Run

P1

5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic. I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic.

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic.

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic. exit code 0 Output: /home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1 /bin/bash: line 1: rg: command not found

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic.

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic. exit code 0 Output: ./README.md ./backend/.env.example ./backend/requirements.txt ./frontend/.env ./frontend/README.md ./frontend/package.json

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic.

Agent context: I’ll first inspect the repository shape and existing agent stack, then I’ll compare managed search/retrieval providers using primary documentation so the recommendation fits the codebase rather than being generic. exit code 0

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next. `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next. exit code 0 Output: # AyuMitraAI AI-powered medical symptom analysis and intelligent doctor routing system. ## Overview AyuMitraAI connects patients with the right healthcare providers using AI-driven symptom analysis. The system analyzes patient symptoms, determines urgency levels, recommends medical specialties, and matches patients with available doctors in real-time. ## Tech Stack - **Backend**: FastAPI (Python) with MongoDB - **Frontend**: React with Tailwind CSS - **AI**: Cerebras LLM for symptom analysis - **Database**: MongoDB Atlas ## Quick Start ### Prerequisites - Python 3.9+ - Node.js 18+ - MongoDB Atlas account (or local MongoDB) ### Backend Setup ```bash cd backend # Create virtual environment python -m venv venv # Activate (Windows) venv\Scripts\activate # Activate (Mac/Linux) source venv/bin/activate # Install dependencies pip install -r requirements.txt pip install dnspython # For MongoDB A

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next. exit code 0 Output: .git .gitignore README.md backend/.env.example backend/agent_endpoints.py backend/auth.py backend/cerebras_service.py backend/config.py backend/langchain_agents.py backend/models.py backend/requirements.txt backend/server.py frontend/.env frontend/.gitignore frontend/README.md frontend/components.json frontend/craco.config.js frontend/jsconfig.json frontend/package-lock.json frontend/package.json frontend/plugins/health-check/health-endpoints.js frontend/plugins/health-check/webpack-health-plugin.js frontend/plugins/visual-edits/babel-metadata-plugin.js frontend/plugins/visual-edits/dev-server-setup.js frontend/postcss.config.js frontend/public/index.html frontend/src/App.css frontend/src/App.js frontend/src/components/Navbar.js frontend/src/components/PaymentGateway.js frontend/src/components/UrgencyBadge.js frontend/src/contexts/ThemeContext.js frontend/src/hooks/use-toast.js frontend/

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next. exit code 0 Output: fastapi==0.110.1 uvicorn==0.25.0 motor==3.3.1 pymongo==4.5.0 pydantic==2.12.4 pydantic-settings==2.12.0 python-dotenv==1.2.1 python-jose==3.5.0 passlib==1.7.4 bcrypt==4.1.3 langchain==0.3.0 langchain-core==0.3.27 langchain-cerebras==0.3.0 langchain-openai==0.2.14 python-multipart==0.0.20 email-validator==2.3.0 Provider mentions: openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next. exit code 0 Output: # MongoDB MONGO_URL=mongodb://localhost:27017 DB_NAME=ayumitra # JWT JWT_SECRET_KEY=[redacted] JWT_ALGORITHM=HS256 JWT_EXPIRATION_MINUTES=30 # Groq AI GROQ_API_KEY=[redacted] # Cerebras AI CEREBRAS_API_KEY=[redacted] # Email Service (Mailgun) MAILGUN_API_KEY=[redacted] MAILGUN_DOMAIN=your_mailgun_domain_here FROM_EMAIL=noreply@ayumitra.ai # Frontend FRONTEND_URL=http://localhost:3000 # CORS CORS_ORIGINS=http://localhost:3000,http://localhost:3001 # App APP_NAME=AyuMitraAI GREETING_PREFIX=Hello

