Taiga is an open-source project management tool designed for agile development teams, combining issue tracking, sprint planning, and visual boards in a unified interface. It’s built for developers, product managers, and teams seeking full control over their project data without vendor lock-in. Taiga supports both Scrum and Kanban methodologies and provides a comprehensive REST API for automation and integration.
The backend (taiga-back) is built with Python and Django, using PostgreSQL as the primary database and Redis for caching and real-time updates. It deploys via Docker or direct server installation, and integrates with GitHub, GitLab, and other Git providers. The frontend is a separate React application, making Taiga a decoupled, API-first platform.
What You Get
- Scrum Boards - Visual sprint planning with backlog, active sprint, and done columns, supporting story points and velocity tracking.
- Kanban Boards - Flexible workflow boards with customizable columns and WIP limits for continuous delivery teams.
- Issue Tracking - Detailed issue management with assignees, priorities, tags, due dates, and hierarchical subtasks.
- REST API - Full-featured public API for automating issue creation, updating statuses, fetching project data, and integrating with CI/CD pipelines.
- Git Integration - Automatic issue linking via commit messages (e.g., #123) and repository webhooks for GitHub and GitLab.
- Role-Based Permissions - Granular access control for project members with roles like admin, member, and guest.
Common Use Cases
- Running a software sprint cycle - A DevOps team uses Taiga to plan sprints, assign user stories, track burn-down charts, and link commits to issues via Git integration.
- Managing a product backlog - A product manager organizes feature requests, prioritizes them with story points, and visualizes progress on a Scrum board.
- Coordinating cross-functional teams - Designers, developers, and QA engineers collaborate on Kanban boards with custom workflow stages and real-time updates.
- Building custom automation workflows - A developer uses Taiga’s REST API to auto-create issues from Jira migration data or trigger notifications when a PR is merged.
Under The Hood
Architecture
- Django-based monolithic structure with tightly coupled models, views, and serializers, lacking clear service or repository layers
- Custom permission logic embedded directly in views without strategy or policy abstraction
- Event hooks implemented as implicit signal listeners, violating inversion of control
- Migration and model dependencies create brittle schema evolution with no domain boundary isolation
- Absence of dependency injection; components are hard-instantiated via Django ORM and settings
- Configuration scattered across multiple files with no unified module-level system
Tech Stack
- Python 3.10 backend powered by Django, with suppressed deprecation warnings for compatibility
- Asynchronous task processing via Celery and Kombu using AMQP message broker
- Comprehensive testing infrastructure built on pytest with custom settings and coverage tooling
- Code quality enforced through flake8 with strict formatting and complexity constraints
- Dependency management via requirements files and tox for environment isolation
- Lightweight templating and asset handling using django-jinja and easy-thumbnails
Code Quality
- Extensive integration test suite covering API endpoints, permissions, and edge cases with realistic data factories
- Clear separation of concerns through well-defined views, serializers, and service layers
- Robust error handling with consistent HTTP status codes for validation, permission, and state errors
- Strong type safety and structured validation in serializers and model factories
- Comprehensive test coverage of complex scenarios including duplicate resources, cross-project conflicts, and webhook integrity
- Consistent naming and modular test organization using pytest fixtures and factory patterns
What Makes It Unique
- Custom DRF viewsets that enforce consistent API patterns across the codebase, reducing boilerplate while preserving flexibility
- Innovative bulk order update system leveraging PostgreSQL’s execute_values for O(n) efficiency and transactional reordering
- Event-driven architecture with decoupled event system enabling real-time collaboration independent of HTTP requests
- Extensible project-specific custom fields and workflows implemented as metadata systems, avoiding hardcoded models
- Atomic persistence of project membership order across nested entities like epics, stories, and tasks
- Built-in serialization layer with automatic inverse relation updates, eliminating client-side state synchronization needs