AgentQL

AgentQL tools provides web interaction and structured data extraction from any web page using an AgentQL query or a Natural Language prompt. AgentQL can be used across multiple languages and web pages without breaking over time and change.

Overview

AgentQL provides the following three tools:

• ExtractWebDataTool extracts structured data as JSON from a web page given a URL using either an AgentQL query or a Natural Language description of the data.

The following two tools are also bundled as AgentQLBrowserToolkit and must be used with a Playwright browser or a remote browser instance via Chrome DevTools Protocal (CDP):

• ExtractWebDataBrowserTool extracts structured data as JSON from the active web page in a browser using either an AgentQL query or a Natural Language description.

• GetWebElementBrowserTool finds a web element on the active web page in a browser using a Natural Language description and returns its CSS selector for further interaction.

Integration details

Class	Package	Serializable	JS support	Package latest
AgentQL	langchain-agentql	❌	❌	1.0.0

Tool features

Tool	Web Data Extraction	Web Element Extraction	Use With Local Browser
ExtractWebDataTool	✅	❌	❌
ExtractWebDataBrowserTool	✅	❌	✅
GetWebElementBrowserTool	❌	✅	✅

Setup

%pip install --quiet -U langchain_agentql

To run this notebook, install Playwright browser and configure Jupyter Notebook's asyncio loop.

!playwright install

# This import is required only for jupyter notebooks, since they have their own eventloop
import nest_asyncio

nest_asyncio.apply()

Credentials

To use the AgentQL tools, you will need to get your own API key from the AgentQL Dev Portal and set the AgentQL environment variable.

import os

os.environ["AGENTQL_API_KEY"] = "YOUR_AGENTQL_API_KEY"

Instantiation

ExtractWebDataTool

You can instantiate ExtractWebDataTool with the following params:

• api_key: Your AgentQL API key from dev.agentql.com. Optional.
• timeout: The number of seconds to wait for a request before timing out. Increase if data extraction times out. Defaults to 900.
• is_stealth_mode_enabled: Whether to enable experimental anti-bot evasion strategies. This feature may not work for all websites at all times. Data extraction may take longer to complete with this mode enabled. Defaults to False.
• wait_for: The number of seconds to wait for the page to load before extracting data. Defaults to 0.
• is_scroll_to_bottom_enabled: Whether to scroll to bottom of the page before extracting data. Defaults to False.
• mode: "standard" uses deep data analysis, while "fast" trades some depth of analysis for speed and is adequate for most usecases. Learn more about the modes in this guide. Defaults to "fast".
• is_screenshot_enabled: Whether to take a screenshot before extracting data. Returned in 'metadata' as a Base64 string. Defaults to False.

ExtractWebDataTool is implemented with AgentQL's REST API, you can view more details about the parameters in the API Reference docs.

from langchain_agentql.tools import ExtractWebDataTool

extract_web_data_tool = ExtractWebDataTool()

ExtractWebDataBrowserTool

To instantiate ExtractWebDataBrowserTool, you need to connect the tool with a browser instance.

You can set the following params:

• timeout: The number of seconds to wait for a request before timing out. Increase if data extraction times out. Defaults to 900.
• wait_for_network_idle: Whether to wait until the network reaches a full idle state before executing. Defaults to True.
• include_hidden: Whether to take into account visually hidden elements on the page. Defaults to True.
• mode: "standard" uses deep data analysis, while "fast" trades some depth of analysis for speed and is adequate for most usecases. Learn more about the modes in this guide. Defaults to "fast".

ExtractWebDataBrowserTool is implemented with AgentQL's SDK. You can find more details about the parameters and the functions in AgentQL's API References.

from langchain_agentql.tools import ExtractWebDataBrowserTool
from langchain_agentql.utils import create_async_playwright_browser

async_browser = await create_async_playwright_browser()

extract_web_data_browser_tool = ExtractWebDataBrowserTool(async_browser=async_browser)

GetWebElementBrowserTool

To instantiate GetWebElementBrowserTool, you need to connect the tool with a browser instance.

