# How to integrate Apiflash MCP with CrewAI

```json
{
  "title": "How to integrate Apiflash MCP with CrewAI",
  "toolkit": "Apiflash",
  "toolkit_slug": "apiflash",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/apiflash/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/apiflash/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:01:32.745Z"
}
```

## Introduction

This guide walks you through connecting Apiflash to CrewAI using the Composio tool router. By the end, you'll have a working Apiflash agent that can capture a screenshot of our homepage now, take screenshots of three competitor sites, check your current apiflash quota usage through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Apiflash account through Composio's Apiflash MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Apiflash with

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

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Apiflash connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Apiflash
- Build a conversational loop where your agent can execute Apiflash operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

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

The Apiflash MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apiflash account. It provides structured and secure access to high-quality website screenshot capture, so your agent can capture web pages, manage batches of screenshots, retrieve quota usage, and access screenshot metadata on your behalf.
- Single and batch website screenshot capture: Instantly direct your agent to take high-resolution screenshots of one or many web pages at once, perfect for monitoring, archiving, or sharing site visuals.
- Customizable screenshot requests: Let your agent use advanced options with POST requests to tailor captures using form data and specific parameters for precise results.
- API quota monitoring: Check your remaining screenshot credits and quota status in real time, so your automations never hit unexpected usage limits.
- Screenshot metadata retrieval: Pull detailed information about previously taken screenshots, including file size and dimensions, for reporting or further processing.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `APIFLASH_APIFLASH_BATCH_CAPTURE_SCREENSHOTS` | Batch Capture Screenshots | Tool to capture screenshots for multiple URLs in a single request. Use when you have a list of pages to snapshot in batch. |
| `APIFLASH_CAPTURE_SCREENSHOT` | Capture Screenshot | Tool to capture a screenshot of a website. Returns a JSON response with URLs to the screenshot (and optionally extracted HTML/text). Supports extensive customization including viewport dimensions, full page capture, quality settings, element selection, geolocation emulation, and more. Use when you need to capture website screenshots for documentation, testing, or monitoring purposes. |
| `APIFLASH_APIFLASH_CAPTURE_WEBSITE_SCREENSHOT_POST` | Capture Website Screenshot (POST) | Capture a screenshot of any website. Returns a URL to the generated screenshot image. Supports full-page captures, custom viewport sizes, and multiple image formats (JPEG, PNG, WebP). Use this tool when you need to visually capture web pages for documentation, monitoring, or analysis. |
| `APIFLASH_GET_QUOTA_INFORMATION` | Get Quota Information | Tool to retrieve current API quota usage and limits. Use after authentication to monitor usage and reset times. |
| `APIFLASH_GET_SCREENSHOT_METADATA` | Get Screenshot Metadata | Retrieve metadata (file size, MIME type) for a previously captured screenshot. Use this tool when you need to check the size or format of a screenshot without downloading the full image. Requires the screenshot URL from a prior capture action. |

## Supported Triggers

None listed.

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

The Apiflash MCP server is an implementation of the Model Context Protocol that connects your AI agent to Apiflash. It provides structured and secure access so your agent can perform Apiflash 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

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account and API key
- A Apiflash connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 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

**What's happening:**
- composio connects your agent to Apiflash via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Apiflash MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

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

### 5. Create a Composio Tool Router session for Apiflash

**What's happening:**
- You create a Apiflash only session through Composio
- Composio returns an MCP HTTP URL that exposes Apiflash tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["apiflash"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["apiflash"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Apiflash through Composio's Tool Router. The agent can perform Apiflash operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## How to build Apiflash MCP Agent with another framework

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

## Related Toolkits

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- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
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- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
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- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.
- [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights.

## Frequently Asked Questions

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

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

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

Yes, you can. CrewAI 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 Apiflash tools.

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

Yes, absolutely. You can configure which Apiflash 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 Apiflash data and credentials are handled as safely as possible.

---
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