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/targeted reads. The repo appears to be a backend/frontend app with Python backend and a JS frontend; I’m checking the agent and dependency surface next. exit code 0 Output: { "name": "frontend", "version": "0.1.0", "private": true, "dependencies": { "@hookform/resolvers": "^5.0.1", "@radix-ui/react-accordion": "^1.2.8", "@radix-ui/react-alert-dialog": "^1.1.11", "@radix-ui/react-aspect-ratio": "^1.1.4", "@radix-ui/react-avatar": "^1.1.7", "@radix-ui/react-checkbox": "^1.2.3", "@radix-ui/react-collapsible": "^1.1.8", "@radix-ui/react-context-menu": "^2.2.12", "@radix-ui/react-dialog": "^1.1.11", "@radix-ui/react-dropdown-menu": "^2.1.12", "@radix-ui/react-hover-card": "^1.1.11", "@radix-ui/react-label": "^2.1.4", "@radix-ui/react-menubar": "^1.1.12", "@radix-ui/react-navigation-menu": "^1.2.10", "@radix-ui/react-popover": "^1.1.11", "@radix-ui/react-progress": "^1.1.4", "@radix-ui/react-radio-group": "^1.3.4", "@radix-ui/react-scroll-area": "^1.2.6", "@radix-ui/react-select": "^2.2.2", "@radix-ui/react-separator": "^1.1.4", "@radix-ui/react-slider": "^1.3.2"

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability. This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability.

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability.

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability. exit code 0 Output: from langchain_cerebras import ChatCerebras from langchain.agents import AgentExecutor, create_tool_calling_agent from langchain_core.tools import tool from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.messages import HumanMessage import json import os import sys sys.path.append(os.path.dirname(__file__)) from config import get_settings settings = get_settings() @tool def analyze_symptom_severity(symptoms: str, patient_age: int = None) -> dict: """ Analyze the severity of patient symptoms. Returns urgency level (critical, moderate, mild) and reasoning. """ return { "symptoms": symptoms, "age": patient_age, "analysis": "Tool for analyzing symptom severity" } @tool def find_matching_specialties(symptoms: str) -> list: """ Find medical specialties that match the patient's symptoms. Returns list of recommended specialties with confidence scores. "

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability.

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability. exit code 0 Output: from langchain_cerebras import ChatCerebras from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import JsonOutputParser from pydantic import BaseModel, Field from typing import List import os import sys import json sys.path.append(os.path.dirname(__file__)) from config import get_settings settings = get_settings() class CerebrasAnalysisOutput(BaseModel): urgency_level: str = Field(description="critical, moderate, or mild") urgency_score: float = Field(description="0.0 to 1.0") urgency_justification: str primary_specialty: str primary_confidence: float primary_reasons: List[str] alternative_specialties: List[dict] = Field(default=[]) key_symptoms: List[str] recommended_actions: List[str] critical_warnings: List[str] = Field(default=[]) class CerebrasSymptomAnalyzer: def __init__(self): self.llm = ChatCerebras( model="llama-3.3-70b", api_key=settings.CE

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability.

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability. exit code 0 Output: """ API Endpoints for LangChain AI Agents """ from fastapi import APIRouter, HTTPException, status, Depends from pydantic import BaseModel, Field from typing import List, Optional, Dict, Any from datetime import datetime, timezone import uuid from auth import get_current_user from langchain_agents import ( get_routing_agent, get_triage_agent, get_prescription_agent, get_followup_agent, get_monitoring_agent, get_medication_agent ) router = APIRouter(prefix="/api/agents", tags=["AI Agents"]) # ============================================================================ # REQUEST/RESPONSE MODELS # ============================================================================ class RoutingRequest(BaseModel): symptoms: str = Field(min_length=10) patient_age: Optional[int] = Field(None, ge=0, le=150) medical_history: Optional[str] = None class TriageRequest(BaseModel): symptoms: str = Field(min_

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability.

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability. exit code 0 Output: from pydantic_settings import BaseSettings from functools import lru_cache class Settings(BaseSettings): MONGO_URL: str DB_NAME: str CORS_ORIGINS: str = "*" CEREBRAS_API_KEY: str MAPPLES_API_KEY: str JWT_SECRET_KEY: str = "ayumitra-secret-key-change-in-production-2025" JWT_ALGORITHM: str = "HS256" JWT_EXPIRATION_MINUTES: int = 10080 class Config: env_file = ".env" case_sensitive = True @lru_cache() def get_settings(): return Settings()

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability.

