beastshr

Run

BEASTSHRIRAM/AyuMitraAI

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:51 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

openai-web-search

Product

Surface

provider_selection

Reason

codex selected openai-web-search during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

agent_action

I’ll first inspect the repository shape and existing agent stack, then I’ll compare...

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.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-1779ca87-b526-456a-bf12-ff37a56e8cb2-cap-1 /bin/bash: line 1: rg: command not found

command

/bin/bash -lc "find . -maxdepth 2 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '.env*' \\) -print"

command

/bin/bash -lc "find . -maxdepth 2 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '.env*' \\) -print"

exit code 0 Output: ./README.md ./backend/.env.example ./backend/requirements.txt ./frontend/.env ./frontend/README.md ./frontend/package.json

command

/bin/bash -lc 'git status --short'

command

/bin/bash -lc 'git status --short'

exit code 0

agent_action

`rg` is not installed in this environment, so I’m falling back to `find`/targeted r...

`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.

command

/bin/bash -lc "sed -n '1,220p' README.md"

command

/bin/bash -lc "sed -n '1,220p' README.md"

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

command

/bin/bash -lc "find . -maxdepth 4 -type f | sed 's#"'^./##'"' | sort | head -250"

command

/bin/bash -lc "find . -maxdepth 4 -type f | sed 's#"'^./##'"' | sort | head -250"

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/

command

/bin/bash -lc "sed -n '1,220p' backend/requirements.txt"

command

/bin/bash -lc "sed -n '1,220p' backend/requirements.txt"

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

command

/bin/bash -lc "sed -n '1,220p' backend/.env.example"

command

/bin/bash -lc "sed -n '1,220p' backend/.env.example"

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

command

/bin/bash -lc "sed -n '1,220p' frontend/package.json"

command

/bin/bash -lc "sed -n '1,220p' frontend/package.json"

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_action

This is a medical triage/routing app, so the web grounding choice needs to favor au...

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.

command

/bin/bash -lc "sed -n '1,280p' backend/langchain_agents.py"

command

/bin/bash -lc "sed -n '1,280p' backend/langchain_agents.py"

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. "

command

/bin/bash -lc "sed -n '1,260p' backend/cerebras_service.py"

command

/bin/bash -lc "sed -n '1,260p' backend/cerebras_service.py"

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

command

/bin/bash -lc "sed -n '1,280p' backend/agent_endpoints.py"

command

/bin/bash -lc "sed -n '1,280p' backend/agent_endpoints.py"

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_

command

/bin/bash -lc "sed -n '1,220p' backend/config.py"

command

/bin/bash -lc "sed -n '1,220p' backend/config.py"

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()

command

/bin/bash -lc "sed -n '1,260p' backend/server.py"

command

/bin/bash -lc "sed -n '1,260p' backend/server.py"

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_action

The current “treatment guidelines” tool is a stub, and the primary symptom analyzer...

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.

command

/bin/bash -lc "sed -n '260,620p' backend/langchain_agents.py"

command

/bin/bash -lc "sed -n '260,620p' backend/langchain_agents.py"

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

command

command

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

Tavily API documentation search include_domains extract raw_content chunks_per_source

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_action

I’m going to include OpenAI, Tavily, Exa, Perplexity, and traditional search APIs i...

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.

command

/bin/bash -lc "sed -n '1,220p' /home/user/.codex/skills/.system/openai-docs/SKILL.md"

command

/bin/bash -lc "sed -n '1,220p' /home/user/.codex/skills/.system/openai-docs/SKILL.md"

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

command

command

exit code 0

OpenAI web search tool Responses API citations user_location search_context_size si...

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

web search tool OpenAI Responses API citations search_context_size official docs

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

url

https://brave.com/search/api

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.

site:docs.tavily.com/documentation/api-reference/endpoint/search Tavily Search API ...

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

OpenAI API HIPAA BAA healthcare official docs

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

command

/bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,120p'"

command

/bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,120p'"

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__

command

/bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,130p'"

command

/bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,130p'"

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

command

/bin/bash -lc "nl -ba backend/config.py | sed -n '1,80p'"

command

/bin/bash -lc "nl -ba backend/config.py | sed -n '1,80p'"

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()

command

/bin/bash -lc "nl -ba backend/requirements.txt | sed -n '1,80p'"

command

/bin/bash -lc "nl -ba backend/requirements.txt | sed -n '1,80p'"

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

'Domain filtering'

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.

url

https://developers.openai.com/api/docs/guides/tools-web-search

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.

url

https://developers.openai.com/api/docs/guides/tools-web-search

url

https://developers.openai.com/api/docs/guides/tools-web-search

url

https://help.openai.com/en/articles/8660679-how-can-i-get-a-business-associate-agreement-baa-with-openai

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.

url

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

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.

url

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

url

https://exa.ai/docs/reference/search

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.

url

https://docs.perplexity.ai/guides/search-quickstart

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.

agent_action

**Provider Choice** I’d choose **OpenAI Responses API with the hosted `web_search` ...

**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

result

Selected openai

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit 0

/bin/bash -lc "find . -maxdepth 2 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '.env*' \\) -print"

exit 0

/bin/bash -lc 'git status --short'

exit 0

/bin/bash -lc "sed -n '1,220p' README.md"

exit 0

/bin/bash -lc "find . -maxdepth 4 -type f | sed 's#"'^./##'"' | sort | head -250"

exit 0

/bin/bash -lc "sed -n '1,220p' backend/requirements.txt"

exit 0

/bin/bash -lc "sed -n '1,220p' backend/.env.example"

exit 0

/bin/bash -lc "sed -n '1,220p' frontend/package.json"

exit 0

/bin/bash -lc "sed -n '1,280p' backend/langchain_agents.py"

exit 0

/bin/bash -lc "sed -n '1,260p' backend/cerebras_service.py"

exit 0

/bin/bash -lc "sed -n '1,280p' backend/agent_endpoints.py"

exit 0

/bin/bash -lc "sed -n '1,220p' backend/config.py"

exit 0

/bin/bash -lc "sed -n '1,260p' backend/server.py"

exit 0

/bin/bash -lc "sed -n '260,620p' backend/langchain_agents.py"

exit 0

exit 0

/bin/bash -lc "sed -n '1,220p' /home/user/.codex/skills/.system/openai-docs/SKILL.md"

exit 0

exit 0

/bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,120p'"

exit 0

/bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,130p'"

exit 0

/bin/bash -lc "nl -ba backend/config.py | sed -n '1,80p'"

exit 0

/bin/bash -lc "nl -ba backend/requirements.txt | sed -n '1,80p'"

exit 0

URLs Fetched

https://brave.com/search/api

status not materialized

https://developers.openai.com/api/docs/guides/tools-web-search

status not materialized

https://help.openai.com/en/articles/8660679-how-can-i-get-a-business-associate-agreement-baa-with-openai

status not materialized

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

status not materialized

https://exa.ai/docs/reference/search

status not materialized

https://docs.perplexity.ai/guides/search-quickstart

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

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