G1Joshi 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.
AgentExecutoris 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.