Update agent.py

agent.py

@@ -25,239 +25,484 @@ GROQ_API_KEY   = os.getenv("GROQ_API_KEY")
 TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 
 if not all([UMLS_API_KEY, GROQ_API_KEY, TAVILY_API_KEY]):
-    logger.error("Missing required API keys")
-    raise RuntimeError("Missing API keys")
+    logger.error("Missing one or more required API keys: UMLS_API_KEY, GROQ_API_KEY, TAVILY_API_KEY")
+    raise RuntimeError("Missing required API keys")
 
 # ── Agent Configuration ──────────────────────────────────────────────
+AGENT_MODEL_NAME   = "llama3-70b-8192"
+AGENT_TEMPERATURE  = 0.1
+MAX_SEARCH_RESULTS = 3
+
 class ClinicalPrompts:
     SYSTEM_PROMPT = """
     You are SynapseAI, an expert AI clinical assistant engaged in an interactive consultation...
     [SYSTEM PROMPT CONTENT HERE]
     """
 
-MAX_ITERATIONS = 4
-AGENT_MODEL_NAME = "llama3-70b-8192"
-AGENT_TEMPERATURE = 0.1
+# ── Message Normalization Helpers ─────────────────────────────────────────
+def wrap_message(msg: Any) -> AIMessage:
+    """
+    Ensures the given message is an AIMessage.
+    If it is a dict, extracts the 'content' field (or serializes the dict).
+    Otherwise, converts the message to a string.
+    """
+    if isinstance(msg, AIMessage):
+        return msg
+    elif isinstance(msg, dict):
+        return AIMessage(content=msg.get("content", json.dumps(msg)))
+    else:
+        return AIMessage(content=str(msg))
+
+def normalize_messages(state: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Normalizes all messages in the state to be AIMessage objects.
+    """
+    state["messages"] = [wrap_message(m) for m in state.get("messages", [])]
+    return state
+
+# ── Helper Functions ─────────────────────────────────────────────────────
+UMLS_AUTH_ENDPOINT  = "https://utslogin.nlm.nih.gov/cas/v1/api-key"
+RXNORM_API_BASE     = "https://rxnav.nlm.nih.gov/REST"
+OPENFDA_API_BASE    = "https://api.fda.gov/drug/label.json"
+
+@lru_cache(maxsize=256)
+def get_rxcui(drug_name: str) -> Optional[str]:
+    """Lookup RxNorm CUI for a given drug name."""
+    drug_name = (drug_name or "").strip()
+    if not drug_name:
+        return None
+    logger.info(f"Looking up RxCUI for '{drug_name}'")
+    try:
+        params = {"name": drug_name, "search": 1}
+        r = requests.get(f"{RXNORM_API_BASE}/rxcui.json", params=params, timeout=10)
+        r.raise_for_status()
+        ids = r.json().get("idGroup", {}).get("rxnormId")
+        if ids:
+            logger.info(f"Found RxCUI {ids[0]} for '{drug_name}'")
+            return ids[0]
+        r = requests.get(f"{RXNORM_API_BASE}/drugs.json", params={"name": drug_name}, timeout=10)
+        r.raise_for_status()
+        for grp in r.json().get("drugGroup", {}).get("conceptGroup", []):
+            props = grp.get("conceptProperties")
+            if props:
+                logger.info(f"Found RxCUI {props[0]['rxcui']} via /drugs for '{drug_name}'")
+                return props[0]["rxcui"]
+    except Exception:
+        logger.exception(f"Error fetching RxCUI for '{drug_name}'")
+    return None
+
+@lru_cache(maxsize=128)
+def get_openfda_label(rxcui: Optional[str] = None, drug_name: Optional[str] = None) -> Optional[Dict[str, Any]]:
+    """Fetch the OpenFDA label for a drug by RxCUI or name."""
