DEV Community: Ram Sidhartha

OmniAgent — AI Agents That Work Together as a Software Engineering Team

Ram Sidhartha — Fri, 11 Sep 2026 09:35:35 +0000

I built OmniAgent because I kept running into the same wall: AI coding assistants are great at answering questions, but they can't actually do anything. They suggest. You implement. OmniAgent flips that .

It's a platform where multiple specialized AI agents work together — one writes code, one reviews it, one debugs it, one thinks about the architecture — and they hand work off to each other in a real pipeline. The idea is that you describe a task, and the agents figure out how to tackle it from every angle without you babysitting the process.

🛠️ What it does:
Coding Agent — Takes a description of what you need and writes working code. You give it a problem statement, it produces files.

Review Agent — Analyzes code diffs and pull requests. Grades issues by severity and gives you actionable suggestions.

Debug Agent — You paste in code and an error, it traces the failure, identifies the root cause, and produces a fix.

Architect Agent — Generates project scaffolds, writes architecture decision records, and produces Mermaid diagrams.

Multi-Agent Pipelines — Chain agents together: architect designs the approach, coder implements it, reviewer checks it, debugger fixes what breaks.

Real-Time Streaming — Everything streams over WebSocket. You watch the agent think and write in real time.

Repo Analysis — Point it at a local git repository and it'll parse the structure, understand the language breakdown, and answer questions about the codebase.

🧰 Tech Stack
Backend is Python with FastAPI streaming WebSocket responses. AI providers are OpenAI and Anthropic — you can switch between them. Frontend is React 18 with TypeScript and Tailwind CSS. PostgreSQL stores task history and Redis handles caching.

🔗 Get Started
Check out the repo: github.com/isidhartha/omni-agent

OmniRAG — A Knowledge Engine That Handles Anything You Throw at It

Ram Sidhartha — Fri, 11 Sep 2026 09:34:23 +0000

I got tired of the standard RAG demo that only works on one PDF and falls apart the moment you throw anything more complex at it. OmniRAG is what I built to fix that .

It handles PDFs, images, codebases, audio, and video — all in the same pipeline. You upload your files, ask questions, and get answers with citations that tell you exactly where each piece of information came from.

The part I spent the most time on is the search layer. Most RAG systems use pure vector search, which works okay but misses things that don't embed well — exact identifiers, code symbols, specific numbers. OmniRAG uses hybrid search: vector embeddings for semantic meaning and BM25 keyword search for precision.

🛠️ What it handles:
PDFs — Full text extraction with chunking, table parsing, and metadata. Works on research papers, contracts, and reports.

Images — OCR for text in images plus vision AI for understanding diagrams, charts, and screenshots.

Code Repositories — AST-aware indexing so it understands structure, not just text.

Audio and Video — Transcribes audio using Whisper, then indexes the transcript.

Web Pages — Point it at a URL and it scrapes the content into your knowledge base.

Multi-Turn Conversation — Maintains conversation memory, so follow-up questions work.

Citations — Every answer includes source references with document name and page or timestamp .

⚙️ How it works
When you upload a file, it goes through a routing step that figures out what type it is and sends it to the right parser. The output gets chunked, each chunk gets an embedding, and everything lands in ChromaDB. The BM25 index is built in parallel. When you ask a question, both indexes get queried simultaneously, results are merged and reranked, and the top chunks are passed to the LLM.

🧰 Tech Stack
Python, FastAPI, ChromaDB, PostgreSQL, Redis, Whisper, and optional Ollama for local AI.

🔗 Get Started
Check out the repo: github.com/isidhartha/omni-rag

InsightFlow AI — Self-Hosted Analytics with AI That Tells You What the Data Means

Ram Sidhartha — Fri, 11 Sep 2026 09:33:12 +0000

I wanted analytics for a side project but didn't want to hand my users' data to a third party, and I didn't want to pay for PostHog's cloud plan. So I built InsightFlow — a self-hosted product analytics platform with an AI layer that actually tells you what the data means .

The difference from standard analytics is the AI insights feature. Most analytics dashboards show you numbers and leave the interpretation to you. InsightFlow looks at your data and tells you things like "users who land on the pricing page from Google have a 3x higher conversion rate than those from social media" .

🛠️ What it does:
Event Tracking — Drop a single JavaScript snippet into your site and it starts capturing pageviews, clicks, form submissions, and custom events.

User Analytics — Unique visitors, session counts, pageview trends over time.

Funnel Analysis — Define a series of steps and see where users drop off, with conversion rates at each step.

Retention Tables — Cohort-based retention analysis to see if your product actually has stickiness.

Click Heatmaps — Visual overlay showing where users click on each page .

AI Insights — Runs across your data every day and generates observations and recommendations .

Anomaly Detection — Flags unusual patterns in your metrics automatically.

🧰 Tech Stack
Python, FastAPI, React, TypeScript, PostgreSQL, Redis, and optional Ollama for local AI.

🔗 Get Started
Check out the repo: github.com/isidhartha/insightflow-ai

CyberShield AI — Catch Vulnerabilities Before They Ship

Ram Sidhartha — Fri, 11 Sep 2026 09:31:22 +0000

I built CyberShield AI because the security tools I kept using were either too narrow (one tool for secrets, another for CVEs, another for SAST) or too expensive. CyberShield pulls all of it into one place with an AI layer on top that actually explains what it finds in plain English, not just dumps a list of CVE numbers at you.

The target user is a developer who wants to check their own code before it ships — not a dedicated security team with enterprise tooling. It's self-hosted, runs in Docker, and gives you actionable output rather than walls of raw scanner output.

🛠️ What it scans:
Secrets and Credentials — Regex and entropy-based detection for API keys, tokens, private keys, connection strings, and passwords hardcoded anywhere in your codebase.

