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I will build ai agents using langchain, openai, rag, langgraph, python
Ouzbékistan
Je parle Français, Russe, Ouzbek, Anglais
Digital products developer
Hey! I am Software engineer with 3 production internships. I shipped a feature used by 500+ active users and 10,000+ entries in production. Top 10 in AI Healthcare Hackathon 2026 (100+ teams) with a 1...
À propos de ce service
Are you looking for advanced Generative AI (GenAI) web applications that leverage the power of LangChain, LangGraph, AgentAI, OpenAI, Groq, LLaMA, RAG, Transformers, and FastAPI?
You've come to the right place!
What I Offer:
- Custom GenAI Web Apps powered by state-of-the-art models
- LangChain & LangGraph Pipelines for dynamic AI workflows
- AgentAI / AI Agents for autonomous, tool-using, task-solving systems
- RAG (Retrieval-Augmented Generation) for intelligent data retrieval
- FastAPI Integration for high-performance backend APIs
- Transformer Models for NLP tasks (text generation, classification, etc.)
- LLM Deployment with OpenAI, Groq, and LLaMA for real-time applications
- Modular Component-Based Architecture for scalable, maintainable UIs
Why Choose Me?
- Expertise in GenAI, Agents, Agentic AI, and API development
- Scalable, fast, and secure web applications
- Clean, well-documented code and responsive design
- End-to-end project support, from development to deployment
Ready to bring your GenAI idea to life? Contact me now and let's build something revolutionary!
Langage de programmation:
Python
•
R
•
SQL
Frameworks:
Scikit-learn
•
SimpleCV
•
keras
•
PyTorch
Outils:
Jupyter Notebook
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opencv
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tensorflow
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Excel
•
Colab
Mon portfolio
FAQ
What kinds of AI agents can you build?
g agents, tool-calling agents, and autonomous workflow agents using LangGraph. If you can describe the task, I can likely build an agent for it.
What frameworks and models do you work with?
LangChain and LangGraph for orchestration, OpenAI (GPT-4o, GPT-3.5) as the default LLM backbone, and FAISS / ChromaDB for vector storage in RAG pipelines. I can also integrate open-source models if needed.
What is RAG and do I need it?
RAG (Retrieval-Augmented Generation) lets the AI answer questions grounded in your documents or knowledge base instead of relying purely on the model's training data. If you have PDFs, docs, or a database you want the agent to "know," you need RAG.
What do I need to provide?
A clear description of what the agent should do, any documents or data sources it should work with, and your preferred LLM provider/API key. I'll handle the rest.
Can the agent use external tools like web search, APIs, or databases?
Yes. Tool-use is one of LangChain's core strengths — I can connect agents to web search, REST APIs, SQL databases, or custom Python functions.
What will I receive as a deliverable?
Clean, well-commented Python code, a working demo or notebook, setup/run instructions, and a brief explanation of the architecture so you can maintain and extend it yourself.
What if I don't have an OpenAI API key?
I can guide you through getting one, or we can use an alternative provider (Anthropic, Groq, etc.) depending on your preference and budget.
