How to Use the Applitools MCP in LangChain

Q: Can my agent delete test batches?

Yes, the deletebatch tool is available. Just remember this action is irreversible, so you should probably require human approval in your chain before executing it.

Build ReAct agents that inspect UI diffs and manage visual baselines directly through LangChain.

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Connect Applitools MCP to LangChain

Create your Vinkius account to connect Applitools to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Applitools with LangChain

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Trace Applitools MCP Server execution

LangChain gives you full observability over your visual testing pipelines. When your agent calls `list_batches` to find recent test runs, LangSmith tracks the exact token usage and latency of that request. You see exactly how the model decided to proceed based on the returned batch IDs. You build multi-step reasoning chains where visual test data feeds into other tools. If `get_batch_stats` returns failed tests, the agent can parse those unresolved counts and automatically trigger a Jira ticket creation step. Output from one operation becomes the exact input for the next.

Automate baseline management

Managing visual baselines usually requires clicking through a web UI. Your LangChain agent handles this programmatically by chaining `list_baselines` with `delete_baseline`. It identifies outdated environments and clears them out without human intervention. The agent decides which environments to keep based on intermediate results. It checks `list_branch_baselines` to inspect branch-specific visual states before making any irreversible deletions. You define the logic, and the agent executes the cleanup sequence.

Dig into session details

Sometimes a batch fails and you need to know exactly why. A ReAct agent can grab a specific batch ID and immediately call `get_session` to pull the underlying test session details. It parses the response to find the exact point of failure. This data feeds directly into your broader agentic workflow. After pulling session data, the agent might call `list_results` to format a plain-text summary of the visual regressions. You get a clear explanation instead of raw JSON output.

Setup guide

Set up Applitools MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Applitools tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "applitools-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Applitools transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Applitools MCP in LangChain

Install the langchain-mcp-adapters package and use MultiServerMCPClient. Pass your Vinkius MCP endpoint URL and call client.get_tools() to load the integration.

Yes, the `delete_batch` tool is available. Just remember this action is irreversible, so you should probably require human approval in your chain before executing it.

You get composable reasoning. An agent can read visual test failures, analyze the diffs, and decide whether to update the baseline or file a bug.

LangSmith automatically traces every tool call made by your agent. You monitor exactly how long `validate_key` or batch retrieval takes.

Your visual baselines and UI test results stay protected. Vinkius runs the MCP connection in an ephemeral V8 isolate sandbox, meaning your raw test data is never stored outside the active session memory.

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