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Langchain

Skill G1Joshi/Agent-Skills/skills/ai-ml/langchain

LangChain LLM application framework with chains and agents. Use for LLM orchestration.From its SKILL.md

Install
npx -y skills add G1Joshi/Agent-Skills --skill langchain

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One thing to look at

  • 12 stars12 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

1.2 KB, 280 tokens by cl100k_base, as published. Nobody here has run it

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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.