beastshr

Run

BEASTSHRIRAM/AyuMitraAI

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:54 AM

Decision Summary

target

Expected

Provider

Product

Selected

Provider

linkup

Product

Surface

provider_selection

Reason

codex selected Linkup during first-pass provider discovery.

Outcome

target

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

5cb360e502f88fe2c18b163c54d26e7b13ec6bc1

agent_action

I’ll first inspect the repo to understand the agent’s runtime and integration point...

I’ll first inspect the repo to understand the agent’s runtime and integration points, then I’ll check current provider docs for managed search/retrieval options with an emphasis on licensed, reputable source coverage.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3 /bin/bash: line 1: rg: command not found

command

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

command

command

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

exit code 0

command

exit code 127 Output: /bin/bash: line 1: rg: command not found

agent_action

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

`rg` is not installed in this environment, so I’m falling back to `find`/`grep` to map the codebase and locate the agent integration surface.

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | head -250"

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | 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/components/ui/accordion.jsx frontend/src/components/ui/alert-dialo

command

command

exit code 0 Output: ./.gitignore:58:# Development tools ./.gitignore:68:agenthub/agents/youtube/db ./README.md:36:source venv/bin/activate ./backend/agent_endpoints.py:2:API Endpoints for LangChain AI Agents ./backend/agent_endpoints.py:12:from langchain_agents import ( ./backend/agent_endpoints.py:13: get_routing_agent, ./backend/agent_endpoints.py:14: get_triage_agent, ./backend/agent_endpoints.py:15: get_prescription_agent, ./backend/agent_endpoints.py:16: get_followup_agent, ./backend/agent_endpoints.py:17: get_monitoring_agent, ./backend/agent_endpoints.py:18: get_medication_agent ./backend/agent_endpoints.py:21:router = APIRouter(prefix="/api/agents", tags=["AI Agents"]) ./backend/agent_endpoints.py:52:class AgentResponse(BaseModel): ./backend/agent_endpoints.py:53: agent_type: str ./backend/agent_endpoints.py:60:# ROUTING AGENT ENDPOINTS ./backend/agent_endpoints.py:63:@router.post("/routing/analyze"

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 'AGENTS.md' -o -name '.env.example' \\) -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 'AGENTS.md' -o -name '.env.example' \\) -print"

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

agent_action

The repo is a small FastAPI backend with LangChain tool-calling agents plus a React...

The repo is a small FastAPI backend with LangChain tool-calling agents plus a React frontend. I’m reading the backend files now to see where live retrieval would be wired and what constraints the medical-agent domain imposes.

command

/bin/bash -lc "sed -n '1,580p' backend/langchain_agents.py"

command

/bin/bash -lc "sed -n '1,580p' 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 '1,220p' 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 "sed -n '1,330p' backend/agent_endpoints.py"

command

/bin/bash -lc "sed -n '1,330p' 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/requirements.txt && sed -n '1,140p' backend/.env.example && sed -n '1,220p' backend/config.py"

command

/bin/bash -lc "sed -n '1,220p' backend/requirements.txt && sed -n '1,140p' backend/.env.example && sed -n '1,220p' backend/config.py"

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 # 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 from pydantic_settings import Ba

agent_action

The current agents already use LangChain tools, but their “guideline” and interacti...

The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

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 '1,220p' backend/server.py"

command

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

command

/bin/bash -lc "sed -n '220,540p' backend/server.py"

command

/bin/bash -lc "sed -n '220,540p' backend/server.py"

exit code 0 Output: "matching_doctors_count": len(request_doc.get("matched_doctors", [])), "requested_at": request_doc.get("requested_at"), "bill_breakdown": request_doc.get("bill_breakdown") } @api_router.get("/patient/history") async def get_patient_history(current_user: dict = Depends(get_current_user)): """Get patient's consultation history""" # Get patient requests requests = await db.patient_requests.find( {"patient_id": current_user["sub"]}, {"_id": 0} ).sort("requested_at", -1).limit(50).to_list(50) # Enrich with doctor info history = [] for req in requests: item = { "request_id": req.get("request_id"), "symptoms": req.get("symptoms"), "status": req.get("status"), "urgency_level": req.get("urgency_level"), "requested_at": req.get("requested_at"), "bill_breakdown": req.get("bill_breakdown"), "total_paid": req.get("bill_breakdown", {}).get("total") if req.get("bill_breakdown") else None } # Get doctor

managed web search API AI agents licensed sources reputable content provider Tavily...

