When to Choose LangChain: The Prototyping Powerhouse

LangChain is the most "Famous" framework, but in the expert community, it's often criticized for being too complex. However, there are specific scenarios where LangChain Core is still the undisputed king.

1. Speed to Proof-of-Concept (POC)

If you have a meeting in 2 hours and you need to show an agent that can talk to your company's database, use LangChain.

It has "Pre-built chains" (like create_sql_query_chain) that do 90% of the work in 5 lines of code.

2. Abundance of Integrations

LangChain has the best integration library. If you need to talk to a niche system (like a specific medical database or an ancient ERP), LangChain likely already has a "Wrapper" for it.

Tools: 500+ pre-built tools.
Loaders: 100+ document loaders (PDF, PPT, Notion, Jira).

3. Simple, Non-Iterative Tasks

If your agent doesn't need to "Loop" or "Plan"—it just needs to:

Read a prompt.
Search one tool.
Answer. ...then LangChain's AgentExecutor is perfect. Don't build a complex graph for a simple linear task.

4. Visualizing the "Direct Path"

graph LR
    User[Query] --> Chain[LangChain Sequence]
    Chain --> Tool[Search/SQL]
    Tool --> Chain
    Chain --> Response[Answer]

5. Use Case Checklist for LangChain

Should you use LangChain? Yes, if:

You are building a demo or MVP.
You have simple logic (1-2 tools maximum).
You need to integrate with legacy systems (Notion, Jira, etc.).
You are just getting started with Agentic AI.

Key Takeaways

LangChain is for Simplicity and Connectivity.
Its biggest strength is the Community Ecosystem of tools and loaders.
It is the best choice for POCs and MVPs.
Avoid it for missions where you need precise state control or multi-agent validation.

Module 11 Lesson 2: When to use LangChain