You will get a production-ready AI agent built with LangGraph
Project details
You will get a production-ready AI agent built with LangGraph, not a fragile prototype that breaks on real inputs. I specialize in agentic AI systems, designing multi-agent workflows with tool calling, memory, and reasoning that hold up under real usage.
My background is in building AI agents for a regulated industry where every decision an agent makes has to be traceable and correct, so reliability isn't an afterthought, it's the starting point. That same rigor applies to your project.
You get clean, documented source code you fully own, a working agent tested against real scenarios, and a clear architecture you can extend later without needing to hire someone else to understand it.
If you need an AI agent that actually works in production, let's build it right the first time.
My background is in building AI agents for a regulated industry where every decision an agent makes has to be traceable and correct, so reliability isn't an afterthought, it's the starting point. That same rigor applies to your project.
You get clean, documented source code you fully own, a working agent tested against real scenarios, and a clear architecture you can extend later without needing to hire someone else to understand it.
If you need an AI agent that actually works in production, let's build it right the first time.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, NVIDIA AI Platform, PyTorchAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$450
|
Standard
$900
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 7 days | 12 days | 18 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | - | ||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Yuval
Agentic AI Engineer | LangGraph, LLM Agents & RAG Systems
Mumbai, India - 2:27 am local time
Over the past year I've architected and shipped multi-agent LLM systems for a regulated industry where failure isn't an option: every agent decision has to be traceable, auditable, and correct. That constraint made me obsessive about reliability, evaluation, and guardrails, skills that carry directly into any agentic AI build, regulated or not.
What I've shipped:
- Designed a multi-agent LangGraph system that cut task execution time by 30%, lifted task success rate by 40%, and expanded automated workflow coverage by 65%
- Rebuilt a data infrastructure pipeline end to end, cutting operating costs by 5x
- Built a monitoring platform from scratch: distributed systems, ML pipelines, autonomous agents, and CI/CD, deployed to production
- Reimplemented the Qwen3.5 LLM architecture from scratch in pure PyTorch (GQA, SwiGLU, RoPE, RMSNorm). I don't just call APIs, I understand what's happening inside the model
- Built a RAG-based AI research assistant using a 10-stage LangGraph pipeline (paper to structured extraction to summary to diagrams to code)
- Built a stateful LLM agent with persistent memory and emotion-aware control flow, improving session consistency by 70%
Recognition:
Top 1%, Amazon ML Challenge 2024 (274th of 74,824 participants)
4th place of 250+ teams, GenAI Week 2025 Hackathon, Silicon Valley
2x published IEEE researcher (air quality ML, diabetes prediction ML)
What I can build for you:
Custom AI agents and multi-agent systems (LangGraph, CrewAI, MCP, A2A)
RAG pipelines and knowledge-base chatbots (vector DBs, hybrid search, reranking)
LLM workflow automation (document processing, data extraction, validation pipelines)
Production AI infrastructure (FastAPI backends, CI/CD, observability with Langfuse/Arize Phoenix)
Model evaluation and prompt engineering systems
Tech stack: Python, LangGraph, LangChain, PyTorch, FastAPI, Langfuse, PostgreSQL/Redis/FAISS/Qdrant/Weaviate, Docker, AWS/GCP/Azure
If you need an agent that actually works reliably in production, not just in a demo, let's talk about what you're building.
Steps for completing your project
After purchasing the project, send requirements so Yuval can start the project.
Delivery time starts when Yuval receives requirements from you.
Yuval works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Architecture Design
Review your requirements and design the agent architecture, including tool calls, memory, and control flow, before writing any code.
Core Agent Build
Implement the agent using LangGraph, wiring up the required tools, LLM integration, and reasoning logic.