# How to integrate Ticktick MCP with LangChain

```json
{
  "title": "How to integrate Ticktick MCP with LangChain",
  "toolkit": "Ticktick",
  "toolkit_slug": "ticktick",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/ticktick/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/ticktick/framework/langchain.md",
  "updated_at": "2026-05-12T10:28:27.341Z"
}
```

## Introduction

This guide walks you through connecting Ticktick to LangChain using the Composio tool router. By the end, you'll have a working Ticktick agent that can add a new task for tomorrow morning, complete all overdue tasks in your inbox, create a project called 'vacation planning' through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Ticktick account through Composio's Ticktick MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Ticktick with

- [OpenAI Agents SDK](https://composio.dev/toolkits/ticktick/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/ticktick/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ticktick/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ticktick/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ticktick/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ticktick/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ticktick/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ticktick/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ticktick/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/ticktick/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ticktick/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/ticktick/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/ticktick/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Ticktick project to Composio
- Create a Tool Router MCP session for Ticktick
- Initialize an MCP client and retrieve Ticktick tools
- Build a LangChain agent that can interact with Ticktick
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## What is the Ticktick MCP server, and what's possible with it?

The Ticktick MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ticktick account. It provides structured and secure access to your task lists and projects, so your agent can create, manage, and organize tasks, complete or delete items, and streamline your productivity workflows automatically.
- Automated task creation and editing: Let your agent add new tasks, set deadlines, and update task details across your Ticktick projects with ease.
- Project management and organization: Direct your agent to create, retrieve, or delete entire projects, keeping your workspace tidy and aligned with your priorities.
- Seamless task completion and cleanup: Ask your agent to mark tasks as complete or delete tasks you've finished or no longer need, helping you stay on top of your to-do list.
- Comprehensive project data retrieval: Have your agent fetch detailed project information, including all associated tasks and columns, for quick overviews or reporting.
- Effortless OAuth2 authentication management: Benefit from a streamlined, agent-guided authorization flow that securely connects your Ticktick account with minimal friction.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TICKTICK_COMPLETE_TASK` | Complete Task | Marks a TickTick task as complete. Requires both the project_id and task_id, which can be obtained from TICKTICK_LIST_ALL_TASKS or TICKTICK_CREATE_TASK actions. This action is idempotent - completing an already-completed task will succeed without error. Completed tasks are removed from default/active views and appear only in completed task filters. |
| `TICKTICK_CREATE_PROJECT` | Create Project | Create a new project (list) in TickTick. Projects organize tasks and notes. Use this to set up a new project with optional customizations like color, view mode, and type. The created project's ID can be used with other actions like TICKTICK_CREATE_TASK. |
| `TICKTICK_CREATE_TASK` | Create Task | Tool to create a new task in TickTick. Use after you have task details such as title, dates, and optional reminders or subtasks. |
| `TICKTICK_DELETE_PROJECT` | Delete TickTick Project | Permanently deletes a TickTick project by its ID. All tasks within the project will also be deleted. Note: This operation is idempotent - deleting a non-existent project ID returns success. Use TICKTICK_GET_USER_PROJECT to list available projects and their IDs before deletion. |
| `TICKTICK_DELETE_TASK` | Delete Task | Tool to permanently delete a specific task — irreversible, no recovery. Use when you need to remove a task from a project after confirming both project and task IDs. Returns an empty data object on success; check status/success flags rather than response payload. When moving tasks between projects via create+delete, comments and history are lost. |
| `TICKTICK_GET_PROJECT_BY_ID` | Get Project By ID | Tool to retrieve a specific TickTick project by its unique ID. Use when you need detailed information about a particular project after obtaining its project ID. |
| `TICKTICK_GET_PROJECT_WITH_DATA` | Get project with data | Retrieve a project's associated data (incomplete tasks, columns). IMPORTANT: This endpoint only returns INCOMPLETE tasks. Completed tasks are automatically filtered out by the TickTick API. An empty tasks list means either the project has no tasks at all, or all tasks have been completed. For completed tasks, check the TickTick app or web interface directly. Columns are only present for kanban-style projects; list-view projects return an empty columns array. Join tasks to columns via each task's columnId field. For large projects, results may paginate at ~100 items per page — iterate all pages and deduplicate by taskId. Multiple tasks can share the same title; always use taskId for follow-up create, update, or delete calls. All filtering by name, tag, or other fields must be done client-side. Scope is project-only — Inbox and other projects are excluded. |