+    if not (rxcui or drug_name):
+        return None
+    terms = []
+    if rxcui:
+        terms.append(f'spl_rxnorm_code:"{rxcui}" OR openfda.rxcui:"{rxcui}"')
+    if drug_name:
+        dn = drug_name.lower()
+        terms.append(f'(openfda.brand_name:"{dn}" OR openfda.generic_name:"{dn}")')
+    query = " OR ".join(terms)
+    logger.info(f"Looking up OpenFDA label with query: {query}")
+    try:
+        r = requests.get(OPENFDA_API_BASE, params={"search": query, "limit": 1}, timeout=15)
+        r.raise_for_status()
+        results = r.json().get("results", [])
+        if results:
+            return results[0]
+    except Exception:
+        logger.exception("Error fetching OpenFDA label")
+    return None
+
+def search_text_list(texts: List[str], terms: List[str]) -> List[str]:
+    """Return highlighted snippets from a list of texts containing any of the search terms."""
+    snippets = []
+    lowers = [t.lower() for t in terms if t]
+    for text in texts or []:
+        tl = text.lower()
+        for term in lowers:
+            if term in tl:
+                i = tl.find(term)
+                start = max(0, i - 50)
+                end   = min(len(text), i + len(term) + 100)
+                snippet = text[start:end]
+                snippet = re.sub(f"({re.escape(term)})", r"**\1**", snippet, flags=re.IGNORECASE)
+                snippets.append(f"...{snippet}...")
+                break
+    return snippets
+
+def parse_bp(bp: str) -> Optional[tuple[int, int]]:
+    """Parse 'SYS/DIA' blood pressure string into a (sys, dia) tuple."""
+    if m := re.match(r"(\d{1,3})\s*/\s*(\d{1,3})", (bp or "").strip()):
+        return int(m.group(1)), int(m.group(2))
+    return None
+
+def check_red_flags(patient_data: Dict[str, Any]) -> List[str]:
+    """Identify immediate red flags from patient_data."""
+    flags: List[str] = []
+    hpi    = patient_data.get("hpi", {})
+    vitals = patient_data.get("vitals", {})
+    syms   = [s.lower() for s in hpi.get("symptoms", []) if isinstance(s, str)]
+    mapping = {
+        "chest pain": "Chest pain reported",
+        "shortness of breath": "Shortness of breath reported",
+        "severe headache": "Severe headache reported",
+        "syncope": "Syncope reported",
+        "hemoptysis": "Hemoptysis reported"
+    }
+    for term, desc in mapping.items():
+        if term in syms:
+            flags.append(f"Red Flag: {desc}.")
+    temp = vitals.get("temp_c")
+    hr   = vitals.get("hr_bpm")
+    rr   = vitals.get("rr_rpm")
+    spo2 = vitals.get("spo2_percent")
+    bp   = parse_bp(vitals.get("bp_mmhg", ""))
+    if temp is not None and temp >= 38.5:
+        flags.append(f"Red Flag: Fever ({temp}°C).")
+    if hr is not None:
+        if hr >= 120:
+            flags.append(f"Red Flag: Tachycardia ({hr} bpm).")
+        if hr <= 50:
+            flags.append(f"Red Flag: Bradycardia ({hr} bpm).")
+    if rr is not None and rr >= 24:
+        flags.append(f"Red Flag: Tachypnea ({rr} rpm).")
+    if spo2 is not None and spo2 <= 92:
+        flags.append(f"Red Flag: Hypoxia ({spo2}%).")
+    if bp:
+        sys, dia = bp
+        if sys >= 180 or dia >= 110:
+            flags.append(f"Red Flag: Hypertensive urgency/emergency ({sys}/{dia} mmHg).")
+        if sys <= 90 or dia <= 60:
+            flags.append(f"Red Flag: Hypotension ({sys}/{dia} mmHg).")
+    return list(dict.fromkeys(flags))
+
+def format_patient_data_for_prompt(data: Dict[str, Any]) -> str:
+    """Format patient_data dict into a markdown-like prompt section."""
+    if not data:
+        return "No patient data provided."
