Run

P1

5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/.gitignore /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/README.md /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/.gitignore /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/README.md /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/ Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 # AyuMitraAI 2 3 AI-powered medical symptom analysis and intelligent doctor routing system. 4 5 ## Overview 6 7 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. 8 9 ## Tech Stack 10 11 - **Backend**: FastAPI (Pyth Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 { 2 "name": "frontend", 3 "version": "0.1.0", 4 "private": true, 5 "dependencies": { 6 "@hookform/resolvers": "^5.0.1", 7 "@radix-ui/react-accordion": "^1.2.8", 8 "@radix-ui/react-alert-dialog": "^1.1.11", 9 "@radix-ui/react-aspect-ratio": "^1.1.4", 10 "@radix-ui/react-avatar": "^1.1.7", 11 "@radix-ui/react-checkbox": "^1.2.3", 12 "@radix-ui/react-collapsible": "^1.1.8", 13 "@radix-ui/react-context-menu": "^2.2.12" Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: total 28 drwxr-xr-x 4 user user 4096 Jun 6 04:56 . drwxr-xr-x 3 user user 4096 Jun 6 04:56 .. -rw-r--r-- 1 user user 170 Jun 6 04:56 .git -rw-r--r-- 1 user user 1014 Jun 6 04:56 .gitignore -rw-r--r-- 1 user user 3423 Jun 6 04:56 README.md drwxr-xr-x 2 user user 4096 Jun 6 04:56 backend drwxr-xr-x 5 user user 4096 Jun 6 04:56 frontend Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: total 108 drwxr-xr-x 2 user user 4096 Jun 6 04:56 . drwxr-xr-x 4 user user 4096 Jun 6 04:56 .. -rw-r--r-- 1 user user 563 Jun 6 04:56 .env.example -rw-r--r-- 1 user user 8996 Jun 6 04:56 agent_endpoints.py -rw-r--r-- 1 user user 1775 Jun 6 04:56 auth.py -rw-r--r-- 1 user user 4386 Jun 6 04:56 cerebras_service.py -rw-r--r-- 1 user user 502 Jun 6 04:56 config.py -rw-r--r-- 1 user user 16074 Jun 6 04:56 langchain_agents Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend' Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 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 22 Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 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(Ba Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 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 set Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 """ 2 API Endpoints for LangChain AI Agents 3 """ 4 5 from fastapi import APIRouter, HTTPException, status, Depends 6 from pydantic import BaseModel, Field 7 from typing import List, Optional, Dict, Any 8 from datetime import datetime, timezone 9 import uuid 10 11 from auth import get_current_user 12 from langchain_agents import ( 13 get_routing_agent, 14 get_triage_agent, 15 get_prescription_agent, 16 get_followup Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 from fastapi import FastAPI, APIRouter, HTTPException, status, Depends 2 from fastapi.middleware.cors import CORSMiddleware 3 from motor.motor_asyncio import AsyncIOMotorClient 4 from datetime import datetime, timezone 5 import os 6 import sys 7 import uuid 8 import time 9 10 sys.path.append(os.path.dirname(__file__)) 11 from config import get_settings 12 from models import * 13 from auth import hash_password, veri Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 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 Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 from pydantic import BaseModel, Field, EmailStr, ConfigDict 2 from typing import List, Optional, Literal 3 from datetime import datetime, timezone 4 5 class UserCreate(BaseModel): 6 email: EmailStr 7 password: str = Field(min_length=6) 8 full_name: str 9 role: Literal["patient", "doctor", "clinic_admin", "hospital_admin"] = "patient" 10 11 class DoctorRegistration(BaseModel): 12 email: EmailStr 13 password: str = F Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 150 }) 151 152 return { 153 "request_id": request_id, 154 "status": "pending", 155 "urgency_level": urgency.level, 156 "primary_specialty": analysis["primary_specialty"], 157 "matching_doctors": [ 158 { 159 "doctor_id": doc["doctor_id"], 160 "name": doc["full_name"], 161 "specialization": doc["specialization"], 162 "experience_years": doc["experience_years"], 163 "facility_name": doc.get("facility_name"), 164 "is_onl Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 300 primary_specialty=primary_specialty, 301 alternative_specialties=alternative_specialties, 302 recommended_facilities=recommended_facilities, 303 recommended_actions=recommended_actions 304 ) 305 306 analysis_doc = { 307 "request_id": request_id, 308 "user_id": current_user["sub"], 309 "symptom_description": request.symptom_description, 310 "patient_age": request.patient_age, 311 "routing_decision": routing_decisi Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/server.py: search_keywords = set() /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/server.py: search_keywords.update(keywords) /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/server.py: search_keywords.update(keywords) /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610 Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: fastapi==0.110.1 Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 450 facility_name=c["clinic_name"], 451 facility_type="clinic", 452 doctor_name=c["doctor"]["name"], 453 doctor_specialization=c["doctor"]["specialization"], 454 availability=c["doctor"]["availability_hours"], 455 emergency_capable=c.get("accepts_emergencies", False), 456 contact=c.get("contact_phone"), 457 location=c.get("location") 458 )) 459 