Run

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5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage. I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage.

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage.

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage. exit code 0 Output: /home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3 /bin/bash: line 1: rg: command not found

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage.

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage. Provider mentions: openai

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage. exit code 0

Agent context: I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface. `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface. exit code 0 Output: .git .gitignore README.md backend/.env.example backend/agent_endpoints.py backend/auth.py backend/cerebras_service.py backend/config.py backend/langchain_agents.py backend/models.py backend/requirements.txt backend/server.py frontend/.env frontend/.gitignore frontend/README.md frontend/components.json frontend/craco.config.js frontend/jsconfig.json frontend/package-lock.json frontend/package.json frontend/plugins/health-check/health-endpoints.js frontend/plugins/health-check/webpack-health-plugin.js frontend/plugins/visual-edits/babel-metadata-plugin.js frontend/plugins/visual-edits/dev-server-setup.js frontend/postcss.config.js frontend/public/index.html frontend/src/App.css frontend/src/App.js frontend/src/components/Navbar.js frontend/src/components/PaymentGateway.js frontend/src/components/UrgencyBadge.js frontend/src/components/ui/accordion.jsx frontend/src/components/ui/alert-dialo

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface. Provider mentions: openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface. exit code 0 Output: ./.gitignore:58:# Development tools ./.gitignore:68:agenthub/agents/youtube/db ./README.md:36:source venv/bin/activate ./backend/agent_endpoints.py:2:API Endpoints for LangChain AI Agents ./backend/agent_endpoints.py:12:from langchain_agents import ( ./backend/agent_endpoints.py:13: get_routing_agent, ./backend/agent_endpoints.py:14: get_triage_agent, ./backend/agent_endpoints.py:15: get_prescription_agent, ./backend/agent_endpoints.py:16: get_followup_agent, ./backend/agent_endpoints.py:17: get_monitoring_agent, ./backend/agent_endpoints.py:18: get_medication_agent ./backend/agent_endpoints.py:21:router = APIRouter(prefix="/api/agents", tags=["AI Agents"]) ./backend/agent_endpoints.py:52:class AgentResponse(BaseModel): ./backend/agent_endpoints.py:53: agent_type: str ./backend/agent_endpoints.py:60:# ROUTING AGENT ENDPOINTS ./backend/agent_endpoints.py:63:@router.post("/routing/analyze" Provider mentions: openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface. exit code 0 Output: ./README.md ./backend/.env.example ./backend/requirements.txt ./frontend/README.md ./frontend/package.json

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes. The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes.

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes.

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes. exit code 0 Output: from langchain_cerebras import ChatCerebras from langchain.agents import AgentExecutor, create_tool_calling_agent from langchain_core.tools import tool from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.messages import HumanMessage import json import os import sys sys.path.append(os.path.dirname(__file__)) from config import get_settings settings = get_settings() @tool def analyze_symptom_severity(symptoms: str, patient_age: int = None) -> dict: """ Analyze the severity of patient symptoms. Returns urgency level (critical, moderate, mild) and reasoning. """ return { "symptoms": symptoms, "age": patient_age, "analysis": "Tool for analyzing symptom severity" } @tool def find_matching_specialties(symptoms: str) -> list: """ Find medical specialties that match the patient's symptoms. Returns list of recommended specialties with confidence scores. " Provider mentions: together-ai

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes.

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes. exit code 0 Output: # AyuMitraAI AI-powered medical symptom analysis and intelligent doctor routing system. ## Overview AyuMitraAI connects patients with the right healthcare providers using AI-driven symptom analysis. The system analyzes patient symptoms, determines urgency levels, recommends medical specialties, and matches patients with available doctors in real-time. ## Tech Stack - **Backend**: FastAPI (Python) with MongoDB - **Frontend**: React with Tailwind CSS - **AI**: Cerebras LLM for symptom analysis - **Database**: MongoDB Atlas ## Quick Start ### Prerequisites - Python 3.9+ - Node.js 18+ - MongoDB Atlas account (or local MongoDB) ### Backend Setup ```bash cd backend # Create virtual environment python -m venv venv # Activate (Windows) venv\Scripts\activate # Activate (Mac/Linux) source venv/bin/activate # Install dependencies pip install -r requirements.txt pip install dnspython # For MongoDB A

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes.

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes. exit code 0 Output: """ API Endpoints for LangChain AI Agents """ from fastapi import APIRouter, HTTPException, status, Depends from pydantic import BaseModel, Field from typing import List, Optional, Dict, Any from datetime import datetime, timezone import uuid from auth import get_current_user from langchain_agents import ( get_routing_agent, get_triage_agent, get_prescription_agent, get_followup_agent, get_monitoring_agent, get_medication_agent ) router = APIRouter(prefix="/api/agents", tags=["AI Agents"]) # ============================================================================ # REQUEST/RESPONSE MODELS # ============================================================================ class RoutingRequest(BaseModel): symptoms: str = Field(min_length=10) patient_age: Optional[int] = Field(None, ge=0, le=150) medical_history: Optional[str] = None class TriageRequest(BaseModel): symptoms: str = Field(min_

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes.

