langchain-yutori 0.2.0

LangChain integration for the Yutori API — Navigator browser control, browser automation, web research, and monitoring

langchain-yutori

LangChain integration for the Yutori API — Navigator browser control, browser automation, deep research, and recurring web monitors.

Installation

pip install langchain langchain-yutori

This package is implemented as a standalone LangChain integration package. It uses the official yutori Python SDK for Browsing, Research, and Scouts, and wraps Navigator as a LangChain chat model. Installing langchain-yutori also installs the yutori Python package, plus the yutori CLI.

Components

Class	Type	Description
ChatYutoriNavigator	ChatModel	Yutori Navigator browser-control model (OpenAI-compatible, defaults to n1.5)
YutoriBrowsingTool	BaseTool	Execute web browsing tasks on a remote browser
YutoriResearchTool	BaseTool	Perform deep and broad research using 100+ tools
YutoriScoutingTool	BaseTool	Create and manage recurring web monitors with Scouts

Authentication

Recommended:

yutori auth login

This opens your browser and saves your API key locally for the SDK and this package to use.

Or set your API key via environment variable:

export YUTORI_API_KEY="yt_..."

Or pass it directly to each class.

Get your API key at platform.yutori.com.

Usage

ChatYutoriNavigator

Navigator is Yutori's pixels-to-actions LLM for browser navigation. It accepts screenshots and returns browser actions (click, type, scroll, etc.). The current version is n1.5 (the default); older versions like n1 remain selectable via the model argument.

from langchain_yutori import ChatYutoriNavigator
from langchain_core.messages import HumanMessage
from yutori.navigator import aplaywright_screenshot_to_data_url

llm = ChatYutoriNavigator()  # defaults to n1.5-latest, uses YUTORI_API_KEY env var
image_url = await aplaywright_screenshot_to_data_url(page)

message = HumanMessage(content=[
    {"type": "text", "text": "What is the next action to complete the task: 'Add item to cart'?"},
    {"type": "image_url", "image_url": {"url": image_url}},
])
response = llm.invoke([message])
# Returns tool_calls with browser actions

To pin to a specific Navigator version:

llm = ChatYutoriNavigator(model="n1-latest")     # older Navigator n1
llm = ChatYutoriNavigator(model="n1.5-latest")   # current Navigator n1.5 (default)

With Playwright, use the SDK helper to capture the screenshot and re-encode it as a WebP data URL at the resolution Navigator was trained on (1280×800, q90). ChatYutoriNavigator accepts image URLs but doesn't capture or preprocess screenshots itself.

If you execute returned browser actions yourself, Navigator coordinates are normalized to a 1000x1000 space. Convert them back into viewport pixels with the SDK helper:

from yutori.navigator import denormalize_coordinates

coords = [500, 250]
x, y = denormalize_coordinates(coords, width=1280, height=800)
await page.mouse.click(x, y)

Tool sets and request options

Navigator's available action set is server-side and version-tagged. Select it (and optionally disable specific actions) via first-class constructor params; they are forwarded to the OpenAI client's extra_body:

llm = ChatYutoriNavigator(
    tool_set="browser_tools_expanded-20260403",   # adds extract_elements, find, set_element_value, execute_js
    disable_tools=["hold_key", "drag"],
)

Other Navigator-specific request fields (e.g. json_schema for structured output) can be passed through extra_body directly — they're merged with the first-class params:

llm = ChatYutoriNavigator(
    tool_set="browser_tools_core-20260403",
    extra_body={"json_schema": {"type": "object", "properties": {...}}},
)

