Run

P0

5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: frontend/components.json frontend/jsconfig.json frontend/package-lock.json frontend/package.json Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: backend/requirements.txt Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: No files found Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: README.md frontend/README.md Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 28 drwxr-xr-x 4 user user 4096 Jun 6 04:48 . drwxr-xr-x 3 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 170 Jun 6 04:48 .git -rw-r--r-- 1 user user 1014 Jun 6 04:48 .gitignore -rw-r--r-- 1 user user 3423 Jun 6 04:48 README.md drwxr-xr-x 2 user user 4096 Jun 6 04:48 backend drwxr-xr-x 5 user user 4096 Jun 6 04:48 frontend Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/backend/agent_endpoints.py /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/backend/auth.py /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/backend/cerebras_service.py /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/backend/config.py /home/user/worktrees/attempt-3d287bbf-9d98- Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 108 drwxr-xr-x 2 user user 4096 Jun 6 04:48 . drwxr-xr-x 4 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 563 Jun 6 04:48 .env.example -rw-r--r-- 1 user user 8996 Jun 6 04:48 agent_endpoints.py -rw-r--r-- 1 user user 1775 Jun 6 04:48 auth.py -rw-r--r-- 1 user user 4386 Jun 6 04:48 cerebras_service.py -rw-r--r-- 1 user user 502 Jun 6 04:48 config.py -rw-r--r-- 1 user user 16074 Jun 6 04:48 langchain_agents Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

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

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/frontend/craco.config.js /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/frontend/plugins/health-check/health-endpoints.js /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/frontend/plugins/health-check/webpack-health-plugin.js /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/fr Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: frontend/src/utils/api.js frontend/src/utils/auth.js Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 100 request.symptom_description, 101 request.patient_age 102 ) 103 104 urgency = UrgencyLevel( 105 level=analysis["urgency_level"], 106 score=analysis["urgency_score"], 107 justification=analysis["urgency_justification"] 108 ) 109 110 primary_specialty = SpecialtyRecommendation( 111 specialty=analysis["primary_specialty"], 112 confidence=analysis["primary_confidence"], 113 reasons=analysis["primary_reasons"] 114 ) 11 Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: backend/server.py:1:from fastapi import FastAPI, APIRouter, HTTPException, status, Depends backend/server.py:2:from fastapi.middleware.cors import CORSMiddleware backend/server.py:3:from motor.motor_asyncio import AsyncIOMotorClient backend/server.py:4:from datetime import datetime, timezone backend/server.py:5:import os backend/server.py:6:import sys backend/server.py:7:import uuid backend/server.py:8:import time ba Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 918 /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/backend/server.py Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 800 raise HTTPException(status_code=403, detail="Only doctors can accept requests") 801 802 # Verify doctor is in matched_doctors list 803 request_doc = await db.patient_requests.find_one({"request_id": request_id}) 804 if not request_doc or current_user["sub"] not in request_doc.get("matched_doctors", []): 805 raise HTTPException(status_code=404, detail="Request not found or not assigned to you") 806 807 result = aw Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

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

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

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 910 } 911 for d in all_doctors 912 ] 913 } 914 915 app.include_router(api_router) 916 917 @app.on_event("shutdown") Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 28 drwxr-xr-x 4 user user 4096 Jun 6 04:48 . drwxr-xr-x 3 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 170 Jun 6 04:48 .git -rw-r--r-- 1 user user 1014 Jun 6 04:48 .gitignore -rw-r--r-- 1 user user 3423 Jun 6 04:48 README.md drwxr-xr-x 2 user user 4096 Jun 6 04:48 backend drwxr-xr-x 5 user user 4096 Jun 6 04:48 frontend Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/backend/.env.example /home/user/worktrees/attempt-3d287bbf-9d98-4c06-b2b3-ceec5ff881b3-cap-0/frontend/.env Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is 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 tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 164 drwxr-xr-x 2 user user 4096 Jun 6 04:48 . drwxr-xr-x 8 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 17772 Jun 6 04:48 ClinicRegistration.js -rw-r--r-- 1 user user 24860 Jun 6 04:48 DoctorDashboard.js -rw-r--r-- 1 user user 21895 Jun 6 04:48 HospitalRegistration.js -rw-r--r-- 1 user user 5342 Jun 6 04:48 LandingPage.js -rw-r--r-- 1 user user 5198 Jun 6 04:48 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 is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 65 Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 is built.

Agent context: The repo is AyuMitraAI: a FastAPI + LangChain + Cerebras (llama-3.3-70b) medical agent system, Python backend, no existing web search. Now let me actually consult the candidate providers' documentation to make a grounded recommendation. The repo is AyuMitraAI: a FastAPI + LangChain + Cerebras (llama-3.3-70b) medical agent system, Python backend, no existing web search. Now let me actually consult the candidate providers' documentation to make a grounded recommendation.

Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API. Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API.

