langchain-code

name

langchain-code

description

Build LangChain agents in Python — create_agent, chat model initialization, tool binding, structured output, and middleware. Use for a single-agent tool-calling loop with a system prompt and a fixed tool list; use langgraph-code instead when the task needs explicit multi-step state-machine control flow, and deepagents-code instead when it needs a pre-assembled filesystem/planning/subagent harness for long-running work. Triggers on "LangChain agent", "create_agent", "chat model", "tool calling agent", "structured output langchain", "bind_tools".

LangChain

Source: https://docs.langchain.com/oss/python/langchain/overview (fetched 2026-07-06 — re-verify if this content seems stale). Note: docs.langchain.com's top-level llms.txt index is dominated by LangSmith/Agent-Server/Fleet/API-reference content unrelated to this skill — the relevant pages live under oss/python/langchain/* specifically; don't confuse the two when looking for more material.

Core Philosophy

An agent is a model plus a configurable harness: create_agent gives you a minimal, composable base (model, tools, system prompt, optional middleware) rather than a fixed opinionated pipeline. This is the "assemble exactly what you need" layer — see deepagents-code for the pre-assembled, opinionated alternative when the task needs filesystem access, planning, and subagents out of the box.

When to Reach for LangChain vs. Its Siblings

• langchain-code (this skill): a single agent, a fixed tool list, optionally structured output — no explicit multi-step state machine needed.
• langgraph-code: the task needs explicit branching/looping control flow, durable checkpointing, or a pause-for-approval step.
• deepagents-code: the task is long-running (coding/research) and needs filesystem access, task planning, and subagent delegation pre-assembled rather than composed by hand.