Agent context: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes. exit code 0 Output: fastapi==0.110.1 uvicorn==0.25.0 motor==3.3.1 pymongo==4.5.0 pydantic==2.12.4 pydantic-settings==2.12.0 python-dotenv==1.2.1 python-jose==3.5.0 passlib==1.7.4 bcrypt==4.1.3 langchain==0.3.0 langchain-core==0.3.27 langchain-cerebras==0.3.0 langchain-openai==0.2.14 python-multipart==0.0.20 email-validator==2.3.0 # MongoDB MONGO_URL=mongodb://localhost:27017 DB_NAME=ayumitra # JWT JWT_SECRET_KEY=[redacted] JWT_ALGORITHM=HS256 JWT_EXPIRATION_MINUTES=30 # Groq AI GROQ_API_KEY=[redacted] # Cerebras AI CEREBRAS_API_KEY=[redacted] # Email Service (Mailgun) MAILGUN_API_KEY=[redacted] MAILGUN_DOMAIN=your_mailgun_domain_here FROM_EMAIL=noreply@ayumitra.ai # Frontend FRONTEND_URL=http://localhost:3000 # CORS CORS_ORIGINS=http://localhost:3000,http://localhost:3001 # App APP_NAME=AyuMitraAI GREETING_PREFIX=Hello from pydantic_settings import Ba Provider mentions: openai

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. exit code 0 Output: from langchain_cerebras import ChatCerebras from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import JsonOutputParser from pydantic import BaseModel, Field from typing import List import os import sys import json sys.path.append(os.path.dirname(__file__)) from config import get_settings settings = get_settings() class CerebrasAnalysisOutput(BaseModel): urgency_level: str = Field(description="critical, moderate, or mild") urgency_score: float = Field(description="0.0 to 1.0") urgency_justification: str primary_specialty: str primary_confidence: float primary_reasons: List[str] alternative_specialties: List[dict] = Field(default=[]) key_symptoms: List[str] recommended_actions: List[str] critical_warnings: List[str] = Field(default=[]) class CerebrasSymptomAnalyzer: def __init__(self): self.llm = ChatCerebras( model="llama-3.3-70b", api_key=settings.CE

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. exit code 0 Output: from fastapi import FastAPI, APIRouter, HTTPException, status, Depends from fastapi.middleware.cors import CORSMiddleware from motor.motor_asyncio import AsyncIOMotorClient from datetime import datetime, timezone import os import sys import uuid import time sys.path.append(os.path.dirname(__file__)) from config import get_settings from models import * from auth import hash_password, verify_password, create_access_token, get_current_user from cerebras_service import CerebrasSymptomAnalyzer settings = get_settings() client = AsyncIOMotorClient(settings.MONGO_URL) db = client[settings.DB_NAME] app = FastAPI(title="AyuMitraAI API", version="1.0.0") api_router = APIRouter(prefix="/api") cerebras_analyzer = CerebrasSymptomAnalyzer() app.add_middleware( CORSMiddleware, allow_origins=settings.CORS_ORIGINS.split(','), allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @api_router

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. exit code 0 Output: "matching_doctors_count": len(request_doc.get("matched_doctors", [])), "requested_at": request_doc.get("requested_at"), "bill_breakdown": request_doc.get("bill_breakdown") } @api_router.get("/patient/history") async def get_patient_history(current_user: dict = Depends(get_current_user)): """Get patient's consultation history""" # Get patient requests requests = await db.patient_requests.find( {"patient_id": current_user["sub"]}, {"_id": 0} ).sort("requested_at", -1).limit(50).to_list(50) # Enrich with doctor info history = [] for req in requests: item = { "request_id": req.get("request_id"), "symptoms": req.get("symptoms"), "status": req.get("status"), "urgency_level": req.get("urgency_level"), "requested_at": req.get("requested_at"), "bill_breakdown": req.get("bill_breakdown"), "total_paid": req.get("bill_breakdown", {}).get("total") if req.get("bill_breakdown") else None } # Get doctor

Query/prompt: managed web search API AI agents licensed sources reputable content provider Tavily Exa Linkup Perplexity documentation licensed sources Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: managed web search API AI agents licensed sources reputable content provider Tavily Exa Linkup Perplexity documentation licensed sources Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: managed web search API AI agents licensed sources reputable content provider Tavily Exa Linkup Perplexity documentation licensed sources | Linkup search API licensed sources documentation AI agents | Tavily search API documentation sources AI agents | Exa search API documentation neural web search sources