+    lines: List[str] = []
+    for section, value in data.items():
+        title = section.replace("_", " ").title()
+        if isinstance(value, dict) and any(value.values()):
+            lines.append(f"**{title}:**")
+            for k, v in value.items():
+                if v:
+                    lines.append(f"- {k.replace('_',' ').title()}: {v}")
+        elif isinstance(value, list) and value:
+            lines.append(f"**{title}:** {', '.join(map(str, value))}")
+        elif value:
+            lines.append(f"**{title}:** {value}")
+    return "\n".join(lines)
+
+# ── Tool Input Schemas ─────────────────────────────────────────────────────
+class LabOrderInput(BaseModel):
+    test_name: str = Field(...)
+    reason: str    = Field(...)
+    priority: str  = Field("Routine")
+
+class PrescriptionInput(BaseModel):
+    medication_name: str          = Field(...)
+    dosage: str                   = Field(...)
+    route: str                    = Field(...)
+    frequency: str                = Field(...)
+    duration: str                 = Field("As directed")
+    reason: str                   = Field(...)
+
+class InteractionCheckInput(BaseModel):
+    potential_prescription: str
+    current_medications: Optional[List[str]] = Field(None)
+    allergies: Optional[List[str]]             = Field(None)
+
+class FlagRiskInput(BaseModel):
+    risk_description: str = Field(...)
+    urgency: str          = Field("High")
+
+# ── Tool Implementations ───────────────────────────────────────────────────
+@tool("order_lab_test", args_schema=LabOrderInput)
+def order_lab_test(test_name: str, reason: str, priority: str = "Routine") -> str:
+    """
+    Place an order for a laboratory test.
+    """
+    logger.info(f"Ordering lab test: {test_name}, reason: {reason}, priority: {priority}")
+    return json.dumps({
+        "status": "success",
+        "message": f"Lab Ordered: {test_name} ({priority})",
+        "details": f"Reason: {reason}"
+    })
+
+@tool("prescribe_medication", args_schema=PrescriptionInput)
+def prescribe_medication(
+    medication_name: str,
+    dosage: str,
+    route: str,
+    frequency: str,
+    duration: str,
+    reason: str
+) -> str:
+    """
+    Prepare a medication prescription.
+    """
+    logger.info(f"Preparing prescription: {medication_name} {dosage}, route: {route}, freq: {frequency}")
+    return json.dumps({
+        "status": "success",
+        "message": f"Prescription Prepared: {medication_name} {dosage} {route} {frequency}",
+        "details": f"Duration: {duration}. Reason: {reason}"
+    })
+
+@tool("check_drug_interactions", args_schema=InteractionCheckInput)
+def check_drug_interactions(
+    potential_prescription: str,
+    current_medications: Optional[List[str]] = None,
+    allergies: Optional[List[str]] = None
+) -> str:
+    """
+    Check for drug–drug interactions and allergy risks.
+    """
+    logger.info(f"Checking interactions for: {potential_prescription}")
+    warnings: List[str] = []
+    pm = [m.lower().strip() for m in (current_medications or []) if m]
+    al = [a.lower().strip() for a in (allergies or []) if a]
+    if potential_prescription.lower().strip() in al:
+        warnings.append(f"CRITICAL ALLERGY: Patient allergic to '{potential_prescription}'.")
+    rxcui = get_rxcui(potential_prescription)
+    label = get_openfda_label(rxcui=rxcui, drug_name=potential_prescription)
+    if not (rxcui or label):
+        warnings.append(f"INFO: Could not identify '{potential_prescription}'. Checks may be incomplete.")
+    for section in ("contraindications", "warnings_and_cautions", "warnings"):
+        items = label.get(section) if label else None
+        if isinstance(items, list):
+            snippets = search_text_list(items, al)
+            if snippets:
+                warnings.append(f"ALLERGY RISK ({section}): {'; '.join(snippets)}")
+    for med in pm:
+        mrxcui = get_rxcui(med)
+        mlabel = get_openfda_label(rxcui=mrxcui, drug_name=med)
+        for sec in ("drug_interactions",):
+            for src_label, src_name in ((label, potential_prescription), (mlabel, med)):
+                items = src_label.get(sec) if src_label else None
+                if isinstance(items, list):
+                    snippets = search_text_list(items, [med if src_name == potential_prescription else potential_prescription])
+                    if snippets:
+                        warnings.append(f"Interaction ({src_name} label): {'; '.join(snippets)}")
+    status  = "warning" if warnings else "clear"
+    message = (
+        f"{len(warnings)} issue(s) found for '{potential_prescription}'."
