How to integrate Archive.org MCP with CrewAI

This guide walks you through connecting Archive.org to CrewAI using the Composio tool router. By the end, you'll have a working Archive.org agent that can search collections, download item files, inspect wayback captures, fetch iiif manifests, and manage item metadata through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Archive.org account through Composio's Archive.org MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Archive.org logoArchive.org
Api Key

Archive.org is the Internet Archive's digital library of websites, books, audio, video, and images. Use it to preserve, explore, and access historical web captures and public-domain media.

26 Tools

Introduction

This guide walks you through connecting Archive.org to CrewAI using the Composio tool router. By the end, you'll have a working Archive.org agent that can search collections, download item files, inspect wayback captures, fetch iiif manifests, and manage item metadata through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Archive.org account through Composio's Archive.org MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Archive.org with

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Archive.org connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Archive.org
  • Build a conversational loop where your agent can execute Archive.org 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 Archive.org MCP server, and what's possible with it?

The Archive.org MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Archive.org account. It provides structured and secure access to archived content, Wayback captures, item records, usage analytics, and account tasks, so your agent can search collections, retrieve files, manage items, inspect activity, and handle reviews on your behalf.

  • Content search and retrieval: Have your agent search Archive.org by keywords and metadata, inspect complete item records, and download public files.
  • Wayback history research: Instruct your agent to find the closest archived version of a web page or list available captures for a website.
  • Item and file management: Direct your agent to check identifier availability, upload, copy, or delete files, update item metadata, and request new derivatives.
  • Images, manifests, and analytics: Let the agent inspect IIIF image details and manifests, render selected image regions, and review item views, history, geography, and referrers.
  • Account tasks and reviews: Have your agent check your account details, monitor catalog tasks and limits, read task logs, and create, update, retrieve, or delete your reviews.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Archive.org connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Archive.org via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

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
5

Import dependencies

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")
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 Archive.org MCP URL
6

Create a Composio Tool Router session for Archive.org

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["archive_org"])

url = session.mcp.url
What's happening:
  • You create a Archive.org only session through Composio
  • Composio returns an MCP HTTP URL that exposes Archive.org tools
7

Initialize the MCP Server

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,
    )
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.
8

Create a CLI Chatloop and define the Crew

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")
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.

Complete Code

Here's the complete code to get you started with Archive.org and CrewAI:

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=["archive_org"],
)
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 Archive.org through Composio's Tool Router. The agent can perform Archive.org 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
TOOLS

Supported Tools

Every Archive.org action and event your agent gets out of the box.

Advanced Search Archive.org Items

Search Archive.

Check Identifier Availability

Check whether an Archive.

Check Archive.org Upload Limit

Check whether Archive.

Copy Item File

Copy one Archive.

Delete Item File

Submit asynchronous deletion of one named file from an Archive.

Delete My Archive.org Review

Permanently delete the connected Archive.

Derive Archive.org Item

Queue Archive.

Download Item File

Download one public file from an Archive.

Find Wayback Snapshots

Find the closest archived Wayback Machine snapshot for each requested URL, using one before, after, or either direction for the batch.

Get Connected Archive.org User

Return the authenticated Archive.

Get Detailed Archive.org Views

Get bounded geographic view analytics and, for items, top referrers over a recent window or date range.

Get IIIF Image Info

Get normalized IIIF Image API capabilities through Archive.

Get IIIF Manifest

Get a compact IIIF Presentation manifest for an Archive.

Get Item Metadata

Get an Archive.

Get Item View Counts

Get all-time, last-30-day, and last-7-day view counts for one or more Archive.

Get Item View History

Get a bounded daily view history for one Archive.

Get My Review

Get the connected Archive.

Get Archive.org Task Log

Download a plain-text log for a task visible to the connected account.

Get Task Rate Limits

Report the connected account's current task capacity and in-flight counts for one Archive.

List Archive.org Tasks

Return any documented combination of the connected account's task summary, active catalog, and resource-authorized history, one page at a time.

List Wayback Captures

Return one bounded structured CDX page of Wayback captures for a URL or host.

Render IIIF Image

Render a bounded crop, resize, rotation, quality, and format from a fixed Archive.

Search Archive.org Items

Search Archive.

Update Item Metadata

Apply JSON Patch changes to one Archive.

Upload Item File

Create or replace one file through Archive.

Upsert My Archive.org Review

Create or overwrite the connected Archive.

FAQ

Frequently asked questions

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

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 Archive.org tools.

Yes, absolutely. You can configure which Archive.org 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.

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 Archive.org data and credentials are handled as safely as possible.

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