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agents-langchain

Build LangChain agents and RAG pipelines for LLM applications.

24|8|Updated Jan 14, 2026
Search Tags:#langchain#llm#rag#agents#retrieval#tool-calling#vectorstore
One-click install
npx skills add https://github.com/vadimcomanescu/codex-skills --skill agents-langchain
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agents-langchain
Source: https://github.com/vadimcomanescu/codex-skills/tree/main/skills/.curated/ai/agents-langchain
Command: npx skills add https://github.com/vadimcomanescu/codex-skills --skill agents-langchain

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LangChain streamlines building tool-using agents and retrieval-augmented generation (RAG) systems so developers can create chatbots, tool-calling workflows, and document-backed assistants without stitching together providers and vector stores manually.

Core Features & Use Cases

  • Agent construction: Create ReAct and tool-calling agents that reason, call functions, and iterate until tasks complete.
  • RAG pipelines: Load documents, split and embed text, build vector stores, and run retrieval-backed QA and conversational chains.
  • Provider & integration flexibility: Swap LLM providers, connect Chroma/Pinecone/FAISS/Weaviate/Qdrant, and enable LangSmith tracing for observability.
  • Use Case: Build a customer support chatbot that retrieves product docs, calls a pricing API, and returns structured answers with source citations.

Quick Start

Use LangChain to create an agent with a model and one retrieval or function tool to answer a user question.

Frequently Asked Questions about agents-langchain

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

FAQPage Schema
How do I build a tool-calling agent for an LLM application?▼

To build a tool-calling agent, use this Skill to create ReAct agents that reason, call functions, and iterate until tasks complete without manually stitching together providers and vector stores.

How do I create a RAG pipeline for retrieval-augmented document Q&A?▼

Create a RAG pipeline by loading documents, splitting and embedding text, building vector stores, and running retrieval-backed conversational chains for document-backed Q&A with source citations.

Can I swap LLM providers and connect different vector stores in my RAG pipeline?▼

Yes, you can swap LLM providers and connect vector stores including Chroma, Pinecone, FAISS, Weaviate, and Qdrant for flexible integration in prototypes and production environments.

What is the best way to add conversational memory and streaming to a chatbot?▼

The best way to add conversational memory and streaming is by using this Skill's agent construction features, which support integration of memory and streaming for chatbot development workflows.

How do I enable observability and tracing for LLM agents and RAG pipelines?▼

Enable observability and tracing by integrating LangSmith within your agent and RAG pipeline workflows to monitor tool-calling workflows and retrieval-augmented generation processes.

Do I need a specific framework to build agents that retrieve product docs and call APIs?▼

You need a framework like this Skill to build a customer support chatbot that retrieves product docs, calls a pricing API, and returns structured answers without manually integrating components.

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