# How to Connect Devin AI with Microsoft OneDrive

Connecting Devin AI to Microsoft OneDrive allows developers to make large datasets and design documents accessible to coding sandboxes. Because Devin sessions run in ephemeral environments where local state resets, teams must use local directory mapping, Git repositories, or Fast.io remote workspaces to maintain file persistence.

Source: https://fast.io/resources/devin-ai-onedrive/
Author: [Derek Labian](https://fast.io/authors/derek-labian/)
Last reviewed: 2026-08-16

## Why Ephemeral Sandboxes Challenge OneDrive Integration in Devin AI

When evaluated on the SWE-bench coding benchmark, Devin AI correctly resolved approximately 14% of software engineering issues unassisted, setting a new baseline for autonomous agents. However, translating this bench performance into daily enterprise workflows requires bridging the gap between Devin's ephemeral runtime container and your team's persistent storage assets. Devin initializes each session within an isolated virtual machine container running Ubuntu. This container provides a command shell, an editor, and a browser to execute developer actions. Because these containers are short-lived, any local file system modifications or imported assets are permanently lost when the session finishes.

This container isolation makes direct cloud storage access difficult. Microsoft OneDrive does not have a native integration inside Devin's cloud sandbox, and the environment does not include a command-line utility to mount cloud drives. If an engineering team needs Devin to analyze project specifications, database exports, or source assets stored in OneDrive, they must configure an external path to synchronize these documents.

Configuring a reliable path requires choosing an integration method that matches your security policies and file sizes. Teams must decide whether to pass files through local developer machines, use version control repositories, or deploy a dedicated cloud storage bridge that connects directly to agent tool loops.

## Three Methods to Connect Devin AI: An Integration Guide

Connecting Devin AI to OneDrive allows developers and teams to use large datasets and project documents stored in cloud storage within Devin's coding sandbox. Since Devin cannot connect directly to the Microsoft Graph API without custom development, teams rely on three primary integration methods:

1. **Devin Desktop Local Sync**: Developers run the Microsoft OneDrive sync client on their local workstation to mirror cloud folders to a local directory. The Devin Local agent accesses these mirrored folders directly from the host operating system, respecting local permission rules.

2. **Git Versioning**: Teams push the required OneDrive documents to a hosted version control platform like GitHub. Devin clones the repository into its cloud container, edits the code, and pushes updates back to the remote server.

3. **Fast.io Persistent Workspaces**: Developers import OneDrive folders directly into a Fast.io workspace using OAuth. Fast.io indexes the files and exposes them to Devin through a remote Model Context Protocol (MCP) server, establishing a secure, persistent storage layer.

Each method has distinct trade-offs. Devin Desktop local sync keeps data local but requires developer machine resources. Git versioning works well for text-based code but fails to handle large binary datasets or spreadsheet files. Fast.io persistent workspaces bypass local machine dependencies and provide automatic indexing for semantic retrieval.

## How to Configure Devin Local and GitHub Repositories

For developers working with Devin Desktop on local projects, using local directories is a direct sync path. Devin Desktop includes a local agent named Devin Local that runs commands and edits files on the host computer.

To set up local sync, install the Microsoft OneDrive client on your machine and select the folders you want to sync. Once OneDrive downloads the files to your local drive, open Devin Desktop and point the agent to the synced local folder. To secure this setup, configure OS-level sandboxing inside the agent settings. Open the configuration file located at `~/.config/devin/config.json` on macOS or Linux, or `%APPDATA%\devin\config.json` on Windows. You can define directory permission scopes to restrict the agent's file system reach:

```json
{
  "sandbox": {
    "allowed_paths": [
      "/Users/username/OneDrive/ProjectFiles"
    ],
    "denied_paths": [
      "/Users/username/OneDrive/Private"
    ]
  }
}
```

The local agent reads and writes files within the allowed OneDrive folder, and the OneDrive sync client automatically uploads changes to the cloud. The agent also respects standard ignore files, including `.gitignore` and `.devinignore`, preventing Devin from indexing temporary cache folders. You can configure this mapping using [storage for agents](/storage-for-agents/).

When working with the cloud-hosted version of Devin, Git versioning is the standard alternative. To use this method, initialize a GitHub repository inside your local OneDrive folder and push the files to GitHub. When starting a new session on `app.devin.ai`, provide the repository URL. Devin clones the files into its `/workspace` directory, edits the files, and commits the updates.

While local sync and Git versioning are functional, they introduce workflow friction. Local sync relies on the host machine remaining online and active during long-running agent tasks. Git versioning creates clean code history but is inefficient for large files, design assets, or media directories that do not belong in a code repository.

## How to Establish a Persistent Workspace Bridge with Fast.io

Fast.io provides a persistent workspace that connects cloud storage like OneDrive directly to agent tool loops. Instead of maintaining local sync clients or committing non-code assets to Git, developers can bridge OneDrive folders to a Fast.io workspace.

