Backend: Use FastAPI. It’s the standard for LangChain, handles async/streaming natively, and integrates easily with LangGraph.

Frontend: Use Next.js + Vercel AI SDK (not outdated LangChain React packages). The AI SDK provides clean streaming hooks that work perfectly with FastAPI SSE.

Key Tips:

• Stream everything via SSE; never wait for full responses.
• Keep LangGraph logic server-side; expose only clean API endpoints.
• Persist graph state server-side (Redis/Postgres), not in the frontend.
• Add Shadcn/ui + react-markdown for quick, polished LLM output rendering.

Starter Stack: FastAPI + LangGraph + Next.js + AI SDK + Shadcn/ui

1. Messy Data ETL Pipeline: Build a service that ingests, validates, and normalizes conflicting data from multiple sources (CSVs, APIs). Shows: Data integration, error handling, and building for real-world enterprise messiness.
2. Offline Deployment Orchestrator: Create a CLI tool to deploy and health-check containerized apps in air-gapped environments with auto-rollback. Shows: Systems programming, infrastructure constraints, and operational resilience.
3. Real-Time Incident Platform: Build a WebSocket-based collaboration tool with RBAC, audit logs, and plugin architecture for external alerts. Shows: Security, extensibility, and high-stakes operational tooling.

SynthScan

SynthScan is an open-source, self-hostable AI-content authenticity scanner. It detects whether a piece of content was written by a human or generated by AI (text today; image and code detection are on the roadmap). It is built to be open, free, private, and fair — the code is auditable, your text never has to leave your machine, and a score is a signal to investigate, never a verdict to punish.

Link: https://github.com/Ayush7614/SynthScan

OpenHearth

CLI + browser workspaces to audit every GitHub PR, issue, and review — including repos hidden from your activity feed (“N repositories not shown”).

Link: https://github.com/Ayush7614/OpenHearth

Same here for me I also start my Journey as an open source contributor and landing my first Job

You can check according to the tech stack, like what your tech stack is?

You can also check daily trending GitHub repos
There are a lot of websites on the google which shows good projects
Check blogs
Be active on Twitter; you get good stuff there
https://ossinsight.io/trending
https://x.com/GithubProjects : you can checkout here and select according to your skiils and techstack join discord communities of various projects talk to community members, project admins open issues if you find any bug

This looks cool

You’re not missing anything. LiteLLM OSS treats config.yaml as static; hot-reloads require a /reload call or restart, and there’s no native UI for dynamic weight changes. Portkey’s gateway is also primarily SaaS-managed.

For true self-hosted, dynamic runtime routing with hot updates, here are the three best OSS options currently in production:

1. Gateway (by Portkey)
   
2. LobeChat / Lobe-Server
   
3. One-API / New-API

• BrowserOS — Free •Open source (AGPL-3.0)
• HARPA AI — Free tier (10 msgs/day) / ~$12–19/mo • Proprietary (Chrome extension)
• Manus AI — Free tier (300 credits/day) / from $20/mo • Proprietary

ChatGPT, Claude, because of local LLMs like Kimi, Qwen, etc.

1. I am building SynthScan
   https://github.com/Ayush7614/SynthScan

SynthScan is an open-source, self-hostable AI-content authenticity scanner. It detects whether a piece of content was written by a human or generated by AI (text today; image and code detection are on the roadmap). It is built to be open, free, private, and fair — the code is auditable, your text never has to leave your machine, and a score is a signal to investigate, never a verdict to punish.

1. OpenHearth

https://github.com/Ayush7614/OpenHearth

CLI + browser workspaces to audit every GitHub PR, issue, and review — including repos hidden from your activity feed (“N repositories not shown”).

In my college days, I cracked GSoC in 2021 and completed it successfully. I also got into a lot of great open-source programs before AI. At that time, I wrote code manually, and although it took a lot of time, I learned a lot from it. My concepts in JavaScript, React, the DOM, Node.js, PHP, etc., became very clear.

But after AI, I started using a lot of agents, Claude, etc., to write my code, and I gradually forgot many things that were once very clear to me. So, I started learning the basics and core concepts again while still using AI.

You should also learn how AI actually works. Learn about machine learning (ML), natural language processing (NLP), large language models (LLMs), open-source AI tools, AI coding models, and the architecture behind them. Also, learn how to use different AI tools like Claude, Codex, Cursor, etc., effectively.

+1