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English
$
USD
I will build an ai chatbot or agent with rag, langchain, and langgraph
Musanna Ammar
About this gig
Production-Ready LangChain & LangGraph AI Agents
I build custom AI agents, not API wrappers designed to scale and integrate into real products.
What I Deliver
- Custom agentic workflows (LangChain + LangGraph)
- Multi-agent systems that collaborate and hand off tasks
- RAG pipelines for PDFs, websites, and CRM data
- Vector database integration (Pinecone, Chroma, FAISS)
- FastAPI backend to connect agents with your frontend
Why Work With Me
Clean, documented, production-ready code
Clear communication, milestone-based delivery
Fast turnaround and reliable availability
Message me your use case and I'll scope it out.
Get to know Musanna Ammar
Musanna Ammar
I will tape your business cash leaks through smart AI automation
- FromPakistan
- Member sinceJun 2024
- Avg. response time1 hour
Languages
Urdu, English, Punjabi
Hi, I'm a Full Stack Developer expert in AI automation, LLMs, RAG, backend & frontend development.
AI Automation: I build intelligent agents for customer communication, data handling, and CRM integration (Salesforce, GoHighLevel) using n8n, Make.com, LangChain, LangGraph, and LlamaIndex.
LLMs & RAG: Hands-on with LLMs, RAG systems, and vector DBs (Pinecone, Weaviate, Chroma, FAISS, PGVector).
Tech Stack: Python (Django, FastAPI, Flask), React, JavaScript.
Why Me? Customized solutions • Transparent communication • On-time delivery • Long-term partnership.
My Portfolio
FAQ
What are RAG systems and why do I need one?
RAG (Retrieval-Augmented Generation) lets your AI pull real, up-to-date information from your own documents, website, or database before answering — instead of relying only on what it was trained on. You need one if you want accurate, source-grounded answers instead of the AI guessing or making thin
What's the difference between a chatbot and an AI agent?
A chatbot responds to messages. An agent can reason, use tools, call APIs, query databases, and take multi-step actions on its own to complete a task — not just chat.
Why use LangGraph instead of just LangChain?
LangChain is great for building individual chains and tools, but LangGraph lets you orchestrate multiple agents working together with memory, branching logic, and control flow — essential for complex, real-world workflows.
Will the agent hallucinate or give wrong answers?
Any LLM can hallucinate, but I reduce this significantly through RAG grounding, tool validation, and structured prompting so responses stay accurate and traceable to your actual data.
Can this integrate with my existing app or CRM?
Yes — I build with FastAPI so the agent system connects cleanly to your frontend, CRM, or internal tools via REST endpoints, regardless of your existing tech stack.
