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5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: total 28 drwxr-xr-x 4 user user 4096 Jun 6 04:56 . drwxr-xr-x 4 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/backend/.env.example /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/backend/config.py /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/backend/requirements.txt /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/frontend/.env /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 14:from cerebras_service import CerebrasSymptomAnalyzer 24:cerebras_analyzer = CerebrasSymptomAnalyzer() 99: analysis = await cerebras_analyzer.analyze_symptoms( 262:async def analyze_symptoms(request: SymptomAnalysisRequest, current_user: dict = Depends(get_current_user)): 267: analysis = await cerebras_analyzer.analyze_symptoms( Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 1 # MongoDB 2 MONGO_URL=mongodb://localhost:27017 3 DB_NAME=ayumitra 4 5 # JWT 6 JWT_SECRET_KEY=[redacted] 7 JWT_ALGORITHM=HS256 8 JWT_EXPIRATION_MINUTES=30 9 10 # Groq AI 11 GROQ_API_KEY=[redacted] 12 13 # Cerebras AI 14 CEREBRAS_API_KEY=[redacted] 15 16 # Email Service (Mailgun) 17 MAILGUN_API_KEY=[redacted] 18 MAILGUN_DOMAIN=your_mailgun_domain_here 19 FROM_ Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: total 800 drwxr-xr-x 5 user user 4096 Jun 6 04:56 . drwxr-xr-x 4 user user 4096 Jun 6 04:56 .. -rw-r--r-- 1 user user 126 Jun 6 04:56 .env -rw-r--r-- 1 user user 315 Jun 6 04:56 .gitignore -rw-r--r-- 1 user user 3359 Jun 6 04:56 README.md -rw-r--r-- 1 user user 444 Jun 6 04:56 components.json -rw-r--r-- 1 user user 3384 Jun 6 04:56 craco.config.js -rw-r--r-- 1 user user 116 Jun 6 04:56 jsconfig.json -rw-r--r-- 1 user Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/frontend/src/App.js /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/frontend/src/components/Navbar.js /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/frontend/src/components/PaymentGateway.js /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/frontend/src/components/UrgencyBadge Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 total 12 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 915 Jun 6 04:56 ThemeContext.js Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/frontend/src/utils/api.js Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/backend/config.py: MAPPLES_API_KEY: str /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/backend/langchain_agents.py:class MedicationReminderAgent: /home/user/worktrees/attempt-fc331577-39b7-45a3-9a07-e89d82533b42-cap-1/backend/langchain_agents.py: return MedicationReminderAgent() /home/user/worktrees/attempt-fc331577-39b7- Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: from fastapi import FastAPI, APIRouter, HTTPException, status, Depends from fastapi.middleware.cors import CORSMiddleware client = AsyncIOMotorClient(settings.MONGO_URL) db = client[settings.DB_NAME] api_router = APIRouter(prefix="/api") @api_router.post("/auth/register", response_model=TokenResponse, status_code=status.HTTP_201_CREATED) @api_router.post("/auth/login", response_model=TokenResponse) @api_router.get("/ Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 500 @api_router.post("/clinics/register", response_model=ClinicResponse, status_code=status.HTTP_201_CREATED) 501 async def register_clinic(clinic: ClinicRegistration, current_user: dict = Depends(get_current_user)): 502 if current_user["role"] not in ["clinic_admin", "hospital_admin"]: 503 raise HTTPException(status_code=403, detail="Only admins can register facilities") 504 505 clinic_doc = clinic.model_dump() 506 Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. 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 agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 261:@api_router.post("/analyze-symptoms", response_model=SymptomAnalysisResponse) 500:@api_router.post("/clinics/register", response_model=ClinicResponse, status_code=status.HTTP_201_CREATED) 521:@api_router.post("/hospitals/register", response_model=HospitalResponse, status_code=status.HTTP_201_CREATED) 544:@api_router.get("/clinics") 549:@api_router.get("/hospitals") 554:@api_router.get("/history") 562:@api_router. Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 915:app.include_router(api_router) Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 900 online_count = sum(1 for d in all_doctors if d.get("availability", {}).get("is_online", False)) 901 return { 902 "total_doctors": len(all_doctors), 903 "online_doctors": online_count, 904 "doctors": [ 905 { 906 "doctor_id": d.get("doctor_id"), 907 "name": d.get("full_name"), 908 "specialization": d.get("specialization"), 909 "is_online": d.get("availability", {}).get("is_online", False) 910 } 911 for d in all_doc Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 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_tok Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: I'll research the repository structure first to understand the agent architecture and how it's built. Tool result summary: 1 REACT_APP_BACKEND_URL=http://127.0.0.1:8000 2 WDS_SOCKET_PORT=3000 3 REACT_APP_ENABLE_VISUAL_EDITS=false 4 ENABLE_HEALTH_CHECK=false Agent context: I'll research the repository structure first to understand the agent architecture and how it's built.

