presentations. "
+                f"The key differentials here would include: {self.case_info.get('differentials', 'several possibilities')}. "
+                "Which of these fits best with ALL the findings — history, examination, and investigations?"
+            )
+
+        # Progressive hints after 3
+        return (
+            "You're working hard on this, which is good. Let me narrow it down — "
+            "focus on the ONE finding that is most specific. "
+            "What single investigation or sign points you toward the diagnosis? "
+            "Think about what makes this case different from the usual presentation."
+        )
+
+    def get_initial_guidance(self) -> dict:
+        """Generate senior doctor's initial teaching prompt."""
+        difficulty = self.case_info.get("difficulty", "intermediate")
+        specialty = self.case_info.get("specialty", "clinical")
+
+        if difficulty == "advanced":
+            content = (
+                f"Interesting {specialty} case we have here. "
+                "This one will test your clinical reasoning — the presentation may not be straightforward. "
+                "Start by taking a thorough history from the patient. What would you ask first, and why?"
+            )
+        elif difficulty == "beginner":
+            content = (
+                f"Good, let's work through this {specialty} case together. "
+                "Start from the basics — look at the patient's presentation and vital signs. "
+                "What's your initial assessment? Don't worry about getting it perfect, just think aloud."
+            )
+        else:
+            content = (
+                f"Alright, we have a {specialty} case. "
+                "I want you to approach this like you would in your exam — systematically. "
+                "Start with the patient's presenting complaint and vitals. What catches your attention?"
+            )
+
+        return {
+            "agent_type": self.agent_type,
+            "display_name": self.display_name,
+            "content": content,
+        }

backend/app/main.py

@@ -9,7 +9,7 @@ from fastapi.middleware.cors import CORSMiddleware
 # Load .env from backend/ directory
 load_dotenv(Path(__file__).resolve().parent.parent / ".env")
 
-from app.api import cases, student, analytics
+from app.api import cases, student, analytics, agents
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(name)s] %(levelname)s: %(message)s")
 logger = logging.getLogger(__name__)
@@ -52,6 +52,7 @@ app.add_middleware(
 app.include_router(cases.router, prefix="/api/cases", tags=["cases"])
 app.include_router(student.router, prefix="/api/student", tags=["student"])
 app.include_router(analytics.router, prefix="/api/analytics", tags=["analytics"])
+app.include_router(agents.router, prefix="/api/agents", tags=["agents"])
 
 
 @app.get("/")

