langchain-tools

name	langchain-tools
description	Guide to using tool integrations in LangChain including pre-built toolkits, Tavily, Wikipedia, and custom tools
language	python

langchain-tools (Python)

Overview

Tools enable LLMs to interact with external systems, perform calculations, search the web, query databases, and more. They extend model capabilities beyond text generation, making agents truly actionable.

Key Concepts

• Tools: Functions that agents can call to perform specific tasks
• Tool Calling: Models decide when and how to use tools based on user queries
• Toolkits: Collections of related tools
• Tool Schema: Describes tool parameters using Pydantic models

Tool Selection Decision Table

Tool/Toolkit	Best For	Package	Key Features
Tavily Search	Web search	langchain-community	AI-optimized search API
Wikipedia	Encyclopedia queries	langchain-community	Wikipedia API access
DuckDuckGo Search	Privacy-focused search	langchain-community	No API key needed
ArXiv	Academic papers	langchain-community	Research paper search
Vector Store Tools	Semantic search	Based on vector store	Query your data
Custom Tools	Your specific needs	langchain-core	Define any function

When to Choose Each Tool

Choose Tavily if:

• You need high-quality web search
• You want AI-optimized results
• You're building research/RAG applications

Choose Wikipedia if:

• You need encyclopedic knowledge
• Factual information is required
• Free, no API key needed

Choose Custom Tools if:

• You have specific business logic
• You need to integrate proprietary systems
• Built-in tools don't meet your needs

Code Examples

Tavily Search Tool

from langchain_community.tools.tavily_search import TavilySearchResults
import os

# Initialize Tavily (requires API key)
search_tool = TavilySearchResults(
    max_results=3,
    api_key=os.getenv("TAVILY_API_KEY"),
)

# Use directly
results = search_tool.invoke("Latest AI news")
print(results)

# Use with agent
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

model = ChatOpenAI(model="gpt-4")
agent = create_react_agent(model, [search_tool])

response = agent.invoke({
    "messages": [{"role": "user", "content": "What's new in AI today?"}]
})

Wikipedia Tool

from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

wikipedia = WikipediaQueryRun(
    api_wrapper=WikipediaAPIWrapper(
        top_k_results=3,
        doc_content_chars_max=4000,
    )
)

# Query Wikipedia
result = wikipedia.invoke("Artificial Intelligence")
print(result)

DuckDuckGo Search (No API Key)

from langchain_community.tools import DuckDuckGoSearchRun

search_tool = DuckDuckGoSearchRun()

results = search_tool.invoke("LangChain framework")
print(results)

# With results object for more details
from langchain_community.tools import DuckDuckGoSearchResults

search_tool = DuckDuckGoSearchResults(max_results=5)
results = search_tool.invoke("Python programming")

ArXiv Tool

from langchain_community.tools import ArxivQueryRun

arxiv_tool = ArxivQueryRun()

# Search academic papers
results = arxiv_tool.invoke("large language models")
print(results)

Custom Tool with @tool Decorator

from langchain_core.tools import tool
from typing import Optional

@tool
def get_weather(location: str, unit: Optional[str] = "celsius") -> str:
    """Get the current weather for a location.
    
    Args:
        location: The city name, e.g., 'San Francisco'
        unit: Temperature unit, either 'celsius' or 'fahrenheit'
    """
    # Your implementation
    data = fetch_weather(location, unit)
    return f"The weather in {location} is {data['temp']}°{unit[0].upper()}"

# Use with agent
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

model = ChatOpenAI(model="gpt-4")
agent = create_react_agent(model, [get_weather])

response = agent.invoke({
    "messages": [{"role": "user", "content": "What's the weather in London?"}]
})

Custom Tool with Pydantic Schema

from langchain_core.tools import tool
from pydantic import BaseModel, Field

class WeatherInput(BaseModel):
    location: str = Field(description="The city name")
    unit: str = Field(default="celsius", description="Temperature unit")

@tool("get_weather", args_schema=WeatherInput)
def get_weather(location: str, unit: str = "celsius") -> str:
    """Get the current weather for a location."""
    # Implementation
    return f"Weather in {location}: 72°{unit[0].upper()}"

# Tool now has proper schema validation

Custom Tool - Class-Based

from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from typing import Type

class DatabaseInput(BaseModel):
    customer_id: str = Field(description="Customer ID to look up")

class DatabaseQueryTool(BaseTool):
    name: str = "database_query"
    description: str = "Query the customer database for information"
    args_schema: Type[BaseModel] = DatabaseInput
    
    def _run(self, customer_id: str) -> str:
        """Use the tool."""
        # Your database logic
        customer = db.get_customer(customer_id)
        return str(customer)
    
    async def _arun(self, customer_id: str) -> str:
        """Async version."""
        # Async implementation
        raise NotImplementedError("Async not implemented")

db_tool = DatabaseQueryTool()

