Open Work Lang (OWL) is an open-source standard schema language designed to define, structure, and reason about work — including tasks, documents, projects, events, and more. It provides a standardized yet extensible foundation for work-based systems, automation engines, and intelligent agents.
Think of OpenWorkLang like a universal power adapter for work data. Just as a universal adapter allows you to plug your devices into any power outlet worldwide, OpenWorkLang allows your work data to seamlessly connect with any tool, platform, or AI system. Whether you're using Jira, Notion, Slack, or any other work tool, OWL provides a standardized way to understand, transform, and utilize your work data across the entire ecosystem.
OpenWorkLang was born out of the need to solve the growing complexity of work data across different tools and platforms, and the frustration with highly vendor-locked tools that offer no guarantees on data ownership. The project began in Q2 2024 by the team at Plane as an initiative to create a standardized language for work data and make it easy to import data from existing tools and integrate with Plane. However, the team soon realized the broader potential of using it to integrate with AI agents while ensuring users maintain full control and ownership of their work data. Below are some of the key features that make OWL a powerful and flexible standard for work data.
OpenWorkLang provides a unified schema layer that normalizes data from various work tools into a standardized YAML format. The architecture includes connectors for popular tools, a normalization engine, schema validation, and outputs that are ready for both API consumption and AI/agent processing.
Define a standardized schema for work data that works across any tool or platform. Enable consistent representation of tasks, projects, documents, and more, regardless of their source system.
Create integrations that work with any tool by mapping their data to the OWL schema. Build connectors once and use them across multiple platforms, reducing integration complexity.
Enable consistent analytics across tools by using a common data schema. Generate comparable metrics and insights regardless of the underlying work management system.
Train AI models on standardized work data that follows a consistent schema. Enable better machine learning outcomes by eliminating data format variations across tools.
Build new work management tools that natively support the OWL schema. Create interoperable applications that can seamlessly exchange work data with existing systems.
Establish consistent documentation practices across tools by using the OWL schema. Ensure work artifacts are documented in a standardized format regardless of their source.
Enable cross-tool search capabilities by using a common schema for work data. Build search systems that can understand and index work items consistently across platforms.
Simplify tool migrations by using OWL as an intermediate schema. Convert work data between different systems without losing structure or context.
Foster an open ecosystem of work tools that share a common data schema. Enable innovation and competition while maintaining interoperability between systems.
OpenWorkLang follows semantic versioning (MAJOR.MINOR) where:
To ensure smooth transitions, we provide migration scripts and detailed documentation for all breaking changes. The project started in Q2 2024 and is expected to reach a stable v1.0 release by Q4 2025.
OpenWorkLang is licensed under the Apache 2.0 License.
OpenWorkLang (OWL) is developed in the open on GitHub with a structured governance model to ensure high-quality, sustainable development:
We've intentionally kept the initial governance group lean to ensure deliberate, high-quality development. As the project matures and reaches stable versions, we'll gradually expand participation opportunities.