break 460 461 # Also search hospitals for specialists 462 hospitals = awa Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: total 48 drwxr-xr-x 8 user user 4096 Jun 6 04:56 . drwxr-xr-x 5 user user 4096 Jun 6 04:56 .. -rw-r--r-- 1 user user 424 Jun 6 04:56 App.css -rw-r--r-- 1 user user 3144 Jun 6 04:56 App.js drwxr-xr-x 3 user user 4096 Jun 6 04:56 components drwxr-xr-x 2 user user 4096 Jun 6 04:56 contexts drwxr-xr-x 2 user user 4096 Jun 6 04:56 hooks -rw-r--r-- 1 user user 2279 Jun 6 04:56 index.css -rw-r--r-- 1 user user 255 Jun 6 04: Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/App.js /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/components/Navbar.js /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/components/PaymentGateway.js /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/components/UrgencyBadge Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: total 16 drwxr-xr-x 2 user user 4096 Jun 6 04:56 . drwxr-xr-x 8 user user 4096 Jun 6 04:56 .. -rw-r--r-- 1 user user 801 Jun 6 04:56 api.js -rw-r--r-- 1 user user 597 Jun 6 04:56 auth.js Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/utils' Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 import axios from 'axios'; 2 import { getToken, clearAuth } from './auth'; 3 4 const API_BASE_URL = process.env.REACT_APP_BACKEND_URL || 'http://127.0.0.1:8000'; 5 console.log('API_BASE_URL:', API_BASE_URL); 6 7 const api = axios.create({ 8 baseURL: `${API_BASE_URL}/api`, 9 headers: { 10 'Content-Type': 'application/json', 11 }, 12 timeout: 30000, 13 }); 14 15 api.interceptors.request.use( 16 (config) => { 17 const Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 export const setToken = (token) => { 2 localStorage.setItem('ayumitra-token', token); 3 }; 4 5 export const getToken = () => { 6 return localStorage.getItem('ayumitra-token'); 7 }; 8 9 export const setUser = (user) => { 10 localStorage.setItem('ayumitra-user', JSON.stringify(user)); 11 }; 12 13 export const getUser = () => { 14 const user = localStorage.getItem('ayumitra-user'); 15 return user ? JSON.parse(user) : Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 import React from 'react'; 2 import { BrowserRouter, Routes, Route, Navigate } from 'react-router-dom'; 3 import { ThemeProvider } from './contexts/ThemeContext'; 4 import { Toaster } from './components/ui/sonner'; 5 import { isAuthenticated, getUser } from './utils/auth'; 6 import Navbar from './components/Navbar'; 7 import LandingPage from './pages/LandingPage'; 8 import Login from './pages/Login'; 9 import Signu Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: total 164 drwxr-xr-x 2 user user 4096 Jun 6 04:56 . drwxr-xr-x 8 user user 4096 Jun 6 04:56 .. -rw-r--r-- 1 user user 17772 Jun 6 04:56 ClinicRegistration.js -rw-r--r-- 1 user user 24860 Jun 6 04:56 DoctorDashboard.js -rw-r--r-- 1 user user 21895 Jun 6 04:56 HospitalRegistration.js -rw-r--r-- 1 user user 5342 Jun 6 04:56 LandingPage.js -rw-r--r-- 1 user user 5198 Jun 6 04:56 Login.js -rw-r--r-- 1 user user 25772 Jun Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 import React, { useState, useEffect, useRef } from 'react'; 2 import { Button } from '../components/ui/button'; 3 import { Textarea } from '../components/ui/textarea'; 4 import { Input } from '../components/ui/input'; 5 import { Card, CardContent, CardHeader, CardTitle, CardDescription } from '../components/ui/card'; 6 import { Label } from '../components/ui/label'; 7 import { Badge } from '../components/ui/badge'; Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 import React from 'react'; 2 import { useLocation, useNavigate } from 'react-router-dom'; 3 import { Button } from '../components/ui/button'; 4 import { Card, CardContent, CardHeader, CardTitle } from '../components/ui/card'; 5 import UrgencyBadge from '../components/UrgencyBadge'; 6 import { ArrowLeft, MapPin, Phone, Clock, Building2, User } from 'lucide-react'; 7 8 const ResultsPage = () => { 9 const location = u Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 1 from datetime import datetime, timedelta, timezone 2 from jose import JWTError, jwt 3 from passlib.context import CryptContext 4 from fastapi import HTTPException, status, Depends 5 from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials 6 import os 7 import sys 8 9 sys.path.append(os.path.dirname(__file__)) 10 from config import get_settings 11 12 settings = get_settings() 13 14 pwd_context = CryptCo Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 850 851 await db.doctors.update_one( 852 {"doctor_id": current_user["sub"]}, 853 {"$inc": {"patients_treated": 1}} 854 ) 855 856 return {"message": "Request completed", "bill_breakdown": bill_breakdown} 857 858 @api_router.get("/facilities/search") 859 async def search_facilities(query: str = ""): 860 facilities = [] 861 862 clinics = await db.clinics.find( 863 {"clinic_name": {"$regex": query, "$options": "i"}}, 864 Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 918 /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/server.py Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 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 12:from config import get_settings 131: prompt = ChatPromptTemplate.from_messages([ 234: prompt Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers. Tool result summary: 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 11:from config import get_settings 92: prompt = ChatPromptTemplate.from_messages([ Agent context: I'll research the repository structure first to understand the tech stack and how the agent works, then evaluate web-search providers.

