langchain

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

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

langchain is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using langchain should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/langchain/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/langchain/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/langchain/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How langchain Compares

Feature / Agent	langchain	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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](https://python.langchain.com/docs/get_started/introduction)