Vector Store as Tool

from langchain_core.tools import create_retriever_tool
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

# Create vector store
vectorstore = InMemoryVectorStore.from_texts(
    ["LangChain is a framework...", "Agents use tools..."],
    embedding=OpenAIEmbeddings(),
)

# Convert to tool
retriever_tool = create_retriever_tool(
    vectorstore.as_retriever(),
    name="knowledge_base",
    description="Search the knowledge base for information about LangChain",
)

# Use in agent
from langgraph.prebuilt import create_react_agent

agent = create_react_agent(model, [retriever_tool])

Multiple Tools Example

from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent

# Define tools
search_tool = TavilySearchResults(max_results=3)

@tool
def calculator(expression: str) -> str:
    """Evaluate a mathematical expression.
    
    Args:
        expression: A mathematical expression to evaluate (e.g., "2 + 2")
    """
    try:
        result = eval(expression)
        return str(result)
    except Exception as e:
        return f"Error: {str(e)}"

@tool
def custom_lookup(query: str) -> str:
    """Look up custom information.
    
    Args:
        query: The query to look up
    """
    # Your custom logic
    return f"Custom result for: {query}"

# Create agent with multiple tools
agent = create_react_agent(
    ChatOpenAI(model="gpt-4"),
    [search_tool, calculator, custom_lookup],
)

# Agent will choose appropriate tool(s)
response = agent.invoke({
    "messages": [{
        "role": "user",
        "content": "Search for the population of Tokyo and calculate if it doubled"
    }]
})

Tool with Error Handling

from langchain_core.tools import tool
import requests

@tool
def api_call(endpoint: str) -> str:
    """Call external API.
    
    Args:
        endpoint: API endpoint to call
    """
    try:
        response = requests.get(f"https://api.example.com/{endpoint}")
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        return f"API error: {str(e)}"

# Error handling is critical for robust tools

Toolkits

# SQL Database Toolkit
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.utilities import SQLDatabase

db = SQLDatabase.from_uri("sqlite:///example.db")
toolkit = SQLDatabaseToolkit(db=db, llm=model)

tools = toolkit.get_tools()
# Includes: query, schema info, query checker, etc.

# Use in agent
agent = create_react_agent(model, tools)

Boundaries

What Agents CAN Do

✅ Use pre-built tools

• Tavily search, Wikipedia, DuckDuckGo
• ArXiv, calculators, web browsers
• Any tool from LangChain community

✅ Create custom tools

• Define functions with @tool decorator
• Implement class-based tools
• Convert retrievers to tools

✅ Combine multiple tools

• Give agents access to many tools
• Let models choose appropriate tools
• Chain tool calls

✅ Handle tool responses

• Parse tool output
• Use results in conversation
• Error handling

What Agents CANNOT Do

❌ Execute arbitrary code safely

• Cannot run untrusted code
• Need sandboxing for code execution

❌ Bypass authentication

• Tools need proper API keys
• Cannot access protected resources without credentials

❌ Guarantee tool selection

• Model decides which tool to use
• Cannot force specific tool usage (without prompting)

Gotchas

1. Import from Correct Package

# ❌ OLD
from langchain.tools import WikipediaQueryRun

# ✅ NEW
from langchain_community.tools import WikipediaQueryRun

Fix: Use langchain-community for tools.

2. API Keys Required

# ❌ Missing API key
tool = TavilySearchResults()
tool.invoke("query")  # Error!

# ✅ Provide API key
import os
tool = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))

Fix: Set required API keys in environment variables.

3. Model Must Support Tools

# ❌ Model doesn't support tool calling
model = ChatOpenAI(model="gpt-3.5-turbo-instruct")
# This model doesn't support tools!

# ✅ Use tool-capable model
model = ChatOpenAI(model="gpt-4")

Fix: Use models that support function calling.

4. Tool Description Matters

# ❌ Poor description
@tool
def tool1(x: int) -> int:
    """A tool"""  # Too vague!
    return x * 2

# ✅ Clear, specific description
@tool
def double_number(number: int) -> int:
    """Multiply a number by 2. Use this when the user wants to double a value.
    
    Args:
        number: The number to double
    """
    return number * 2

Fix: Write clear docstrings that help the model know when to use the tool.

5. Type Hints Required

# ❌ Missing type hints
@tool
def my_tool(x):  # No type hints!
    return x

# ✅ Include type hints
@tool
def my_tool(x: str) -> str:
    """Process input.
    
    Args:
        x: Input string
    """
    return x.upper()

Fix: Always include type hints for tool parameters.

Links and Resources

Official Documentation

• LangChain Python Tools
• Custom Tools Guide
• Tavily

Package Installation

# Community tools
pip install langchain-community

# Specific tools
pip install tavily-python  # For Tavily
pip install wikipedia  # For Wikipedia
pip install duckduckgo-search  # For DuckDuckGo