langchain
LangChain LLM application framework with chains and agents. Use for LLM orchestration.
Install
npx skillscat add g1joshi/agent-skills/langchain Install via the SkillsCat registry.
LangChain is an orchestration framework used to chain large language model components and build stateful, cyclic agents via LangGraph. It solves the complexity of managing data flows between prompts, models, and external tools. Developers should use it when building complex agentic workflows that require memory, error handling, or integration with various vector databases and APIs.
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.
References
Install
npx skillscat add g1joshi/agent-skills/langchain Install via the SkillsCat registry.