You can set the following params:

• timeout: The number of seconds to wait for a request before timing out. Increase if data extraction times out. Defaults to 900.
• wait_for_network_idle: Whether to wait until the network reaches a full idle state before executing. Defaults to True.
• include_hidden: Whether to take into account visually hidden elements on the page. Defaults to False.
• mode: "standard" uses deep data analysis, while "fast" trades some depth of analysis for speed and is adequate for most usecases. Learn more about the modes in this guide. Defaults to "fast".

GetWebElementBrowserTool is implemented with AgentQL's SDK. You can find more details about the parameters and the functions in AgentQL's API References.`

from langchain_agentql.tools import GetWebElementBrowserTool

extract_web_element_tool = GetWebElementBrowserTool(async_browser=async_browser)

Invocation

ExtractWebDataTool

This tool uses AgentQL's REST API under the hood, sending the publically available web page's URL to AgentQL's endpoint. This will not work with private pages or logged in sessions. Use ExtractWebDataBrowserTool for those usecases.

• url: The URL of the web page you want to extract data from.
• query: The AgentQL query to execute. Use AgentQL query if you want to extract precisely structured data. Learn more about how to write an AgentQL query in the docs or test one out in the AgentQL Playground.
• prompt: A Natural Language description of the data to extract from the page. AgentQL will infer the data’s structure from your prompt. Use prompt if you want to extract data defined by free-form language without defining a particular structure.

Note: You must define either a query or a prompt to use AgentQL.

# You can invoke the tool with either a query or a prompt

# extract_web_data_tool.invoke(
#     {
#         "url": "https://www.agentql.com/blog",
#         "prompt": "the blog posts with title, url, date of post and author",
#     }
# )

extract_web_data_tool.invoke(
    {
        "url": "https://www.agentql.com/blog",
        "query": "{ posts[] { title url date author } }",
    },
)

{'data': {'posts': [{'title': 'Launch Week Recap—make the web AI-ready',
    'url': 'https://www.agentql.com/blog/2024-launch-week-recap',
    'date': 'Nov 18, 2024',
    'author': 'Rachel-Lee Nabors'},
   {'title': 'Accurate data extraction from PDFs and images with AgentQL',
    'url': 'https://www.agentql.com/blog/accurate-data-extraction-pdfs-images',
    'date': 'Feb 1, 2025',
    'author': 'Rachel-Lee Nabors'},
   {'title': 'Introducing Scheduled Scraping Workflows',
    'url': 'https://www.agentql.com/blog/scheduling',
    'date': 'Dec 2, 2024',
    'author': 'Rachel-Lee Nabors'},
   {'title': 'Updates to Our Pricing Model',
    'url': 'https://www.agentql.com/blog/2024-pricing-update',
    'date': 'Nov 19, 2024',
    'author': 'Rachel-Lee Nabors'},
   {'title': 'Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5',
    'url': 'https://www.agentql.com/blog/data-rest-api',
    'date': 'Nov 15, 2024',
    'author': 'Rachel-Lee Nabors'}]},
 'metadata': {'request_id': '0dc1f89c-1b6a-46fe-8089-6cd0f082f094',
  'generated_query': None,
  'screenshot': None}}

ExtractWebDataBrowserTool

• query: The AgentQL query to execute. Use AgentQL query if you want to extract precisely structured data. Learn more about how to write an AgentQL query in the docs or test one out in the AgentQL Playground.
• prompt: A Natural Language description of the data to extract from the page. AgentQL will infer the data’s structure from your prompt. Use prompt if you want to extract data defined by free-form language without defining a particular structure.

Note: You must define either a query or a prompt to use AgentQL.

