Maige

What is Maige?

Maige is a workflow platform for GitHub code repositories, developed by the Canadian AI application team Rubric Labs. It enables maintainers to describe rules in natural language, allowing AI to carry out tasks such as adding tags, assigning responsibilities, leaving comments, conducting code reviews, and performing simple coding tasks when Issues and Pull Requests arise.

The current version defines the product as an open-source infrastructure that enables natural-language workflows for running code libraries. It offers both hosting services and the complete source code of the repositories, but self-hosting is still considered not to be officially supported.

Core working method

1. Install the Maige GitHub App in the selected repository and grant it the permissions required to handle Issues, comments, and Pull Requests.
2. The system creates Webhooks for the connected warehouses, clones and splits the code, establishes an index for the code repository, and prepares a sandbox environment for running tasks.
3. Maintainers write rules in plain language to specify which events should trigger which actions, and they can refer to contribution guidelines or project conventions.
4. When an Issue, comment, push, or Pull Request event occurs, Maige reads the relevant context and selects the appropriate GitHub API action.
5. Users can view the operation records, usage data, and results in the control panel, provide feedback on any erroneous behavior, and adjust the rules accordingly.

Main functions

Issue automation

• Automatic tagging: Add or remove tags for new issues based on the title, content, file type, or natural language rules.
• Automatic assignment: Issues can be assigned to specific members based on criteria such as interface, backend, document, or other responsibilities.
• Automatic comments: Generate clarifying questions, handling instructions, or maintenance tips to reduce repetitive initial responses.
• Custom rules: For example, add a specific tag when a certain technical keyword appears, without the need to write traditional conditional scripts.
• Contextual Q&A: It is possible to ask which packages are used in a repository or where a particular implementation can be found; the answers rely on code embedding retrieval.

Pull Request review

• Rule review: All new Pull Requests can be required to be checked against the contribution guidelines and project rules.
• File sensitivity rules: It is possible to add an approval mark to environment configurations, key-related items, or high-risk paths.
• Comments and feedback: The system can provide review comments or additional explanations for changes via the GitHub API.
• Code repository context: The review takes into account not only the differences but also allows for the retrieval of relevant files that have been indexed.
• The results still require manual verification: AI may overlook cross-module impacts, misinterpret business rules, or propose unworkable modifications.

Code execution and generation

• Sandbox execution: The managed version allows for the creation of isolated environments in which simple code snippets can be run, making it suitable for limited testing or auxiliary processing tasks.
• Code generation: The Standard plan includes a code generation feature, which can be implemented based on an Issue or a command.
• Warehouse synchronization: Push events can trigger embedded updates to keep the index in line with the changes in the main branch code as much as possible.
• GitHub operations: The product description states that it is possible to use the GitHub API flexibly, but the actual scope of usage is limited by the app’s permissions and its functional capabilities.
• High-risk actions require approval: merging, publishing, changing keys, and making production configurations should not be left to unmonitored rules.

Example of a typical rule

• Assign all interface-related issues to the designated front-end maintainer.
• When a Pull Request modifies the environment configuration files and the contributor is not the maintainer, an approval tag is added.
• Each new Pull Request is reviewed in accordance with the project contribution guidelines.
• When the Issue mentions containerization, add a self-hosted tag along with an explanation of the existing discussions.
• Answer questions regarding project dependencies, file locations, or implementation relationships to help contributors quickly understand the codebase.

Input, output, and applicable scenarios

Suitable for users

• Open-source project maintainers: Automatically handle a large number of duplicate tags, assignments, and initial comments.
• Small R&D teams: Write the rules for routing internal issues in natural language to reduce manual maintenance.
• Code review supervisor: AI is used for the first round of rule checks, after which it is approved by a qualified engineer.
• Platform Engineering Team: Researching how to combine GitHub Webhooks, code retrieval, and agent actions into workflows.
• Organizations that require private deployment: They can evaluate open-source code, but the self-hosting experience is not yet mature at present.