Multi-turn loop and message-history shape

After Navigator returns tool_calls, you execute the action in your browser, capture a fresh screenshot, and feed the result back as a ToolMessage whose content is a multimodal list of [text, image_url]. Navigator requires the new screenshot inside the tool message — not in a separate HumanMessage:

from langchain_core.messages import AIMessage, HumanMessage
from langchain_yutori import navigator_tool_result, trim_navigator_history
from yutori.navigator import aplaywright_screenshot_to_data_url, denormalize_coordinates

history = [
    HumanMessage(content=[
        {"type": "text", "text": "Search for Yutori on Google."},
        {"type": "image_url", "image_url": {"url": await aplaywright_screenshot_to_data_url(page)}},
    ])
]

while True:
    response: AIMessage = llm.invoke(trim_navigator_history(history))
    history.append(response)
    if not response.tool_calls:
        break  # Navigator finished the task; response.content has the summary

    last_idx = len(response.tool_calls) - 1
    for i, call in enumerate(response.tool_calls):
        result_text = await execute_in_browser(page, call)  # your code; use denormalize_coordinates etc.
        history.append(navigator_tool_result(
            tool_call_id=call["id"],
            result_text=result_text,
            current_url=page.url,
            # only the last result in a batched turn carries the next screenshot
            screenshot_data_url=(
                await aplaywright_screenshot_to_data_url(page) if i == last_idx else None
            ),
        ))

Notes:

• Don't add a system message. Navigator's docs recommend placing extra instructions in the first user message instead.

• Include Current URL: ... in the tool result text — it improves grounding.

• Don't drop messages — only old screenshots. For long trajectories, use trim_navigator_history to drop image_url blocks from older messages while keeping every message and its text intact:

  from langchain_yutori import trim_navigator_history
  
  response = llm.invoke(trim_navigator_history(history))   # default keep_recent=2
  

  The Yutori SDK exposes equivalent helpers (trim_images_to_fit, trimmed_messages_to_fit, create_trimmed / acreate_trimmed) that operate on dict-shaped messages and bypass LangChain — use those if you call the SDK directly, otherwise prefer the LangChain helper above for llm.invoke(...) flows.

For the full Navigator input requirements and action schema, see the Yutori docs: https://docs.yutori.com/llm-quickstart and https://docs.yutori.com/reference/navigator

YutoriBrowsingTool

Runs a browser automation agent on Yutori's cloud browser. The tool creates the task and polls until it completes (up to 20 minutes by default; tasks typically take 5–15 minutes).

from langchain_yutori import YutoriBrowsingTool

tool = YutoriBrowsingTool()

result = tool.run({
    "task": "Find the price of the MacBook Pro 14-inch M4",
    "start_url": "https://www.apple.com",
})
print(result)  # JSON string with task result

In a LangChain agent:

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_yutori import YutoriBrowsingTool

tools = [YutoriBrowsingTool()]
llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with web browsing capabilities."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
executor.invoke({"input": "What's the current price of AAPL on Yahoo Finance?"})

YutoriResearchTool

Performs deep and broad research using Yutori's research agent (100+ MCP tools). Creates the task and polls until complete.

from langchain_yutori import YutoriResearchTool

tool = YutoriResearchTool()

result = tool.run({
    "query": "What are the top 5 AI coding assistants in 2026 and how do their pricing models compare?",
    "user_location": "San Francisco, CA, US",
})
print(result)  # JSON string with research report

YutoriScoutingTool

Manages Yutori Scouts — recurring web monitors that run on a schedule and surface findings.

from langchain_yutori import YutoriScoutingTool

tool = YutoriScoutingTool()

# Create a scout (runs daily by default)
result = tool.run({
    "action": "create",
    "query": "Monitor Hacker News for posts about browser automation agents",
    "output_interval": 3600,  # hourly
})

# List all scouts
scouts = tool.run({"action": "list"})

# Get updates from a specific scout
updates = tool.run({
    "action": "get_updates",
    "scout_id": "abc123...",
    "limit": 10,
})

# Pause / resume / delete
tool.run({"action": "pause", "scout_id": "abc123..."})
tool.run({"action": "resume", "scout_id": "abc123..."})
tool.run({"action": "delete", "scout_id": "abc123..."})

Configuration

Browsing and Research tools accept poll_interval (seconds between status checks, default 60, minimum 60) and timeout (max wait seconds, default 1200):

tool = YutoriBrowsingTool(
    api_key="yt_...",
    poll_interval=10.0,
    timeout=300.0,
)

Links

• Yutori documentation
• API platform
• PyPI
• Yutori Python SDK