langchain-quercle 1.0.3

LangChain tools for Quercle web search and URL fetching

langchain-quercle

Quercle web search, fetch, and extraction tools for LangChain.

Installation

uv add langchain-quercle
# or
pip install langchain-quercle

Setup

Set your API key as an environment variable:

export QUERCLE_API_KEY=qk_...

Get your API key at quercle.dev.

Quick Start

from quercle_langchain import QuercleSearchTool, QuercleFetchTool

search = QuercleSearchTool()
result = search.invoke("latest developments in AI agents")
print(result)

Tools

Tool	Description	Key Args
QuercleSearchTool	AI-synthesized web search with citations	query, allowed_domains, blocked_domains
QuercleFetchTool	Fetch a URL and analyze content with AI	url, prompt
QuercleRawSearchTool	Raw web search results (markdown/JSON)	query, format, use_safeguard
QuercleRawFetchTool	Raw URL content (markdown/HTML)	url, format, use_safeguard
QuercleExtractTool	Extract relevant content chunks from a URL	url, query, format, use_safeguard

Direct Tool Usage

Sync

from quercle_langchain import (
    QuercleSearchTool,
    QuercleFetchTool,
    QuercleRawSearchTool,
    QuercleRawFetchTool,
    QuercleExtractTool,
)

# AI-synthesized search
search = QuercleSearchTool()
result = search.invoke("best practices for building AI agents")
print(result)

# Search with domain filtering
result = search.invoke({
    "query": "Python documentation",
    "allowed_domains": ["docs.python.org"],
})

# Fetch and analyze a page with AI
fetch = QuercleFetchTool()
result = fetch.invoke({
    "url": "https://en.wikipedia.org/wiki/Python_(programming_language)",
    "prompt": "Summarize the key features of Python",
})
print(result)

# Raw search results
raw_search = QuercleRawSearchTool()
result = raw_search.invoke({"query": "Python tutorials", "format": "json"})
print(result)

# Raw URL content
raw_fetch = QuercleRawFetchTool()
result = raw_fetch.invoke({"url": "https://example.com", "use_safeguard": True})
print(result)

# Extract relevant content from a URL
extract = QuercleExtractTool()
result = extract.invoke({
    "url": "https://en.wikipedia.org/wiki/Python_(programming_language)",
    "query": "What are Python's main features?",
    "format": "json",
})
print(result)

Async

import asyncio
from quercle_langchain import (
    QuercleSearchTool,
    QuercleFetchTool,
    QuercleRawSearchTool,
    QuercleRawFetchTool,
    QuercleExtractTool,
)

async def main():
    # AI-synthesized search
    search = QuercleSearchTool()
    result = await search.ainvoke("latest AI agent frameworks")
    print(result)

    # Fetch and analyze with AI
    fetch = QuercleFetchTool()
    result = await fetch.ainvoke({
        "url": "https://en.wikipedia.org/wiki/TypeScript",
        "prompt": "What is TypeScript?",
    })
    print(result)

    # Raw search results
    raw_search = QuercleRawSearchTool()
    result = await raw_search.ainvoke({"query": "Python tutorials", "format": "json"})
    print(result)

    # Raw URL content
    raw_fetch = QuercleRawFetchTool()
    result = await raw_fetch.ainvoke({"url": "https://example.com"})
    print(result)

    # Extract relevant content
    extract = QuercleExtractTool()
    result = await extract.ainvoke({
        "url": "https://en.wikipedia.org/wiki/TypeScript",
        "query": "What is TypeScript used for?",
    })
    print(result)

asyncio.run(main())

Custom API Key

search = QuercleSearchTool(api_key="qk_...")
fetch = QuercleFetchTool(api_key="qk_...")
raw_search = QuercleRawSearchTool(api_key="qk_...")
raw_fetch = QuercleRawFetchTool(api_key="qk_...")
extract = QuercleExtractTool(api_key="qk_...")

Agentic Usage

With LangGraph ReAct Agent

from quercle_langchain import (
    QuercleSearchTool,
    QuercleFetchTool,
    QuercleRawSearchTool,
    QuercleRawFetchTool,
    QuercleExtractTool,
)
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

model = ChatOpenAI(model="gpt-4o")
tools = [
    QuercleSearchTool(),
    QuercleFetchTool(),
    QuercleRawSearchTool(),
    QuercleRawFetchTool(),
    QuercleExtractTool(),
]

agent = create_react_agent(model, tools)

response = agent.invoke({
    "messages": [
        {"role": "user", "content": "Search for the latest AI news and summarize the top story"}
    ]
})

print(response["messages"][-1].content)

Streaming

for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "Research WebAssembly trends"}]},
    stream_mode="values",
):
    print(chunk["messages"][-1].content)

With Anthropic

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = create_react_agent(model, tools)

API Reference

QuercleSearchTool

AI-synthesized web search with citations.

Parameter	Type	Required	Description
query	str	Yes	The search query
allowed_domains	list[str]	No	Only include results from these domains
blocked_domains	list[str]	No	Exclude results from these domains

QuercleFetchTool

Fetch a URL and analyze its content with AI.

Parameter	Type	Required	Description
url	str	Yes	The URL to fetch
prompt	str	Yes	Instructions for content analysis

QuercleRawSearchTool

Raw web search results without AI synthesis.

Parameter	Type	Required	Description
query	str	Yes	The search query
format	str	No	Output format ("markdown" or "json")
use_safeguard	bool	No	Enable prompt-injection detection

QuercleRawFetchTool

Raw URL content without AI processing.

Parameter	Type	Required	Description
url	str	Yes	The URL to fetch
format	str	No	Output format ("markdown" or "html")
use_safeguard	bool	No	Enable prompt-injection detection

QuercleExtractTool

Extract content chunks relevant to a query from a URL.

Parameter	Type	Required	Description
url	str	Yes	The URL to fetch and extract from
query	str	Yes	What information to extract
format	str	No	Output format ("markdown" or "json")
use_safeguard	bool	No	Enable prompt-injection detection

Configuration

All tools accept these constructor parameters:

Parameter	Default	Description
api_key	QUERCLE_API_KEY env var	Your Quercle API key
timeout	None	Request timeout in seconds

License

MIT