frontend/src/components/avatars/NurseAvatar.tsx

@@ -0,0 +1,83 @@
+import React from 'react';
+
+interface NurseAvatarProps {
+  size?: number;
+  urgencyLevel?: 'routine' | 'attention' | 'urgent' | 'critical';
+  className?: string;
+}
+
+export const NurseAvatar: React.FC<NurseAvatarProps> = ({
+  size = 40,
+  urgencyLevel = 'routine',
+  className = '',
+}) => {
+  const isAlert = urgencyLevel === 'urgent' || urgencyLevel === 'critical';
+
+  return (
+    <svg
+      width={size}
+      height={size}
+      viewBox="0 0 32 32"
+      fill="none"
+      xmlns="http://www.w3.org/2000/svg"
+      className={className}
+    >
+      {/* Head */}
+      <circle cx="16" cy="14" r="10" fill="#f0d9b5" stroke="#8B7355" strokeWidth="0.5" />
+
+      {/* Hair - tied back */}
+      <path
+        d="M 7 13 Q 6 5 16 4 Q 26 5 25 13"
+        fill="#1a0a00"
+        stroke="#0d0500"
+        strokeWidth="0.3"
+      />
+      {/* Hair bun */}
+      <circle cx="22" cy="7" r="3" fill="#1a0a00" />
+
+      {/* Nurse cap */}
+      <rect x="10" y="3" width="12" height="4" rx="1" fill="white" stroke="#4a90d9" strokeWidth="0.5" />
+      <line x1="14" y1="3" x2="14" y2="7" stroke="#e74c3c" strokeWidth="0.4" />
+      <line x1="18" y1="3" x2="18" y2="7" stroke="#e74c3c" strokeWidth="0.4" />
+      <line x1="12" y1="5" x2="20" y2="5" stroke="#e74c3c" strokeWidth="0.4" />
+
+      {/* Eyes */}
+      <circle cx="12" cy="13" r="1.1" fill="#2C1810" />
+      <circle cx="20" cy="13" r="1.1" fill="#2C1810" />
+
+      {/* Friendly smile */}
+      <path d="M 13 19 Q 16 21 19 19" stroke="#8B4513" strokeWidth="0.6" fill="none" strokeLinecap="round" />
+
+      {/* Scrubs collar */}
+      <path d="M 9 24 L 12 22 L 16 24 L 20 22 L 23 24" stroke="#4a90d9" strokeWidth="1.2" fill="none" />
+
+      {/* Stethoscope */}
+      <path
+        d="M 14 24 Q 12 27 14 29 Q 16 30 18 29 Q 20 27 18 24"
+        stroke="#555"
+        strokeWidth="0.7"
+        fill="none"
+      />
+      <circle cx="16" cy="30" r="1.2" fill="#666" stroke="#444" strokeWidth="0.3" />
+
+      {/* Alert indicator for urgent/critical */}
+      {isAlert && (
+        <g>
+          <circle cx="27" cy="5" r="4" fill={urgencyLevel === 'critical' ? '#e74c3c' : '#f39c12'}>
+            <animate attributeName="r" values="3.5;4.5;3.5" dur="1s" repeatCount="indefinite" />
+          </circle>
+          <text
+            x="27"
+            y="7"
+            textAnchor="middle"
+            fill="white"
+            fontSize="5"
+            fontWeight="bold"
+          >
+            !
+          </text>
+        </g>
+      )}
+    </svg>
+  );
+};

frontend/src/components/avatars/PatientAvatar.tsx

@@ -0,0 +1,87 @@
+import React from 'react';
+
+interface PatientAvatarProps {
+  size?: number;
+  distressLevel?: 'low' | 'moderate' | 'high' | 'critical';
+  className?: string;
+}
+
+export const PatientAvatar: React.FC<PatientAvatarProps> = ({