To extract data, first you must navigate to a web page using LangChain's Playwright tool.

from langchain_community.tools.playwright import NavigateTool

navigate_tool = NavigateTool(async_browser=async_browser)
await navigate_tool.ainvoke({"url": "https://www.agentql.com/blog"})

API Reference:NavigateTool

'Navigating to https://www.agentql.com/blog returned status code 200'

# You can invoke the tool with either a query or a prompt

# await extract_web_data_browser_tool.ainvoke(
#     {'query': '{ blogs[] { title url date author } }'}
# )

await extract_web_data_browser_tool.ainvoke(
    {"prompt": "the blog posts with title, url, date of post and author"}
)

/usr/local/lib/python3.11/dist-packages/agentql/_core/_utils.py:167: UserWarning: 🚨 The function get_data_by_prompt_experimental is experimental and may not work as expected 🚨
  warnings.warn(

{'blog_posts': [{'title': 'Launch Week Recap—make the web AI-ready',
   'url': 'https://www.agentql.com/blog/2024-launch-week-recap',
   'date': 'Nov 18, 2024',
   'author': 'Rachel-Lee Nabors'},
  {'title': 'Accurate data extraction from PDFs and images with AgentQL',
   'url': 'https://www.agentql.com/blog/accurate-data-extraction-pdfs-images',
   'date': 'Feb 1, 2025',
   'author': 'Rachel-Lee Nabors'},
  {'title': 'Introducing Scheduled Scraping Workflows',
   'url': 'https://www.agentql.com/blog/scheduling',
   'date': 'Dec 2, 2024',
   'author': 'Rachel-Lee Nabors'},
  {'title': 'Updates to Our Pricing Model',
   'url': 'https://www.agentql.com/blog/2024-pricing-update',
   'date': 'Nov 19, 2024',
   'author': 'Rachel-Lee Nabors'},
  {'title': 'Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5',
   'url': 'https://www.agentql.com/blog/data-rest-api',
   'date': 'Nov 15, 2024',
   'author': 'Rachel-Lee Nabors'}]}

GetWebElementBrowserTool

• prompt: A Natural Language description of the web element to find on the page.

selector = await extract_web_element_tool.ainvoke({"prompt": "Next page button"})
selector

"[tf623_id='194']"

from langchain_community.tools.playwright import ClickTool

# Disabling 'visible_only' will allow us to click on elements that are not visible on the page
await ClickTool(async_browser=async_browser, visible_only=False).ainvoke(
    {"selector": selector}
)

API Reference:ClickTool

"Clicked element '[tf623_id='194']'"

from langchain_community.tools.playwright import CurrentWebPageTool

await CurrentWebPageTool(async_browser=async_browser).ainvoke({})

API Reference:CurrentWebPageTool

'https://www.agentql.com/blog/page/2'

Chaining

You can use AgentQL tools in a chain by first binding one to a tool-calling model and then calling it:

Instantiate LLM

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

from langchain.chat_models import init_chat_model

llm = init_chat_model(model="gpt-4o", model_provider="openai")

API Reference:init_chat_model

Execute Tool Chain

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig, chain

prompt = ChatPromptTemplate(
    [
        ("system", "You are a helpful assistant in extracting data from website."),
        ("human", "{user_input}"),
        ("placeholder", "{messages}"),
    ]
)

# specifying tool_choice will force the model to call this tool.
llm_with_tools = llm.bind_tools(
    [extract_web_data_tool], tool_choice="extract_web_data_with_rest_api"
)

llm_chain = prompt | llm_with_tools


@chain
def tool_chain(user_input: str, config: RunnableConfig):
    input_ = {"user_input": user_input}
    ai_msg = llm_chain.invoke(input_, config=config)
    tool_msgs = extract_web_data_tool.batch(ai_msg.tool_calls, config=config)
    return {"messages": tool_msgs}


tool_chain.invoke(
    "Extract data from https://www.agentql.com/blog using the following agentql query: { posts[] { title url date author } }"
)

