How do I find my Habitify API Key?

Open the Habitify mobile app, go to **Settings > API Credentials** (or Settings > Account > API), and tap **Generate New Key**. Note that API access may require a premium subscription.

Can I record progress for my habits via the AI agent?

Yes! Use the `add_habit_log` tool. You can specify the habit ID and the value you want to record (e.g., '10 reps' or '30 minutes').

Does the integration support viewing statistics?

Yes, you can use the `get_habit_stats` tool to retrieve performance data for any habit within a specific date range, helping you analyze your consistency.

Can I create new habits through this integration?

Yes! The `create_habit` tool allows you to add new habits to your Habitify account directly from your conversational interface.

Habitify MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Habitify
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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({
        "habitify": {
            "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 Habitify, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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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 Habitify MCP Server

Connect your Habitify account to any AI agent and take full control of your personal growth and habit-tracking workflows through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Habitify through native MCP adapters. Connect 10 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

Habit Oversight — List all habits you are tracking and retrieve detailed information for each.
Journal Monitoring — Get a daily overview of your completion status and progress for any specific date.
Log Management — Record progress for your habits (reps, minutes, etc.) and view history logs efficiently.
Statistical Insights — Retrieve performance statistics for any habit within a custom date range.
Personalized Growth — Create new habits or update existing ones directly from your chat or IDE.
Area Categorization — Organize and browse your habits by areas of focus like Health, Work, or Mindset.

The Habitify MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Habitify to LangChain via MCP

Follow these steps to integrate the Habitify MCP Server with LangChain.

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 10 tools from Habitify via MCP

Why Use LangChain with the Habitify MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Habitify 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 Habitify queries for multi-turn workflows

Habitify + LangChain Use Cases

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

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

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

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

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

Habitify MCP Tools for LangChain (10)

These 10 tools become available when you connect Habitify to LangChain via MCP:

add_habit_log

g., number of reps, minutes, or completion) to a habit. Record progress for a specific habit

create_habit

Create a new habit to track

delete_habit

Permanently delete a habit

get_habit

Get detailed information about a specific habit

get_habit_stats

Get statistics for a habit within a date range

get_journal

Get habits with completion status for a specific date

list_areas

List all habit areas (categories)

list_habit_logs

List all logs for a specific habit

list_habits

List all habits in your Habitify account

update_habit

Update an existing habit details

Example Prompts for Habitify in LangChain

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

"What habits do I need to complete today?"

"Log 30 minutes of reading for today."

"Show me my stats for 'Morning Meditation' from last week."

Troubleshooting Habitify MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Habitify + LangChain FAQ

Common questions about integrating Habitify 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.