Agent context: This is a healthcare AI agent (FastAPI + LangChain + Cerebras) for medical symptom triage with no existing web retrieval. The key constraint is **licensed, reputable sources over scraped search results** — and it's a medical domain. Let me research the managed web-search providers and consult their docs. This is a healthcare AI agent (FastAPI + LangChain + Cerebras) for medical symptom triage with no existing web retrieval. The key constraint is **licensed, reputable sources over scraped search results** — and it's a medical domain. Let me research the managed web-search providers and consult their docs.

Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage. Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage.

Query/prompt: Valyu API licensed web search retrieval LLM medical academic sources Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage. Query: Valyu API licensed web search retrieval LLM medical academic sources 8 search result links captured Tool result summary: Based on the search results, here's what I found about Valyu API for licensed web search retrieval with LLM and medical/academic sources: ## Overview Valyu offers an API to search millions of academic papers, providing access to arXiv, PubMed, bioRxiv, and licensed journals with full-text retrieval for AI agents and research applications. ## Key Features for Medical and Academic Research The API lets you search the o Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage.

Query/prompt: Linkup API search licensed premium content sources LLM grounding Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage. Query: Linkup API search licensed premium content sources LLM grounding 10 search result links captured Tool result summary: ## Search Results for Linkup API Based on the web search results, here's what I found about Linkup API and its role in accessing licensed premium content for LLM grounding: ### Overview Linkup is a high-performance web search engine and API designed to ground AI agents in real-time, LLM-ready data from premium sources. The French startup Linkup is building an API that lets developers access web content from premium, Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage.

Query/prompt: Tavily vs Exa vs Perplexity Sonar API web search for AI agents comparison 2026 Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage. Query: Tavily vs Exa vs Perplexity Sonar API web search for AI agents comparison 2026 10 search result links captured Tool result summary: ## Search Results: Tavily vs Exa vs Perplexity Sonar API Comparison 2026 I've found comprehensive information comparing these three AI search APIs for agents. Here's what the latest 2026 benchmarks and analyses show: ### Performance & Benchmarking In a recent LangSmith benchmark with GPT-4o grading, Perplexity scored highest, Exa was a close second, Gemini third, and Tavily last. However, when benchmarks were scaled Agent context: Now let me research the candidate providers, focusing on licensed-content offerings and medical/academic source coverage.

Agent context: Valyu stands out for medical (PubMed, clinical trials, licensed journals); Linkup focuses on business/corporate content. Let me read the actual documentation for the top candidates. Valyu stands out for medical (PubMed, clinical trials, licensed journals); Linkup focuses on business/corporate content. Let me read the actual documentation for the top candidates.