Query/prompt: Extract: what this API does, whether it's designed for LLM/RAG/agents, pricing/free tier, Python SDK availability, LangChain integration, and what the response format includes (clean content, raw content, etc.). Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API. Fetch prompt: Extract: what this API does, whether it's designed for LLM/RAG/agents, pricing/free tier, Python SDK availability, LangChain integration, and what the response format includes (clean content, raw content, etc.). Tool result summary: # Tavily Search API Summary **What It Does:** Tavily Search is a REST API providing "seamless access to Tavily Search, a powerful search engine for LLM agents" along with web extraction capabilities. It executes search queries and returns ranked results with multiple content formats. **LLM/RAG/Agent Design:** Yes—the documentation explicitly describes it as "a powerful search engine for LLM agents," making it purpose Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API.

Query/prompt: Extract: what Exa search does, neural/semantic search capabilities, whether designed for LLM/RAG/agents, Python SDK, LangChain integration, pricing/free tier, and content retrieval features. Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API. Fetch prompt: Extract: what Exa search does, neural/semantic search capabilities, whether designed for LLM/RAG/agents, Python SDK, LangChain integration, pricing/free tier, and content retrieval features. 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: "Extract: what Exa sea Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API.

Query/prompt: Extract: what the Perplexity Sonar API does, whether it returns grounded answers with citations, models available, pricing, Python/OpenAI SDK compatibility, and whether it's a search API vs an answer API. Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API. Fetch prompt: Extract: what the Perplexity Sonar API does, whether it returns grounded answers with citations, models available, pricing, Python/OpenAI SDK compatibility, and whether it's a search API vs an answer API. Tool result summary: # Perplexity API Overview Based on the provided documentation, here's what I found: ## What Perplexity Sonar Does The platform offers "real-time, web-wide research and Q&A capabilities" through multiple API endpoints. However, the specific "Sonar API" isn't explicitly detailed in this overview page. ## Available APIs The documentation highlights three main services: - **Agent API**: Accesses third-party models with w Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API. Provider mentions: openai

Query/prompt: Extract: what the Brave Search API does, pricing/free tier, query volume limits, response format, and whether it's positioned for LLM/RAG grounding. Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API. Fetch prompt: Extract: what the Brave Search API does, pricing/free tier, query volume limits, response format, and whether it's positioned for LLM/RAG grounding. Tool result summary: # Brave Search API Summary ## What It Does The Brave Search API provides "access to our comprehensive index of web pages, enabling you to retrieve relevant results" across billions of indexed sites. It offers multiple search verticals including web, news, video, image, and place searches, plus rich data enrichments for sports, weather, stocks, and more. ## Pricing & Free Tier The documentation references a pricing pa Agent context: Let me consult the documentation of the leading candidates in parallel: Tavily, Exa, Perplexity Sonar, and Brave Search API.

Agent context: Let me follow the Exa redirect and check Tavily's LangChain integration and pricing details. Let me follow the Exa redirect and check Tavily's LangChain integration and pricing details.

Query/prompt: Extract: what Exa search does, neural/semantic search capabilities, whether designed for LLM/RAG/agents, Python SDK, LangChain integration, pricing/free tier, and content retrieval features (like get contents / highlights). Agent context: Let me follow the Exa redirect and check Tavily's LangChain integration and pricing details. Fetch prompt: Extract: what Exa search does, neural/semantic search capabilities, whether designed for LLM/RAG/agents, Python SDK, LangChain integration, pricing/free tier, and content retrieval features (like get contents / highlights). Tool result summary: # Exa Search Engine Overview **What Exa Does:** Exa is "a search engine made for AIs" with four core functionalities: semantic search via embeddings, content retrieval, direct question answering, and automated research tasks. **Neural/Semantic Search:** The platform uses "embeddings-based search and other intelligent methods" to locate precise web content, enabling AI-native retrieval beyond traditional keyword match Agent context: Let me follow the Exa redirect and check Tavily's LangChain integration and pricing details.

Query/prompt: Tavily LangChain integration TavilySearch tool pricing free tier 2026 Agent context: Let me follow the Exa redirect and check Tavily's LangChain integration and pricing details. Query: Tavily LangChain integration TavilySearch tool pricing free tier 2026 10 search result links captured Tool result summary: Based on the search results, here's what I found about Tavily's LangChain integration, the TavilySearch tool, and pricing information: ## LangChain Integration Tavily is particularly popular in the LangChain community, with native integrations that make adding real-time web search to your agent straightforward. LangChain uses Tavily as its default search tool, and OpenAI referenced it in their function-calling exampl Agent context: Let me follow the Exa redirect and check Tavily's LangChain integration and pricing details. Provider mentions: openai