+        if warnings else
+        f"No major interactions or allergy issues identified for '{potential_prescription}'."
+    )
+    return json.dumps({"status": status, "message": message, "warnings": warnings})
+
+@tool("flag_risk", args_schema=FlagRiskInput)
+def flag_risk(risk_description: str, urgency: str = "High") -> str:
+    """
+    Flag a clinical risk with given urgency.
+    """
+    logger.info(f"Flagging risk: {risk_description} (urgency={urgency})")
+    return json.dumps({
+        "status": "flagged",
+        "message": f"Risk '{risk_description}' flagged with {urgency} urgency."
+    })
 
-# ── State Definition ─────────────────────────────────────────────────
+# ── Include Tavily search tool ─────────────────────────────────────────────
+search_tool = TavilySearchResults(max_results=MAX_SEARCH_RESULTS, name="tavily_search_results")
+all_tools   = [order_lab_test, prescribe_medication, check_drug_interactions, flag_risk, search_tool]
+
+# ── LLM & Tool Executor ───────────────────────────────────────────────────
+llm              = ChatGroq(temperature=AGENT_TEMPERATURE, model=AGENT_MODEL_NAME)
+model_with_tools = llm.bind_tools(all_tools)
+tool_executor    = ToolExecutor(all_tools)
+
+# ── State Definition ─────────────────────────────────────────────────────
 class AgentState(TypedDict):
     messages: List[Any]
     patient_data: Optional[Dict[str, Any]]
     summary: Optional[str]
     interaction_warnings: Optional[List[str]]
-    done: bool
-    iterations: int
+    done: Optional[bool]
+    iterations: Optional[int]
 
+# Helper to propagate state fields between nodes
 def propagate_state(new: Dict[str, Any], old: Dict[str, Any]) -> Dict[str, Any]:
-    """Merge new state changes with existing state"""
-    return {**old, **new}
+    for key in ["iterations", "done", "patient_data", "summary", "interaction_warnings"]:
+        if key in old and key not in new:
+            new[key] = old[key]
+    return new
 
-# ── Core Agent Node ──────────────────────────────────────────────────
+# ── Graph Nodes ─────────────────────────────────────────────────────────
 def agent_node(state: AgentState) -> Dict[str, Any]:
-    """Main agent node with iteration tracking"""
-    state = dict(state)  # Create mutable copy
-    
-    # Check termination conditions
+    state = normalize_messages(state)
     if state.get("done", False):
         return state
-    
-    # Update iteration count
-    iterations = state.get("iterations", 0) + 1
-    state["iterations"] = iterations
-    
-    # Enforce iteration limit
-    if iterations >= MAX_ITERATIONS:
-        return {
-            "messages": [AIMessage(content="Consultation concluded. Maximum iterations reached.")],
-            "done": True,
-            **state
-        }
-    
-    # Prepare message history
-    messages = state.get("messages", [])
-    if not messages or not isinstance(messages[0], SystemMessage):
-        messages = [SystemMessage(content=ClinicalPrompts.SYSTEM_PROMPT)] + messages
-    
+    msgs = state.get("messages", [])
+    if not msgs or not isinstance(msgs[0], SystemMessage):
+        msgs = [SystemMessage(content=ClinicalPrompts.SYSTEM_PROMPT)] + msgs
+    logger.info(f"Invoking LLM with {len(msgs)} messages")
     try:
-        # Generate response
-        llm_response = ChatGroq(
-            temperature=AGENT_TEMPERATURE,
-            model=AGENT_MODEL_NAME
-        ).invoke(messages)
-        
-        return propagate_state({
-            "messages": [llm_response],
-            "done": "consultation complete" in llm_response.content.lower()
-        }, state)
-    
+        response = model_with_tools.invoke(msgs)
+        response = wrap_message(response)
+        new_state = {"messages": [response]}
+        return propagate_state(new_state, state)
     except Exception as e:
-        logger.error(f"Agent error: {str(e)}")
-        return propagate_state({
-            "messages": [AIMessage(content=f"System Error: {str(e)}")],
-            "done": True
-        }, state)
-
-# ── Tool Handling Nodes ──────────────────────────────────────────────
-tool_executor = ToolExecutor([
-    TavilySearchResults(max_results=3),
-    # Include other tools here...
