langchain

name	langchain
description	Build production-ready LLM applications with chains, agents, memory, tools, and RAG pipelines using the LangChain framework
version	1.0.0
author	workspace-hub
category	ai-prompting
type	skill
trigger	manual
auto_execute	false
capabilities	["chain_composition","agent_orchestration","memory_management","tool_integration","rag_pipelines","vector_stores","document_processing","streaming_responses"]
tools	["Read","Write","Bash","Grep"]
tags	["langchain","llm","chains","agents","rag","embeddings","vector-stores","memory","tools"]
platforms	["python"]
related_skills	["prompt-engineering","dspy","pandasai"]
scripts_exempt	true

Langchain

Quick Start

# Install LangChain ecosystem
pip install langchain langchain-openai langchain-community langchain-core

# Install vector store dependencies
pip install chromadb faiss-cpu

# Install document loaders
pip install unstructured pypdf docx2txt

# Set API key
export OPENAI_API_KEY="your-api-key"

When to Use This Skill

USE when:

• Building complex LLM applications with multiple components
• Need agents that can use tools and make autonomous decisions
• Implementing RAG (Retrieval Augmented Generation) systems
• Integrating with various LLM providers (OpenAI, Anthropic, local models)
• Building chatbots with conversation memory
• Processing and querying document collections
• Need streaming responses for real-time applications
• Orchestrating multi-step reasoning workflows

DON'T USE when:

• Simple single-prompt LLM calls (use direct API)
• Optimizing prompts programmatically (use DSPy instead)
• Building UI-focused chat applications (use Streamlit/Gradio directly)
• Need minimal dependencies and maximum control
• Performance-critical applications requiring custom optimizations

Prerequisites

# Core installation
pip install langchain>=0.2.0 langchain-openai>=0.1.0 langchain-core>=0.2.0

# For RAG applications
pip install chromadb>=0.4.0 faiss-cpu>=1.7.0

# For document processing
pip install unstructured>=0.10.0 pypdf>=3.0.0

# For web search and tools
pip install duckduckgo-search wikipedia arxiv

# Optional: Local LLMs
pip install langchain-community ollama

# Environment setup
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Complete Examples

Example 1: Engineering Documentation Assistant

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 langchain_community.vectorstores  Chroma
 langchain_community.document_loaders  DirectoryLoader, PyPDFLoader
 langchain.text_splitter  RecursiveCharacterTextSplitter
 langchain_core.prompts  ChatPromptTemplate, MessagesPlaceholder
 langchain_core.runnables  RunnablePassthrough
 langchain_core.runnables.history  RunnableWithMessageHistory
 langchain_community.chat_message_histories  ChatMessageHistory
 pathlib  Path

*See sub-skills  full details.*


```python
 langchain_openai  ChatOpenAI
 langchain.agents  AgentExecutor, create_openai_tools_agent
 langchain_core.prompts  ChatPromptTemplate, MessagesPlaceholder
 langchain_core.tools  tool
 langchain_community.tools  DuckDuckGoSearchRun, WikipediaQueryRun
 langchain_community.utilities  WikipediaAPIWrapper
 pydantic  BaseModel, Field
 typing  , 
 json

*See sub-skills  full details.*





```python
 fastapi  FastAPI
 langserve  add_routes
 langchain_openai  ChatOpenAI
 langchain_core.prompts  ChatPromptTemplate
 langchain_core.output_parsers  StrOutputParser


app = FastAPI(
    title=,

*See sub-skills  full details.*


```python
 os
 langchain_openai  ChatOpenAI
 langchain_core.prompts  ChatPromptTemplate


os.environ[] = 
os.environ[] = 
os.environ[] = 


chain = ChatPromptTemplate.from_template() | ChatOpenAI()
response = chain.invoke({: })