How to Connect Devin AI to Bitbucket Repositories and Manage Pull Requests

When an autonomous coding agent executes tasks inside an isolated virtual machine, code edits and build telemetry disappear the instant the session terminates. An agent operating without a direct source code management integration leaves engineering teams stranded. Developers must manually inspect code diffs, copy patched files across environments, and reconstruct test logs by hand. This disconnected pattern introduces human error, destroys the development audit trail, and prevents autonomous tooling from participating in regular code review routines.

Atlassian serves a customer base of over 300,000 organizations worldwide across its collaboration and development tools. Within this extensive ecosystem, teams increasingly rely on autonomous engineers to accelerate software delivery cycles. The Devin AI Bitbucket integration links Cognition's autonomous software engineer directly to Atlassian Bitbucket repositories for automated branch checkouts, PR authoring, and diff resolution. Devin AI runs in a containerized Linux environment equipped with a shell, code editor, and browser. Instead of treating Devin as an external code generator, connecting it to Atlassian Bitbucket allows the agent to function as a collaborative contributor in your source control ecosystem. The agent clones target repositories, navigates project structures, edits files across multiple directories, runs local validation tests, and pushes clean branches.

Integrating Bitbucket is especially critical for enterprise engineering teams invested in the Atlassian stack. Bitbucket connects natively with Jira issue tracking and Bitbucket Pipelines, establishing a unified lifecycle for backlog tasks, automated tests, and deployment gates. When Devin connects directly to Bitbucket, teams can route Jira tickets directly to the agent, inspect autonomous branch progress in Bitbucket, and review generated pull requests using standard team workflows.

However, source control only solves half of the autonomous engineering challenge. While Bitbucket manages git commits and pull request diffs, Devin produces secondary project assets that do not belong in git history. These outputs include bulky build logs, test coverage reports, browser execution traces, and architectural documentation. Committing these files directly to Bitbucket bloats repository storage and slows down clone operations for human teammates. Pairing the Devin AI Bitbucket integration with Fast.io workspaces establishes a persistent storage layer where Devin can upload build deliverables, share test reports, and preserve session artifacts across tasks.

Connecting Bitbucket Cloud to Devin AI establishes the authentication channel that allows Devin to clone repositories, push code branches, and author pull requests. Setting up the connection properly requires adhering to enterprise access control standards. Rather than connecting an individual developer's personal Bitbucket account, organizations should create a dedicated service account specifically for Devin, such as devin@yourcompany.com. A dedicated service account ensures that all commits and pull requests authored by the agent are clearly attributable in the Bitbucket audit log, preventing confusion during security audits or team reviews.

To configure the Bitbucket Cloud connection in Devin, complete the following steps:

1. Create a dedicated Bitbucket user account for Devin using your organization's domain email and invite the account to your Atlassian Bitbucket workspace with developer-level repository permissions.

2. Log in to your Devin account, navigate to the Connections section under the Settings menu, and locate the Bitbucket integration option.

3. Click Connect next to Bitbucket, which redirects your browser to Atlassian's authorization screen.

4. Log in using the dedicated Bitbucket account created for Devin, rather than your personal login credentials, and approve the requested OAuth permissions.

5. Confirm that the integration displays an active status in Devin Settings, then open Devin's Machine dashboard to configure your active repository targets.

Once the connection is active, Devin can read repository files, check out designated branches, make code modifications, and author pull requests directly in Bitbucket.

Engineering teams should note an important operational characteristic regarding Bitbucket pull request comments. While Devin can inspect and address feedback left on pull requests when prompted directly by an engineer in session chat, it does not wake up automatically in response to Bitbucket pull request comments. Unlike GitHub or GitLab webhook integrations that trigger automatic session wakeups, Bitbucket workflows currently require an engineer to prompt Devin or provide the pull request link to initiate revisions. Establishing this expectation ensures smooth handoffs between human reviewers and the autonomous agent.

Enterprise engineering organizations require strict control over which source code repositories an autonomous agent can access. Devin provides granular permission management at the organization level, allowing Enterprise Admins to enforce the principle of least privilege across teams and projects.

In Devin Enterprise, all Source Code Management integrations are managed through centralized administrative screens. Enterprise Admins navigate to Enterprise Settings, open the Integrations panel, and link the organization's SCM provider. Once the connection exists, administrators manage specific repository access rules under Enterprise Settings within the Repository Permissions view.

To grant or update Bitbucket repository access for a Devin Organization:

1. Select the target Devin Organization from the dropdown menu at the top of the Repository Permissions page.

2. Click Add Permissions to open the access configuration flyout.

3. Select your desired permission scope. You can choose Group-level access, which in Bitbucket maps directly to an entire Bitbucket Workspace, granting Devin access to all repositories contained in that workspace. Alternatively, you can select Repository-level access to grant access to a single, specific repository.

4. Filter by Git Provider if your enterprise links multiple source control systems, select the target repositories, and click Add permissions to save your configuration.

Devin Organizations cannot access or clone any repository until an administrator explicitly grants permission. If a project concludes or access needs to be removed, administrators can revoke permissions instantly by selecting the trash icon next to any active grant or deselecting the repository in the permissions flyout.