Static Code Analysis — Pattern matching against OWASP Top 10 vulnerabilities: SQL injection, command injection, XSS, and more across Python, JavaScript, TypeScript, Go, and Java .

Dependency CVEs — Reads your requirements files and package manifests, checks each dependency against the OSV.dev database .

AI Security Review — Goes beyond pattern matching. Catches things like "this looks like a race condition that could allow privilege escalation" that a regex pattern would miss .

Remediation Suggestions — For every finding, it tells you what to change. Not just "this is a SQL injection" but "replace this f-string with a parameterized query" .

Report Generation — Produces clean HTML security reports you can share or save.

🧰 Tech Stack
Python, FastAPI, OWASP dependency-check, OSV.dev API, Redis, and optional Ollama for AI features.

🔗 Get Started
Check out the repo: github.com/isidhartha/cybershield-ai

CodeForge AI — A Browser-Based IDE with an AI That Understands Your Project

Ram Sidhartha — Fri, 11 Sep 2026 09:28:13 +0000

The premise is simple: your IDE should be running everywhere, and it should understand what you're building. CodeForge AI is a browser-based coding environment with an AI assistant built into the core — not bolted on as a chat sidebar.

You get a Monaco editor (the same engine that powers VS Code), a real terminal, a file explorer, and an AI that has context about your entire project and can act on it. Run it once in Docker and you've got a full IDE accessible from any browser on your network.

🛠️ What it does:
Monaco Editor — Full VS Code-like editing experience in the browser with syntax highlighting for 20+ languages.

AI Coding Assistant — A chat panel that's aware of the file you're editing. Ask it to explain a function, suggest a refactor, or generate a test.

Code Generation from Specs — Describe what you want in plain English and it produces working code.

AI Debugging — Paste in an error trace and it explains what went wrong, why, and shows you the fix.

Smart Autocomplete — AI-powered completions that consider what you're building, not just symbols in scope.

Interactive Terminal — Real xterm.js terminal running in the browser over WebSocket.

Project Semantic Search — Index your codebase and ask questions about it like "Where is the database connection pool initialized?".

code-server Integration — The full VS Code experience in the browser is also available at port 8080.

🧰 Tech Stack
Python, FastAPI, Next.js, Monaco Editor, xterm.js, WebSocket, Redis, and optional Ollama for local AI.

🔗 Get Started
Check out the repo: github.com/isidhartha/codeforge-ai

InfraOS AI — Stop Staring at Graphs, Just Ask Your Cluster

Ram Sidhartha — Fri, 11 Sep 2026 09:25:51 +0000

I was on-call for a service once and got paged at 2am for a Kubernetes cluster that was falling apart in three different ways simultaneously. I spent 45 minutes running kubectl commands to understand what was happening before I could even start fixing it. InfraOS AI is my answer to that problem .

It's a DevOps operations platform that connects to your Kubernetes cluster, pulls in everything that's happening — pods, deployments, resource usage, events, Prometheus metrics — and gives you an AI layer to make sense of it all. You can ask "why is this pod crashing?" in plain English and get a real answer, not a wall of log output .

🛠️ What it does:
Cluster Monitoring — Live view of your nodes, pods, deployments, and services. Health status, restart counts, resource requests vs. limits.

Natural Language Queries — Ask questions like "Which pods have restarted more than 5 times in the last hour?" and it translates these into the right queries.

Root Cause Analysis — When something breaks, it traces back through events, logs, and metrics to figure out why.

Anomaly Detection — Watches your metrics over time and flags unusual patterns before they become incidents.

Automated Remediation — For common problems it can take action directly: restart a crashed pod, scale a deployment, or cordon a misbehaving node.

Mock Mode — No Kubernetes cluster? No problem. Set K8S_MOCK_MODE=true and it generates realistic fake cluster data for exploring the UI.

🧰 Tech Stack
Python, FastAPI, Kubernetes API, Prometheus, Grafana, Redis, PostgreSQL, and optional Ollama for local AI.

🔗 Get Started
Check out the repo: github.com/isidhartha/infra-os

NexusOS — Your Personal Jarvis, Powered by AI

Ram Sidhartha — Fri, 11 Sep 2026 09:21:59 +0000

Have you ever wanted a Jarvis? Not a toy demo, but something that actually controls your computer, understands what you're asking, and takes action? That's exactly what I built with NexusOS

NexusOS is an AI-powered operating environment that runs locally. You say "Hey Nexus", it wakes up, listens, figures out what you want, and does it. Open Chrome, search for something, move files around, run a script, or control your smart home devices — all through voice or text, without touching the keyboard

🛠️ What it does:
Voice Pipeline — Listens for a wake word, transcribes with Whisper, and speaks back using TTS. The whole loop takes about 2-3 seconds [2†L14-L17].

Computer Control — Moves your mouse, clicks buttons, and types text using PyAutoGUI

Browser Automation — Built on Playwright, it can open browsers, navigate, fill forms, and read page content back to you [2†L20-L22].

File Management — Create, move, rename, search, and delete files through voice commands .

Autonomous Workflows — Define multi-step routines like "morning routine" that run in sequence .

AI Memory — Remembers things across sessions using vector embeddings, so it actually knows context from previous conversations .

Smart Home — Connects to MQTT, working with Home Assistant and most IoT devices .

Plugin System — Drop a Python file into the plugins folder and it loads automatically .

🧰 Tech Stack
The backend is Python with FastAPI, Whisper for speech recognition, PyAutoGUI for computer control, Playwright for browser automation, and SQLite with vector embeddings for memory. The frontend is React with Tailwind CSS. Everything runs in Docker .

🔗 Get Started
Check out the repo: (https://github.com/isidhartha/nexus-os)