+  size = 40,
+  distressLevel = 'moderate',
+  className = '',
+}) => {
+  const faceColor = distressLevel === 'critical' ? '#e8d5d0' : distressLevel === 'high' ? '#edd9c5' : '#f0d9b5';
+  const mouthPath =
+    distressLevel === 'critical'
+      ? 'M 14 22 Q 16 20 18 22' // grimace
+      : distressLevel === 'high'
+        ? 'M 14 21 Q 16 19 18 21' // frown
+        : distressLevel === 'moderate'
+          ? 'M 14 20 L 18 20' // flat
+          : 'M 14 20 Q 16 22 18 20'; // slight smile
+
+  const eyeY = distressLevel === 'critical' ? '14' : '13';
+  const browOffset = distressLevel === 'high' || distressLevel === 'critical' ? -1 : 0;
+
+  return (
+    <svg
+      width={size}
+      height={size}
+      viewBox="0 0 32 32"
+      fill="none"
+      xmlns="http://www.w3.org/2000/svg"
+      className={className}
+    >
+      {/* Head */}
+      <circle cx="16" cy="14" r="10" fill={faceColor} stroke="#8B7355" strokeWidth="0.5" />
+
+      {/* Hair */}
+      <path
+        d="M 6 14 Q 6 5 16 4 Q 26 5 26 14"
+        fill="#2C1810"
+        stroke="#1a0f09"
+        strokeWidth="0.3"
+      />
+
+      {/* Eyes */}
+      <circle cx="12" cy={eyeY} r="1.2" fill="#2C1810" />
+      <circle cx="20" cy={eyeY} r="1.2" fill="#2C1810" />
+
+      {/* Eyebrows */}
+      <line x1="10" y1={10 + browOffset} x2="14" y2={9.5 + browOffset} stroke="#2C1810" strokeWidth="0.6" strokeLinecap="round" />
+      <line x1="18" y1={9.5 + browOffset} x2="22" y2={10 + browOffset} stroke="#2C1810" strokeWidth="0.6" strokeLinecap="round" />
+
+      {/* Mouth */}
+      <path d={mouthPath} stroke="#8B4513" strokeWidth="0.7" fill="none" strokeLinecap="round" />
+
+      {/* Hospital gown collar */}
+      <path d="M 10 24 Q 16 26 22 24" stroke="#6B9BD2" strokeWidth="1.5" fill="none" />
+
+      {/* Distress indicators */}
+      {(distressLevel === 'high' || distressLevel === 'critical') && (
+        <>
+          {/* Sweat drops */}
+          <ellipse cx="7" cy="12" rx="0.6" ry="1" fill="#87CEEB" opacity="0.7">
+            <animate attributeName="cy" values="12;14;12" dur="1.5s" repeatCount="indefinite" />
+          </ellipse>
+          <ellipse cx="25" cy="11" rx="0.5" ry="0.8" fill="#87CEEB" opacity="0.6">
+            <animate attributeName="cy" values="11;13;11" dur="1.8s" repeatCount="indefinite" />
+          </ellipse>
+        </>
+      )}
+
+      {distressLevel === 'critical' && (
+        <>
+          {/* Pain lines */}
+          <line x1="3" y1="8" x2="5" y2="9" stroke="#cc0000" strokeWidth="0.5" opacity="0.6">
+            <animate attributeName="opacity" values="0.6;0.2;0.6" dur="0.8s" repeatCount="indefinite" />
+          </line>
+          <line x1="27" y1="8" x2="29" y2="9" stroke="#cc0000" strokeWidth="0.5" opacity="0.6">
+            <animate attributeName="opacity" values="0.6;0.2;0.6" dur="0.8s" repeatCount="indefinite" />
+          </line>
+        </>
+      )}
+    </svg>
+  );
+};

