langchain-tools

Define and manage LangChain tools with structured schemas and usage patterns.

3|1|Updated Jan 31, 2026

Search Tags:#python #langchain #tools #ai-agents #tool-calling #decorators

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Tools extend what agents can do—letting them fetch real-time data, execute code, query databases, and take actions in the world. Tools are callable functions with well-defined inputs and outputs that models can invoke based on context.

Core Features & Use Cases

Tools have a name, description, and schema (input parameters)
Models decide when to call tools and what arguments to provide
Tools can be functions or coroutines (async)
Some providers offer built-in server-side tools (web search, code interpreter)

Quick Start

Create a sample tool, bind it to a model, and invoke it to see a tool call.

Frequently Asked Questions about langchain-tools

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

FAQPage Schema

How do I define custom tools for LangChain AI agents?▼

To define custom LangChain tools, create callable functions with a name, description, and input schema. You can apply Python decorators to structure tool metadata, enabling models to invoke them based on context for data extraction or API interactions.

Can I use async functions as tools with LangChain?▼

Yes, LangChain tools can be coroutines. You can define async tools to handle non-blocking tasks, allowing AI agents to execute operations like data fetching or code execution concurrently within Python workflows.

What's the best way to structure input schemas for tool calling?▼

The best way to structure tool calling schemas is to provide clear input parameter definitions alongside a descriptive tool name. This structured metadata helps the model accurately determine when to call the tool and what arguments to provide.

Does LangChain support built-in server-side tools for AI agents?▼

Yes, some providers offer built-in server-side tools for LangChain agents, such as web search and code interpreter. These run on the provider's infrastructure and can be used alongside your custom Python tool definitions.

Why do my LangChain tools fail to trigger during agent execution?▼

LangChain tools fail to trigger when their metadata is unclear. Models rely on accurate descriptions and deterministic usage patterns to decide when to call tools, so poorly defined schemas or missing return types prevent proper invocation.

Do I need Python decorators to use LangChain tools?▼

No, Python decorators are not strictly required, but they provide efficient guidance on schemas and return types. Using decorators streamlines the process of binding structured tool definitions to models for AI agent workflows.