You will get A Tool Calling and Web Search Agent
Rising Talent
Rising Talent
Project details
Most AI chatbots only talk. I build AI agents that š®š°š, searching the web, executing code, querying databases, and calling APIs autonomously to complete real-world tasks.
I develop production-grade AI agents powered by LangChain, LangGraph, and OpenAI Function Calling, capable of multi-step reasoning, dynamic tool selection, and autonomous decision-making.
šš®š°šµ š®š“š²š»š š¶š š²š¾šš¶š½š½š²š± šš¶ššµ š² š½š¼šš²šæš³šš¹ šš¼š¼š¹š:
ā” Web Search ā real-time information retrieval via Tavily or SerpAPI
ā” Calculator ā accurate mathematical computations and analysis
ā” Database Query ā structured data retrieval from SQL or NoSQL
ā” Code Execution ā dynamic Python code generation and execution
ā” API Calls ā seamless integration with any third-party service
ā” File Reader ā extract and process data from uploaded documents
ššš¶š¹š šš¶ššµ:
LangChain | LangGraph | OpenAI API | Claude API | Tavily | SerpAPI | FastAPI | Python
Whether you need a simple web search agent or a fully autonomous multi-tool AI system, I deliver reliable, scalable, production-ready solutions.
I develop production-grade AI agents powered by LangChain, LangGraph, and OpenAI Function Calling, capable of multi-step reasoning, dynamic tool selection, and autonomous decision-making.
šš®š°šµ š®š“š²š»š š¶š š²š¾šš¶š½š½š²š± šš¶ššµ š² š½š¼šš²šæš³šš¹ šš¼š¼š¹š:
ā” Web Search ā real-time information retrieval via Tavily or SerpAPI
ā” Calculator ā accurate mathematical computations and analysis
ā” Database Query ā structured data retrieval from SQL or NoSQL
ā” Code Execution ā dynamic Python code generation and execution
ā” API Calls ā seamless integration with any third-party service
ā” File Reader ā extract and process data from uploaded documents
ššš¶š¹š šš¶ššµ:
LangChain | LangGraph | OpenAI API | Claude API | Tavily | SerpAPI | FastAPI | Python
Whether you need a simple web search agent or a fully autonomous multi-tool AI system, I deliver reliable, scalable, production-ready solutions.
What's included
| Service Tiers |
Starter
$80
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 18 days |
Number of Revisions | 0 | 1 | 2 |
Design Customization | - | - | - |
Content Upload | - | - | - |
Responsive Design | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$30
Additional Custom API
(+ 3 Days)
+$50Frequently asked questions
About Hassaan
AI Developer | Agentic AI | LLM & RAG Systems | Voice AI | Python & ML
Lahore, PakistanĀ - 12:49 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.
Step 1: Requirement Gathering
Define the agent use case, tools needed, and expected workflow
Step 2: Architecture & Planning
Design agent architecture, tool selection, and tech stack