frontend/src/components/avatars/SeniorDoctorAvatar.tsx

@@ -0,0 +1,78 @@
+import React from 'react';
+
+interface SeniorDoctorAvatarProps {
+  size?: number;
+  isThinking?: boolean;
+  className?: string;
+}
+
+export const SeniorDoctorAvatar: React.FC<SeniorDoctorAvatarProps> = ({
+  size = 40,
+  isThinking = false,
+  className = '',
+}) => {
+  return (
+    <svg
+      width={size}
+      height={size}
+      viewBox="0 0 32 32"
+      fill="none"
+      xmlns="http://www.w3.org/2000/svg"
+      className={className}
+    >
+      {/* Head */}
+      <circle cx="16" cy="14" r="10" fill="#e8c99b" stroke="#8B7355" strokeWidth="0.5" />
+
+      {/* Hair - grey/distinguished */}
+      <path
+        d="M 7 14 Q 6 4 16 3 Q 26 4 25 14"
+        fill="#888"
+        stroke="#666"
+        strokeWidth="0.3"
+      />
+      {/* Slightly receding hairline */}
+      <path d="M 9 8 Q 12 6 16 5.5 Q 20 6 23 8" fill="#e8c99b" />
+
+      {/* Glasses */}
+      <rect x="9" y="11" width="5.5" height="4" rx="1.5" stroke="#333" strokeWidth="0.6" fill="none" />
+      <rect x="17.5" y="11" width="5.5" height="4" rx="1.5" stroke="#333" strokeWidth="0.6" fill="none" />
+      <line x1="14.5" y1="13" x2="17.5" y2="13" stroke="#333" strokeWidth="0.5" />
+      <line x1="9" y1="12.5" x2="7" y2="12" stroke="#333" strokeWidth="0.4" />
+      <line x1="23" y1="12.5" x2="25" y2="12" stroke="#333" strokeWidth="0.4" />
+
+      {/* Eyes behind glasses */}
+      <circle cx="12" cy="13" r="0.9" fill="#2C1810" />
+      <circle cx="20" cy="13" r="0.9" fill="#2C1810" />
+
+      {/* Slight mustache */}
+      <path d="M 13 17.5 Q 16 18.5 19 17.5" stroke="#777" strokeWidth="0.5" fill="none" />
+
+      {/* Confident smile */}
+      <path d="M 13 19.5 Q 16 21 19 19.5" stroke="#8B4513" strokeWidth="0.5" fill="none" strokeLinecap="round" />
+
+      {/* White coat collar */}
+      <path d="M 8 24 L 12 21 L 16 23 L 20 21 L 24 24" stroke="#fff" strokeWidth="1.5" fill="white" />
+      <line x1="16" y1="23" x2="16" y2="28" stroke="#ddd" strokeWidth="0.5" />
+
+      {/* Name badge */}
+      <rect x="18" y="24" width="5" height="3" rx="0.5" fill="#4a90d9" stroke="#3a7bc8" strokeWidth="0.3" />
+      <text x="20.5" y="26.2" textAnchor="middle" fill="white" fontSize="2">DR</text>
+
+      {/* Thinking bubble when active */}
+      {isThinking && (
+        <g>
+          <circle cx="5" cy="5" r="1" fill="#ddd" opacity="0.6">
+            <animate attributeName="opacity" values="0.3;0.8;0.3" dur="1.5s" repeatCount="indefinite" />
+          </circle>
+          <circle cx="3" cy="3" r="1.8" fill="#ddd" opacity="0.5">
+            <animate attributeName="opacity" values="0.2;0.7;0.2" dur="1.5s" repeatCount="indefinite" begin="0.3s" />
+          </circle>
+          <ellipse cx="0" cy="0" rx="3" ry="2" fill="#eee" stroke="#ccc" strokeWidth="0.3" opacity="0.7">
+            <animate attributeName="opacity" values="0.4;0.9;0.4" dur="1.5s" repeatCount="indefinite" begin="0.6s" />
+          </ellipse>
+          <text x="0" y="1" textAnchor="middle" fill="#666" fontSize="2.5" opacity="0.8">?</text>
+        </g>
+      )}
+    </svg>
+  );
+};

frontend/src/components/avatars/index.ts

@@ -0,0 +1,3 @@
+export { PatientAvatar } from './PatientAvatar';
+export { NurseAvatar } from './NurseAvatar';
+export { SeniorDoctorAvatar } from './SeniorDoctorAvatar';

