Build a lightweight, educational version of LangChain.js
Learn to rebuild LangChain's core patterns with clear, educational code. Understand what frameworks are actually doing, making you far more effective at using them.
Build it yourself. Understand it deeply. Use LangChain confidently.
You've just built AI agents with node-llama-cpp. You know how to call LLMs, format prompts, parse responses, and create agent loops. That's awesome—you understand the fundamentals!
But you probably noticed some friction:
This tutorial fixes those problems. Instead of jumping straight into LangChain's complex codebase, you'll rebuild its core patterns yourself with clear, educational code. You'll transform the script-style code you wrote into clean, composable abstractions.
Start with problems you've encountered
Build the abstraction that solves it
See how it connects to LangChain's API
Understand frameworks deeply
Transform the patterns you already use into reusable components.
→ Build the Runnable pattern for composability
→ Create Message types for structure
→ Design LLM wrappers for flexibility
→ Implement Context for memory
Dive deeper into prompt engineering and chain complex operations together.
→ Build reusable PromptTemplates with variables
→ Create OutputParsers for reliable data extraction
→ Design LLMChain to compose operations
→ Use piping to connect Runnables
Agents and tools for decision-making and action execution.
State machines and workflows for complex agent orchestration.
Complete these capstone projects to solidify your learning:
After Part 1
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Happy learning! 🚀