Agent context: This is a medical triage/routing app, so the web grounding choice needs to favor authoritative sources and citations over generic breadth. I’m reading the backend agent code to see whether the provider should plug in as a LangChain tool, a direct API call, or a model-native capability. exit code 0 Output: from fastapi import FastAPI, APIRouter, HTTPException, status, Depends from fastapi.middleware.cors import CORSMiddleware from motor.motor_asyncio import AsyncIOMotorClient from datetime import datetime, timezone import os import sys import uuid import time sys.path.append(os.path.dirname(__file__)) from config import get_settings from models import * from auth import hash_password, verify_password, create_access_token, get_current_user from cerebras_service import CerebrasSymptomAnalyzer settings = get_settings() client = AsyncIOMotorClient(settings.MONGO_URL) db = client[settings.DB_NAME] app = FastAPI(title="AyuMitraAI API", version="1.0.0") api_router = APIRouter(prefix="/api") cerebras_analyzer = CerebrasSymptomAnalyzer() app.add_middleware( CORSMiddleware, allow_origins=settings.CORS_ORIGINS.split(','), allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @api_router

Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing. The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing.

Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing.

Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing. exit code 0 Output: try: result = self.agent.invoke({"input": input_message}) return { "status": "success", "triage_assessment": result.get("output", "") } except Exception as e: return { "status": "error", "error": str(e) } # ============================================================================ # PRESCRIPTION ANALYSIS AGENT # ============================================================================ class PrescriptionAnalysisAgent: """ AI Agent for analyzing prescriptions and drug interactions. Helps identify potential issues and provides guidance. """ def __init__(self): self.llm = ChatCerebras( model="llama-3.3-70b", api_key=settings.CEREBRAS_API_KEY, temperature=0.2, max_tokens=1500 ) async def analyze_prescription(self, medications: list, patient_age: int = None, allergies: list = None) -> dict: """ Analyze prescription for potential issues. Args: medications: List of medications with dosages Provider mentions: together-ai

Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing.

Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing. exit code 0 Output: backend/langchain_agents.py:75:def get_treatment_guidelines(specialty: str, condition: str) -> dict: backend/langchain_agents.py:77: Get evidence-based treatment guidelines for a specific condition. backend/langchain_agents.py:83: "guidelines": "Treatment guidelines" backend/langchain_agents.py:111: get_treatment_guidelines backend/langchain_agents.py:207: get_treatment_guidelines backend/server.py:349: search_keywords = set() backend/server.py:352: search_keywords.update(keywords) backend/server.py:355: search_keywords.update(keywords) backend/server.py:359: if not search_keywords: backend/server.py:360: search_keywords.add(specialty_lower) backend/server.py:362: print(f"[DEBUG] AI specialty: '{specialty}' → Search keywords: {search_keywords}") backend/server.py:377: for keyword in search_keywords: backend/server.py:461: # Also search hospitals for specialists backend/server.py:858:@api

Query/prompt: Tavily API documentation search include_domains extract raw_content chunks_per_source Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing. Query: Tavily API documentation search include_domains extract raw_content chunks_per_source Agent context: The current “treatment guidelines” tool is a stub, and the primary symptom analyzer is a direct Cerebras call with no retrieval step. That makes provider quality and citation control more important than an SDK fit alone, because this is patient-facing medical routing. Expanded queries: Tavily API documentation search include_domains extract raw_content chunks_per_source | Exa API documentation search contents includeDomains highlights summaries | Perplexity API documentation Sonar citations search domain filter

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. exit code 0 Output: --- name: "openai-docs" description: "Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains." --- # OpenAI Docs Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. "Docs MCP" means `mcp__openaiDeveloperDocs__search_openai_docs` and `mcp__openaiDeveloperDocs__fetch_openai_doc`; for API reference, schema, parameter, or required-field questions, also use `mcp__openaiDeveloperDocs__get_openapi_spec` when available. Official-domain web search is fal Provider mentions: openai

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior.

