langchain-tools

name	langchain-tools
description	LangChain framework utilities for chains, agents, and RAG
allowed-tools	["Bash","Read","Grep","Glob"]
category	ai-tools
requires-env	[]
optional-env	["LANGCHAIN_API_KEY","LANGCHAIN_TRACING_V2"]

LangChain Tools Skill

Overview

The LangChain Tools skill provides configuration utilities and template generation for LangChain framework components, including chains, agents, RAG systems, and LangSmith tracing integration.

Context Savings: 90%+ reduction vs raw documentation by providing focused, task-specific interfaces to LangChain configurations.

Use Cases:

• Chain configuration and template generation
• Agent setup and tool integration
• Document loader configuration
• Vector store setup
• Embedding provider configuration
• LangSmith tracing and evaluation

Important: This skill helps configure LangChain components, not run them directly. Actual chain execution happens in application code.

Requirements

Python Dependencies:

pip install langchain langchain-community langchain-openai

Optional Dependencies (based on use case):

# For OpenAI models
pip install openai

# For vector stores
pip install chromadb faiss-cpu pinecone-client

# For document loaders
pip install pypdf docx2txt

# For LangSmith tracing
pip install langsmith

Installation Verification:

python -c "import langchain; print(langchain.__version__)"

Tools (Progressive Disclosure)

Chain Utilities

Tool	Description	Example
list-chain-types	List available chain types	Show all chain types
chain-template	Get chain configuration template	Get LLMChain template
validate-chain	Validate chain configuration	Validate chain config file

Agent Utilities

Tool	Description	Example
list-agent-types	List available agent types	Show all agent types
agent-template	Get agent configuration template	Get ReAct agent template
list-tools	List available agent tools	Show built-in tools

Document Loaders

Tool	Description	Example
list-loaders	List available document loaders	Show all loader types
loader-config	Get loader configuration template	Get PDF loader config

Vector Stores

Tool	Description	Example
list-vectorstores	List vector store types	Show all vector stores
vectorstore-config	Get vector store configuration	Get Chroma config

Embeddings

Tool	Description	Example
list-embeddings	List embedding providers	Show all embedding models
embedding-config	Get embedding configuration	Get OpenAI embeddings config

LangSmith (Tracing)

Tool	Description	Example
list-traces	List recent traces	Show last 10 traces
trace-details	Get trace details	Get trace by ID
list-datasets	List evaluation datasets	Show all datasets

Quick Reference

Chain Types

# List available chain types
python -c "
from langchain.chains import (
    LLMChain,
    ConversationChain,
    SequentialChain,
    SimpleSequentialChain,
    MapReduceChain,
    RetrievalQA
)

print('Available Chain Types:')
print('- LLMChain: Basic chain with LLM and prompt')
print('- ConversationChain: Chat with memory')
print('- SequentialChain: Multiple chains in sequence')
print('- SimpleSequentialChain: Simple sequential chain')
print('- MapReduceChain: Map-reduce pattern')
print('- RetrievalQA: RAG question-answering')
"

Create Basic LLM Chain

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Define prompt template
template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

# Create LLM
llm = ChatOpenAI(temperature=0, model="gpt-4")

# Create chain
chain = LLMChain(llm=llm, prompt=prompt)

# Run chain
result = chain.run(question="What is quantum computing?")
print(result)

Agent Types

# List available agent types
python -c "
from langchain.agents import AgentType

print('Available Agent Types:')
print('- ZERO_SHOT_REACT_DESCRIPTION: ReAct agent with tool descriptions')
print('- CONVERSATIONAL_REACT_DESCRIPTION: Chat ReAct agent')
print('- CHAT_ZERO_SHOT_REACT_DESCRIPTION: Chat-optimized ReAct')
print('- STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION: Structured output ReAct')
print('- OPENAI_FUNCTIONS: OpenAI function calling agent')
print('- OPENAI_MULTI_FUNCTIONS: Multi-function OpenAI agent')
"

