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 🔥

I’ve been working on Astaleria and I’m thinking about selling it as a brand package, including the design assets and Astaleria.com.

I put together this little video to show how it all comes together.

Would you buy a brand with the matching .com instead of starting from scratch? Curious what you think, and if you could see yourself building something with this one.

Wonderful design, really nice