Agent context: Repo understood: Python/FastAPI backend using LangChain + Cerebras (llama-3.3-70b), a medical symptom-triage app with no existing web search. Now I'll consult real provider documentation to ground the recommendation. Repo understood: Python/FastAPI backend using LangChain + Cerebras (llama-3.3-70b), a medical symptom-triage app with no existing web search. Now I'll consult real provider documentation to ground the recommendation.

Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations. I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations.

Query/prompt: Summarize Tavily's search API: what it returns (sources, URLs, content, scores), citation/source support, accuracy/quality features, whether it's built for LLM/RAG grounding, and any medical/factual reliability mentions. List key response fields. Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations. Fetch prompt: Summarize Tavily's search API: what it returns (sources, URLs, content, scores), citation/source support, accuracy/quality features, whether it's built for LLM/RAG grounding, and any medical/factual reliability mentions. List key response fields. Tool result summary: # Tavily Search API Summary ## What It Returns Tavily's POST `/search` endpoint delivers: - **Search results array** with ranked items by relevance - **Title, URL, and content** summaries for each source - **Relevance scores** (float values like 0.81) - **Raw HTML content** (optional, in markdown or plain text) - **Images** with optional descriptions - **Favicon URLs** for visual source identification ## Citation & S Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations.

Query/prompt: Summarize Exa's API: search and /answer endpoints, what it returns (sources, highlights, content, citations), how it grounds answers, neural/embeddings search quality, and any accuracy/source-quality features relevant to factual grounding for an LLM. Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations. Fetch prompt: Summarize Exa's API: search and /answer endpoints, what it returns (sources, highlights, content, citations), how it grounds answers, neural/embeddings search quality, and any accuracy/source-quality features relevant to factual grounding for an LLM. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/getting-started Redirect URL: https://exa.ai/docs/reference/getting-started Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/getting-started" - prompt: "Summarize Exa's API: Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations.