API Reference:ChatPromptTemplate | RunnableConfig | chain

{'messages': [ToolMessage(content='{"data": {"posts": [{"title": "Launch Week Recap—make the web AI-ready", "url": "https://www.agentql.com/blog/2024-launch-week-recap", "date": "Nov 18, 2024", "author": "Rachel-Lee Nabors"}, {"title": "Accurate data extraction from PDFs and images with AgentQL", "url": "https://www.agentql.com/blog/accurate-data-extraction-pdfs-images", "date": "Feb 1, 2025", "author": "Rachel-Lee Nabors"}, {"title": "Introducing Scheduled Scraping Workflows", "url": "https://www.agentql.com/blog/scheduling", "date": "Dec 2, 2024", "author": "Rachel-Lee Nabors"}, {"title": "Updates to Our Pricing Model", "url": "https://www.agentql.com/blog/2024-pricing-update", "date": "Nov 19, 2024", "author": "Rachel-Lee Nabors"}, {"title": "Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5", "url": "https://www.agentql.com/blog/data-rest-api", "date": "Nov 15, 2024", "author": "Rachel-Lee Nabors"}]}, "metadata": {"request_id": "1a84ed12-d02a-497d-b09d-21fe49342fa3", "generated_query": null, "screenshot": null}}', name='extract_web_data_with_rest_api', tool_call_id='call_z4Rl1MpjJZNcbLlq1OCneoMF')]}

Use within an agent

You can use AgentQL tools with an AI Agent using the AgentQLBrowserToolkit . This toolkit includes ExtractDataBrowserTool and GetWebElementBrowserTool. Here's an example of agentic browser actions that combine AgentQL's toolkit with the Playwright tools.

Instantiate Toolkit

from langchain_agentql.utils import create_async_playwright_browser

async_agent_browser = await create_async_playwright_browser()

from langchain_agentql import AgentQLBrowserToolkit

agentql_toolkit = AgentQLBrowserToolkit(async_browser=async_agent_browser)
agentql_toolkit.get_tools()

[ExtractWebDataBrowserTool(async_browser=<Browser type=<BrowserType name=chromium executable_path=/root/.cache/ms-playwright/chromium-1155/chrome-linux/chrome> version=133.0.6943.16>),
 GetWebElementBrowserTool(async_browser=<Browser type=<BrowserType name=chromium executable_path=/root/.cache/ms-playwright/chromium-1155/chrome-linux/chrome> version=133.0.6943.16>)]

from langchain_community.tools.playwright import ClickTool, NavigateTool

# we hand pick the following tools to allow more precise agentic browser actions
playwright_toolkit = [
    NavigateTool(async_browser=async_agent_browser),
    ClickTool(async_browser=async_agent_browser, visible_only=False),
]
playwright_toolkit

API Reference:ClickTool | NavigateTool

[NavigateTool(async_browser=<Browser type=<BrowserType name=chromium executable_path=/root/.cache/ms-playwright/chromium-1155/chrome-linux/chrome> version=133.0.6943.16>),
 ClickTool(async_browser=<Browser type=<BrowserType name=chromium executable_path=/root/.cache/ms-playwright/chromium-1155/chrome-linux/chrome> version=133.0.6943.16>, visible_only=False)]

Use with a ReAct Agent

%pip install --quiet -U langgraph

from langgraph.prebuilt import create_react_agent

# You need to set up an llm, please refer to the chaining section
agent_executor = create_react_agent(
    llm, agentql_toolkit.get_tools() + playwright_toolkit
)

API Reference:create_react_agent

prompt = """
Navigate to https://news.ycombinator.com/,
extract the news titles on the current page,
show the current page url,
find the button on the webpage that direct to the next page,
click on the button,
show the current page url,
extract the news title on the current page
extract the news titles that mention "AI" from the two pages.
"""

events = agent_executor.astream(
    {"messages": [("user", prompt)]},
    stream_mode="values",
)
async for event in events:
    event["messages"][-1].pretty_print()

================================ Human Message =================================


Navigate to https://news.ycombinator.com/,
extract the news titles on the current page,
show the current page url,
find the button on the webpage that direct to the next page,
click on the button,
show the current page url,
extract the news title on the current page
extract the news titles that mention "AI" from the two pages.

