langchain-fundamentals

SKILL.md

Build production LangChain agents with create_agent(), tools, and middleware patterns.

Use create_agent() with model, tools list, and system prompt; configure state persistence with checkpointer and thread_id for conversation memory across invocations

Define tools via @tool decorator (Python) or tool() function (TypeScript) with clear descriptions so agents know when to call them

Add middleware like HumanInTheLoopMiddleware for approval workflows, custom error handling, and human-in-the-loop control over agent decisions

Set recursion_limit in invoke config to prevent infinite loops, and access results via result["messages"][-1].content rather than direct content access

<create_agent>

Creating Agents with create_agent

create_agent() is the recommended way to build agents. It handles the agent loop, tool execution, and state management.

Agent Configuration Options

Parameter
Purpose
Example

model
LLM to use
"anthropic:claude-sonnet-4-5" or model instance

tools
List of tools
[search, calculator]

system_prompt / systemPrompt
Agent instructions
"You are a helpful assistant"

checkpointer
State persistence
MemorySaver()

middleware
Processing hooks
[HumanInTheLoopMiddleware] (Python) / [humanInTheLoopMiddleware({...})] (TypeScript)

</create_agent>