Tabstack for LangChain (Python)

langchain-tabstack gives your LangChain agents reliable web access: schema-enforced extraction, multi-source research, AI transformation, and browser automation, all as native LangChain tools backed by the official tabstack SDK.

LangChain’s built-in loaders (WebBaseLoader, PlaywrightURLLoader) are fine for prototypes and brittle in production: unpredictable parsing, Playwright binaries to maintain, and behavior that drifts across LangChain releases. This package replaces them with a hosted API. See Tabstack vs. LangChain browser tools for the full comparison.

Install

pip

pip install langchain-tabstack

uv

uv add langchain-tabstack

poetry

poetry add langchain-tabstack

Requires Python 3.9+. For the agent example below you also need a chat model, for example pip install langchain langchain-openai.

Create and Setup Your API Key

Before you can start using Tabstack API, you’ll need to create an API key and set it up in your environment.

1. Create Your API Key

1. Visit the Tabstack Console
2. Sign in to your account (or create one if you haven’t already)
3. Navigate to the API Keys section and click the “Manage API Keys”
4. Once you are on the API Keys page, Click “Create New API Key”
5. Give your key a descriptive name (e.g., “Development”, “Production”) and click the “Create API Key”
6. Copy the generated API key and store it securely

Your API key will only be shown once. Make sure to copy and store it in a secure location.

2. Set Up Environment Variable

For security and convenience, we recommend storing your API key as an environment variable rather than hardcoding it in your scripts.

macOS/Linux

# Add to your shell profile (~/.bashrc, ~/.zshrc, or ~/.bash_profile)
export TABSTACK_API_KEY="your_api_key_here"

# Or set it temporarily for the current session
export TABSTACK_API_KEY="your_api_key_here"

# Reload your shell or run:
source ~/.bashrc  # or ~/.zshrc

Windows (Command Prompt)

# Set temporarily for current session
set TABSTACK_API_KEY=your_api_key_here

# Set permanently (requires restart)
setx TABSTACK_API_KEY "your_api_key_here"

Windows (PowerShell)

# Set temporarily for current session
$env:TABSTACK_API_KEY = "your_api_key_here"

# Set permanently for current user
[Environment]::SetEnvironmentVariable("TABSTACK_API_KEY", "your_api_key_here", "User")

3. Verify Your Setup

Test that your environment variable is set correctly:

macOS/Linux/Windows (Git Bash):

echo $TABSTACK_API_KEY

Windows (Command Prompt):

echo %TABSTACK_API_KEY%

Windows (PowerShell):

echo $env:TABSTACK_API_KEY

You should see your API key printed in the terminal.

Quickstart

from langchain.agents import create_agent
from langchain_tabstack import TABSTACK_TOOLS, TabstackToolError

agent = create_agent(
    "openai:gpt-4o",
    tools=TABSTACK_TOOLS,
    system_prompt=(
        "You are a research assistant with web intelligence tools. "
        "Use extract_structured_data for specific fields from a URL, "
        "extract_page_content for full page text, and research_question for multi-source research."
    ),
)

try:
    result = agent.invoke(
        {"messages": [{"role": "user", "content": "What are Vercel's pricing plans?"}]}
    )
    print(result["messages"][-1].content)
except TabstackToolError as err:
    print(f"Tabstack failed ({err.status or 'no status'}): {err}")

TABSTACK_TOOLS reads TABSTACK_API_KEY from the environment and is ready to drop into any agent.

Using LangChain 0.x? create_agent is the LangChain 1.x replacement for AgentExecutor + create_tool_calling_agent. The tools themselves work with either.

The tools

Tool	What it does
extract_structured_data	Pull specific fields from a URL into a JSON shape you define.
extract_page_content	Fetch a page as clean markdown.
research_question	Synthesize a cited answer across multiple pages.
generate_structured_data	Fetch a page, then AI-transform it into derived or reshaped JSON.
automate_browser_task	Run a multi-step, natural-language browser task.

Every tool is exported individually too, so you can use one directly without an agent. Tools return a JSON string (use json.loads); extract_page_content returns markdown text directly. Each raises TabstackToolError on failure; the per-tool examples below omit the try/except for brevity, see Error handling.

extract_structured_data

import json
from langchain_tabstack import extract_structured_data_tool

raw = extract_structured_data_tool.invoke(
    {
        "url": "https://news.ycombinator.com",
        # json_schema_json is a JSON-encoded JSON Schema describing the fields you want.
        "json_schema_json": json.dumps(
            {
                "type": "object",
                "properties": {
                    "stories": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "title": {"type": "string"},
                                "points": {"type": "number", "description": "Story points"},
                            },
                        },
                    }
                },
            }
        ),
    }
)
data = json.loads(raw)

