You will get a stateful LangGraph agent workflow built for your use case
Project details
You will get a production-ready LangGraph agent workflow tailored to your use case — stateful, testable, and built to extend.
LangGraph is the go-to framework for building reliable multi-step AI agents with branching logic, memory, and human-in-the-loop control. But designing the graph correctly from the start takes real experience.
I build LangGraph workflows in production. My open-source project EasyOref runs a multi-node enrichment pipeline in production: fan-out extraction, consensus voting, and stateful message editing — all in LangGraph.
What you get:
– StateSchema with Zod validation
– Typed nodes with clean separation of concerns
– Conditional edges and routing logic
– Checkpointing and memory (when needed)
– Full TypeScript or Python codebase with README
I work async, communicate clearly, and deliver code you can actually maintain.
LangGraph is the go-to framework for building reliable multi-step AI agents with branching logic, memory, and human-in-the-loop control. But designing the graph correctly from the start takes real experience.
I build LangGraph workflows in production. My open-source project EasyOref runs a multi-node enrichment pipeline in production: fan-out extraction, consensus voting, and stateful message editing — all in LangGraph.
What you get:
– StateSchema with Zod validation
– Typed nodes with clean separation of concerns
– Conditional edges and routing logic
– Checkpointing and memory (when needed)
– Full TypeScript or Python codebase with README
I work async, communicate clearly, and deliver code you can actually maintain.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$49
|
Standard
$89
|
Advanced
$149
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
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 | - | - | - |
About Mikhail
Agentic AI Engineer | MCP | LangGraph | LLM Infrastructure
Tel Aviv, Israel - 6:02 am local time
10+ years in production engineering. Led a platform team from 1 to 13 engineers. Shipped products to 5M+ users while keeping 99.5–100% crash-free rate in production.
My flagship project is CyberMem — a self-hosted MCP memory platform I designed and shipped end-to-end, used by Claude, GPT, Cursor, Gemini, and Perplexity. It runs on Docker, Kubernetes, and Raspberry Pi with Prometheus/Grafana observability, Traefik zero-trust auth, automated versioned releases via GitHub Actions, and Vitest test suite. 550+ commits, 25 versioned releases. Live at cybermem.dev.
I also built EasyOref: a LangGraph-based multi-step alert agent with MCP tool use, BullMQ queues, RAG enrichment, and Telegram delivery — running live in production.
What I work on:
- MCP server development (memory, API bridges, file gateways, custom tools) — Docker/K8s deployed, schema-driven, with auth, observability, and docs
- LangGraph / LangChain workflows — stateful agents with tool use, conditional branching, human-in-the-loop, rollback
- RAG pipelines — document ingestion, embeddings, vector store, retrieval tuning, clean Q&A API
- MCP client setup — Claude Desktop, Cursor, Windsurf, Perplexity — wired correctly with working auth
- Architecture reviews — for teams building agent systems before committing to a design
Positioning: AI Infra Engineer who ships production systems, not demos.
Stack: TypeScript/Node.js, Python, LangGraph, LangChain, FastMCP, Docker, Kubernetes, Redis, BullMQ, Prometheus, Vitest, GitHub Actions.
Send me a short message with your use case, and I'll tell you the fastest way to ship it.
Portfolio: mikhailkogan.dev
Steps for completing your project
After purchasing the project, send requirements so Mikhail can start the project.
Delivery time starts when Mikhail receives requirements from you.
Mikhail works on your project following the steps below.
Revisions may occur after the delivery date.
Design graph architecture
Define nodes, edges, state schema and conditional routing based on your use case
Build & iterate
Implement nodes, wire the graph, add checkpointing and test end-to-end