frontend/src/components/case/AgentMessage.tsx

@@ -0,0 +1,97 @@
+import React from 'react';
+import { PatientAvatar, NurseAvatar, SeniorDoctorAvatar } from '../avatars';
+
+export interface AgentMessageData {
+  id: string;
+  agent_type: 'patient' | 'nurse' | 'senior_doctor' | 'student';
+  display_name: string;
+  content: string;
+  distress_level?: string;
+  urgency_level?: string;
+  timestamp: Date;
+}
+
+interface AgentMessageProps {
+  message: AgentMessageData;
+}
+
+const agentStyles: Record<string, { bg: string; border: string; nameColor: string }> = {
+  patient: {
+    bg: 'bg-amber-50',
+    border: 'border-amber-200',
+    nameColor: 'text-amber-700',
+  },
+  nurse: {
+    bg: 'bg-blue-50',
+    border: 'border-blue-200',
+    nameColor: 'text-blue-700',
+  },
+  senior_doctor: {
+    bg: 'bg-emerald-50',
+    border: 'border-emerald-200',
+    nameColor: 'text-emerald-700',
+  },
+  student: {
+    bg: 'bg-warm-gray-50',
+    border: 'border-warm-gray-200',
+    nameColor: 'text-text-primary',
+  },
+};
+
+const AgentAvatarIcon: React.FC<{ agentType: string; distressLevel?: string; urgencyLevel?: string }> = ({
+  agentType,
+  distressLevel,
+  urgencyLevel,
+}) => {
+  switch (agentType) {
+    case 'patient':
+      return (
+        <PatientAvatar
+          size={28}
+          distressLevel={(distressLevel as 'low' | 'moderate' | 'high' | 'critical') || 'moderate'}
+        />
+      );
+    case 'nurse':
+      return (
+        <NurseAvatar
+          size={28}
+          urgencyLevel={(urgencyLevel as 'routine' | 'attention' | 'urgent' | 'critical') || 'routine'}
+        />
+      );
+    case 'senior_doctor':
+      return <SeniorDoctorAvatar size={28} />;
+    default:
+      return (
+        <div className="w-7 h-7 rounded-full bg-warm-gray-200 flex items-center justify-center text-xs font-bold text-text-primary">
+          You
+        </div>
+      );
+  }
+};
+
+export const AgentMessage: React.FC<AgentMessageProps> = ({ message }) => {
+  const isStudent = message.agent_type === 'student';
+  const style = agentStyles[message.agent_type] || agentStyles.student;
+
+  return (
+    <div className={`flex gap-2 ${isStudent ? 'flex-row-reverse' : 'flex-row'}`}>
+      <div className="flex-shrink-0 mt-1">
+        <AgentAvatarIcon
+          agentType={message.agent_type}
+          distressLevel={message.distress_level}
+          urgencyLevel={message.urgency_level}
+        />
+      </div>
+      <div
+        className={`p-3 rounded-xl text-sm leading-relaxed border max-w-[85%] ${style.bg} ${style.border}`}
+      >
+        {!isStudent && (
+          <div className={`text-xs font-semibold mb-1 ${style.nameColor}`}>
+            {message.display_name}
+          </div>
+        )}
+        <div className="text-text-primary">{message.content}</div>
+      </div>
+    </div>
+  );
+};

frontend/src/hooks/useApi.ts

@@ -153,3 +153,50 @@ export function fetchPerformance(): Promise<PerformanceData> {
 export function fetchRecommendations(): Promise<RecommendationItem[]> {
   return request('/analytics/recommendations');
 }
+
+// --- Multi-Agent System ---
+
+export interface AgentMessageDTO {
+  agent_type: 'patient' | 'nurse' | 'senior_doctor' | 'student';
+  display_name: string;
+  content: string;
+  distress_level?: string;
+  urgency_level?: string;
+  thinking?: string;
+}
+
+export interface AgentSessionResponse {
+  session_id: string;
+  messages: AgentMessageDTO[];
+  vitals: {
+    vitals: { bp: string; hr: number; rr: number; temp: number; spo2: number };
+    urgency_level: string;
+    patient_distress: string;
+  };
+}
+
+export function initializeAgents(caseId: string): Promise<AgentSessionResponse> {
+  return request('/agents/initialize', {
+    method: 'POST',
+    body: JSON.stringify({ case_id: caseId }),
+  });
+}
+
+export function sendAgentAction(
+  sessionId: string,
+  actionType: string,
+  studentInput?: string,
+): Promise<AgentSessionResponse> {
+  return request('/agents/action', {
+    method: 'POST',
+    body: JSON.stringify({
+      session_id: sessionId,
+      action_type: actionType,
+      student_input: studentInput,
+    }),
+  });
+}
+
+export function fetchAgentVitals(sessionId: string): Promise<AgentSessionResponse['vitals']> {
+  return request(`/agents/vitals/${sessionId}`);
+}

frontend/src/pages/CaseInterface.tsx

@@ -1,8 +1,16 @@
 import React, { useState, useRef, useEffect } from 'react';
 import { useNavigate, useParams, useSearchParams } from 'react-router-dom';
 import { Button, Card, Badge, Input } from '../components/ui';
-import type { Message } from '../types';
-import { fetchCase, generateCase, submitDiagnosis, sendTutorMessage, type GeneratedCase, type DiagnosisResult } from '../hooks/useApi';
+import { AgentMessage, type AgentMessageData } from '../components/case/AgentMessage';
+import {
+  fetchCase,
+  generateCase,
+  submitDiagnosis,
+  initializeAgents,
+  sendAgentAction,
+  type GeneratedCase,
+  type DiagnosisResult,
+} from '../hooks/useApi';
 
 const stageLabels: Record<string, string> = {
   history: 'History',
@@ -16,6 +24,8 @@ const stageIcons: Record<string, string> = {
   labs: '🔬',
 };
 
