Tag Archives: Lang Chain

Lang Chain and Lang Graph

Leave a reply

1. Why Do We Need LangChain or LangGraph?

So far in the series, we’ve learned:

  • LLMs → The brains
  • Embeddings → The “understanding” of meaning
  • Vector DBs → The memory store

But…
How do you connect them into a working application?
How do you manage complex multi-step reasoning?
That’s where LangChain and LangGraph come in.

2. What is LangChain?

LangChain is an AI application framework that makes it easier to:

  • Chain multiple AI calls together
  • Connect LLMs to external tools and APIs
  • Handle retrieval from vector databases
  • Manage prompts and context

It acts as a middleware layer between your LLM and the rest of your app.

Example:
A chatbot that:

  1. Takes user input
  2. Searches a vector database for context
  3. Calls an LLM to generate a response
  4. Optionally hits an API for fresh data

3. LangGraph — The Next Evolution

LangGraph is like LangChain’s “flowchart” version:

  • Allows graph-based orchestration of AI agents and tools
  • Built for agentic AI (LLMs that make decisions and choose actions)
  • Makes state management easier for multi-step, branching workflows

Think of LangChain as linear and LangGraph as non-linear — perfect for complex applications like:

  • Multi-agent systems
  • Research assistants
  • AI-powered workflow automation

4. Core Concepts in LangChain

  • LLM Wrappers → Interface to models (OpenAI, Anthropic, local models)
  • Prompt Templates → Reusable, parameterized prompts
  • Chains → A sequence of calls (e.g., “Prompt → LLM → Post-process”)
  • Agents → LLMs that decide which tool to use next
  • Memory → Store conversation history or retrieved context
  • Toolkits → Prebuilt integrations (SQL, Google Search, APIs)

5. Where LangChain/LangGraph Fits in a RAG Pipeline

  1. User Query → Passed to LangChain
  2. Retriever → Pulls embeddings from a vector DB
  3. LLM Call → Uses retrieved docs for context
  4. Response Generation → Returned to user or sent to next step in LangGraph flow

6. Key Questions

  • Q: How is LangChain different from directly calling an LLM API?
    A: LangChain provides structure, chaining, memory, and tool integration — making large workflows maintainable.
  • Q: When to use LangGraph over LangChain?
    A: LangGraph is better for non-linear, branching, multi-agent applications.
  • Q: What is an Agent in LangChain?
    A: An LLM that dynamically chooses which tool or action to take next based on the current state.