For organizations running self-managed source code infrastructure, Devin also supports Bitbucket Data Center. Configuring Bitbucket Data Center requires an Enterprise Admin to create a dedicated service account on the on-premise or private cloud Bitbucket instance. In Devin Settings under the Connections menu, select Bitbucket Data Center. Enter your custom Bitbucket Data Center instance URL and the service account authentication credentials. After saving, assign the service account appropriate project and repository read and write permissions within your Bitbucket Data Center administration portal to activate secure internal code checkout.

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When Devin AI works on a Bitbucket repository, its development activities generate more than git diffs. A typical autonomous programming session involves installing dependencies, running unit and integration test suites, generating visual regression screenshots, and compiling detailed execution logs. If Devin stores these files only within its temporary sandbox virtual machine, all supporting data is erased when the session closes. If it commits these assets to Bitbucket, repository clone speeds degrade and git history becomes cluttered with binary artifacts.

Fast.io solves this challenge by providing shared cloud workspaces where humans and AI agents collaborate on the same files and context. Teams can connect Devin AI to Fast.io 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 configuring Devin with the authenticated endpoint https://mcp.fast.io/mcp/key (where key is your workspace API key), Devin gains direct access to read, write, and organize files in shared team workspaces.

Alternatively, Devin can interact directly with the Fast.io REST API at https://api.fast.io/current/. The upload endpoint uses a nested, singular path: POST /current/workspace/{workspace_id}/storage/{parent_id}/addfile/.

The following Python script illustrates how Devin can upload a generated test report to a persistent Fast.io workspace using standard library modules:

import http.client
import json

def upload_build_report(api_key, workspace_id, folder_id, file_path):
    connection = http.client.HTTPSConnection("api.fast.io")
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/octet-stream"
    }
    
    with open(file_path, "rb") as file_handle:
        file_content = file_handle.read()
        
    endpoint = f"/current/workspace/{workspace_id}/storage/{folder_id}/addfile/"
    connection.request("POST", endpoint, body=file_content, headers=headers)
    response = connection.getresponse()
    print(f"Upload completed with status: {response.status}")
    connection.close()

upload_build_report("YOUR_FASTIO_API_KEY", "ws_12345", "fld_67890", "coverage_report.html")

Fast.io workspaces provide built-in intelligence that enhances autonomous engineering workflows. When you enable Intelligence Mode on a workspace, all uploaded documents, log files, and architectural notes are automatically indexed for semantic search, allowing human engineers to query test results and error patterns with citation-backed answers. In addition, teams can deploy Fast.io Metadata Views to structure unstructured test summaries, extracting status codes, execution durations, and defect counts into a live spreadsheet.

To build reactive verification loops without polling, external automation scripts can monitor workspace events using the Fast.io activity long-poll endpoint: GET /current/activity/poll/{entity_id}?wait=95&lastactivity={timestamp}. When Devin finishes uploading new test artifacts, the long-poll notifies the test orchestrator to initiate subsequent verification stages.

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. | Plans are Starter at $29/mo, Business at $99/mo, and Growth at $299/mo. Once Devin completes an initial repository onboarding or migration workspace, developers can use ownership transfer to transfer the organization and workspaces from the agent to a human lead, who joins, starts the trial, and maintains full administrative access.

Connecting Devin AI to Bitbucket enables a complete, closed-loop development cycle that combines autonomous code authoring, automated pipeline validation, and human code review.

The complete workflow proceeds through seven defined stages:

1. Task Assignment: An engineering lead assigns a task or bug fix to Devin, providing a Jira issue reference and the target Bitbucket repository.

2. Branch Checkout: Devin connects to Bitbucket, checks out a fresh branch from the main branch, and reviews the existing codebase to plan modifications.

3. Code Authoring and Local Testing: Devin edits the necessary source files, installs required project libraries in its sandbox, and runs local test suites.

4. Artifact Persistence: Before submitting code, Devin uploads its full test execution logs, browser screenshots, and benchmark metrics directly to the project Fast.io shared workspace.

5. Pull Request Creation: Devin pushes its feature branch to Bitbucket and opens a pull request, detailing what changes were made, why each file was modified, and linking to the uploaded build artifacts in Fast.io.

6. Bitbucket Pipelines Execution: Bitbucket automatically detects the new pull request and triggers Bitbucket Pipelines. The CI/CD pipeline runs automated linters, unit tests, and security scans against Devin's branch.

7. Human Review and Merge: A senior developer reviews the pull request in Bitbucket, inspects the associated test logs in Fast.io, co-edits any architectural notes using Fast.io Collaborative Notes, and merges the pull request into main once all checks pass.

To maintain repository stability, engineering teams should enforce branch protection rules in Bitbucket settings. Configure your repository so that pull requests to main require at least one human approval and successful completion of all Bitbucket Pipelines builds before merging. By combining Bitbucket's branch protection with Fast.io's granular access permissions and append-only audit log, teams maintain complete visibility into every line of code Devin authors, ensuring reliable software delivery without sacrificing engineering velocity.