+type AgentTarget = 'talk_to_patient' | 'ask_nurse' | 'consult_senior';
+
 export const CaseInterface: React.FC = () => {
   const navigate = useNavigate();
   const { id } = useParams<{ id: string }>();
@@ -23,20 +33,20 @@ export const CaseInterface: React.FC = () => {
   const [caseData, setCaseData] = useState<GeneratedCase | null>(null);
   const [loadingCase, setLoadingCase] = useState(true);
   const [revealedStages, setRevealedStages] = useState<Set<number>>(new Set());
-  const [messages, setMessages] = useState<Message[]>([
-    {
-      id: '1',
-      role: 'ai',
-      content: "I see you've started a new case. Take a look at the patient presentation and vital signs. What's your initial assessment? What differential diagnoses come to mind?",
-      timestamp: new Date(),
-    },
-  ]);
+
+  // Multi-agent state
+  const [agentSessionId, setAgentSessionId] = useState<string | null>(null);
+  const [agentMessages, setAgentMessages] = useState<AgentMessageData[]>([]);
+  const [activeTarget, setActiveTarget] = useState<AgentTarget>('talk_to_patient');
   const [inputValue, setInputValue] = useState('');
   const [sendingMessage, setSendingMessage] = useState(false);
+
+  // Diagnosis state
   const [showDiagnosis, setShowDiagnosis] = useState(false);
   const [studentDiagnosis, setStudentDiagnosis] = useState('');
   const [showDiagnosisInput, setShowDiagnosisInput] = useState(false);
   const [diagnosisResult, setDiagnosisResult] = useState<DiagnosisResult | null>(null);
+
   const chatEndRef = useRef<HTMLDivElement>(null);
 
   // Load existing case by ID, or generate new one
@@ -44,11 +54,9 @@ export const CaseInterface: React.FC = () => {
     setLoadingCase(true);
 
     if (id && id !== 'new') {
-      // Try to load an existing case from the backend
       fetchCase(id)
         .then((data) => setCaseData(data))
         .catch(() => {
-          // Case not found (expired from memory) — generate a fresh one
           const specialty = searchParams.get('specialty') || 'cardiology';
           const difficulty = searchParams.get('difficulty') || 'intermediate';
           return generateCase(specialty, difficulty).then((data) => setCaseData(data));
@@ -56,7 +64,6 @@ export const CaseInterface: React.FC = () => {
         .catch(() => setCaseData(null))
         .finally(() => setLoadingCase(false));
     } else {
-      // Explicitly generating a new case
       const specialty = searchParams.get('specialty') || 'cardiology';
       const difficulty = searchParams.get('difficulty') || 'intermediate';
       generateCase(specialty, difficulty)
@@ -66,44 +73,85 @@ export const CaseInterface: React.FC = () => {
     }
   }, [id, searchParams]);
 
+  // Initialize multi-agent session once case loads
+  useEffect(() => {
+    if (!caseData) return;
+
+    initializeAgents(caseData.id)
+      .then((res) => {
+        setAgentSessionId(res.session_id);
+        const initialMsgs: AgentMessageData[] = res.messages.map((m, i) => ({
+          id: `init-${i}`,
+          agent_type: m.agent_type,
+          display_name: m.display_name,
+          content: m.content,
+          distress_level: m.distress_level,
+          urgency_level: m.urgency_level,
+          timestamp: new Date(),
+        }));
+        setAgentMessages(initialMsgs);
+      })
+      .catch(() => {
+        // Fallback: show a default welcome message
+        setAgentMessages([
+          {
+            id: 'fallback-1',
+            agent_type: 'senior_doctor',
+            display_name: 'Dr. Sharma',
+            content:
+              "Let's work through this case together. Start by examining the patient's presentation and vitals. What catches your attention?",
+            timestamp: new Date(),
+          },
+        ]);
+      });
+  }, [caseData]);
+
   useEffect(() => {
     chatEndRef.current?.scrollIntoView({ behavior: 'smooth' });
-  }, [messages]);
+  }, [agentMessages]);
 
   const revealStage = (index: number) => {
     setRevealedStages((prev) => new Set(prev).add(index));
   };
 
   const sendMessage = async () => {
-    if (!inputValue.trim() || !caseData) return;
+    if (!inputValue.trim() || !agentSessionId) return;
 