| `TICKTICK_GET_TASK_BY_PROJECT_AND_ID` | Get Task By Project And ID | Tool to retrieve a specific TickTick task by project ID and task ID. Use when you need detailed information about a particular task within a project. |
| `TICKTICK_GET_USER_PROJECT` | Get User Projects | Retrieves all projects accessible to the authenticated user, including personal and shared projects. Use this tool to list available projects before creating tasks or to get project IDs for other operations. Returns project metadata including name, color, view mode, and organization details. Always use returned projectId values (not project names) when calling TICKTICK_CREATE_TASK, TICKTICK_UPDATE_TASK, or TICKTICK_GET_PROJECT_WITH_DATA. The inbox project may not appear in results — omit projectId in TICKTICK_CREATE_TASK to target the inbox. Non-kanban projects return an empty columns array; check viewMode before assuming columns exist. |
| `TICKTICK_LIST_ALL_TASKS` | List All Tasks | Tool to list all open/undone tasks across all user projects in one call. This is a composite operation that internally fetches all projects and aggregates their tasks. Use when you need a comprehensive view of pending work across the entire account. Note: Only returns open/undone tasks; completed task history is not available in TickTick OpenAPI v1. |
| `TICKTICK_OAUTH2_AUTHORIZATION_STEP1` | Generate OAuth2 Authorization URL | Tool to generate the TickTick OAuth2 authorization URL. Use to redirect the user to obtain the authorization code (step 1). Re-run this tool if downstream TickTick calls return HTTP 401 (expired/invalid token) to obtain a fresh authorization code. |
| `TICKTICK_UPDATE_PROJECT` | Update Project | Tool to update an existing project. Use when you need to modify project details like name, color, sort order, view mode, or kind after selecting a project ID. |
| `TICKTICK_UPDATE_TASK` | Update Task | Tool to update an existing task. Use after confirming the taskId and projectId. Omitting optional fields resets them to null — include all existing field values in every payload. Cannot move a task to a different project; use TICKTICK_CREATE_TASK + TICKTICK_DELETE_TASK instead. Fields outside the input schema (e.g., columnId, assignee) are silently ignored. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Ticktick MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ticktick. It provides structured and secure access so your agent can perform Ticktick operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Ticktick tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Ticktick tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
- This approach allows the agent to dynamically load and use Ticktick tools as needed
```python
# Create Tool Router session for Ticktick
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['ticktick']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['ticktick']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "ticktick-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "ticktick-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Ticktick related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Ticktick related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['ticktick']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "ticktick-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Ticktick related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['ticktick']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "ticktick-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Ticktick related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Ticktick through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Ticktick MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/ticktick/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/ticktick/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ticktick/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ticktick/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ticktick/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ticktick/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ticktick/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ticktick/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ticktick/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/ticktick/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ticktick/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/ticktick/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/ticktick/framework/crew-ai)

## Related Toolkits

- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
- [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.
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- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
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- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place.
- [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place.
- [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place.
- [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives.
- [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page.
- [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes.
- [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights.
- [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Ticktick MCP?

With a standalone Ticktick MCP server, the agents and LLMs can only access a fixed set of Ticktick tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ticktick and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Ticktick tools.

### Can I manage the permissions and scopes for Ticktick while using Tool Router?

Yes, absolutely. You can configure which Ticktick scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Ticktick data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)