-])
+        logger.exception("Error in agent_node")
+        new_state = {"messages": [wrap_message(AIMessage(content=f"Error: {e}"))]}
+        return propagate_state(new_state, state)
 
 def tool_node(state: AgentState) -> Dict[str, Any]:
-    """Execute tool calls from last agent message"""
-    state = dict(state)
-    messages = state["messages"]
-    last_message = messages[-1]
-    
-    if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
+    state = normalize_messages(state)
+    if state.get("done", False):
         return state
-    
-    tool_calls = last_message.tool_calls
-    outputs = []
-    
-    for tool_call in tool_calls:
-        try:
-            output = tool_executor.invoke(tool_call)
-            outputs.append(
-                ToolMessage(
-                    content=json.dumps(output),
-                    tool_call_id=tool_call["id"],
-                    name=tool_call["name"]
-                )
-            )
-        except Exception as e:
-            logger.error(f"Tool error: {str(e)}")
-            outputs.append(
-                ToolMessage(
-                    content=json.dumps({"error": str(e)}),
-                    tool_call_id=tool_call["id"],
-                    name=tool_call["name"]
-                )
-            )
-    
-    return propagate_state({
-        "messages": outputs,
-        "interaction_warnings": detect_interaction_warnings(outputs)
-    }, state)
-
-def detect_interaction_warnings(tool_messages: List[ToolMessage]) -> List[str]:
-    """Parse tool outputs for interaction warnings"""
-    warnings = []
-    for msg in tool_messages:
-        try:
-            content = json.loads(msg.content)
-            if content.get("status") == "warning":
-                warnings.extend(content.get("warnings", []))
-        except json.JSONDecodeError:
-            continue
-    return warnings
-
-# ── Safety Reflection Node ───────────────────────────────────────────
+    messages_list = state.get("messages", [])
+    if not messages_list:
+        logger.warning("tool_node invoked with no messages")
+        new_state = {"messages": []}
+        return propagate_state(new_state, state)
+    last = wrap_message(messages_list[-1])
+    # Safely retrieve pending tool_calls
+    tool_calls = last.__dict__.get("tool_calls")
+    if not (isinstance(last, AIMessage) and tool_calls):
+        logger.warning("tool_node invoked without pending tool_calls")
+        new_state = {"messages": []}
+        return propagate_state(new_state, state)
+    calls = tool_calls
+    blocked_ids = set()
+    for call in calls:
+        if call["name"] == "prescribe_medication":
+            med = call["args"].get("medication_name", "").lower()
+            if not any(
+                c["name"] == "check_drug_interactions" and
+                c["args"].get("potential_prescription", "").lower() == med
+                for c in calls
+            ):
+                logger.warning(f"Blocking prescribe_medication for '{med}' without interaction check")
+                blocked_ids.add(call["id"])
+    to_execute = [c for c in calls if c["id"] not in blocked_ids]
+    pd = state.get("patient_data", {})
+    for call in to_execute:
+        if call["name"] == "check_drug_interactions":
+            call["args"].setdefault("current_medications", pd.get("medications", {}).get("current", []))
+            call["args"].setdefault("allergies", pd.get("allergies", []))
+    messages: List[ToolMessage] = []
+    warnings: List[str] = []
+    try:
+        responses = tool_executor.batch(to_execute, return_exceptions=True)
+        for call, resp in zip(to_execute, responses):
+            if isinstance(resp, Exception):
+                logger.exception(f"Error executing tool {call['name']}")
+                content = json.dumps({"status": "error", "message": str(resp)})
+            else:
