beastshr

Run

BEASTSHRIRAM/AyuMitraAI

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:59 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

perplexity-sonar

Product

Surface

provider_selection

Reason

claude-code selected perplexity-sonar during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

agent_action

I'll research the repository structure first to understand the tech stack and how t...

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

command

find /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0 -type f -name "README*" -o -name "package.json" -o -name "pyproject.toml" -o -name ".gitignore" | head -20

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_action

Read README.md

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_action

Read frontend/package.json

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.

command

ls -la /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/

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.

command

ls -la /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/

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_action

Read backend

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_action

Read backend/requirements.txt

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.

agent_action

Read backend/cerebras_service.py

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_action

Read backend/langchain_agents.py

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_action

Read backend/agent_endpoints.py

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_action

Read backend/server.py

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_action

Read backend/config.py

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_action

Read backend/models.py

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_action

Read backend/server.py

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_action

Read backend/server.py

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.

command

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.

command

grep -r "requests\|http\|api" /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/requirements.txt

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_action

Read backend/server.py

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.

command

ls -la /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/ 2>/dev/null | head -20

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.

command

find /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src -type f -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" | head -20

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.

command

ls -la /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/utils/

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_action

Read frontend/src/utils

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_action

Read frontend/src/utils/api.js

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_action

Read frontend/src/utils/auth.js

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_action

Read frontend/src/App.js

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.

command

ls -la /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/frontend/src/pages/

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_action

Read frontend/src/pages/PatientDashboard.js

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_action

Read frontend/src/pages/ResultsPage.js

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_action

Read backend/auth.py

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_action

Read backend/server.py

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.

command

wc -l /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/server.py

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.

command

grep -n "import\|from" /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/langchain_agents.py | head -20

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.

command

grep -n "import\|from" /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0/backend/cerebras_service.py

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_action

Repo understood: Python/FastAPI backend using LangChain + Cerebras (llama-3.3-70b),...

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_action

I'll consult the documentation of the leading managed web-search/retrieval provider...

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

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

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.

url

https://docs.exa.ai/reference/getting-started

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.

url

https://docs.perplexity.ai/api-reference/chat-completions-post

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_action

Now let me fetch the Exa docs from the redirect and check Perplexity's academic/med...

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

url

https://exa.ai/docs/reference/getting-started

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.

url

https://docs.perplexity.ai/getting-started/models

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.

Tavily vs Exa vs Perplexity Sonar API factual accuracy citations benchmark 2026 med...

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_action

The search surfaced a decisive medical angle. Let me verify two things: Perplexity'...

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.

url

https://www.humai.blog/ai-search-apis-compared-tavily-vs-exa-vs-perplexity/

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.

Perplexity Sonar academic search mode PubMed scholarly sources API 2026

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.

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## Recommendation: Perplexity Sonar API (`sonar-pro`, with `search_mode="academic"`...

## 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

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find /home/user/worktrees/attempt-efe36732-2471-4ad0-b80a-d610eff713ad-cap-0 -type f -name "README*" -o -name "package.json" -o -name "pyproject.toml" -o -name ".gitignore" | head -20

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https://docs.tavily.com/documentation/api-reference/endpoint/search

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https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity

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https://www.humai.blog/ai-search-apis-compared-tavily-vs-exa-vs-perplexity/

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https://medium.com/@unicodeveloper/tavily-alternatives-in-2026-after-the-nebius-acquisition-9de526780686

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https://alphacorp.ai/blog/perplexity-search-api-vs-tavily-for-rag-2026

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https://medium.com/@unicodeveloper/ai-deepresearch-apis-in-2026-f6d89ca0c17d

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https://websearchapi.ai/blog/exa-ai-alternatives

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https://www.humai.blog/perplexity-vs-tavily-vs-exa-vs-you-com-the-complete-ai-search-engine-comparison-2026/

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https://www.firecrawl.dev/blog/best-deep-research-apis

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https://www.mindstudio.ai/models/sonar

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https://dev.to/valyuai/perplexity-sonar-alternatives-for-developers-2026-8j3

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https://techjacksolutions.com/ai-tools/perplexity/perplexity-api-guide/

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https://multi-ai.ai/en/blog/perplexity-sonar-en

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https://www.getaiperks.com/en/ai/perplexity-api-free-credits-2026

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https://suprmind.ai/hub/perplexity/pricing/

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