The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
This is basically a tutorial for beginners who want to learn LangChain, I have curated relevant sources and provided my understanding in here to help learners move fast, and dont need to dig hours for relevant study material.
I hope this helps!
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Built a LangChain-based agentic application that turns a learning goal into a structured, personalised learning plan.
The application supports two execution modes:
⚡ ReAct Agent Mode
A tool-using agent dynamically selects custom tools for course lookup, completion-time calculation, and learning-plan generation.
🔍 Explain Mode — LCEL
A deterministic LCEL pipeline executes the planning workflow step-by-step, making the process more transparent, predictable, and easier to debug.
The project also uses Pydantic structured outputs, Groq, Python, Streamlit, and custom LangChain tools, with deterministic logic kept outside the LLM wherever possible.
This project was built to explore the practical engineering side of AI agents — tool calling, agent orchestration, structured outputs, deterministic workflows, and explainability.
The project is not currently deployed, but the complete source code and implementation are available on GitHub.
Tech: Python · LangChain · ReAct · LCEL · Groq · Pydantic · Streamlit
turning a learning goal into an actual plan is such a useful agent use case 🔥
Built an AI-powered Social Media Post Analyzer that analyzes social media content and generates structured insights using LLMs.
The application uses LangChain and Pydantic to process the input and return structured results through an interactive Streamlit interface.
Built with: Python, LangChain, Pydantic, Hugging Face, Streamlit, and LangSmith.
This is Mikuna. From WhatsApp chaos to a confirmed lesson.
Small language studios shouldn't have to manage every student through WhatsApp.
Mikuna started from a simple problem: a student wants to book a lesson, but the process quickly becomes a chain of messages about language, goals, tutors, availability, payment, changes and confirmations.
So I designed a system where the student handles the coordination themselves, while the tutor gets a ready-to-teach agenda.
The student can:
→ Choose a language and learning goal
→ Find the right tutor
→ Pick an available time
→ Purchase credits
→ Book the lesson
→ See their upcoming lessons and remaining credits
The tutor gets:
→ An organized agenda
→ Upcoming and past lessons
→ Relevant student context
→ One-click access to the lesson
Behind the interface, the system also handles tutor availability, booking conflicts, credits, payments and cancellation rules.
The idea was simple:
From managing conversations to managing lessons.
Mikuna was built for the Contra × Lovable Challenge, starting from an existing brand identity created at SUR Studio and reimagining it as a complete appointment-based product.
Love this! Such a clean solution to a very real problem for language studios. The shift from WhatsApp chaos to a seamless booking experience is brilliant.