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I will build a langgraph ai agent with memory, tools and humanintheloop
Kiranfiaz
About this gig
About This Gig
Looking for an AI chatbot that actually remembers context, follows logic, and can be audited? I build production-grade AI assistants using LangChain + LangGraph not fragile prototypes.
Most gigs stop at "basic RAG pipeline." I go further: I design stateful agents that handle multi-step conversations, tool use, human-in-the-loop approval, and durable execution the same infrastructure used by production AI products.
What I Will Build for You
Core: LangChain-Powered RAG System
- Document loading from PDFs, websites, databases, or APIs
- Smart chunking with hybrid search and re-ranking
- Vector database integration (Pinecone, Chroma, FAISS, Qdrant)
- Source citations so answers are grounded in YOUR data
Advanced: LangGraph Agent Orchestration
- Stateful multi-turn conversations with persistent memory
- Explicit control flow: loops, branches, retries
- Tool calling: let the agent search, calculate, fetch APIs, or trigger actions
- Human-in-the-loop: approve or modify agent decisions before execution
- Durable execution: agent survives failures and resumes
Optional Add-ons
- FastAPI backend for easy integration
- Web interface or API endpoint
- Docker deployment
- Cost optimization & token tracking
Get to know Kiranfiaz
Kiranfiaz
Artificial Intelligence Machine Learning Natural Language Processi
- FromPakistan
- Member sinceJan 2021
- Avg. response time1 hour
- Last delivery3 years
Languages
Urdu, English, Chinese, Spanish
Hi, I am Kiran Fiaz. I am by profession a data scientist and has an excellent grip on python programming, can assist you in deep learning, machine learning, and other NLP related tasks. I have 3 years of experience in handling machine learning and deep learning tasks.
FAQ
What is the difference between LangChain and LangGraph?
LangChain is a framework for building LLM applications quickly — document loaders, retrievers, chains. LangGraph is a lower-level runtime for building stateful agents with explicit control flow, memory, and production features like durable execution and human-in-the-loop. I use both: LangChain for r
Can you make the chatbot remember previous conversations?
Yes. LangGraph supports thread-level and cross-thread persistence, so the agent remembers context across sessions. This is one of the key differences from a basic RAG chatbot.
Can humans review the agent's decisions before they execute?
Yes. I can implement human-in-the-loop interrupts — the agent pauses for approval before performing actions like sending emails, updating databases, or triggering workflows.
Do I need my own API key?
Yes, for most cases. I'll guide you on setup. If you prefer open-source models, I can integrate Llama or Mistral for lower cost.
