Euni
Euni Agent is an AI-powered development tool that automatically identifies, fixes, and commits code issues in software repositories. It understands code context like a human developer, enabling faster bug resolution and more efficient software delivery. The agent demonstrates strong performance on industry benchmarks, including 71.2% on SWE-bench Verified and 35.3% on SWE-bench Lite.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Software development teams spend significant time manually identifying, debugging, and fixing code issues, which slows down release cycles and diverts developer attention from feature work. Automated tools often lack the contextual understanding needed to resolve complex bugs without human intervention, leaving teams with tedious, error-prone troubleshooting processes.
Solution
Euni Agent provides an AI-driven development assistant that understands codebases like a developer, automatically detecting bugs, generating fixes, and committing the corrections directly to the repository. The agent integrates with GitHub workflows, allowing users to submit a repository URL and issue number to trigger an automated analysis and repair cycle. It applies advanced machine learning models trained on software engineering tasks, as evidenced by its benchmark results, to deliver reliable, context-aware code patches. By handling the full fix-and-commit pipeline, Euni Agent reduces manual debugging effort and helps teams ship software faster with fewer defects.
Target Audience
Primary users are software development teams and individual developers who want to automate bug fixing and accelerate their code delivery cycles within GitHub-based workflows.
Features
- Automated bug detection and code fix generation with direct commit capabilities
- GitHub integration that accepts repository URLs and issue numbers for streamlined workflow
- Support for private repositories via GitHub token authentication
- Strong performance on SWE-bench Verified (71.2%), SWE-bench Lite (35.3%), and SWE-bench Multilingual (25.7%) benchmarks
- Invitation-code-based access model for controlled rollout and user onboarding