langchain

name	langchain
description	LangChain LLM application framework with chains and agents. Use for LLM orchestration.

LangChain

LangChain is the standard framework for chaining LLM components. In 2025, the focus shifted to LangGraph for building stateful, cyclic agents.

When to Use

• Orchestration: Chaining "Prompt -> LLM -> Parser".
• Agents: Using LangGraph to build agents that can loop, retry, and keep state.
• Integrations: 1000+ connectors for vector DBs, APIs, and tools.

Core Concepts

LangGraph

The successor to AgentExecutor. A graph-based way to define agent flows with cycles (loops).

LCEL (LangChain Expression Language)

The declarative pipe syntax: prompt | llm | output_parser.

LangSmith

Observability platform to trace and debug complex chains.

Best Practices (2025)

Do:

• Use LangGraph: For any non-trivial agent. AgentExecutor is legacy.
• Use LCEL: It enables streaming and async out of the box.
• Trace everything: Connect to LangSmith to see why your agent failed.

Don't:

• Don't over-abstract: If a simple Python function works, don't wrap it in a Chain.

References

• LangChain Documentation