kodezi/chronos

Kodezi Chronos is a debugging-first language model that achieves state-of-the-art results on SWE-bench Lite (80.33%) and 67% real-world fix accuracy, over...

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Summary Information

Updated 36 minutes ago
Added to GitGenius on September 14th, 2026
Created on July 21st, 2025
Open Issues & Pull Requests: 1 (+0)
GitHub issues: Enabled
Number of forks: 210
Total Stargazers: 4,942 (+0)
Total Subscribers: 42 (+0)

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Open issues: 1
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Detailed Description

Kodezi Chronos is a debugging-first language model designed to autonomously identify and fix bugs in repository-scale codebases. The tool addresses the fundamental gap between code generation and debugging capabilities in general-purpose language models, which typically achieve over 70% accuracy on code generation tasks but drop below 15% on debugging benchmarks. Chronos uses Adaptive Graph-Guided Retrieval and Persistent Debug Memory to maintain context across repository structures and learn from previous debugging attempts, enabling it to understand code relationships and fix patterns that general models miss.

Developers should adopt Chronos if they need autonomous bug fixing at scale, particularly for complex repositories where understanding code interdependencies is critical. The tool is purpose-built for debugging rather than code generation, making it fundamentally different from general-purpose models like Claude or GPT-4. It suits teams working on software maintenance, automated program repair, and large codebases where manual debugging is expensive. The model is proprietary and available exclusively through Kodezi OS, with access beginning in beta and expanding to general availability through that platform.

The project maintains an active research focus with detailed benchmark documentation and evaluation results published in its repository. Development activity centers on establishing and validating performance metrics against industry-standard benchmarks, with comprehensive documentation of architecture decisions and comparative analysis against competing approaches. The team publishes detailed performance breakdowns across different repository types and maintains a leaderboard tracking results, indicating ongoing commitment to transparent evaluation and benchmark tracking.