Create ReAct Agent

from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI

# Create LLM
llm = ChatOpenAI(temperature=0, model="gpt-4")

# Load tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)

# Create agent
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# Run agent
result = agent.run("What is the square root of 144?")
print(result)

Document Loaders

# PDF Loader
from langchain.document_loaders import PyPDFLoader

loader = PyPDFLoader("document.pdf")
documents = loader.load()

# Directory Loader
from langchain.document_loaders import DirectoryLoader

loader = DirectoryLoader("./data", glob="**/*.txt")
documents = loader.load()

# Text Loader
from langchain.document_loaders import TextLoader

loader = TextLoader("document.txt")
documents = loader.load()

# Web Loader
from langchain.document_loaders import WebBaseLoader

loader = WebBaseLoader("https://example.com")
documents = loader.load()

Vector Store Setup

# Chroma Vector Store
from langchain.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

# Create vector store
vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=embeddings,
    persist_directory="./chroma_db"
)

# FAISS Vector Store
from langchain.vectorstores import FAISS

vectorstore = FAISS.from_documents(
    documents=documents,
    embedding=embeddings
)

# Pinecone Vector Store
from langchain.vectorstores import Pinecone
import pinecone

pinecone.init(api_key="your-api-key", environment="us-west1-gcp")

vectorstore = Pinecone.from_documents(
    documents=documents,
    embedding=embeddings,
    index_name="my-index"
)

Embedding Providers

# OpenAI Embeddings
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")

# Hugging Face Embeddings
from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

# Cohere Embeddings
from langchain.embeddings import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-v2.0")

RAG System Setup

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load documents
loader = DirectoryLoader("./data", glob="**/*.txt")
documents = loader.load()

# Split documents
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
texts = text_splitter.split_documents(documents)

# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)

# Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

# Create QA chain
llm = ChatOpenAI(temperature=0, model="gpt-4")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True
)

# Run query
result = qa_chain({"query": "What is the main topic?"})
print(result["result"])
print(result["source_documents"])

LangSmith Tracing

import os

# Enable tracing
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
os.environ["LANGCHAIN_PROJECT"] = "my-project"

# Your LangChain code here
# All executions will be traced in LangSmith

from langchain.chains import LLMChain
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate

template = "Tell me a joke about {topic}"
prompt = PromptTemplate(template=template, input_variables=["topic"])
llm = ChatOpenAI(temperature=0.9)
chain = LLMChain(llm=llm, prompt=prompt)

# This will be traced
result = chain.run(topic="programming")

Configuration

Environment Variables

Variable	Purpose	Default
LANGCHAIN_API_KEY	LangSmith API key for tracing	None
LANGCHAIN_TRACING_V2	Enable LangSmith tracing	false
LANGCHAIN_PROJECT	LangSmith project name	default
LANGCHAIN_ENDPOINT	Custom LangSmith endpoint	https://api.smith.langchain.com
OPENAI_API_KEY	OpenAI API key for models/embeddings	None

LangSmith Setup

# Method 1: Environment variables
export LANGCHAIN_API_KEY="your-api-key"
export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_PROJECT="my-project"

# Method 2: Python configuration
from langsmith import Client

client = Client(api_key="your-api-key")

Chain Configuration Template

# config/chain_config.py
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

class ChainConfig:
    """Base chain configuration"""

    def __init__(self, temperature=0, model="gpt-4"):
        self.temperature = temperature
        self.model = model

    def create_llm(self):
        return ChatOpenAI(
            temperature=self.temperature,
            model=self.model
        )

    def create_chain(self, template, input_variables):
        prompt = PromptTemplate(
            template=template,
            input_variables=input_variables
        )
        llm = self.create_llm()
        return LLMChain(llm=llm, prompt=prompt)

Agent Integration

Primary Agents

Agent	Use Case
llm-architect	LangChain system design, RAG architecture, agent design
developer	Chain implementation, agent integration, RAG setup
architect	System architecture, integration patterns

Secondary Agents

Agent	Use Case
qa	Chain testing, agent validation
performance-engineer	Chain optimization, embedding performance
security-architect	Security review, prompt injection prevention

Integration Pattern

# LLM Architect: Design RAG system
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma

# Define architecture
architecture = {
    "loader": "DirectoryLoader",
    "splitter": "RecursiveCharacterTextSplitter",
    "embeddings": "OpenAIEmbeddings",
    "vectorstore": "Chroma",
    "chain_type": "stuff",
    "retriever": {"k": 3}
}

# Developer: Implement RAG system
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI

loader = DirectoryLoader("./data", glob="**/*.txt")
documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

llm = ChatOpenAI(temperature=0, model="gpt-4")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever
)

# QA: Test RAG system
test_queries = [
    "What is the main topic?",
    "Summarize the key points",
    "What are the conclusions?"
]

for query in test_queries:
    result = qa_chain({: query})
    ()
    ()
    ()

Examples

Example 1: Simple Question-Answering Chain

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Define prompt
template = """You are a helpful assistant. Answer the following question concisely.

Question: {question}

Answer:"""

prompt = PromptTemplate(template=template, input_variables=["question"])

# Create chain
llm = ChatOpenAI(temperature=0, model="gpt-4")
chain = LLMChain(llm=llm, prompt=prompt)

# Run chain
questions = [
    "What is machine learning?",
    "Explain neural networks",
    "What is transfer learning?"
]

for question in questions:
    result = chain.run(question=question)
    print(f"Q: {question}")
    print(f"A: {result}\n")

Example 2: Conversational Agent with Memory

from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.memory import ConversationBufferMemory
from langchain_openai import ChatOpenAI

# Create LLM
llm = ChatOpenAI(temperature=0, model="gpt-4")

# Load tools
tools = load_tools(["llm-math"], llm=llm)

# Create memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# Create agent
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory,
    verbose=True
)

# Conversation
agent.run("What is 25 * 4?")
agent.run("Add 10 to the previous result")
agent.run("What was the first calculation I asked?")

Example 3: Document Q&A with Sources

from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings, ChatOpenAI

# Load document
loader = TextLoader("document.txt")
documents = loader.load()

# Split text
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(texts, embeddings)

# Create QA chain
llm = ChatOpenAI(temperature=0, model="gpt-4")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

# Query with sources
result = qa_chain({"query": "What are the key findings?"})
print(f"Answer: {result['result']}\n")
print("Sources:")
for doc in result['source_documents']:
    print(f"- {doc.page_content[:100]}...")

Example 4: Custom Tool Agent

from langchain.agents import Tool, AgentType, initialize_agent
from langchain_openai import ChatOpenAI

# Define custom tools
def get_weather(location: str) -> str:
    """Get weather for a location"""
    return f"The weather in {location} is sunny and 72°F"

def calculate_tip(bill: str) -> str:
    """Calculate 20% tip for a bill"""
    amount = float(bill)
    tip = amount * 0.20
    return f"20% tip on ${amount:.2f} is ${tip:.2f}"

tools = [
    Tool(
        name="Weather",
        func=get_weather,
        description="Get weather for a location. Input should be a city name."
    ),
    Tool(
        name="TipCalculator",
        func=calculate_tip,
        description="Calculate 20% tip. Input should be the bill amount."
    )
]

# Create agent
llm = ChatOpenAI(temperature=0, model="gpt-4")
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# Use agent
agent.run("What's the weather in San Francisco?")
agent.run("Calculate tip for a $50 bill")

Example 5: Multi-Step Chain

from langchain.chains import SequentialChain, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0.7, model="gpt-4")

# First chain: Generate topic
topic_template = "Generate a topic for a blog post about {subject}"
topic_prompt = PromptTemplate(template=topic_template, input_variables=["subject"])
topic_chain = LLMChain(llm=llm, prompt=topic_prompt, output_key="topic")

# Second chain: Generate outline
outline_template = "Create an outline for a blog post about: {topic}"
outline_prompt = PromptTemplate(template=outline_template, input_variables=["topic"])
outline_chain = LLMChain(llm=llm, prompt=outline_prompt, output_key="outline")

# Third chain: Write introduction
intro_template = "Write an introduction for this outline:\n{outline}"
intro_prompt = PromptTemplate(template=intro_template, input_variables=["outline"])
intro_chain = LLMChain(llm=llm, prompt=intro_prompt, output_key="introduction")

# Combine chains
overall_chain = SequentialChain(
    chains=[topic_chain, outline_chain, intro_chain],
    input_variables=["subject"],
    output_variables=["topic", "outline", "introduction"],
    verbose=True
)

# Run sequential chain
result = overall_chain({"subject": "artificial intelligence"})
print(f"Topic: {result['topic']}")
print(f"\nOutline: {result['outline']}")
print()

Troubleshooting

Common Issues

Issue: ImportError: No module named 'langchain'

# Solution: Install the package
pip install langchain langchain-community langchain-openai

Issue: ValueError: Did not find openai_api_key

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

# Or in code
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"

Issue: ChromaDB connection error

# Solution: Install ChromaDB
pip install chromadb

# Or use in-memory mode
from langchain.vectorstores import Chroma

vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=embeddings
    # No persist_directory = in-memory
)

Issue: Agent stuck in loop or gives poor results

# Solution: Adjust temperature and add max_iterations
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent

llm = ChatOpenAI(temperature=0, model="gpt-4")  # Lower temperature

agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    max_iterations=5,  # Limit iterations
    early_stopping_method="generate",  # Force generation
    verbose=True
)

Issue: Memory errors with large documents

# Solution: Use smaller chunk sizes and streaming
from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,  # Smaller chunks
    chunk_overlap=50
)

# Or use streaming for vector store
from langchain.vectorstores import Chroma

vectorstore = Chroma.from_documents(
    documents=texts[:100],  # Process in batches
    embedding=embeddings
)

Issue: LangSmith traces not appearing

# Solution: Verify environment variables
echo $LANGCHAIN_TRACING_V2  # Should be "true"
echo $LANGCHAIN_API_KEY      # Should be your API key
echo $LANGCHAIN_PROJECT      # Should be your project name

# Check connection
python -c "from langsmith import Client; client = Client(); print('Connected')"

Debug Mode

# Enable verbose logging
import logging

logging.basicConfig(level=logging.DEBUG)

# Enable chain verbose mode
from langchain.chains import LLMChain

chain = LLMChain(llm=llm, prompt=prompt, verbose=True)

# Enable agent verbose mode
from langchain.agents import initialize_agent

agent = initialize_agent(tools, llm, verbose=True)

Verify Installation

# Check package versions
pip list | grep -E "langchain|openai|chromadb"

# Test basic functionality
python -c "
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

print('LangChain installation verified')
"

Best Practices

1. Use environment variables: Store API keys securely in environment variables
2. Enable tracing: Use LangSmith for debugging and monitoring production chains
3. Optimize chunk sizes: Balance chunk size between context and granularity (500-1000 chars)
4. Use appropriate embeddings: Choose embeddings based on use case (ada-002 for general, sentence-transformers for local)
5. Implement error handling: Add retries and fallbacks for API calls
6. Cache vector stores: Persist vector stores to disk to avoid recomputing embeddings
7. Monitor token usage: Track token consumption for cost optimization
8. Version control prompts: Store prompt templates in version control

Related Skills

• huggingface-hub: Alternative embedding providers and models
• repo-rag: Search codebase for existing LangChain patterns
• evaluator: Evaluate chain outputs and agent performance
• test-generator: Generate tests for chains and agents
• dependency-analyzer: Check for compatibility issues

Additional Resources

• LangChain Documentation
• LangChain Agent Documentation
• LangChain Chains Documentation
• LangSmith Documentation
• LangChain Templates