To set up the Fast.io bridge, follow these steps:

1. Log in to your Fast.io account and select your organization.
2. Create a new workspace dedicated to your coding project.
3. Click the Import button, select OneDrive, and authenticate the connection using the PKCE OAuth pop-up.
4. Select the specific folders you want to make accessible to Devin and click Import. Fastio transfers the folders server-to-server.
5. Enable Intelligence Mode in your workspace settings. Fast.io automatically indexes the documents for semantic search and retrieval.

Once the files are imported, Fast.io exposes them to Devin through a remote Model Context Protocol (MCP) server. The Fast.io MCP server is hosted at `mcp.fast.io` and communicates over Streamable HTTP or legacy SSE transport. You do not need to install local npm packages or configure Entra ID applications. You can learn more about how to set up secure workspace imports on the [Fast.io cloud import](/product/cloud-import/) page.

To configure Devin to access the workspace, generate a scoped API key in the Fast.io dashboard under Developer Access. Then, define the remote endpoint inside your MCP configuration settings. If your team uses a client like Cline or Claude Code alongside Devin, add the hosted endpoint details under the `mcpServers` configuration block:

```json
{
  "mcpServers": {
    "fastio-storage": {
      "url": "https://mcp.fast.io/mcp/code",
      "type": "streamableHttp",
      "headers": {
        "Authorization": "Bearer YOUR_FASTIO_API_KEY"
      }
    }
  }
}
```

When Devin executes a task, it calls the Fast.io MCP tools to scan folders, read documents, and write changes. Because Fast.io indexes the workspace automatically, the agent can perform hybrid search, combining exact keyword matching with semantic retrieval. Devin can query the workspace for project context without downloading entire folders into its local memory:

* User: Find the database schema in our OneDrive imports and list the table structures.
* Devin (Tool Call): search(query="database schema")
* Devin: The schema document defines three tables. I will write the migration scripts based on these definitions.

## Collaborative File History and Human-Agent Handoff Patterns

Using a persistent cloud workspace establishes a collaborative space where humans and AI agents work on the same file systems. In a shared workspace, team members can view agent activity, review file edits, and manage project handoffs.

Because agents work rapidly, tracking edits is critical for security. Fast.io records all workspace events in a WebSocket activity feed and a detailed activity log, providing a detailed record of file creations, modifications, and downloads. If Devin generates incorrect code or overwrites an active configuration, the workspace version history allows developers to inspect the diffs and restore any prior file version.

For real-time collaboration, human developers and agents can edit documents simultaneously using Collaborative Notes. These notes support multiplayer cursors, allowing team members to outline requirements, list bugs, and write documentation in the same workspace where Devin is coding.

When a coding project is complete, Fast.io supports ownership transfer. An agent can sign up free, initialize the workspaces, import the OneDrive assets, and organize the codebase. The agent then transfers the organization to a human administrator via a claim link. While creating an account is free, performing active work requires an organization on a paid subscription. Every organization starts with a 30-day free trial that requires a credit card, allowing teams to test the setup. Once the human admin claims the organization, the team can manage access rights and connect Devin to the workspace tools. Teams can get started by opening the [Fast.io pricing page](/pricing/) and initiating a trial workspace.

## Frequently asked questions

### Can Devin AI access files from OneDrive?

Devin AI cannot access OneDrive folders directly because it runs in an isolated container without native Graph API sync tools. To grant access, you must sync OneDrive folders locally for Devin Desktop, push the files to a GitHub repository, or import them into a Fast.io workspace connected via MCP.

### How do I give Devin AI access to local files?

You can give Devin AI access to local files by using Devin Desktop and running the Devin Local agent. You manage permissions by editing the configuration file at `~/.config/devin/config.json` to define allowed and denied paths, ensuring the agent remains sandboxed.

### Does Devin AI have native OneDrive integration?

No, Devin AI does not have a native OneDrive integration or sync command. You must bridge the connection by using local directory mirroring, linking a Git repository, or connecting Devin's tool loops to a Fast.io persistent workspace.

## Sources

- [Cognition AI](https://www.cognition.ai/blog/introducing-devin): Devin correctly resolves approximately 14% of the issues end-to-end on the SWE-bench benchmark.

## About Fast.io

Fast.io provides shared workspaces where people and AI agents work on the same files, with built-in semantic search and citation-backed chat over what they hold. Agents reach it through a remote MCP server, a REST API at https://api.fast.io/current/, and a command line client published on npm as @vividengine/fastio-cli. MCP setup is at https://mcp.fast.io/docs: Claude and most MCP clients connect to https://mcp.fast.io/mcp/tools, ChatGPT to https://mcp.fast.io/mcp/operations, and coding agents to https://mcp.fast.io/mcp/code.