================================== Ai Message ==================================
Tool Calls:
  navigate_browser (call_3eY5a0BRwyYj7kaNpAxkquTD)
 Call ID: call_3eY5a0BRwyYj7kaNpAxkquTD
  Args:
    url: https://news.ycombinator.com/
================================= Tool Message =================================
Name: navigate_browser

Navigating to https://news.ycombinator.com/ returned status code 200
================================== Ai Message ==================================
Tool Calls:
  extract_web_data_from_browser (call_WvRrZKGGo8mq3JewRlaIS5xx)
 Call ID: call_WvRrZKGGo8mq3JewRlaIS5xx
  Args:
    prompt: Extract all the news titles from this page.
``````output
/usr/local/lib/python3.11/dist-packages/agentql/_core/_utils.py:167: UserWarning: 🚨 The function get_data_by_prompt_experimental is experimental and may not work as expected 🚨
  warnings.warn(
``````output
================================= Tool Message =================================
Name: extract_web_data_from_browser

{"news_item": [{"title": "I Went to SQL Injection Court"}, {"title": "Framework's first desktop is a strange–but unique–mini ITX gaming PC"}, {"title": "Hyperspace"}, {"title": "The XB-70 (2019)"}, {"title": "How core Git developers configure Git"}, {"title": "Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs [pdf]"}, {"title": "Hard problems that reduce to document ranking"}, {"title": "Ggwave: Tiny Data-over-Sound Library"}, {"title": "Bald eagles are thriving again after near extinction"}, {"title": "Forum with 2.6M posts being deleted due to UK Online Safety Act"}, {"title": "Launch HN: Browser Use (YC W25) – open-source web agents"}, {"title": "Part two of Grant Sanderson's video with Terry Tao on the cosmic distance ladder"}, {"title": "New maps of the chaotic space-time inside black holes"}, {"title": "Knitting Your Parachute"}, {"title": "Chicory: A JVM native WebAssembly runtime"}, {"title": "Low Overhead Allocation Sampling with VMProf in PyPy's GC"}, {"title": "Sigma BF Camera"}, {"title": "DeepSearcher: A local open-source Deep Research"}, {"title": "Xonsh – A Python-powered shell"}, {"title": "A possible future of Python in the browser"}, {"title": "Show HN: GoatDB – A lightweight, offline-first, realtime NoDB for Deno and React"}, {"title": "Embedding Python in Elixir, it's fine"}, {"title": "The Deep Research problem"}, {"title": "Why are QR Codes with capital letters smaller than QR codes with lower case?"}, {"title": "Show HN: My new wiki for Silicon Graphics stuff"}, {"title": "AI is blurring the line between PMs and engineers?"}, {"title": "I recreated Shazam's algorithm with Go [video]"}, {"title": "Dogs may have domesticated themselves because they liked snacks, model suggests"}, {"title": "Show HN: Txtl – Fast static website of text utilities"}, {"title": "Have we been wrong about why Mars is red?"}]}
================================== Ai Message ==================================
Tool Calls:
  get_web_element_from_browser (call_B6jn5ItasceNW7eeb640UhQQ)
 Call ID: call_B6jn5ItasceNW7eeb640UhQQ
  Args:
    prompt: button or link to go to the next page
  extract_web_data_from_browser (call_Wyh2VH76bzrlDozp7gpkVBl7)
 Call ID: call_Wyh2VH76bzrlDozp7gpkVBl7
  Args:
    prompt: Extract the current page URL
``````output
/usr/local/lib/python3.11/dist-packages/agentql/_core/_utils.py:167: UserWarning: 🚨 The function get_data_by_prompt_experimental is experimental and may not work as expected 🚨
  warnings.warn(
``````output
================================= Tool Message =================================
Name: extract_web_data_from_browser