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. exit code 0

Query/prompt: OpenAI web search tool Responses API citations user_location search_context_size site:platform.openai.com OR site:developers.openai.com Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: OpenAI web search tool Responses API citations user_location search_context_size site:platform.openai.com OR site:developers.openai.com Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Expanded queries: OpenAI web search tool Responses API citations user_location search_context_size site:platform.openai.com OR site:developers.openai.com | Brave Search API documentation web search snippets freshness site:api-dashboard.search.brave.com OR site:brave.com/search/api | Google Programmable Search JSON API documentation site restricted search site:developers.google.com/custom-search Provider mentions: openai

Query/prompt: web search tool OpenAI Responses API citations search_context_size official docs Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: web search tool OpenAI Responses API citations search_context_size official docs Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Expanded queries: web search tool OpenAI Responses API citations search_context_size official docs | OpenAI Responses API web_search_preview citation annotations official docs | OpenAI built in tools web search API guide official docs Provider mentions: openai

Query/prompt: https://brave.com/search/api Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://brave.com/search/api Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: site:docs.tavily.com/documentation/api-reference/endpoint/search Tavily Search API include_domains raw_content chunks_per_source Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: site:docs.tavily.com/documentation/api-reference/endpoint/search Tavily Search API include_domains raw_content chunks_per_source Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Expanded queries: site:docs.tavily.com/documentation/api-reference/endpoint/search Tavily Search API include_domains raw_content chunks_per_source | site:exa.ai/docs/reference/search-api Exa search API includeDomains contents highlights text | site:docs.perplexity.ai/guides/search-domain-filters Perplexity search_domain_filter citations Sonar API Provider mentions: openai

Query/prompt: OpenAI API HIPAA BAA healthcare official docs Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: OpenAI API HIPAA BAA healthcare official docs Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Expanded queries: OpenAI API HIPAA BAA healthcare official docs | Exa API HIPAA compliance mode documentation | Tavily HIPAA compliance API documentation Provider mentions: openai

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior.

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. exit code 0 Output: 1 from langchain_cerebras import ChatCerebras 2 from langchain_core.prompts import ChatPromptTemplate 3 from langchain_core.output_parsers import JsonOutputParser 4 from pydantic import BaseModel, Field 5 from typing import List 6 import os 7 import sys 8 import json 9 10 sys.path.append(os.path.dirname(__file__)) 11 from config import get_settings 12 13 settings = get_settings() 14 15 class CerebrasAnalysisOutput(BaseModel): 16 urgency_level: str = Field(description="critical, moderate, or mild") 17 urgency_score: float = Field(description="0.0 to 1.0") 18 urgency_justification: str 19 primary_specialty: str 20 primary_confidence: float 21 primary_reasons: List[str] 22 alternative_specialties: List[dict] = Field(default=[]) 23 key_symptoms: List[str] 24 recommended_actions: List[str] 25 critical_warnings: List[str] = Field(default=[]) 26 27 class CerebrasSymptomAnalyzer: 28 def __init__

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior.

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. exit code 0 Output: 1 2 from langchain_cerebras import ChatCerebras 3 from langchain.agents import AgentExecutor, create_tool_calling_agent 4 from langchain_core.tools import tool 5 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder 6 from langchain_core.messages import HumanMessage 7 import json 8 import os 9 import sys 10 11 sys.path.append(os.path.dirname(__file__)) 12 from config import get_settings 13 14 settings = get_settings() 15 16 @tool 17 def analyze_symptom_severity(symptoms: str, patient_age: int = None) -> dict: 18 """ 19 Analyze the severity of patient symptoms. 20 Returns urgency level (critical, moderate, mild) and reasoning. 21 """ 22 return { 23 "symptoms": symptoms, 24 "age": patient_age, 25 "analysis": "Tool for analyzing symptom severity" 26 } 27 28 @tool 29 def find_matching_specialties(symptoms: str) -> list: 30 """ 31 Find medical specialties that match the p

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior.