Query/prompt: Summarize Perplexity Sonar API: how it grounds answers in web search, citation support (does it return source URLs/citations), accuracy claims, models available (sonar, sonar-pro), and reliability for factual answers. Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations. Fetch prompt: Summarize Perplexity Sonar API: how it grounds answers in web search, citation support (does it return source URLs/citations), accuracy claims, models available (sonar, sonar-pro), and reliability for factual answers. Tool result summary: # Perplexity Sonar API Summary ## Web Search Grounding The API integrates web search capabilities to inform responses. Users can configure search behavior through parameters like `search_mode` (web, academic, or sec), `search_type` (fast, pro, or auto), and context size (low, medium, high). The `disable_search` option allows responses based solely on training data when needed. ## Citations & Source URLs The API expli Agent context: I'll consult the documentation of the leading managed web-search/retrieval providers in parallel, focusing on accuracy, source quality, and citations.

Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option. Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option.

Query/prompt: Summarize Exa's API: search and /answer endpoints, what it returns (sources, highlights, content, citations), how it grounds answers, neural/embeddings search quality, and any accuracy/source-quality features relevant to factual grounding for an LLM. Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option. Fetch prompt: Summarize Exa's API: search and /answer endpoints, what it returns (sources, highlights, content, citations), how it grounds answers, neural/embeddings search quality, and any accuracy/source-quality features relevant to factual grounding for an LLM. Tool result summary: # Exa API Overview Based on the provided documentation, here's what Exa offers: ## Core Endpoints **Search**: Uses "embeddings-based search and other intelligent methods" to locate webpages. The /search endpoint leverages neural capabilities for discovering relevant content. **Answer**: The "/answer" endpoint "Get[s] direct answers to questions" — providing a dedicated tool for factual responses rather than just sear Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option.

Query/prompt: List Perplexity Sonar models and what distinguishes sonar-pro for factual accuracy. Does it offer an academic search mode? Any benchmarks on factual accuracy or hallucination reduction (e.g. SimpleQA)? Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option. Fetch prompt: List Perplexity Sonar models and what distinguishes sonar-pro for factual accuracy. Does it offer an academic search mode? Any benchmarks on factual accuracy or hallucination reduction (e.g. SimpleQA)? Tool result summary: # Perplexity Sonar Models Overview Based on the documentation provided, here are the Sonar models available: 1. **Sonar** - "Lightweight, cost-effective search model with grounding" 2. **Sonar Pro** - "Advanced search offering with grounding, supporting complex queries and follow-ups" 3. **Sonar Reasoning Pro** - "Precise reasoning offering with Chain of Thought (CoT)" 4. **Sonar Deep Research** - "Expert-level resea Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option.

Query/prompt: Tavily vs Exa vs Perplexity Sonar API factual accuracy citations benchmark 2026 medical grounding Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option. Query: Tavily vs Exa vs Perplexity Sonar API factual accuracy citations benchmark 2026 medical grounding 10 search result links captured Tool result summary: Based on my search results, here's what I found regarding benchmark comparisons of Tavily, Exa, and Perplexity Sonar API in 2026: ## Factual Accuracy Benchmarks In a 2026 LangSmith benchmark comparing these APIs against factual queries, Perplexity scored highest, Exa was a close second, and Tavily ranked last. The evaluation tested four search APIs (Tavily, Perplexity API, Exa, and Google Gemini Search Grounding) aga Agent context: Now let me fetch the Exa docs from the redirect and check Perplexity's academic/medical search mode plus one more retrieval-focused option.

Agent context: The search surfaced a decisive medical angle. Let me verify two things: Perplexity's academic search mode and the medical-source claim, since this is a clinical triage app. The search surfaced a decisive medical angle. Let me verify two things: Perplexity's academic search mode and the medical-source claim, since this is a clinical triage app.

