You will get an AI Deep Research Agent with Auto Report Generation
Rising Talent
Rising Talent
Project details
Manual research is slow, expensive, and inconsistent. I build production-grade AI Deep Research Agents that autonomously plan, search, verify, and synthesize comprehensive research reports, powered by LangGraph, OpenAI Agents SDK, and Tavily web search.
Inspired by OpenAI Deep Research architecture, my implementation supports two research modes with specialized agent pipelines delivering 20+ page detailed reports with full source citations.
What you get across all packages:
š¹ Starter ā Simple mode with a single iterative researcher loop, web search, and crawler integration, and an auto-generated cited report for fast, narrower topic research
š¹ Standard ā Enhanced simple mode with full Thinking, Knowledge Gap, and Tool Selector agent pipeline, iterative verification loops, and structured reports with source citations
š¹ Advanced ā Full deep parallel mode with a Planner agent, multiple concurrent iterative researchers per sub-topic running async in parallel, a Proofreader agent for synthesis, human-in-the-loop approval, and 20+ page reports in PDF, DOCX, and Markdown formats
Built with:
LangGraph | OpenAI Agents SDK | Tavily | SerpAPI | OpenAI API | Claude API | FastAPI | Python
Inspired by OpenAI Deep Research architecture, my implementation supports two research modes with specialized agent pipelines delivering 20+ page detailed reports with full source citations.
What you get across all packages:
š¹ Starter ā Simple mode with a single iterative researcher loop, web search, and crawler integration, and an auto-generated cited report for fast, narrower topic research
š¹ Standard ā Enhanced simple mode with full Thinking, Knowledge Gap, and Tool Selector agent pipeline, iterative verification loops, and structured reports with source citations
š¹ Advanced ā Full deep parallel mode with a Planner agent, multiple concurrent iterative researchers per sub-topic running async in parallel, a Proofreader agent for synthesis, human-in-the-loop approval, and 20+ page reports in PDF, DOCX, and Markdown formats
Built with:
LangGraph | OpenAI Agents SDK | Tavily | SerpAPI | OpenAI API | Claude API | FastAPI | Python
AI Development Type
Knowledge RepresentationWhat's included
| Service Tiers |
Starter
$100
|
Standard
$180
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 6 days | 12 days | 18 days |
Number of Revisions | 0 | 1 | 2 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$30Frequently asked questions
About Hassaan
AI Developer | Agentic AI | LLM & RAG Systems | Voice AI | Python & ML
Lahore, PakistanĀ - 1:00 am local time
ā 4+ Years Experience in AI/ML & Production AI Systems
ā 50+ Projects Successfully Delivered Across Career
ā 100% Client Satisfaction Rate Throughout Career
ā Rising Talent on Upwork
ā Certified in Python | LangChain | Machine Learning
ā Available 20ā25 hrs/week | 24/7 Client Communication
š“ Specialized in transforming AI prototypes into reliable, production-grade systems.
š“ Delivering intelligent AI solutions for startups, SaaS businesses, and enterprise clients.
Organizations rarely struggle with AI ideas ā they struggle with execution, reliability, and production deployment.
I'm šš®ššš®š®š», an AI Engineer with š°+ šš²š®šæš š¼š³ š²š š½š²šæš¶š²š»š°š² building production-grade AI systems. Across š±š¬+ š½šæš¼š·š²š°šš, I have consistently achieved:
ā¢ šÆš¬āš³š¬% reduction in manual workloads through AI automation
ā¢ š®š±āš²š¬% improvement in LLM response accuracy via RAG & prompt engineering
ā¢ š°š¬āš³š¬% improvement in retrieval precision through advanced vector search
ā¢ š®āš±š faster workflow execution using autonomous multi-agent systems
šš²š šš & ššš šš š½š²šæšš¶šš²:
š¹ AI Agents & Agentic AI: LangGraph, CrewAI, Agno, Google ADK, MCP
š¹ Large Language Models: OpenAI GPT, Claude, Gemini, Llama, Mistral
š¹ RAG Systems: LangChain, Pinecone, Weaviate, ChromaDB, FAISS, Milvus
š¹ Prompt Engineering: Structured Outputs, Function Calling, Multi-step Reasoning
š¹ Voice AI & Real-Time Systems: LiveKit, Conversational AI
š¹ Machine Learning & Deep Learning: TensorFlow, PyTorch, Scikit-learn
š¹ MLOps & Deployment: Docker, Kubernetes, AWS, Azure, GCP
šŖšµš®š š šš®š» ššš¶š¹š±:
ā Autonomous AI Agents with memory, reasoning, and tool usage
ā Multi-Agent Systems for complex workflow orchestration
ā Production-ready LLM Applications and AI SaaS products
ā RAG Pipelines with optimized vector database retrieval
ā AI Chatbots, Voice AI, and Conversational AI systems
ā End-to-End ML pipelines and AI workflow automation
ā Cloud AI deployment and custom API integrations
šš²š š§š²š°šµš»š¶š°š®š¹ š¦šš®š°šø:
š¹ Agentic AI: LangGraph, CrewAI, Agno, Google ADK, MCP
š¹ LLM Frameworks: LangChain, OpenAI SDK, Claude API, Gemini API
š¹ Open-Source LLMs: Llama, Mistral, Gemma
š¹ Vector Databases: Pinecone, Weaviate, ChromaDB, FAISS, Milvus
š¹ Backend: Python, FastAPI, Flask
š¹ ML: TensorFlow, PyTorch, Scikit-learn, NLP, Computer Vision
š¹ Cloud: AWS, Azure, GCP, Docker, Kubernetes
š¹ Databases: PostgreSQL, MongoDB, SQL, NoSQL
šš¼šæš² š¦šøš¶š¹š¹š:
AI Agents | Agentic AI | Multi-Agent Systems | LLM | RAG | LangChain | LangGraph | CrewAI | Agno | MCP | OpenAI API | Claude API | Gemini API | Prompt Engineering | Pinecone | ChromaDB | FAISS | Python | FastAPI | TensorFlow | PyTorch | NLP | MLOps | Docker | AWS | Azure | GCP | Voice AI | LiveKit | AI Automation
My goal is never just to complete a task, it's to build a relationship rooted in trust, consistent delivery, and results that exceed expectations. Every project gets my full technical commitment because I genuinely care about the impact my work creates.
š šæš²šš½š¼š»š± šš¼ š®š¹š¹ šŗš²ššš®š“š²š šš¶ššµš¶š» šµš¼ššæš ā š®š°/š³.
Let's build something that actually works in production.
šŖš®šæšŗ šæš²š“š®šæš±š,
šš®ššš®š®š»
Steps for completing your project
After purchasing the project, send requirements so Hassaan can start the project.
Delivery time starts when Hassaan receives requirements from you.
Hassaan works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
Define research topic, report depth, output format, and preferred LLM model
Architecture Design
Plan the agent pipeline based on the chosen package, simple or deep parallel mode