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Raunak ALI
Raunak ALI

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LangChain & LangGraph: What You Need to Know Before You Build Agents and GenAI Applications

#genai #llm #langchain #webdev

LangChain & LangGraph: What You Need to Know Before You Build Agents and GenAI Applications

1. Why LangChain?

LangChain is one of the most widely adopted frameworks for building LLM-powered applications. While LLMs on their own can generate text, LangChain connects them to:

  • Tools (like APIs, calculators, databases).
  • Memory (short-term & long-term).
  • Chains & Agents (structured multi-step reasoning).

Think of it as the glue layer between an LLM and the real world.

Official Docs: LangChain Documentation

2. What About LangGraph?

LangGraph is an extension built by the same team, focused on agent workflows.
Where LangChain gives you chains and tool usage, LangGraph adds state machines and graphs — ideal for orchestrating multi-step or multi-agent systems.

-> In short:

  • LangChain → Great for prototyping and connecting models.
  • LangGraph → Great for scaling to real-world, reliable agent systems.

Official Docs: LangGraph Documentation

3. Why This Tutorial Uses LangChain And LangGraph

In this series, our focus is Agentic AI. LangChain & LangGraph give us:

  • Rapid prototyping (easy chains, tools, prompts).
  • Production patterns (memory, state management, graphs).
  • Community support (huge ecosystem, tutorials, integrations).

They’re not the only option, but they balance beginner-friendliness and professional depth.

4. Alternatives You Should Know

Depending on your use case, you might explore:

  • LlamaIndex (GPT Index) → Strong for RAG (Retrieval-Augmented Generation) use cases.
  • Haystack → Powerful for search + retrieval pipelines.
  • Guidance → Focused on controlling LLM output formats.
  • AutoGPT / BabyAGI → More experimental “autonomous” frameworks.

Each has trade-offs, but LangChain remains the most versatile entry point.

5. Limitations of LangChain / LangGraph

  • Complexity → Large learning curve for beginners beyond simple chains.
  • Performance → Agents can be slow/expensive if not optimized (tool overuse, excessive prompts).
  • Still evolving → APIs change quickly; keeping up with updates is necessary.

6. Prerequisites

Before we jump into our practicals in the next chapter, let’s talk prerequisites.

To follow along with LangChain implementations, you should ideally be comfortable with:

  • Python basics → writing/understanding simple scripts.
  • pip → installing Python packages.
  • Virtual environments (venv / conda) → optional but useful to keep projects isolated.

  • Jupyter / Google Colab → running Python code in notebooks.

  • If you’re new, don’t worry — this module series will provide ready-to-use Google Colab notebooks alongside the explanations. That way, you don’t need to set up a full dev environment on your laptop right away.

Next Build your own GenAI-powered HR research assistant!

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