Query/prompt: What sources does Valyu provide access to for academic/medical use cases? List the specific datasets (PubMed, clinical trials, journals, etc.), whether content is licensed/full-text, how the search API works, integration options (LangChain etc.), and any details about medical/healthcare coverage. Also note authentication and how to call the API. Agent context: Valyu stands out for medical (PubMed, clinical trials, licensed journals); Linkup focuses on business/corporate content. Let me read the actual documentation for the top candidates. Fetch prompt: What sources does Valyu provide access to for academic/medical use cases? List the specific datasets (PubMed, clinical trials, journals, etc.), whether content is licensed/full-text, how the search API works, integration options (LangChain etc.), and any details about medical/healthcare coverage. Also note authentication and how to call the API. Tool result summary: # Valyu Academic Search API Overview ## Available Datasets Valyu provides access to five primary sources: - **ArXiv**: Preprints across all research fields - **PubMed**: Medical and life sciences literature - **BioRxiv**: Life sciences preprints - **MedRxiv**: Clinical research and health preprints - **Wiley Finance Papers**: Economics and finance research The platform also mentions "closed-access journals and books" Agent context: Valyu stands out for medical (PubMed, clinical trials, licensed journals); Linkup focuses on business/corporate content. Let me read the actual documentation for the top candidates.

Query/prompt: What are Linkup's core concepts and source coverage? What types of premium/licensed sources does it cover (business, news, medical?)? How does the search API work, what response formats does it support (sourced answer vs raw), and what integrations exist? Note whether it covers medical/healthcare/academic sources. Agent context: Valyu stands out for medical (PubMed, clinical trials, licensed journals); Linkup focuses on business/corporate content. Let me read the actual documentation for the top candidates. Fetch prompt: What are Linkup's core concepts and source coverage? What types of premium/licensed sources does it cover (business, news, medical?)? How does the search API work, what response formats does it support (sourced answer vs raw), and what integrations exist? Note whether it covers medical/healthcare/academic sources. Tool result summary: # Linkup Search API: Core Capabilities ## How the Search API Works Linkup offers a synchronous web search endpoint with three operational depths. The system accepts natural-language queries and returns ranked sources with optional processing. According to the documentation, **"standard" and "deep" modes invoke agentic search**, where the system interprets queries, plans retrieval, and evaluates results. The "fast" mo Agent context: Valyu stands out for medical (PubMed, clinical trials, licensed journals); Linkup focuses on business/corporate content. Let me read the actual documentation for the top candidates.