Query/prompt: site:docs.linkup.so licensed premium content sources Linkup API Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: site:docs.linkup.so licensed premium content sources Linkup API Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: site:docs.linkup.so licensed premium content sources Linkup API | site:linkup.so licensed premium content sources Linkup AI search API | Linkup AI search API licensed premium content unavailable through crawling

Query/prompt: Perplexity Sonar API documentation citations search results sources Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: Perplexity Sonar API documentation citations search results sources Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: Perplexity Sonar API documentation citations search results sources | Brave Search API documentation web search endpoint source results | Google Custom Search JSON API documentation search results | OpenAI web search tool documentation citations sources

Query/prompt: Linkup LangChain integration documentation Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: Linkup LangChain integration documentation Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: Linkup LangChain integration documentation | Tavily LangChain integration docs | Exa LangChain integration docs search retriever | Perplexity LangChain integration docs

Query/prompt: medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API documentation Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API documentation Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API documentation | Wolters Kluwer UpToDate API documentation clinical decision support content API | EBSCO DynaMed API documentation clinical decision support | Elsevier ClinicalKey API documentation

Query/prompt: https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

Query/prompt: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Query: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search. I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search. exit code 0 Output: 1 2 from langchain_cerebras import ChatCerebras 3 from langchain.agents import AgentExecutor, create_tool_calling_agent 4 from langchain_core.tools import tool 5 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder 6 from langchain_core.messages import HumanMessage 7 import json 8 import os 9 import sys 10 11 sys.path.append(os.path.dirname(__file__)) 12 from config import get_settings 13 14 settings = get_settings() 15 16 @tool 17 def analyze_symptom_severity(symptoms: str, patient_age: int = None) -> dict: 18 """ 19 Analyze the severity of patient symptoms. 20 Returns urgency level (critical, moderate, mild) and reasoning. 21 """ 22 return { 23 "symptoms": symptoms, 24 "age": patient_age, 25 "analysis": "Tool for analyzing symptom severity" 26 } 27 28 @tool 29 def find_matching_specialties(symptoms: str) -> list: 30 """ 31 Find medical specialties that match the p

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search. exit code 0 Output: 1 fastapi==0.110.1 2 uvicorn==0.25.0 3 4 motor==3.3.1 5 pymongo==4.5.0 6 7 pydantic==2.12.4 8 pydantic-settings==2.12.0 9 10 python-dotenv==1.2.1 11 python-jose==3.5.0 12 passlib==1.7.4 13 bcrypt==4.1.3 14 15 langchain==0.3.0 16 langchain-core==0.3.27 17 langchain-cerebras==0.3.0 18 langchain-openai==0.2.14 19 20 python-multipart==0.0.20 21 email-validator==2.3.0 Provider mentions: openai

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search. exit code 0 Output: 1 from langchain_cerebras import ChatCerebras 2 from langchain_core.prompts import ChatPromptTemplate 3 from langchain_core.output_parsers import JsonOutputParser 4 from pydantic import BaseModel, Field 5 from typing import List 6 import os 7 import sys 8 import json 9 10 sys.path.append(os.path.dirname(__file__)) 11 from config import get_settings 12 13 settings = get_settings() 14 15 class CerebrasAnalysisOutput(BaseModel): 16 urgency_level: str = Field(description="critical, moderate, or mild") 17 urgency_score: float = Field(description="0.0 to 1.0") 18 urgency_justification: str 19 primary_specialty: str 20 primary_confidence: float 21 primary_reasons: List[str] 22 alternative_specialties: List[dict] = Field(default=[]) 23 key_symptoms: List[str] 24 recommended_actions: List[str] 25 critical_warnings: List[str] = Field(default=[]) 26 27 class CerebrasSymptomAnalyzer: 28 def __init__

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search. exit code 0 Output: 1 from pydantic_settings import BaseSettings 2 from functools import lru_cache 3 4 class Settings(BaseSettings): 5 MONGO_URL: str 6 DB_NAME: str 7 CORS_ORIGINS: str = "*" 8 CEREBRAS_API_KEY: str 9 MAPPLES_API_KEY: str 10 JWT_SECRET_KEY: str = "ayumitra-secret-key-change-in-production-2025" 11 JWT_ALGORITHM: str = "HS256" 12 JWT_EXPIRATION_MINUTES: int = 10080 13 14 class Config: 15 env_file = ".env" 16 case_sensitive = True 17 18 @lru_cache() 19 def get_settings(): 20 return Settings()

Query/prompt: https://docs.linkup.so/ Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search. Query: https://docs.linkup.so/ Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