{"current_page_url": "https://news.ycombinator.com/news"}
================================== Ai Message ==================================
Tool Calls:
  click_element (call_NLGIW1lLutkZ6k0vqkfGbOD7)
 Call ID: call_NLGIW1lLutkZ6k0vqkfGbOD7
  Args:
    selector: [tf623_id='944']
================================= Tool Message =================================
Name: click_element

Clicked element '[tf623_id='944']'
================================== Ai Message ==================================
Tool Calls:
  extract_web_data_from_browser (call_QPt8R2hqiSgytUvLcWUUORKF)
 Call ID: call_QPt8R2hqiSgytUvLcWUUORKF
  Args:
    prompt: Extract the current page URL
``````output
/usr/local/lib/python3.11/dist-packages/agentql/_core/_utils.py:167: UserWarning: 🚨 The function get_data_by_prompt_experimental is experimental and may not work as expected 🚨
  warnings.warn(
``````output
================================= Tool Message =================================
Name: extract_web_data_from_browser

{"current_page_url": "https://news.ycombinator.com/news?p=2"}
================================== Ai Message ==================================
Tool Calls:
  extract_web_data_from_browser (call_ZZOPrIfVaVQ1A26j8EGE913W)
 Call ID: call_ZZOPrIfVaVQ1A26j8EGE913W
  Args:
    prompt: Extract all the news titles from this page.
``````output
/usr/local/lib/python3.11/dist-packages/agentql/_core/_utils.py:167: UserWarning: 🚨 The function get_data_by_prompt_experimental is experimental and may not work as expected 🚨
  warnings.warn(
``````output
================================= Tool Message =================================
Name: extract_web_data_from_browser

{"news_item": [{"title": "'Hey Number 17 '"}, {"title": "Building and operating a pretty big storage system called S3 (2023)"}, {"title": "Ghost House – software for automatic inbetweens"}, {"title": "Ask HN: Former devs who can't get a job, what did you end up doing for work?"}, {"title": "DeepSeek open source DeepEP – library for MoE training and Inference"}, {"title": "SETI's hard steps and how to resolve them"}, {"title": "A Defense of Weird Research"}, {"title": "DigiCert: Threat of legal action to stifle Bugzilla discourse"}, {"title": "Show HN: Tach – Visualize and untangle your Python codebase"}, {"title": "Ask HN: A retrofitted C dialect?"}, {"title": "“The closer to the train station, the worse the kebab” – a “study”"}, {"title": "Brewing Clean Water: The metal-remediating benefits of tea preparation"}, {"title": "Invoker Commands (Explainer)"}, {"title": "Freelancing: How I found clients, part 1"}, {"title": "Claude 3.7 Sonnet and Claude Code"}, {"title": "Clean Code vs. A Philosophy Of Software Design"}, {"title": "Show HN: While the world builds AI Agents, I'm just building calculators"}, {"title": "History of CAD"}, {"title": "Fans are better than tech at organizing information online (2019)"}, {"title": "Some Programming Language Ideas"}, {"title": "The independent researcher (2018)"}, {"title": "The best way to use text embeddings portably is with Parquet and Polars"}, {"title": "Show HN: Prioritize Anything with Stacks"}, {"title": "Ashby (YC W19) Is Hiring Principal Product Engineers"}, {"title": "GibberLink [AI-AI Communication]"}, {"title": "Show HN: I made a site to tell the time in corporate"}, {"title": "It’s still worth blogging in the age of AI"}, {"title": "What would happen if we didn't use TCP or UDP?"}, {"title": "Closing the “green gap”: energy savings from the math of the landscape function"}, {"title": "Larry Ellison's half-billion-dollar quest to change farming"}]}
================================== Ai Message ==================================

Here's a summary of the actions and results:

