LangChain is the orchestration layer that turns raw LLMs into practical applications—adding tools (function calls and APIs), memory, and control flow so your model can search, fetch data, calculate, and reason across steps. In this post, we’ll cover the core building blocks (prompts, tools, chains, agents), show a minimal agent that uses two simple tools, and share good practices for reliability and observability so you know when to use a chain versus an agent—and how to keep both predictable.

Why LangChain?

What LangChain adds on top of LLMs: tools, memory, routing, and agents.

• When you need orchestration vs. a single prompt.
• Alternatives to know: LlamaIndex, Haystack.

Core Building Blocks

• LLM wrappers and prompt templates.
• Tools (functions/APIs) and tool calling.
• Chains vs. Agents: when to use each.
• Memory: short-term vs. long-term.

Minimal Example

• One agent with two tools (search + calculator).
• Outline: define tools → configure agent → run query.

Good Practices

• Deterministic prompts and testing.
• Observability/logging of intermediate steps.
• Rate limits, retries, and timeouts.

Where to Go Next

• Add memory to your agent.
• Plug in a retrieval step (RAG).

Next up: we’ll build a small retrieval-augmented agent end to end.