-    const studentMsg: Message = {
+    const studentMsg: AgentMessageData = {
       id: Date.now().toString(),
-      role: 'student',
+      agent_type: 'student',
+      display_name: 'You',
       content: inputValue,
       timestamp: new Date(),
     };
-    setMessages((prev) => [...prev, studentMsg]);
+    setAgentMessages((prev) => [...prev, studentMsg]);
+    const currentInput = inputValue;
     setInputValue('');
     setSendingMessage(true);
 
     try {
-      const res = await sendTutorMessage(caseData.id, inputValue);
-      const aiResponse: Message = {
-        id: (Date.now() + 1).toString(),
-        role: 'ai',
-        content: res.response,
+      const res = await sendAgentAction(agentSessionId, activeTarget, currentInput);
+      const newMsgs: AgentMessageData[] = res.messages.map((m, i) => ({
+        id: `${Date.now()}-${i}`,
+        agent_type: m.agent_type,
+        display_name: m.display_name,
+        content: m.content,
+        distress_level: m.distress_level,
+        urgency_level: m.urgency_level,
         timestamp: new Date(),
-      };
-      setMessages((prev) => [...prev, aiResponse]);
+      }));
+      setAgentMessages((prev) => [...prev, ...newMsgs]);
     } catch {
-      const aiResponse: Message = {
-        id: (Date.now() + 1).toString(),
-        role: 'ai',
-        content: "Good thinking. Can you explain your reasoning further? What evidence supports your hypothesis?",
-        timestamp: new Date(),
-      };
-      setMessages((prev) => [...prev, aiResponse]);
+      setAgentMessages((prev) => [
+        ...prev,
+        {
+          id: (Date.now() + 1).toString(),
+          agent_type: 'senior_doctor',
+          display_name: 'Dr. Sharma',
+          content: 'Good thinking. Can you explain your reasoning further? What evidence supports your hypothesis?',
+          timestamp: new Date(),
+        },
+      ]);
     } finally {
       setSendingMessage(false);
     }
@@ -117,21 +165,27 @@ export const CaseInterface: React.FC = () => {
       const result = await submitDiagnosis(caseData.id, studentDiagnosis, '');
       setDiagnosisResult(result);
 
-      const aiMsg: Message = {
-        id: Date.now().toString(),
-        role: 'ai',
-        content: result.feedback,
-        timestamp: new Date(),
-      };
-      setMessages((prev) => [...prev, aiMsg]);
+      setAgentMessages((prev) => [
+        ...prev,
+        {
+          id: Date.now().toString(),
+          agent_type: 'senior_doctor',
+          display_name: 'Dr. Sharma',
+          content: result.feedback,
+          timestamp: new Date(),
+        },
+      ]);
     } catch {
-      const aiMsg: Message = {
-        id: Date.now().toString(),
-        role: 'ai',
-        content: `You diagnosed: "${studentDiagnosis}". Review the case details and learning points for feedback.`,
-        timestamp: new Date(),
-      };
-      setMessages((prev) => [...prev, aiMsg]);
+      setAgentMessages((prev) => [
+        ...prev,
+        {
+          id: Date.now().toString(),
+          agent_type: 'senior_doctor',
+          display_name: 'Dr. Sharma',
+          content: `You diagnosed: "${studentDiagnosis}". Review the case details and learning points for feedback.`,
+          timestamp: new Date(),
+        },
+      ]);
     }
   };
 