Query: managed web search API AI agents licensed sources reputable content provider Tavily Exa Linkup Perplexity documentation licensed sources Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: managed web search API AI agents licensed sources reputable content provider Tavily Exa Linkup Perplexity documentation licensed sources | Linkup search API licensed sources documentation AI agents | Tavily search API documentation sources AI agents | Exa search API documentation neural web search sources

site:docs.linkup.so licensed premium content sources Linkup API

Query: site:docs.linkup.so licensed premium content sources Linkup API Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: site:docs.linkup.so licensed premium content sources Linkup API | site:linkup.so licensed premium content sources Linkup AI search API | Linkup AI search API licensed premium content unavailable through crawling

Perplexity Sonar API documentation citations search results sources

Query: Perplexity Sonar API documentation citations search results sources Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: Perplexity Sonar API documentation citations search results sources | Brave Search API documentation web search endpoint source results | Google Custom Search JSON API documentation search results | OpenAI web search tool documentation citations sources

Linkup LangChain integration documentation

Query: Linkup LangChain integration documentation Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: Linkup LangChain integration documentation | Tavily LangChain integration docs | Exa LangChain integration docs search retriever | Perplexity LangChain integration docs

medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API doc...

Query: medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API documentation Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses. Expanded queries: medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API documentation | Wolters Kluwer UpToDate API documentation clinical decision support content API | EBSCO DynaMed API documentation clinical decision support | Elsevier ClinicalKey API documentation

url

https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect

Query: https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

url

https://docs.linkup.so/pages/documentation/endpoints/search/overview

Query: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: The current agents already use LangChain tools, but their “guideline” and interaction tools are placeholders returning static strings. That makes the best integration path a backend retrieval service plus one LangChain tool shared by the medical agents, with citations carried through responses.

agent_action

I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus ...

I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

command

/bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,230p'"

command

/bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,230p'"

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/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

command

/bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,140p'"

command

/bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,140p'"

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/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()

url

https://docs.linkup.so/

Query: https://docs.linkup.so/ Agent context: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus general AI web-search APIs. Because this repo’s agent is doing symptom triage, prescription review, follow-up care, vitals, and medication scheduling, the clinical content providers deserve more weight than general web search.

agent_action

**Provider I’d choose: Wolters Kluwer UpToDate Connect.** This repo is a medical tr...

**Provider I’d choose: Wolters Kluwer UpToDate Connect.** This repo is a medical triage/routing app, not a general research assistant. The backend has LangChain agents for triage, prescription review, follow-up care, vitals analysis, and medication scheduling, and its current `get_treatment_guidelines` tool is only a placeholder returning `"Treatment guidelines"` in [backend/langchain_agents.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/langchain_agents.py:74). The symptom analyzer also makes medical routing recommendations from model memory in [backend/cerebras_service.py](/home/user/worktrees/attempt-ff821f2f-d798-4e29-9701-5522f12af5be-cap-3/backend/cerebras_service.py:37). For this use case, I would prioritize licensed clinical decision-support content over web search breadth. [UpToDate Connect](https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect) is explicitly an API-driven clinical decision support integration for digital health platforms and EHR workflows, with trusted evidence-based UpToDate content. Wolters Kluwer also describes UpToDate as clinician-authored, expert-reviewed, covering 25+ specialti

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

exit 0

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

exit 0

exit 127

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | head -250"

exit 0

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 'AGENTS.md' -o -name '.env.example' \\) -print"

exit 0

/bin/bash -lc "sed -n '1,580p' backend/langchain_agents.py"

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,330p' backend/agent_endpoints.py"

exit 0

/bin/bash -lc "sed -n '1,220p' backend/requirements.txt && sed -n '1,140p' backend/.env.example && sed -n '1,220p' backend/config.py"

exit 0

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

exit 0

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

exit 0

/bin/bash -lc "sed -n '220,540p' backend/server.py"

exit 0

/bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,230p'"

exit 0

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

exit 0

/bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,140p'"

exit 0

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

exit 0

URLs Fetched

https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect

status not materialized

https://docs.linkup.so/pages/documentation/endpoints/search/overview

status not materialized

https://docs.linkup.so/

status not materialized

Changed Files

Artifact only

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

Runs