### Page 1
- **URL:** [https://news.ycombinator.com/news](https://news.ycombinator.com/news)
- **News Titles:**
  1. I Went to SQL Injection Court
  2. Framework's first desktop is a strange–but unique–mini ITX gaming PC
  3. Hyperspace
  4. The XB-70 (2019)
  5. How core Git developers configure Git
  6. Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs [pdf]
  7. Hard problems that reduce to document ranking
  8. Ggwave: Tiny Data-over-Sound Library
  9. Bald eagles are thriving again after near extinction
  10. Forum with 2.6M posts being deleted due to UK Online Safety Act
  11. Launch HN: Browser Use (YC W25) – open-source web agents
  12. Part two of Grant Sanderson's video with Terry Tao on the cosmic distance ladder
  13. New maps of the chaotic space-time inside black holes
  14. Knitting Your Parachute
  15. Chicory: A JVM native WebAssembly runtime
  16. Low Overhead Allocation Sampling with VMProf in PyPy's GC
  17. Sigma BF Camera
  18. DeepSearcher: A local open-source Deep Research
  19. Xonsh – A Python-powered shell
  20. A possible future of Python in the browser
  21. Show HN: GoatDB – A lightweight, offline-first, realtime NoDB for Deno and React
  22. Embedding Python in Elixir, it's fine
  23. The Deep Research problem
  24. Why are QR Codes with capital letters smaller than QR codes with lower case?
  25. Show HN: My new wiki for Silicon Graphics stuff
  26. **AI is blurring the line between PMs and engineers?**
  27. I recreated Shazam's algorithm with Go [video]
  28. Dogs may have domesticated themselves because they liked snacks, model suggests
  29. Show HN: Txtl – Fast static website of text utilities
  30. Have we been wrong about why Mars is red?

### Page 2
- **URL:** [https://news.ycombinator.com/news?p=2](https://news.ycombinator.com/news?p=2)
- **News Titles:**
  1. 'Hey Number 17'
  2. Building and operating a pretty big storage system called S3 (2023)
  3. Ghost House – software for automatic inbetweens
  4. Ask HN: Former devs who can't get a job, what did you end up doing for work?
  5. DeepSeek open source DeepEP – library for MoE training and Inference
  6. SETI's hard steps and how to resolve them
  7. A Defense of Weird Research
  8. DigiCert: Threat of legal action to stifle Bugzilla discourse
  9. Show HN: Tach – Visualize and untangle your Python codebase
  10. Ask HN: A retrofitted C dialect?
  11. “The closer to the train station, the worse the kebab” – a “study”
  12. Brewing Clean Water: The metal-remediating benefits of tea preparation
  13. Invoker Commands (Explainer)
  14. Freelancing: How I found clients, part 1
  15. Claude 3.7 Sonnet and Claude Code
  16. Clean Code vs. A Philosophy Of Software Design
  17. **Show HN: While the world builds AI Agents, I'm just building calculators**
  18. History of CAD
  19. Fans are better than tech at organizing information online (2019)
  20. Some Programming Language Ideas
  21. The independent researcher (2018)
  22. The best way to use text embeddings portably is with Parquet and Polars
  23. Show HN: Prioritize Anything with Stacks
  24. Ashby (YC W19) Is Hiring Principal Product Engineers
  25. **GibberLink [AI-AI Communication]**
  26. Show HN: I made a site to tell the time in corporate
  27. **It’s still worth blogging in the age of AI**
  28. What would happen if we didn't use TCP or UDP?
  29. Closing the “green gap”: energy savings from the math of the landscape function
  30. Larry Ellison's half-billion-dollar quest to change farming

### News Titles Mentioning "AI":
1. Page 1: **AI is blurring the line between PMs and engineers?**
2. Page 2:
   - **Show HN: While the world builds AI Agents, I'm just building calculators**
   - **GibberLink [AI-AI Communication]**
   - **It’s still worth blogging in the age of AI**

API reference

For more information on how to use this integration, please refer to the git repo or the langchain integration documentation

Related

• Tool conceptual guide
• Tool how-to guides