langchain-agents

Create and configure LangChain agents with Python's create_agent.

3|1|Updated Jan 31, 2026

Search Tags:#workflow #python #middleware #langchain #agents #tools #create_agent

Ready to run

npx skills add https://github.com/christian-bromann/langchain-skills --skill langchain-agents-christian-bromann

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-agents
Source: https://github.com/christian-bromann/langchain-skills/tree/main/skills/langchain-agents/python
Command: npx skills add https://github.com/christian-bromann/langchain-skills --skill langchain-agents-christian-bromann

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create and configure LangChain agents using Python's create_agent, including tool selection, agent loops, stopping conditions, and middleware integration for Python workflows.

Core Features & Use Cases

Create and configure LangChain agents using create_agent with model, tools, and prompts
Define agent loops, stopping criteria, and middleware hooks to tailor behavior
Use cases include automating tasks that require stepwise tool usage, multi-tool coordination, and persistent state with checkpointer
Real-world example: build an agent that queries tools, reasons, and revises its plan until it reaches a final answer

Quick Start

Use create_agent with a model and a set of tools to start an iterative tool-calling loop.

Frequently Asked Questions about langchain-agents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I configure LangChain agents using Python?▼

To configure LangChain agents in Python, use the create_agent function with a specified model, a set of tools, and prompts to establish an iterative tool-calling loop for task automation.

What are agent loops and stopping conditions in LangChain?▼

Agent loops in LangChain allow an agent to iteratively query tools, reason, and revise its plan until meeting defined stopping conditions. This ensures the agent halts appropriately upon reaching a final answer.

Can I integrate middleware into my LangChain agent workflow?▼

Yes, you can define middleware hooks when creating LangChain agents in Python. This allows you to tailor agent behavior and integrate typical workflow components within the agent loop.

How do I manage persistent state for multi-tool coordination in LangChain?▼

You can manage persistent state during multi-tool coordination by using a checkpointer. This enables the LangChain agent to maintain context and state across iterative tool-calling steps.

What is the best way to automate stepwise tool usage in Python?▼

The best way to automate stepwise tool usage is using LangChain's create_agent. It configures a model with multiple tools to iteratively execute, reason, and revise plans until a final answer is reached.

Does create_agent support modular guidance for production-ready agents?▼

Yes, create_agent provides modular guidance and practical examples for configuring production-ready agents. It defines clear boundaries for tool selection, agent loops, and middleware integration.