Agent context: ## Recommendation: Valyu For AyuMitraAI — a FastAPI + LangChain + Cerebras medical symptom-triage agent with no existing retrieval layer — the best fit is **Valyu** (`api.valyu.ai/v1/search`). **Why Valyu wins for this repo:** - **Licensed, medical-grade sources** — the explicit requirement. Valyu indexes PubMed, MedRxiv, BioRxiv, clinical trials, and closed-access licensed journals/books with **full-text retrieval**, not scraped SERPs. For a clinical-triage agent, grounding in peer-reviewed medical literature is the single most important factor, and Valyu is the only candidate purpose-built around licensed academic/medical corpora. - **Drop-in fit** — LangChain/LlamaIndex compatible, so it slots beside the existing `langchain_agents.py` / `cerebras_service.py` chains. Simple `x-api-key` auth fits the existing `config.py` env pattern (alongside `CEREBRAS_API_KEY`). - **Pay-per-result pricing** ($0.003/web result, $0.01+/proprietary) — no subscriptions, easy to gate behind the symptom-analysis endpoint. - **Source filtering** (`included_sources`, date ranges, citation/DOI metadata) lets the agent cite reputable references back to clinicians. **Main alternatives considered:** - **Linkup** — strong "licensed, no-scraping" model with publisher deals, but coverage is **business/corporate-focused** (Statista, Xerfi, news). No medical/academic depth — wrong domain. - **Perplexity Sonar** — clean cited answers, but a 50-calls/min cap and finished-answer format fight the agent's own Cerebras reasoning; sources are general web. - **Exa** — good neural web search, but raw web index, no licensed medical corpus; you build the summarizer. - **Tavily** — popular agent search, but generic web grounding (and recently acquired by Nebius). Not licensed/medical. - **SerpAPI/Serper** — explicitly excluded: scraped Google results, the opposite of the requirement. **Documentation I actually consulted:** - Valyu — academic/medical use-case docs (`docs.valyu.ai/use-cases/academic`): confirmed PubMed/MedRxiv/BioRxiv/licensed-journal coverage, full-text retrieval, `POST /v1/search`, `x-api-key`, Python/JS SDKs. - Linkup — concepts docs (`docs.linkup.so/.../concepts`): confirmed fast/standard/deep modes and `sourcedAnswer`/`structured` formats, but no medical/academic source coverage. - Plus comparison research on Tavily/Exa/Perplexity (benchmarks, rate limits, formats). **Bottom line:** Linkup was the runner-up on the "licensed not scraped" axis, but Valyu beats it decisively because the corpus is medical. Want me to wire a Valyu retrieval tool into the LangChain agents next? Sources: - [Valyu Academic/Medical Search API](https://docs.valyu.ai/use-cases/academic) - [Valyu Search API](https://www.valyu.ai/search-api) - [Linkup Concepts — Docs](https://docs.linkup.so/pages/documentation/get-started/concepts) - [Linkup connects LLMs with premium content (TechCrunch)](https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/) - [AI Search APIs Compared: Tavily vs Exa vs Perplexity](https://www.humai.blog/ai-search-apis-compared-tavily-vs-exa-vs-perplexity/) ## Recommendation: Valyu For AyuMitraAI — a FastAPI + LangChain + Cerebras medical symptom-triage agent with no existing retrieval layer — the best fit is **Valyu** (`api.valyu.ai/v1/search`). **Why Valyu wins for this repo:** - **Licensed, medical-grade sources** — the explicit requirement. Valyu indexes PubMed, MedRxiv, BioRxiv, clinical trials, and closed-access licensed journals/books with **full-text retrieval**, not scraped SERPs. For a clinical-triage agent, grounding in peer-reviewed medical literature is the single most important factor, and Valyu is the only candidate purpose-built around licensed academic/medical corpora. - **Drop-in fit** — LangChain/LlamaIndex compatible, so it slots beside the existing `langchain_agents.py` / `cerebras_service.py` chains. Simple `x-api-key` auth fits the existing `config.py` env pattern (alongside `CEREBRAS_API_KEY`). - **Pay-per-result pricing** ($0.003/web result, $0.01+/proprietary) — no subscriptions, easy to gate behind the symptom-analysis endpoint. - **Source filtering** (`included_sources`, date ranges, citation/DOI metadata) lets the agent cite reputable references back to clinicians. **Main alternatives considered:** - **Lin

## Recommendation: Valyu For AyuMitraAI — a FastAPI + LangChain + Cerebras medical symptom-triage agent with no existing retrieval layer — the best fit is **Valyu** (`api.valyu.ai/v1/search`). **Why Valyu wins for this repo:** - **Licensed, medical-grade sources** — the explicit requirement. Valyu indexes PubMed, MedRxiv, BioRxiv, clinical trials, and closed-access licensed journals/books with **full-text retrieval**, not scraped SERPs. For a clinical-triage agent, grounding in peer-reviewed medical literature is the single most important factor, and Valyu is the only candidate purpose-built around licensed academic/medical corpora. - **Drop-in fit** — LangChain/LlamaIndex compatible, so it slots beside the existing `langchain_agents.py` / `cerebras_service.py` chains. Simple `x-api-key` auth fits the existing `config.py` env pattern (alongside `CEREBRAS_API_KEY`). - **Pay-per-result pricing** ($0.003/web result, $0.01+/proprietary) — no subscriptions, easy to gate behind the symptom-analysis endpoint. - **Source filtering** (`included_sources`, date ranges, citation/DOI metadata) lets the agent cite reputable references back to clinicians. **Main alternatives considered:** - **Linkup** — strong "licensed, no-scraping" model with publisher deals, but coverage is **business/corporate-focused** (Statista, Xerfi, news). No medical/academic depth — wrong domain. - **Perplexity Sona