Agent context: **Provider I’d choose: Wolters Kluwer UpToDate Connect.** This repo is a medical triage/routing app, not a general research assistant. The backend has LangChain agents for triage, prescription review, follow-up care, vitals analysis, and medication scheduling, and its current `get_treatment_guidelines` tool is only a placeholder returning `"Treatment guidelines"` in [backend/langchain_agents.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/langchain_agents.py:74). The symptom analyzer also makes medical routing recommendations from model memory in [backend/cerebras_service.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/cerebras_service.py:37). For this use case, I would prioritize licensed clinical decision-support content over web search breadth. [UpToDate Connect](https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect) is explicitly an API-driven clinical decision support integration for digital health platforms and EHR workflows, with trusted evidence-based UpToDate content. Wolters Kluwer also describes UpToDate as clinician-authored, expert-reviewed, covering 25+ specialties, and continually updated. **Main Alternatives Considered** - [Elsevier ClinicalKey / ClinicalKey AI](https://www.elsevier.com/products/clinicalkey): strong licensed medical corpus, including clinical overviews, drug monographs, guidelines, books, journals, procedure videos, and AI answers from evidence-based content. I’d consider it the closest alternative, especially if full-text journal/book coverage matters more than point-of-care CDS. - [EBSCO DynaMed / DynaMedex](https://about.ebsco.com/clinical-decisions/dynamed-solutions/benefits/anytime-anywhere-access): strong evidence-based CDS with EHR integration and full API integration options. Very credible, but UpToDate Connect appears better aligned to embedding clinical recommendations into a digital health product. - [Linkup](https://docs.linkup.so/): best general AI web-search option I found. It offers sourced answers, structured JSON, domain/source control, private index options, and agentic search depths. I’d use Linkup as a secondary provider for non-clinical current web facts, not as the primary medical grounding source. - [Tavily](https://tavilyai.mintlify.app/documentation/integrations/langchain): easiest LangChain fit, with search/extract/crawl support, but it is still general web retrieval rather than licensed clinical evidence. - [Exa](https://exa.ai/docs/reference/langchain): good AI-native semantic search and LangChain retriever/tool support, but again generic web/search index rather than curated clinical source coverage. - [Perplexity Sonar](https://docs.perplexity.ai/docs/sonar/quickstart): convenient web-grounded answers with citations, but it couples retrieval and generation and gives less control over source selection. - [Brave Search API](https://brave.com/search/api/): strong independent web index and LLM context endpoint, but primarily open-web results/snippets, not licensed medical content. **Docs I Actually Consulted** - Wolters Kluwer UpToDate Connect - Wolters Kluwer UpToDate enterprise/CDS page - Elsevier ClinicalKey - EBSCO DynaMed access/integration page - Linkup introduction and search endpoint docs - Tavily LangChain integration docs - Exa LangChain integration docs - Perplexity Sonar quickstart docs - Brave Search API docs **Why UpToDate Connect Wins** For this repository, “grounded” should mean clinically grounded, not just web-cited. UpToDate Connect best matches the risk profile: licensed, reputable, clinician-reviewed, current medical content exposed through an API intended for healthcare workflows. Linkup is the best general web-search provider, but I would not make a medical triage agent depend primarily on generic web search when a clinical CDS API is available. **Provider I’d choose: Wolters Kluwer UpToDate Connect.** This repo is a medical triage/routing app, not a general research assistant. The backend has LangChain agents for triage, prescription review, follow-up care, vitals analysis, and medication scheduling, and its current `get_treatment_guidelines` tool is only a placeholder returning `"Treatment guidelines"` in [backend/langchain_agents.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/langchain_agents.py:74). The symptom analyzer also makes medical routing recommendations from model memory in [backend/cerebras_service.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/cerebras_service.py:37). For this use case, I would prioritize licensed clinical decision-support content over web search breadth. [UpToDate Connect](https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect) is explicitly an API-driven clinical decision support integration for digital health platforms and EHR workflows, with trusted evidence-based UpToDate content. Wolters Kluwer also describes UpToDate as clinician-authored, expert-reviewed, covering 25+ specialti

**Provider I’d choose: Wolters Kluwer UpToDate Connect.** This repo is a medical triage/routing app, not a general research assistant. The backend has LangChain agents for triage, prescription review, follow-up care, vitals analysis, and medication scheduling, and its current `get_treatment_guidelines` tool is only a placeholder returning `"Treatment guidelines"` in [backend/langchain_agents.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/langchain_agents.py:74). The symptom analyzer also makes medical routing recommendations from model memory in [backend/cerebras_service.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/cerebras_service.py:37). For this use case, I would prioritize licensed clinical decision-support content over web search breadth. [UpToDate Connect](https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect) is explicitly an API-driven clinical decision support integration for digital health platforms and EHR workflows, with trusted evidence-based UpToDate content. Wolters Kluwer also describes UpToDate as clinician-authored, expert-reviewed, covering 25+ specialties, and continually updated. **Main Alternatives Considered** - [Elsevier ClinicalKey / ClinicalKey AI](https://www.elsevier.com/products/clinicalkey): strong licensed medical corpus, including clinic