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. exit code 0 Output: 1 from pydantic_settings import BaseSettings 2 from functools import lru_cache 3 4 class Settings(BaseSettings): 5 MONGO_URL: str 6 DB_NAME: str 7 CORS_ORIGINS: str = "*" 8 CEREBRAS_API_KEY: str 9 MAPPLES_API_KEY: str 10 JWT_SECRET_KEY: str = "ayumitra-secret-key-change-in-production-2025" 11 JWT_ALGORITHM: str = "HS256" 12 JWT_EXPIRATION_MINUTES: int = 10080 13 14 class Config: 15 env_file = ".env" 16 case_sensitive = True 17 18 @lru_cache() 19 def get_settings(): 20 return Settings()

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior.

Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. exit code 0 Output: 1 fastapi==0.110.1 2 uvicorn==0.25.0 3 4 motor==3.3.1 5 pymongo==4.5.0 6 7 pydantic==2.12.4 8 pydantic-settings==2.12.0 9 10 python-dotenv==1.2.1 11 python-jose==3.5.0 12 passlib==1.7.4 13 bcrypt==4.1.3 14 15 langchain==0.3.0 16 langchain-core==0.3.27 17 langchain-cerebras==0.3.0 18 langchain-openai==0.2.14 19 20 python-multipart==0.0.20 21 email-validator==2.3.0 Provider mentions: openai

Query/prompt: 'Domain filtering' Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: 'Domain filtering' Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://developers.openai.com/api/docs/guides/tools-web-search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://developers.openai.com/api/docs/guides/tools-web-search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://developers.openai.com/api/docs/guides/tools-web-search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://developers.openai.com/api/docs/guides/tools-web-search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://developers.openai.com/api/docs/guides/tools-web-search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://developers.openai.com/api/docs/guides/tools-web-search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://help.openai.com/en/articles/8660679-how-can-i-get-a-business-associate-agreement-baa-with-openai Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://help.openai.com/en/articles/8660679-how-can-i-get-a-business-associate-agreement-baa-with-openai Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://exa.ai/docs/reference/search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://exa.ai/docs/reference/search Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Query/prompt: https://docs.perplexity.ai/guides/search-quickstart Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Query: https://docs.perplexity.ai/guides/search-quickstart Agent context: I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs in the comparison. For OpenAI-specific facts I’m using the OpenAI docs workflow so I don’t rely on memory for current API behavior. Provider mentions: openai

