LangChain Integration

LangChain tools and document loader for Plasmate - browse the web with ~10x fewer tokens than raw HTML.

Plasmate's SOM output compiles web pages into compact, structured representations that preserve interactive elements and content hierarchy. This integration provides LangChain-native tools for stateless fetching, persistent browsing, and batch document loading.

Interactive form controls include action-state cues such as [disabled], [enabled], [required], [group="Billing"], [current="page"], [controls="panel-id"], [haspopup="menu"], and [description="..."] when Plasmate emits those SOM attrs. Agents should use those cues before calling plasmate_click or plasmate_type.

For agent prompts that need a compact action menu, use the shared action-plan helpers before constructing messages:

from langchain_plasmate import (
    action_target_index,
    find_action_targets_by_action,
    find_action_targets_by_role,
)

index = action_target_index(som, enabled_only=True)
buttons = find_action_targets_by_role(som, "button", enabled_only=True)
clicks = find_action_targets_by_action(som, "click", enabled_only=True)

Source: integrations/langchain/

Installation

pip install langchain-plasmate

Requires the plasmate binary on your PATH:

curl -fsSL https://plasmate.app/install.sh | sh

Quick Start

Fetch a page (stateless)

from langchain_plasmate import PlasmateFetchTool

fetch = PlasmateFetchTool()
result = fetch.invoke("https://news.ycombinator.com")
print(result)

Agent with browsing tools

from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_plasmate import get_plasmate_tools

tools = get_plasmate_tools()
llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You can browse the web using Plasmate tools."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({
    "input": "Go to Hacker News and tell me the top 3 stories"
})
print(result["output"])

Document loader

from langchain_plasmate import PlasmateSOMLoader

loader = PlasmateSOMLoader([
    "https://example.com",
    "https://news.ycombinator.com",
])
docs = loader.load()

for doc in docs:
    print(f"{doc.metadata['title']} -  {doc.metadata['element_count']} elements")
    print(doc.page_content[:200])
    print()

Available Tools

`PlasmateFetchTool`

Stateless page fetch. Each call creates a fresh connection, fetches the URL, and returns SOM text. Best for one-off page reads.

from langchain_plasmate import PlasmateFetchTool

fetch = PlasmateFetchTool()
result = fetch.invoke("https://example.com")

`PlasmateNavigateTool`

Opens a URL in a persistent browser session. Use with PlasmateClickTool and PlasmateTypeTool for multi-step browsing workflows.

from langchain_plasmate import get_plasmate_tools

tools = get_plasmate_tools()
navigate, click, type_tool, fetch = tools

`PlasmateClickTool`

Clicks an interactive element by its SOM element ID (e.g., e_a1b2c3d4e5f6). Requires an active session from PlasmateNavigateTool.

`PlasmateTypeTool`

Types text into a form input or textarea by SOM element ID. Takes element_id and text as input.

`get_plasmate_tools()`

Returns all four tools with a shared Plasmate client and browser session:

from langchain_plasmate import get_plasmate_tools
from plasmate import Plasmate

# Default client
tools = get_plasmate_tools()

# Custom client
client = Plasmate(binary="/path/to/plasmate", timeout=60)
tools = get_plasmate_tools(client=client)

PlasmateSOMLoader

Loads web pages as LangChain Document objects with SOM text as page_content.

PlasmateSOMLoader(
    urls=["https://example.com"],
    budget=2000,          # optional token budget per page
    javascript=True,      # enable JS execution (default)
    client=None,          # optional Plasmate instance
)

Document metadata includes: url, title, lang, html_bytes, som_bytes, element_count, interactive_count, and optionally description, open_graph, json_ld.

Token Efficiency

Compared to LangChain's WebBaseLoader which passes raw HTML:

SOM preserves all interactive elements, headings, and content structure while stripping scripts, styles, hidden elements, and layout-only markup.