How to Connect Devin AI to GitLab and Fast.io Workspaces

GitLab serves a developer ecosystem of more than 30 million registered users, as noted in the platform's S-1 registration filing with the Securities and Exchange Commission. Within this extensive footprint, development teams increasingly use autonomous software engineers to edit repositories, fix bugs, and run tests. Devin AI functions as an autonomous agent that operates in a containerized cloud sandbox, providing the tools needed to install dependencies, run scripts, and browse the web. However, a containerized sandbox is temporary. When a development session ends, all build logs, compilation metrics, and testing artifacts disappear. This ephemeral structure isolates the agent's work, making it difficult for teams to inspect results or troubleshoot failures.

To resolve this isolation, developers must build a continuous pipeline that links the agent's workspace to source control and persistent storage. Connecting Devin AI to GitLab enables the agent to check out code branches, modify files, and submit merge requests. Pairing this integration with Fast.io workspaces creates a durable, shared storage layer where the agent can upload logs and reports. This coordination ensures that all intermediate outputs are preserved, searchable, and reviewable by human developers. Fast.io serves as the collaborative platform where agents write and humans review, preventing code from being lost in a black box.

Fast.io supports this workflow with granular permission controls at the organization, workspace, folder, and file level. Human developers can configure specific workspaces for different projects, limiting Devin AI's access to only the repositories and directories it needs. All data is protected with encryption in transit and at rest. Fast.io runs on cloud infrastructure partners, including Google Cloud Platform and Cloudflare, that are certified to industry-leading security standards. This setup allows teams to manage agent access securely, keeping credentials private while maintaining a full history of all modifications.

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Integrating Devin AI with GitLab.com requires a direct connection to allow the agent to read issues, write code, and collaborate on merge requests. To ensure a clean audit trail, organizations should create a dedicated GitLab user profile for Devin AI rather than connecting a developer's personal account. Using a dedicated account prevents commit history confusion and makes it simple to track the agent's contributions. Once the GitLab profile is ready, you can establish the integration through the Devin web interface.

To add GitLab connections in Devin, follow these 4 steps:

1. Create a dedicated GitLab account for Devin's usage to keep agent contributions separate from human developer profiles.

2. Navigate to the Connections section under your Devin settings page and click the Connect button next to the GitLab option.

3. Log in using the dedicated GitLab account credentials when redirected, and authorize Devin's access to the required repositories.

4. Return to the Connections dashboard in your Devin settings, click the Manage menu next to the new GitLab connection, and select the webhook option to establish real-time event updates.

Configuring the webhook is necessary to enable real-time collaboration. Without webhooks, Devin AI must rely on polling to check for updates, which introduces delays and consumes API limits. Once the webhook is active, GitLab immediately notifies Devin AI when a team member opens a merge request, leaves a review comment, or updates an issue. The agent can process these notifications immediately, editing its code or replying to comments within seconds. This creates a responsive feedback loop between human developers and the autonomous agent.

Many enterprise engineering departments deploy GitLab Self-Managed on private cloud infrastructure or on-premise servers. Connecting Devin AI to a self-hosted GitLab instance requires additional configuration by an Enterprise Admin to establish secure access. The process relies on service accounts and Personal Access Tokens (PATs) to authenticate the agent without exposing personal user credentials.

The Enterprise Admin must first navigate to the target GitLab group settings and create a service account for Devin AI. The service account must be added as a member of the group with the Developer role, granting the permissions needed to write code and open merge requests. The admin then manages the service account to generate a new Personal Access Token. This token must carry the api scope and be stored securely, as it will act as the authentication key. In Devin's Enterprise Settings under SCM Connections, the admin selects the Self-Hosted option, inputs the self-hosted GitLab URL, and provides the service account token.

To allow individual developers to authenticate and link their identities, the admin must also register an OAuth application on the self-hosted GitLab server. This configuration allows developers to connect their personal GitLab profiles under Personal Connections in Devin. By linking personal profiles, Devin AI can perform Git operations and publish comments on behalf of the specific developer, maintaining clear identity tracking across the team's repositories.

Persist Devin AI outputs in a shared workspace

Connect your AI coding agents to Fast.io workspaces using our Model Context Protocol endpoint. Keep your logs, artifacts, and test reports versioned and searchable in one central hub. Every organization starts with a 14-day free trial, which requires a credit card.