Agent context: I have enough grounded information. Here's my recommendation. ## Recommendation: **Tavily** For AyuMitraAI — a Python/FastAPI backend running LangChain tool-calling agents on Cerebras `llama-3.3-70b` — Tavily is the best fit. **Why Tavily wins for this repo specifically:** - **Native LangChain integration.** The repo already uses LangChain agents (`langchain_agents.py`, with `MedicalRoutingAgent`/`TriageAgent` etc. as tool-calling agents). The `langchain-tavily` package ships a drop-in `TavilySearch` tool you register alongside existing tools — minimal new code, no new agent framework. It's LangChain's de-facto default search tool. - **Single-call grounding.** Tavily returns cleaned, extracted page content (and an optional synthesized `answer`) directly. SERP-style APIs (Serper, Brave web endpoint, SerpAPI) return only URLs + snippets, forcing a second scraping step inside your agent loop — extra latency and code. - **Purpose-built for LLM agents**, with a Python SDK (`from tavily import TavilyClient`) matching the backend, plus a usable free tier (1,000 credits/month) for dev, and predictable PAYG pricing. **Main alternatives I considered:** - **Exa** — strong embeddings/neural search + `/contents` endpoint; excellent for semantic discovery, but no first-class LangChain tool and more setup for agent tool-calling. - **Perplexity (Sonar / Search + Agent API)** — great grounded-answer quality with citations, but the Agent API bundles its *own* models, which conflicts with your committed Cerebras stack; the raw Search API loses that advantage. - **Brave Search API** — has a dedicated "LLM Context" endpoint built for AI consumption and good privacy/independence, but no native LangChain integration and weaker out-of-the-box agent ergonomics. - **Serper / SerpAPI** — cheap Google SERP, but snippet-only (no content extraction) → not real grounding without extra scraping. **Documentation I actually consulted:** - Tavily Search API reference (`docs.tavily.com`) + `langchain-tavily` (PyPI/GitHub) and LangChain's Tavily integration docs - Exa docs (`exa.ai/docs`) - Perplexity API docs (`docs.perplexity.ai`) - Brave Search API docs (`api-dashboard.search.brave.com`) **One caveat to flag:** Tavily was acquired by Nebius (2026), prompting "Tavily alternatives" discussion. It remains the lowest-friction choice for *this* LangChain codebase, but if vendor independence is a hard requirement, Brave's LLM Context endpoint is the strongest fallback. Want me to wire up the `TavilySearch` tool into the existing LangChain agents? Sources: - [Tavily Search API reference](https://docs.tavily.com/documentation/api-reference/endpoint/search) - [langchain-tavily (PyPI)](https://pypi.org/project/langchain-tavily/) - [langchain-tavily (GitHub)](https://github.com/tavily-ai/langchain-tavily) - [Tavily search integration — LangChain docs](https://docs.langchain.com/oss/javascript/integrations/tools/tavily_search) - [Exa docs](https://exa.ai/docs/reference/getting-started) - [Perplexity API docs](https://docs.perplexity.ai/home) - [Brave Search API docs](https://api-dashboard.search.brave.com/app/documentation/web-search/get-started) - [Tavily Alternatives in 2026 (after Nebius acquisition)](https://medium.com/@unicodeveloper/tavily-alternatives-in-2026-after-the-nebius-acquisition-9de526780686) I have enough grounded information. Here's my recommendation. ## Recommendation: **Tavily** For AyuMitraAI — a Python/FastAPI backend running LangChain tool-calling agents on Cerebras `llama-3.3-70b` — Tavily is the best fit. **Why Tavily wins for this repo specifically:** - **Native LangChain integration.** The repo already uses LangChain agents (`langchain_agents.py`, with `MedicalRoutingAgent`/`TriageAgent` etc. as tool-calling agents). The `langchain-tavily` package ships a drop-in `TavilySearch` tool you register alongside existing tools — minimal new code, no new agent framework. It's LangChain's de-facto default search tool. - **Single-call grounding.** Tavily returns cleaned, extracted page content (and an optional synthesized `answer`) directly. SERP-style APIs (Serper, Brave web endpoint, SerpAPI) return only URLs + snippets, forcing a second scraping step inside your agent loop — extra latency and code. - **Purpose-built for LLM agents**, with a Python SDK (`from tavily import TavilyClient`) matching the backend, plus a usable free tier (1,000 credits/month) for dev, and predictable PAYG pricing. **Main alternatives I considered:** - **Exa** — strong embeddings/neural se

I have enough grounded information. Here's my recommendation. ## Recommendation: **Tavily** For AyuMitraAI — a Python/FastAPI backend running LangChain tool-calling agents on Cerebras `llama-3.3-70b` — Tavily is the best fit. **Why Tavily wins for this repo specifically:** - **Native LangChain integration.** The repo already uses LangChain agents (`langchain_agents.py`, with `MedicalRoutingAgent`/`TriageAgent` etc. as tool-calling agents). The `langchain-tavily` package ships a drop-in `TavilySearch` tool you register alongside existing tools — minimal new code, no new agent framework. It's LangChain's de-facto default search tool. - **Single-call grounding.** Tavily returns cleaned, extracted page content (and an optional synthesized `answer`) directly. SERP-style APIs (Serper, Brave web endpoint, SerpAPI) return only URLs + snippets, forcing a second scraping step inside your agent loop — extra latency and code. - **Purpose-built for LLM agents**, with a Python SDK (`from tavily import TavilyClient`) matching the backend, plus a usable free tier (1,000 credits/month) for dev, and predictable PAYG pricing. **Main alternatives I considered:** - **Exa** — strong embeddings/neural search + `/contents` endpoint; excellent for semantic discovery, but no first-class LangChain tool and more setup for agent tool-calling. - **Perplexity (Sonar / Search + Agent API)** — great grounded-a