Query/prompt: Compare Tavily, Exa, and Perplexity for factual accuracy, source quality, and citations. What does each return for grounding? Which is best when hallucination cannot be tolerated? Any notes on medical/academic source access, SimpleQA scores, and the Tavily Nebius acquisition. Agent context: The search surfaced a decisive medical angle. Let me verify two things: Perplexity's academic search mode and the medical-source claim, since this is a clinical triage app. Fetch prompt: Compare Tavily, Exa, and Perplexity for factual accuracy, source quality, and citations. What does each return for grounding? Which is best when hallucination cannot be tolerated? Any notes on medical/academic source access, SimpleQA scores, and the Tavily Nebius acquisition. Tool result summary: # AI Search APIs: Factual Accuracy and Citation Quality ## Accuracy Metrics **Exa leads on SimpleQA benchmarks**, achieving "around 94.9% accuracy" according to the article, with Tavily close at "93.3% when feeding retrieved content to GPT-4.1." Perplexity measures differently since it returns synthesized answers rather than raw results. ## Citation and Grounding Approach Each API handles citations distinctly: - **Pe Agent context: The search surfaced a decisive medical angle. Let me verify two things: Perplexity's academic search mode and the medical-source claim, since this is a clinical triage app.

Query/prompt: Perplexity Sonar academic search mode PubMed scholarly sources API 2026 Agent context: The search surfaced a decisive medical angle. Let me verify two things: Perplexity's academic search mode and the medical-source claim, since this is a clinical triage app. Query: Perplexity Sonar academic search mode PubMed scholarly sources API 2026 6 search result links captured Tool result summary: Based on the search results, here's what I found about Perplexity Sonar and its academic search capabilities: ## Perplexity Sonar API Overview Perplexity launched its Sonar API in late 2025, marking a significant evolution in how enterprises and developers can integrate advanced search capabilities into their applications. ## Academic and Scholarly Source Access Regarding access to academic sources like PubMed, the s Agent context: The search surfaced a decisive medical angle. Let me verify two things: Perplexity's academic search mode and the medical-source claim, since this is a clinical triage app.

