langchain-agent-development

Develop LangChain ReAct agents with tool integration and semantic routing.

Updated Jul 15, 2025

Search Tags:#caching #react #langchain #agent-development #tool #semantic-routing #tool-registry

Ready to run

npx skills add https://github.com/d-dragon/dp-stock-investment-assistant --skill langchain-agent-development

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-agent-development
Source: https://github.com/d-dragon/dp-stock-investment-assistant/tree/main/.github/skills/langchain-agent-development
Command: npx skills add https://github.com/d-dragon/dp-stock-investment-assistant --skill langchain-agent-development

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured approach to building LangChain ReAct agents with integrated tool usage, caching, and semantic routing for stock-investment assistants, helping teams prototype and extend AI capabilities quickly.

Core Features & Use Cases

Tool-driven agent architecture: Standardizes how tools are created, registered, and cached using CachingTool and ToolRegistry.
Semantic routing and debugging: Enables classification of user queries into distinct routes and supports LangGraph Studio debugging.
Rapid extension patterns: Guides adding new tools, routes, and debugging workflows for scalable AI agents in finance contexts.

Quick Start

Install dependencies, explore the repository, and run a small experiment to bootstrap a ReAct agent with a tool, then observe and iterate.

Frequently Asked Questions about langchain-agent-development

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

FAQPage Schema

How do I build a LangChain ReAct agent with custom tools?▼

To build a LangChain ReAct agent with custom tools, you create new tools, register them using a ToolRegistry, and apply caching via CachingTool. This Skill provides a structured architecture to prototype and extend agent capabilities quickly.

What is semantic routing in LangGraph and when do I need it?▼

Semantic routing in LangGraph classifies user queries into distinct routes for specialized handling. You need it when building agents that must dynamically direct conversations, such as routing stock-investment queries to specific financial tools.

How do I debug LangGraph agent issues in LangGraph Studio?▼

You can debug LangGraph agent issues by accessing LangGraph Studio to observe and iterate on your ReAct agent's execution flow. This Skill supports semantic routing and tool integration debugging workflows within the studio environment.

Do I need a working Python repository to use LangChain tool caching?▼

Yes, you need a working Python repository context with existing components like CachingTool and ToolRegistry. This Skill requires Python, LangChain, and access to LangGraph Studio to bootstrap and extend your agents.

What is the best way to extend LangChain agent capabilities for finance contexts?▼

The best way to extend LangChain agent capabilities for finance contexts is following rapid extension patterns that guide adding new tools, routes, and debugging workflows. This approach standardizes tool creation and caching for scalable AI agents.