See Schema design for patterns that produce reliable extraction.

extract_page_content

from langchain_tabstack import extract_page_content_tool

markdown = extract_page_content_tool.invoke({"url": "https://example.com/blog/article"})

research_question

import json
from langchain_tabstack import research_question_tool

result = json.loads(
    research_question_tool.invoke(
        {"query": "What are the latest developments in quantum error correction?"}
    )
)
print(result["answer"])
for source in result["sources"]:  # [{"title": ..., "url": ...}, ...]
    print(source["url"])

generate_structured_data

import json
from langchain_tabstack import generate_structured_data_tool

raw = generate_structured_data_tool.invoke(
    {
        "url": "https://news.ycombinator.com",
        "instructions": "For each story, categorize it and write a one-sentence summary.",
        "json_schema_json": json.dumps(
            {
                "type": "object",
                "properties": {
                    "summaries": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "title": {"type": "string"},
                                "category": {"type": "string"},
                                "summary": {"type": "string"},
                            },
                        },
                    }
                },
            }
        ),
    }
)

automate_browser_task

import json
from langchain_tabstack import automate_browser_task_tool

result = json.loads(
    automate_browser_task_tool.invoke(
        {
            "task": "Find the top 3 trending repositories and their star counts",
            "url": "https://github.com/trending",
            "guardrails": "browse and extract only, do not submit forms",
        }
    )
)
# {"answer", "success", "iterations", "actions", "duration_ms", "extracted", "pages_visited"}

Optional inputs

Pass these alongside the required inputs for finer control. They are sent only when provided.

• extract_structured_data, extract_page_content, generate_structured_data:
  • effort: "min", "standard", or "max". Use "max" for JS-heavy pages (the PlaywrightURLLoader replacement). See Effort levels.
  • nocache: True to bypass the cache.
  • country: ISO 3166-1 alpha-2 code for geotargeting.
• research_question: mode ("fast" or "balanced"), nocache.
• automate_browser_task: guardrails, data (context for form filling), country, max_iterations, max_validation_attempts.

# Render a JS-heavy SPA, fresh, from the UK:
extract_page_content_tool.invoke(
    {"url": url, "effort": "max", "nocache": True, "country": "GB"}
)

Async

Every tool supports ainvoke natively, backed by AsyncTabstack, so it runs in your event loop without a thread-pool bounce:

markdown = await extract_page_content_tool.ainvoke({"url": "https://example.com"})

Configuration

For a custom API key or base URL, or to reuse one client, build the tools explicitly:

from langchain_tabstack import create_tabstack_tools

tools = create_tabstack_tools(api_key="...")
# or pass clients you already have:
# create_tabstack_tools(client=..., async_client=...)

Error handling

Tool calls raise TabstackToolError with a normalized message and, for API failures, an HTTP status:

from langchain_tabstack import TabstackToolError, extract_page_content_tool

try:
    extract_page_content_tool.invoke({"url": "https://example.com"})
except TabstackToolError as err:
    print(f"Tabstack failed ({err.status or 'no status'}): {err}")

Beyond tools

The tools cover the agent use case. For pipelines where Tabstack is a plain step rather than a tool the model chooses, call the tabstack SDK directly.

LCEL chains

Wrap a Tabstack call as a RunnableLambda and compose it like any other step:

import os
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
from tabstack import Tabstack

client = Tabstack(api_key=os.environ["TABSTACK_API_KEY"])

def fetch_page_content(url: str) -> str:
    return client.extract.markdown(url=url).content

summarize_chain = (
    RunnableLambda(fetch_page_content)
    | ChatPromptTemplate.from_template("Summarize this in 3 bullet points: {text}")
    | ChatOpenAI(model="gpt-4o-mini")
    | StrOutputParser()
)

print(summarize_chain.invoke("https://example.com/blog/article"))

RAG document loading

Tabstack’s markdown endpoint returns clean content plus optional metadata, ideal for enriching documents before embedding:

import os
from langchain_core.documents import Document
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from tabstack import Tabstack

client = Tabstack(api_key=os.environ["TABSTACK_API_KEY"])

def tabstack_to_document(url: str) -> Document:
    result = client.extract.markdown(url=url, metadata=True)
    meta = result.metadata
    return Document(
        page_content=result.content,
        metadata={
            "source": url,
            "title": meta.title if meta else None,
            "author": meta.author if meta else None,
            "published": meta.created_at if meta else None,
        },
    )

urls = ["https://docs.example.com/getting-started", "https://docs.example.com/api-reference"]
docs = [tabstack_to_document(u) for u in urls]
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever()

Related

• Integrations overview
• Tabstack Python SDK
• Schema design
• Tabstack vs. LangChain browser tools
• LangChain.js integration and Vercel AI SDK integration