@@ -345,10 +399,11 @@ export const CaseInterface: React.FC = () => {
           )}
         </div>
 
-        {/* AI Tutor Sidebar (1/3) */}
+        {/* Multi-Agent Hospital Chat Sidebar (1/3) */}
         <div className="lg:col-span-1">
           <div className="sticky top-24">
             <Card padding="md" className="h-[calc(100vh-8rem)] flex flex-col">
+              {/* Header */}
               <div className="flex items-center gap-3 mb-4 pb-4 border-b border-warm-gray-100">
                 <div className="w-10 h-10 bg-forest-green/10 rounded-xl flex items-center justify-center">
                   <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="#2D5C3F" strokeWidth="2">
@@ -356,23 +411,40 @@ export const CaseInterface: React.FC = () => {
                   </svg>
                 </div>
                 <div>
-                  <h3 className="text-base font-semibold text-text-primary">AI Tutor</h3>
-                  <p className="text-xs text-text-tertiary">Socratic reasoning coach</p>
+                  <h3 className="text-base font-semibold text-text-primary">Hospital Ward</h3>
+                  <p className="text-xs text-text-tertiary">Patient, Nurse, Senior Doctor</p>
                 </div>
               </div>
 
-              <div className="flex-1 overflow-y-auto space-y-3 mb-4">
-                {messages.map((msg) => (
-                  <div
-                    key={msg.id}
-                    className={`p-3 rounded-xl text-sm leading-relaxed ${
-                      msg.role === 'ai'
-                        ? 'bg-forest-green/5 border border-forest-green/10 text-text-primary'
-                        : 'bg-warm-gray-50 border border-warm-gray-100 text-text-primary ml-4'
+              {/* Agent target selector */}
+              <div className="flex gap-1 mb-3 p-1 bg-warm-gray-50 rounded-lg">
+                {([
+                  { key: 'talk_to_patient' as AgentTarget, label: 'Patient', color: 'amber' },
+                  { key: 'ask_nurse' as AgentTarget, label: 'Nurse', color: 'blue' },
+                  { key: 'consult_senior' as AgentTarget, label: 'Dr. Sharma', color: 'emerald' },
+                ]).map((target) => (
+                  <button
+                    key={target.key}
+                    onClick={() => setActiveTarget(target.key)}
+                    className={`flex-1 text-xs font-medium py-1.5 px-2 rounded-md transition-colors ${
+                      activeTarget === target.key
+                        ? target.color === 'amber'
+                          ? 'bg-amber-100 text-amber-800'
+                          : target.color === 'blue'
+                            ? 'bg-blue-100 text-blue-800'
+                            : 'bg-emerald-100 text-emerald-800'
+                        : 'text-text-tertiary hover:text-text-secondary'
                     }`}
                   >
-                    {msg.content}
-                  </div>
+                    {target.label}
+                  </button>
+                ))}
+              </div>
+
+              {/* Messages */}
+              <div className="flex-1 overflow-y-auto space-y-3 mb-4">
+                {agentMessages.map((msg) => (
+                  <AgentMessage key={msg.id} message={msg} />
                 ))}
                 {sendingMessage && (
                   <div className="p-3 rounded-xl text-sm bg-forest-green/5 border border-forest-green/10 text-text-tertiary animate-pulse">
@@ -382,11 +454,18 @@ export const CaseInterface: React.FC = () => {
                 <div ref={chatEndRef} />
               </div>
 
+              {/* Input */}
               <div className="mt-auto">
                 <div className="flex gap-2">
                   <input
                     type="text"
-                    placeholder="Type your reasoning..."
+                    placeholder={
+                      activeTarget === 'talk_to_patient'
+                        ? 'Talk to the patient...'
+                        : activeTarget === 'ask_nurse'
+                          ? 'Ask Nurse Priya...'
+                          : 'Discuss with Dr. Sharma...'
+                    }
                     value={inputValue}
                     onChange={(e) => setInputValue(e.target.value)}
                     onKeyPress={(e) => e.key === 'Enter' && sendMessage()}