Agent context: **Provider Choice** I’d choose **OpenAI Responses API with the hosted `web_search` tool**. Repo fit: the backend is a Python FastAPI medical routing app. Its current critical path uses Cerebras directly in [backend/cerebras_service.py](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/cerebras_service.py:27), and the LangChain “treatment guidelines” tool is only a stub in [backend/langchain_agents.py](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/langchain_agents.py:74). `langchain-openai` is already present in [backend/requirements.txt](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/requirements.txt:15), but I’d use the current OpenAI SDK/Responses API directly for the grounding layer. Why OpenAI over the others: OpenAI’s `web_search` supports live access, inline URL citations, full source metadata, page-open/find-in-page behavior with reasoning models, and domain filtering. The docs explicitly show medical-source allowlisting such as PubMed, ClinicalTrials.gov, WHO, CDC, and FDA, plus `include: ["web_search_call.action.sources"]` to audit all consulted sources. That combination is the strongest fit for a medical product where bad sources are worse than slow answers. It also has a current healthcare/BAA path for API use with PHI. I would implement it as a guarded evidence step: `allowed_domains` for authoritative medical sources, `external_web_access: true`, `search_context_size: high` for medical questions, return citations/source metadata to the frontend, and fail closed when evidence is missing or uncited. **Alternatives Considered** - **Tavily**: strong standalone RAG search API, good Python fit, domain allowlists up to 300, raw page content, date filters, and advanced snippets. I’d pick it if the priority were “keep Cerebras and add a retrieval tool quickly.” I would not pick it first here because OpenAI gives tighter end-to-end cited answer generation and source auditing. - **Exa**: excellent retrieval/extraction API with highlights, full text, live crawl controls, large domain filters, and even an enterprise HIPAA mode. Strong runner-up for evidence retrieval, but OpenAI’s integrated cited reasoning loop is better for this high-stakes answer path. - **Perplexity Search/Sonar**: good real-time search and cited answer API. The weaker point is source control: its domain filter max is 20 domains, and Sonar is more of a black-box answer engine. - **Brave Search API**: strong independent index, freshness claims, snippets, Goggles, and answer/citation products. Better as a general web index than as the primary clinical grounding layer. - **Google Programmable Search / site-restricted search**: high-quality index and source restriction, but the JSON API is mostly search-result metadata, not a managed evidence-to-cited-answer workflow. Google’s site-restricted API docs also point customers toward Vertex AI Search. **Docs I Actually Consulted** - OpenAI Web Search docs: https://developers.openai.com/api/docs/guides/tools-web-search - OpenAI BAA/HIPAA API help: https://help.openai.com/en/articles/8660679-how-can-i-get-a-business-associate-agreement-baa-with-openai - Tavily Search docs: https://docs.tavily.com/documentation/api-reference/endpoint/search - Exa Search and Contents docs: https://exa.ai/docs/reference/search and https://exa.ai/docs/reference/contents-retrieval - Perplexity Search/Sonar/domain filter docs: https://docs.perplexity.ai/docs/search/quickstart, https://docs.perplexity.ai/docs/search/filters/domain-filter, https://docs.perplexity.ai/docs/sonar/quickstart - Brave Search API docs: https://brave.com/search/api/ - Google Custom Search docs: https://developers.google.com/custom-search/v1/ and https://developers.google.com/custom-search/v1/using_rest **Provider Choice** I’d choose **OpenAI Responses API with the hosted `web_search` tool**. Repo fit: the backend is a Python FastAPI medical routing app. Its current critical path uses Cerebras directly in [backend/cerebras_service.py](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/cerebras_service.py:27), and the LangChain “treatment guidelines” tool is only a stub in [backend/langchain_agents.py](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/langchain_agents.py:74). `langchain-openai` is already present in [backend/requirements.txt](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/requirements.txt:15), but I’d use the current OpenAI SDK/Responses API directly for the grounding layer. Why OpenAI over the others: OpenAI’s `web_search` supports live access, inline URL citations, full source metadata, page-open/find-in-page behavior with reasoning models, and domain filtering. The docs explicitly show medical-source allowlisting such as PubMed, ClinicalTrials.gov, WHO, CDC, and FDA, plus `include: ["web_search_call.action.sources"]` to audit all consulted sources. That combination is the Provider mentions: openai

**Provider Choice** I’d choose **OpenAI Responses API with the hosted `web_search` tool**. Repo fit: the backend is a Python FastAPI medical routing app. Its current critical path uses Cerebras directly in [backend/cerebras_service.py](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/cerebras_service.py:27), and the LangChain “treatment guidelines” tool is only a stub in [backend/langchain_agents.py](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/langchain_agents.py:74). `langchain-openai` is already present in [backend/requirements.txt](/home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1/backend/requirements.txt:15), but I’d use the current OpenAI SDK/Responses API directly for the grounding layer. Why OpenAI over the others: OpenAI’s `web_search` supports live access, inline URL citations, full source metadata, page-open/find-in-page behavior with reasoning models, and domain filtering. The docs explicitly show medical-source allowlisting such as PubMed, ClinicalTrials.gov, WHO, CDC, and FDA, plus `include: ["web_search_call.action.sources"]` to audit all consulted sources. That combination is the strongest fit for a medical product where bad sources are worse than slow answers. It also has a current healthcare/BAA path for API use with PHI. I would implement it as a guarded evidence step: `al Provider mentions: openai