Agent context: ## Recommendation: Perplexity Sonar API (`sonar-pro`, with `search_mode="academic"`) For a clinical symptom-triage app that cannot tolerate hallucination, Perplexity Sonar is the best fit. It is the only one of the major three that is *purpose-built* for citation-critical, high-stakes domains, and it has the two properties that matter most here. **Why Perplexity wins on your stated priorities:** - **Verifiable citations** — every response returns a `citations` array (source URLs) plus a structured `search_results` array (title, URL, snippet, publish date). Citations are tied to claims, designed explicitly for "applications where users verify information." Reviews directly recommend it for healthcare/legal where everything must be source-verified. - **Source quality** — `search_mode` supports `academic`, biasing toward scholarly/peer-reviewed sources rather than open web — important for medical grounding. Recency and domain filters let you further constrain to trusted medical domains. - **Lower hallucination risk in *your* architecture** — your backend runs Cerebras `llama-3.3-70b` (a smaller model). Perplexity returns a *grounded, synthesized* answer + citations, so you don't rely on the local model to both retrieve and synthesize. With raw-snippet providers, llama-3.3-70b would do the synthesis itself — more room to fabricate. It drops in cleanly: you already use `langchain-openai==0.2.14` (in requirements.txt), and Sonar is OpenAI-compatible, so it wires into `langchain_agents.py` as a tool or a grounding pre-step in `cerebras_service.py` with minimal change. **Main alternatives considered:** - **Exa** — highest raw SimpleQA accuracy (~94.9% via its Research API feeding a frontier model), embeddings/neural search. But it emphasizes *raw sources* with less citation formatting; you'd have to write the summarizer, pushing synthesis (and hallucination risk) back onto your small local model. Strong if you later add a heavier grounding model. - **Tavily** — excellent RAG plumbing (clean chunked snippets, relevance scores), but ranked *last* on factual accuracy in the LangSmith comparison, makes no medical/academic source claims, and is now under Nebius ownership. It's a retrieval layer, not an accuracy-first answer engine. - **Valyu** (specialist) — the one provider with native PubMed / ClinicalTrials.gov / bioRxiv access. If you decide you need true *biomedical-database* grounding rather than academic-leaning web search, this is the specialist to evaluate. None of the big three (Perplexity/Exa/Tavily) natively integrate PubMed. **Documentation I actually consulted:** - Tavily Search API reference — `docs.tavily.com/.../endpoint/search` - Exa getting-started / reference — `exa.ai/docs/reference/getting-started` - Perplexity Sonar chat-completions API reference and models pages — `docs.perplexity.ai/api-reference/chat-completions-post` and `/getting-started/models` - Plus third-party comparison/benchmark write-ups (LangSmith/SimpleQA comparisons) for cross-checking accuracy claims. **Why Perplexity over the others, in one line:** Exa is marginally more accurate on raw retrieval but offloads synthesis to your hallucination-prone local model; Tavily is the weakest on factual accuracy; Perplexity is the only option engineered around per-claim citations + an academic source mode, which matches your "accuracy and verifiable citations above all" requirement for a medical product. If clinical-grade biomedical sourcing becomes a hard requirement, layer in Valyu. Want me to implement it — a `perplexity_service.py` grounding step plus a LangChain tool in `langchain_agents.py`? Sources: - [Tavily vs Exa vs Perplexity comparison](https://www.humai.blog/ai-search-apis-compared-tavily-vs-exa-vs-perplexity/) - [Compare Tavily, Perplexity, Exa with LLM-as-Judge (LangSmith)](https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity) - [Perplexity Sonar Alternatives for Developers 2026 (medical source note)](https://dev.to/valyuai/perplexity-sonar-alternatives-for-developers-2026-8j3) - [Perplexity API & Developer Guide 2026](https://techjacksolutions.com/ai-tools/perplexity/perplexity-api-guide/) ## Recommendation: Perplexity Sonar API (`sonar-pro`, with `search_mode="academic"`) For a clinical symptom-triage app that cannot tolerate hallucination, Perplexity Sonar is the best fit. It is the only one of the major three that is *purpose-built* for citation-critical, high-stakes domains, and it has the two properties that matter most here. **Why Perplexity wins on your stated priorities:** - **Verifiable citations** — every response returns a `citations` array (source URLs) plus a structured `search_results` array (title, URL, snippet, publish date). Citations are tied to claims, designed explicitly for "applications where users verify information." Reviews directly recommend it for healthcare/legal where everything must be source-verified. - **Source quality** — `search_mode` supports `academic`, biasing toward scholarly/peer-reviewed sources rather than open web — important for medical grounding. Recency and domain filters let you further constrain to trusted medical domains. - **Lower hallucination risk in *your* architecture** — your backend runs Cerebras `llama-3.3-70b` (a smaller model). Perplexity returns a *grounded, synthesized* answer + citations, so you don't rely Provider mentions: openai

## Recommendation: Perplexity Sonar API (`sonar-pro`, with `search_mode="academic"`) For a clinical symptom-triage app that cannot tolerate hallucination, Perplexity Sonar is the best fit. It is the only one of the major three that is *purpose-built* for citation-critical, high-stakes domains, and it has the two properties that matter most here. **Why Perplexity wins on your stated priorities:** - **Verifiable citations** — every response returns a `citations` array (source URLs) plus a structured `search_results` array (title, URL, snippet, publish date). Citations are tied to claims, designed explicitly for "applications where users verify information." Reviews directly recommend it for healthcare/legal where everything must be source-verified. - **Source quality** — `search_mode` supports `academic`, biasing toward scholarly/peer-reviewed sources rather than open web — important for medical grounding. Recency and domain filters let you further constrain to trusted medical domains. - **Lower hallucination risk in *your* architecture** — your backend runs Cerebras `llama-3.3-70b` (a smaller model). Perplexity returns a *grounded, synthesized* answer + citations, so you don't rely on the local model to both retrieve and synthesize. With raw-snippet providers, llama-3.3-70b would do the synthesis itself — more room to fabricate. It drops in cleanly: you already use `langchain-op Provider mentions: openai