Price packages

• Only the first 30 issues are available for free on this page; there is no information provided regarding how the cost is calculated per repository, organization, or account, nor is there any indication of the price for additional issues after the trial period.
• Standard does not disclose any limits regarding Issues, PRs, model calls, sandbox time, the number of members, or the number of repositories.
• The enterprise version has not yet been launched, so private deployment, support commitments, or enterprise-level security features cannot be considered as already available.
• Details regarding refunds, the timing of cancellation, automatic renewal, and tax rules are not yet available; these should be checked on the actual payment page before making a payment.

GitHub permissions and data flow

Open source and self-hosting

• Maige’s main repository makes the complete source code of its web applications available, under the GNU Affero General Public License 3.0 or a later version.
• Users can view, modify, and deploy code; when providing modified services to users on the network, it is necessary to comply with the requirements of AGPL regarding the provision of the corresponding source code.
• The warehouse instructions state clearly that self-hosting is not yet officially supported; the installation process requires Bun, a database, GitHub App, Webhooks, authentication, as well as the configuration of various external services.
• The current code relies on OpenAI embeddings, the Weaviate vector database, the E2B sandbox, Stripe, GitHub, databases, and tracking services; the costs associated with the underlying infrastructure are borne by the person who deploys it.
• Open-source licenses cover only the code in the repository; they do not automatically grant rights to use the Maige trademark, hosting credits, third-party models, or cloud services.

Privacy and security

• When using the managed version for connection, an embedding of the entire code repository is created; as a result, private code, comments, context related to the commit history, and file metadata may be processed by external infrastructure.
• The current code uses OpenAI’s text-embedding-3-small to generate embeddings, and Weaviate is used to store those vectors; it is not explicitly guaranteed that the same configuration will be used in production environments.
• The environment examples also list E2B, LangChain tracking, search services, databases, and GitHub credentials; the deployer needs to review the data rules of these third-party services separately.
• The unloading process in the warehouse deletes the project database records associated with a particular organization or user; however, the available documentation does not specify a unified deadline for deleting vector indexes, backups, logs, and copies processed by third parties.
• The current site does not provide an identifiable separate privacy policy, terms of service, data processing appendix, or security guidelines page.
• In the absence of appropriate policies, repositories containing client secrets, personal data, unrevealed vulnerabilities, production keys, or regulated code should not be connected.

Usage restrictions

• Natural language rules can be ambiguous; the same sentence may lead to inconsistent behaviors in different issues.
• Code embedding cannot fully represent runtime states, external dependencies, database contents, and implicit business constraints.
• AI review is suitable for identifying initial issues, but it cannot replace testing, static analysis, dependency scanning, security audits, and approval by authorized personnel.
• The fact that a sandbox can run simple code snippets does not mean that it is possible to execute any code from a repository safely; the details regarding networking, resources, and isolation levels are not disclosed.
• The new version of the product was released in August 2026; its stability, support responsiveness, and corporate governance capabilities still need to be assessed through actual use.

Safety tips for getting started

1. First, install it in a test repository that does not contain sensitive data, and restrict the GitHub App to access only that repository.
2. Start with rules for tags and comments that do not cause any damage, and do not grant permission for automatic merging, publishing, or making changes right away.
3. Check the range of the initial code index, and remove from the repository any keys, personal data, and files that should not be sent to third parties.
4. For each rule, specify the trigger conditions, exceptions, target users, and allowed actions, and then verify them using multiple simulated issues.
5. Manual review is retained to continuously monitor operation logs, error comments, permission changes, and abnormal usage levels.
6. When ceasing use, uninstall the GitHub App and verify with the service provider regarding the deletion status of project data, vector indexes, logs, and backups.

Summary

Maige’s advantage lies in its ability to abstract GitHub maintenance tasks into natural-language workflows, and it integrates code search with sandbox environments for handling issues and pull requests. It is suitable for reducing redundant tasks and carrying out the initial rounds of review, but it is not appropriate for making critical code changes without any oversight.

The managed version has a simple pricing structure, but its data policies and detailed quota settings are not yet complete; the open-source version provides a foundation that allows for inspection and modification, yet it does not offer seamless self-hosting support. Teams should first use a low-risk repository to verify the reliability of permissions, data flows, and rules.