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I will build stateful ai agents and agentic rag using langgraph and mcp
Sami Ullah
Über diesen Service
Upgrade your application from static AI to dynamic, decision-making agents.
Standard Generative AI models lack context, and basic RAG only fetches data. I specialize in Agentic RAGwhere the AI reasons, evaluates confidence, and autonomously decides when to query databases or execute custom functions.
Using my Computer Science background, I architect stateful, multi-step LLM workflows that solve complex business logic without hallucinating.
What I Offer:
- Agentic RAG Systems: Adding reasoning layers so the AI evaluates data and dynamically searches external tools if information is missing.
- LangGraph Orchestration: Building stateful, multi-agent pipelines for complex workflows with supervisor and specialized agents.
- MCP Integration: Implementing the Model Context Protocol to securely bridge your AI agents with local files, databases, or enterprise APIs.
- Vector DB Engineering: Efficient chunking, embeddings, and semantic search using Pinecone, ChromaDB, or FAISS.
The Tech Stack:
Python | LangChain | LangGraph | GPT-4o | Claude | Ollama
Please message me before ordering so we can scope your custom workflow perfectly.
Lerne Sami Ullah kennen
Sami Ullah
AI and ML Engineer, Agentic AI, LangGraph, and RAG Specialist
- AusPakistan
- Mitglied seitJuni 2026
- ⌀ Antwortzeit1 Stunde
Sprachen
Urdu, Englisch
As a Computer Science graduate from Quaid-i-Azam University, I build production-ready Generative AI systems beyond standard chatbots. I specialize in stateful, multi-agent architectures and Agentic RAG applications that reason and interact with external APIs. I orchestrate complex workflows using LangChain, LangGraph, and Vector Databases, while implementing the Model Context Protocol (MCP) to connect LLMs to your data. I deliver scalable AI agents and customized multi-agent pipelines tailored to your business needs.
FAQ
What is the difference between standard RAG and Agentic RAG?
Standard RAG simply fetches the top matching documents for a query and passes them to the LLM. Agentic RAG utilizes an agent to evaluate the query first, decide which databases or tools to search, assess the quality of the retrieved context, and iteratively search again if the answer isn't fully res
Why use LangGraph instead of just LangChain?
While LangChain is excellent for linear sequences, LangGraph is specifically designed for stateful, multi-actor workflows. It allows agents to loop, self-correct, remember past interactions across long sessions, and hand off tasks to other specialized agents.
What is MCP and why do I need it?
The Model Context Protocol (MCP) is an open standard that gives AI agents a universal "USB port" to securely connect to external tools, data sources, and APIs without requiring hard-coded, brittle integrations.
Which LLMs do you work with?
I can integrate your system with leading commercial models like OpenAI's GPT-4o, Anthropic's Claude 3.5, Google Gemini, or open-source local models via Ollama.
