How to Use the Chameleon.io MCP in LangChain

Q: Does Chameleon.io work with LangGraph?

It works out of the box. You map specific tools like trackuserevent to individual graph nodes. This lets you build complex routing logic based on user behavior.

Build multi-step product adoption workflows using LangChain ReAct agents and real-time user data.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Chameleon.io MCP to LangChain

Create your Vinkius account to connect Chameleon.io 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.

GDPR Free for Subscribers

Setup Chameleon.io with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Chameleon.io MCP Server events

Connect product adoption metrics directly to your LangChain reasoning pipelines. Your ReAct agents can grab recent activity using `list_chameleon_events` and immediately pass that context to an email generation node. No manual data extraction required. The agent decides what happens next based on the user's behavior. If they ignored a tour, your script triggers `track_user_event` to log the drop-off and queues up an alternative onboarding sequence. LangSmith traces every token and tool call along the way.

Build dynamic segment updates

User segments change constantly. Instead of building brittle cron jobs, you set up an autonomous script that checks `list_user_segments` and compares it against your internal database. When the script spots a discrepancy, it calls `identify_chameleon_user` to sync the profile data. The output of the segment check becomes the input for the identity update. You get a self-healing data pipeline running entirely within your framework.

Aggregate microsurvey feedback

Processing qualitative feedback usually means staring at spreadsheets. Now you point a text summarization chain at `list_microsurvey_responses` and let it chew through the raw text. The chain pulls the latest answers, feeds them through a sentiment analysis prompt, and outputs a clean markdown report. You can even configure it to flag negative responses and trigger `get_experience_details` to see exactly which product tour caused the frustration.

Setup guide

Set up Chameleon.io 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 Chameleon.io 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({
    "chameleonio-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 Chameleon.io transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Chameleon. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Chameleon.io now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Chameleon.io MCP in LangChain

Install the langchain-mcp-adapters package. Use MultiServerMCPClient with your endpoint URL, then pass the output of client.get_tools() to your agent creation function.

Yes. LangSmith automatically logs every interaction with the server. You see the exact input parameters sent to the tools and the raw JSON returned.

The client connection is stateless by default. Call client.session() to maintain persistent context across multiple interactions with product tours or survey data.

It works out of the box. You map specific tools like track_user_event to individual graph nodes. This lets you build complex routing logic based on user behavior.

Vinkius runs this server inside an isolated V8 sandbox that gets destroyed after execution. It processes sensitive product adoption metrics and microsurvey responses without writing anything to persistent disk.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript