Kunj Shah

AI Agent Developer · Developer Tools · LLM Systems

I build AI-powered tools that ship — not demos, not experiments, real things.
Currently building AgentForge, a memory-driven career operating system.
Author of sentinel-cli — local-first AI code reviewer on npm.

Ahmedabad, India · Open to roles & freelance · 4th Year CS @ Indus University

I got tired of building chatbots. So I started building tools that actually do things.

Most of my work lives at the intersection of LLMs and developer workflows — code reviewers, agent pipelines, memory systems. I care about tools that work offline, respect your data, and don't need a SaaS subscription to be useful.

Right now I'm deep in:

• Multi-agent orchestration with LangGraph
• Memory architectures for long-running AI systems
• MCP (Model Context Protocol) and what it changes for agent tooling
• LLM evaluation — because "it feels right" isn't a metric

My development philosophy: ship first, refine second, don't tutorial-grind. The best way to understand a system is to break one.

Languages Python TypeScript JavaScript SQL Bun

Frontend React Next.js Tailwind CSS

Backend FastAPI Node.js PostgreSQL Redis

AI/ML LangChain LangGraph OpenAI SDK Anthropic SDK HuggingFace Transformers CrewAI

Vector Databases Pinecone ChromaDB FAISS Weaviate

Cloud & DevOps Docker Kubernetes GitHub Actions Vercel Google Cloud

APIs & Integrations OpenAI Anthropic Claude Groq Gemini OpenRouter

A memory-driven autonomous career operating system. Not another job board wrapper — a multi-agent system that tracks your career trajectory, surfaces opportunities, preps you for interviews, and remembers everything across sessions.

Why it matters: Most career tools treat you as a static resume. AgentForge treats your career as a stateful, evolving system.

Stack: Next.js · FastAPI · LangGraph · PostgreSQL · Pinecone · Anthropic Claude

Status: Architecture phase. Backend scaffolding in progress.

A terminal-first AI agent orchestration system. Think of it as an OS layer for running, managing, and chaining AI agents from the command line — built for developers who live in the terminal.

Why it matters: Most agent frameworks are GUI-first or notebook-first. Neuron OS is built for composability, piping agents like Unix commands.

Stack: TypeScript · Bun · LangGraph · MCP

Status: Active development. → Repository

Your codebase, reviewed locally. No SaaS. No data leaks. Your keys, your rules.

Problem: Existing code review tools are either cloud-dependent, expensive, or shallow.

Solution: A local-first CLI with 13+ specialized analyzers covering security, accessibility, TypeScript strictness, React patterns, and dependency vulnerabilities. Plugs into any LLM (OpenAI, Claude, Groq, Gemini).

Stack: Node.js · TypeScript · Multi-LLM APIs · Docker

Key Features:

• SQL injection, XSS, secrets detection
• WCAG 2.1 accessibility checks
• GitHub PR integration via CLI
• SARIF output for GitHub Security tab
• Auto-fix for common issues
• Works 100% offline for static analysis

→ Repository · → npm Package

Nine ANN algorithms. Hand-rolled. No shortcuts.

Problem: Most developers use vector DBs as black boxes. I wanted to understand what's actually happening inside.

Solution: A production-grade vector database built from scratch in Python featuring nine hand-rolled ANN algorithms (HNSW, IVF, PQ, LSH, and more), a full RAG pipeline, multimodal ingestion, FastAPI server, and Kubernetes deployment configs.

Stack: Python · FastAPI · NumPy · Docker · Kubernetes

Key Features:

• 9 ANN algorithms implemented from scratch
• RAG pipeline with multimodal support
• REST API via FastAPI
• K8s deployment configs for production
• Benchmarking suite across algorithms

→ Repository

A framework for agents that plan, not just respond.

Problem: LangChain is powerful but complex. Most teams need a simpler, opinionated wrapper to build reliable agents fast.

Solution: Modular agent framework with built-in tool integration, state monitoring, and persistent memory. REST API included for deployment.

Stack: Python · LangChain · OpenAI GPT-4 · FastAPI · PostgreSQL

→ Repository

Ancient script. Modern tokenization. BPE from scratch.

Problem: Sanskrit is morphologically complex. No production-grade tokenizer existed for GPT fine-tuning workflows.

Solution: Custom BPE tokenizer built from scratch. Fine-tuning pipeline for GPT-3.5/4 on Devanagari text. Multilingual support across Sanskrit, Hindi, English.

Stack: Python · PyTorch · Transformers · OpenAI API

→ Repository

Stop applying blindly. Know your fit score before you apply.

Problem: Job seekers waste hours applying to roles they're underqualified or overqualified for.

Solution: An intelligent agent that parses resumes with NER, semantically matches them to job descriptions, surfaces skills gaps, and gives ATS optimization suggestions.

Stack: Python · spaCy · Transformers · FastAPI · React · PostgreSQL

→ Repository

What's standing between you and your target role?

Problem: Developers don't know which skills to prioritize for career growth.

Solution: TypeScript-based skills gap analyzer that maps your current skills against target job requirements and surfaces the highest-ROI things to learn next.

Stack: TypeScript · Node.js · LLM APIs

→ Repository

Because good prompts deserve version control.

Stack: TypeScript · React · Node.js

→ Repository

I push when things work, not when they're perfect. Readmes matter. Tests matter more. If a project has no README, it might as well not exist.

I prefer small, focused repos over monorepo sprawl. I'd rather have five sharp tools than one giant project that tries to do everything.

Commits tell a story — I try to make them readable.

Always happy to connect with builders, founders, and curious developers.

If you're working on something in AI tooling, agent systems, or developer productivity — reach out.