OpenAdapt - AI Productivity Tool

Overview

OpenAdapt is an open-source generative tool designed for process observation and productivity measurement to help teams and systems optimize workflows. It combines generation-driven scenario modeling with telemetry-aware observation to let operators simulate alternate process flows, augment sparse traces, and measure productivity metrics such as cycle time and throughput. The project emphasizes reproducible, exportable outputs that can be used for downstream analytics and capacity planning. According to the GitHub repository, OpenAdapt is published under an MIT license and has an active codebase (last recorded commit on 2025-12-19). With a focus on practical optimization, the project provides instrumentation and analysis primitives that can be integrated into existing pipelines to capture event streams, generate synthetic process traces, and produce measurement reports for prioritizing system improvements. Its open-source nature makes it suitable for teams that need transparent models and the ability to extend or adapt analysis modules to domain-specific processes.

GitHub Statistics

• Stars: 1,463
• Forks: 212
• Contributors: 17
• License: MIT
• Primary Language: Python
• Last Updated: 2025-12-19T21:26:39Z
• Latest Release: v0.46.0

According to the GitHub repository, OpenAdapt has 1,463 stars, 212 forks, and 17 contributors, and is released under the MIT license. The repository shows recent maintenance activity with the last recorded commit on 2025-12-19, indicating ongoing development. The star and fork counts suggest a moderate but engaged user base; a contributor count of 17 points to an active core team rather than a very large community. For the most current issue/PR activity and release cadence, review the repository’s Issues and Pull Requests pages directly.

Installation

Install via pip:

git clone https://github.com/OpenAdaptAI/OpenAdapt.git

cd OpenAdapt

pip install -r requirements.txt

pip install -e .

Key Features

• Generative trace synthesis to augment sparse telemetry and simulate process variants.
• Event- and metric-based productivity measurement (cycle time, throughput, bottleneck detection).
• Automated optimization recommendations based on observed patterns and generative scenarios.
• CLI and SDK for instrumenting processes and collecting observation data from systems.
• Exportable JSON/CSV reports for downstream analysis and dashboarding.

Community

OpenAdapt’s primary community hub is the GitHub repository, which shows 1,463 stars, 212 forks, and 17 contributors. The project is maintained under an MIT license and has recent commits, indicating active development. Users and contributors typically engage via issues and pull requests on GitHub; for project-specific support channels (chat, mailing lists) check the repository README or documentation.

Key Information

• Category: Productivity
• Type: AI Productivity Tool