GrowthBook MCP Server for LangChainGive LangChain instant access to 15 tools to Create Environment, Create Feature, Create Project, and more

Name: GrowthBook
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LangChain is the leading Python framework for composable LLM applications. Connect GrowthBook through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Ask AI about this MCP Server for LangChain

Open in ChatGPT Open in Claude Open in Perplexity

The GrowthBook MCP Server for LangChain is a standout in the Developer Tools category — giving your AI agent 15 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "growthbook": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using GrowthBook, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About GrowthBook MCP Server

Connect your GrowthBook account to any AI agent to streamline your experimentation and feature management workflows through natural language.

LangChain's ecosystem of 500+ components combines seamlessly with GrowthBook through native MCP adapters. Connect 15 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Feature Management — List, create, and toggle feature flags across production and staging environments to control rollouts.
Project Control — Organize your experimentation roadmap by managing projects, their descriptions, and specific settings.
Environment Visibility — Audit and list all configured environments to ensure flags are deployed correctly across your stack.
Full Lifecycle — Create, update, or delete projects and environments as your infrastructure and team needs evolve.
Deep Inspection — Retrieve detailed metadata for specific features and projects to understand their current configuration and state.

The GrowthBook MCP Server exposes 15 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 15 GrowthBook tools available for LangChain

When LangChain connects to GrowthBook through Vinkius, your AI agent gets direct access to every tool listed below — spanning feature-flags, a-b-testing, experimentation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create environment on GrowthBook

Create a new GrowthBook environment

create

Create feature on GrowthBook

Create a new GrowthBook feature flag (v2)

create

Create project on GrowthBook

Create a new GrowthBook project

delete

Delete environment on GrowthBook

Delete a GrowthBook environment

delete

Delete feature on GrowthBook

Delete a GrowthBook feature flag (v2)

delete

Delete project on GrowthBook

Delete a GrowthBook project

get

Get feature on GrowthBook

Get a single GrowthBook feature flag (v2)

get

Get project on GrowthBook

Get a single GrowthBook project by ID

list

List environments on GrowthBook

g., production, staging) used for per-environment feature flag control. List all GrowthBook environments

list

List features on GrowthBook

List all GrowthBook feature flags (v2)

list

List projects on GrowthBook

List all GrowthBook projects

toggle

Toggle feature on GrowthBook

Toggle a GrowthBook feature flag on or off

update

Update environment on GrowthBook

Update an existing GrowthBook environment

update

Update feature on GrowthBook

Partially update a GrowthBook feature flag (v2)

update

Update project on GrowthBook

Edit an existing GrowthBook project

Connect GrowthBook to LangChain via MCP

Follow these steps to wire GrowthBook into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 15 tools from GrowthBook via MCP

Why Use LangChain with the GrowthBook MCP Server

LangChain provides unique advantages when paired with GrowthBook through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine GrowthBook MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across GrowthBook queries for multi-turn workflows

GrowthBook + LangChain Use Cases

Practical scenarios where LangChain combined with the GrowthBook MCP Server delivers measurable value.

RAG with live data: combine GrowthBook tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query GrowthBook, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain GrowthBook tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every GrowthBook tool call, measure latency, and optimize your agent's performance

Example Prompts for GrowthBook in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with GrowthBook immediately.

"List all feature flags for the project 'frontend-v2'."

"Enable the 'dark-mode-beta' feature flag in production."

"Get the details and settings for project ID 'proj_123'."

Troubleshooting GrowthBook MCP Server with LangChain

Common issues when connecting GrowthBook to LangChain through Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

GrowthBook + LangChain FAQ

Common questions about integrating GrowthBook MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

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