+                content = str(resp)
+                if call["name"] == "check_drug_interactions":
+                    data = json.loads(content)
+                    if data.get("status") == "warning":
+                        warnings.extend(data.get("warnings", []))
+            messages.append(ToolMessage(content=content, tool_call_id=call["id"], name=call["name"]))
+    except Exception as e:
+        logger.exception("Critical error in tool_node")
+        for call in to_execute:
+            messages.append(ToolMessage(
+                content=json.dumps({"status": "error", "message": str(e)}),
+                tool_call_id=call["id"],
+                name=call["name"]
+            ))
+    new_state = {"messages": messages, "interaction_warnings": warnings or None}
+    return propagate_state(new_state, state)
+
 def reflection_node(state: AgentState) -> Dict[str, Any]:
-    """Analyze potential safety issues"""
-    warnings = state.get("interaction_warnings", [])
-    if not warnings:
+    state = normalize_messages(state)
+    if state.get("done", False):
         return state
-    
-    prompt = f"""Analyze these clinical warnings:
-    {chr(10).join(warnings)}
-    
-    Provide concise safety recommendations:"""
-    
+    warns = state.get("interaction_warnings")
+    if not warns:
+        logger.warning("reflection_node called without warnings")
+        new_state = {"messages": []}
+        return propagate_state(new_state, state)
+    triggering = None
+    for msg in reversed(state.get("messages", [])):
+        wrapped = wrap_message(msg)
+        if isinstance(wrapped, AIMessage) and wrapped.__dict__.get("tool_calls"):
+            triggering = wrapped
+            break
+    if not triggering:
+        new_state = {"messages": [AIMessage(content="Internal Error: reflection context missing.")]}
+        return propagate_state(new_state, state)
+    prompt = (
+        "You are SynapseAI, performing a focused safety review of the following plan:\n\n"
+        f"{triggering.content}\n\n"
+        "Highlight any issues based on these warnings:\n" +
+        "\n".join(f"- {w}" for w in warns)
+    )
     try:
-        reflection = ChatGroq(
-            temperature=0.0,  # Strict safety mode
-            model=AGENT_MODEL_NAME
-        ).invoke([HumanMessage(content=prompt)])
-        
-        return propagate_state({
-            "messages": [reflection],
-            "summary": f"Safety Review:\n{reflection.content}"
-        }, state)
-    
+        resp = llm.invoke([SystemMessage(content="Safety reflection"), HumanMessage(content=prompt)])
+        new_state = {"messages": [wrap_message(resp)]}
+        return propagate_state(new_state, state)
     except Exception as e:
-        logger.error(f"Reflection error: {str(e)}")
-        return propagate_state({
-            "messages": [AIMessage(content=f"Safety review unavailable: {str(e)}")],
-            "summary": "Failed safety review"
-        }, state)
-
-# ── State Routing Logic ──────────────────────────────────────────────
-def route_state(state: AgentState) -> str:
-    """Determine next node in workflow"""
-    if state.get("done", False):
-        return "end"
-    
-    messages = state.get("messages", [])
-    
-    # Prioritize safety reflection
+        logger.exception("Error during reflection")
+        new_state = {"messages": [AIMessage(content=f"Error during reflection: {e}")]}
+        return propagate_state(new_state, state)
+
+# ── Routing Functions ────────────────────────────────────────────────────
+def should_continue(state: AgentState) -> str:
+    state = normalize_messages(state)
+    state.setdefault("iterations", 0)
+    state["iterations"] += 1
+    logger.info(f"Iteration count: {state['iterations']}")
+    if state["iterations"] >= 4:
+        state.setdefault("messages", []).append(AIMessage(content="Final output: consultation complete."))
+        state["done"] = True
+        return "end_conversation_turn"
+    if not state.get("messages"):
+        state["done"] = True
+        return "end_conversation_turn"
+    last = wrap_message(state["messages"][-1])
+    if not isinstance(last, AIMessage):
+        state["done"] = True
+        return "end_conversation_turn"
+    if last.__dict__.get("tool_calls"):
+        return "continue_tools"
+    if "consultation complete" in last.content.lower():
+        state["done"] = True
+        return "end_conversation_turn"
+    state["done"] = False
+    return "agent"
+
+def after_tools_router(state: AgentState) -> str:
     if state.get("interaction_warnings"):
         return "reflection"
-    
-    # Check for tool calls
-    if messages and isinstance(messages[-1], AIMessage):
-        if messages[-1].tool_calls:
-            return "tools"
-    
-    return "agent"
+    return "end_conversation_turn"
 
-# ── Workflow Construction ────────────────────────────────────────────
+# ── ClinicalAgent ─────────────────────────────────────────────────────────
 class ClinicalAgent:
     def __init__(self):
-        self.workflow = StateGraph(AgentState)
-        
-        # Define nodes
-        self.workflow.add_node("agent", agent_node)
-        self.workflow.add_node("tools", tool_node)
-        self.workflow.add_node("reflection", reflection_node)
-        
-        # Configure edges
-        self.workflow.set_entry_point("agent")
-        
-        self.workflow.add_conditional_edges(
-            "agent",
-            lambda state: "tools" if state.get("messages")[-1].tool_calls else "end",
-            {"tools": "tools", "end": END}
-        )
-        
-        self.workflow.add_conditional_edges(
-            "tools",
-            lambda state: "reflection" if state.get("interaction_warnings") else "agent",
-            {"reflection": "reflection", "agent": "agent"}
-        )
-        
-        self.workflow.add_edge("reflection", "agent")
-        
-        self.app = self.workflow.compile()
-    
-    def consult(self, initial_state: Dict) -> Dict:
-        """Execute full consultation workflow"""
+        logger.info("Building ClinicalAgent workflow")
+        wf = StateGraph(AgentState)
+        wf.add_node("agent", agent_node)
+        wf.add_node("tools", tool_node)
+        wf.add_node("reflection", reflection_node)
+        wf.set_entry_point("agent")
+        wf.add_conditional_edges("agent", should_continue, {
+            "continue_tools": "tools",
+            "end_conversation_turn": END
+        })
+        wf.add_conditional_edges("tools", after_tools_router, {
+            "reflection": "reflection",
+            "end_conversation_turn": END
+        })
+        # Removed edge from reflection back to agent.
+        self.graph_app = wf.compile()
+        logger.info("ClinicalAgent ready")
+
+    def invoke_turn(self, state: Dict[str, Any]) -> Dict[str, Any]:
         try:
-            return self.app.invoke(
-                initial_state,
-                {"recursion_limit": MAX_ITERATIONS + 2}
-            )
+            result = self.graph_app.invoke(state, {"recursion_limit": 100})
+            result.setdefault("summary", state.get("summary"))
+            result.setdefault("interaction_warnings", None)
+            return result
         except Exception as e:
-            logger.error(f"Consultation failed: {str(e)}")
+            logger.exception("Error during graph invocation")
             return {
-                "error": str(e),
-                "trace": traceback.format_exc(),
-                "done": True
+                "messages": state.get("messages", []) + [AIMessage(content=f"Error: {e}")],
+                "patient_data": state.get("patient_data"),
+                "summary": state.get("summary"),
+                "interaction_warnings": None
             }
-
-# ── Example Usage ────────────────────────────────────────────────────
-if __name__ == "__main__":
-    agent = ClinicalAgent()
-    
-    initial_state = {
-        "messages": [HumanMessage(content="Patient presents with chest pain")],
-        "patient_data": {
-            "age": 45,
-            "vitals": {"bp": "150/95", "hr": 110}
-        },
-        "done": False,
-        "iterations": 0
-    }
-    
-    result = agent.consult(initial_state)
-    print("Final State:", json.dumps(result, indent=2))