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While GitLab manages source code, developers need a place to store the bulk assets generated during autonomous runs. When Devin AI executes a test suite, it produces build logs, HTML test coverage reports, and screenshots of UI states. Committing these files to Git bloats the repository. A common alternative is uploading them to local drives or standard cloud storage. However, standard storage lacks the integration needed for multi-agent workflows. Fast.io provides persistent workspaces that integrate directly with developer tools and AI models.

Developers can connect Devin AI to Fast.io workspaces using the Model Context Protocol (MCP) server. The Fast.io MCP server is remote, hosted at https://mcp.fast.io/mcp with a legacy SSE transport at https://mcp.fast.io/sse. By adding the MCP endpoint https://mcp.fast.io/mcp/key (replacing key with a Fast.io API key) to Devin's configuration, the agent can write files directly to the workspace. If Devin AI needs to upload files using the REST API, it can send requests to https://api.fast.io/current/. The upload endpoint uses a nested, singular path: POST /current/workspace/{workspace_id}/storage/{parent_id}/addfile/.

Fast.io workspaces also provide built-in RAG capabilities through Intelligence Mode. When you enable Intelligence Mode on a workspace, all uploaded files are automatically indexed. Human developers can search the workspace using semantic queries to locate specific test logs or build metrics, receiving citation-backed answers. Additionally, teams can use Fastio Metadata Views to turn folders of build reports into a structured database. By defining an extraction schema in natural language, Fast.io matches files in the workspace and extracts test outcomes, run durations, and error messages into a sortable spreadsheet.

Fast.io does not use file-event webhooks or folder-monitoring triggers. If you need to build a reactive pipeline that responds to new files uploaded by Devin AI, the agent can monitor the workspace activity feed. Fast.io supports this through the workspace activity long-poll endpoint, GET /current/activity/poll/{entity_id}?wait=95&lastactivity={timestamp}. This long-poll allows your local scripts or orchestration systems to wait for file changes and trigger verification steps when Devin AI finishes writing build artifacts.

To automate this schema sync, developers can deploy a Python script that listens to file changes and posts the updated files to the Fast.io workspace using the Fast.io Model Context Protocol page and agent onboarding documentation for configuration guidance:

import http.client
import json

def upload_artifact(api_key, workspace_id, folder_id, file_path):
    conn = http.client.HTTPSConnection("api.fast.io")
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/octet-stream"
    }
    
    with open(file_path, "rb") as f:
        file_data = f.read()
        
    path = f"/current/workspace/{workspace_id}/storage/{folder_id}/addfile/"
    conn.request("POST", path, body=file_data, headers=headers)
    response = conn.getcall() # or getcall()
    response = conn.getresponse()
    print(response.status, response.read().decode())

upload_artifact("YOUR_FASTIO_API_KEY", "ws_98765", "dir_12345", "build_log.txt")

Creating an account is free; doing real work requires an organization on a paid subscription. Every organization starts with a 14-day free trial, which requires a credit card. | Paid plans are Starter at $29/mo | Business at $99/mo | Growth at $299/mo. Once Devin AI completes a coding task, developers can use the ownership transfer feature to transfer the workspace from the agent to a human team member. The human receives a link, joins or creates the organization, and takes full ownership of the assets while maintaining the agent's developer access. Detailed tiers are visible on the Fast.io API pricing page.

The true value of connecting Devin AI to GitLab and Fast.io is the ability to run automated verification loops. When Devin AI writes code, it submits the modifications by opening a merge request in GitLab. This action immediately triggers a GitLab CI/CD pipeline. The pipeline runs unit tests, linting scripts, and security scanners to verify the changes. GitLab pipelines automate test runs for AI agents, ensuring that autonomous writes are subject to the same quality checks as human code.

If the GitLab pipeline fails, Devin AI can observe the outcome. The agent can download the raw pipeline logs from GitLab, identify the error, and modify the code to resolve the issue. By automating this loop, the agent can self-heal syntax mistakes or test failures before notifying human developers. Once the pipeline passes, Devin AI posts a status update, and human team members can review the merge request.

Humans and agents can co-edit project boards or update checklists in real time using Collaborative Notes inside the Fast.io workspace. If the human developer requires changes, they can write comments on the merge request. GitLab's webhook immediately alerts Devin AI, which checks out the branch, implements the modifications, and pushes the code to trigger a new pipeline run. This continuous coordination enables engineering teams to deploy autonomous agents with confidence, knowing that GitLab pipelines validate the code and Fast.io preserves the history.