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✓ verifiedFreemium

Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.

👁 775K/mo
Code Autopilot
✓ verifiedFreemium

AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.

Qoder
Freemium
👁 2.7M/mo32K
devActivity
✓ verifiedFreemium

GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.

👁 52K/mo
Pricing

No public pricing

No public pricing

No public pricing

Free trial available

Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
Core features
  • Open-source AI code assistant
  • Customizable autocomplete
  • In-editor AI chat
  • Community-built coding agent
  • Chat inside GitHub issues and PRs
  • Task-to-implementation plans with code
  • Automatic bug-fix suggestions
  • Pull-request summaries for faster review
  • Full-codebase context
  • GitHub-native integration
  • Enhanced Context Engineering for deep codebase analysis and adaptive memory
  • Intelligent Agents for autonomous planning, coding, and testing
  • Spec-Driven Development for clarifying requirements and automating execution
  • Intelligent Codebase Search and Advanced Repository Insight
  • Context-aware code completions and next-edit suggestions
  • Support for leading AI models (Claude, GPT, Gemini)
  • Contribution and work-quality analytics
  • Automated, AI-powered performance reviews
  • Retrospective insights
  • Operational bottleneck alerts
  • Gamification with XP, levels and leaderboards
  • Uses Git metadata without accessing source code
Use cases
  • Get AI code completions while coding
  • Ask questions about code in the editor
  • Build on an open-source coding-agent foundation
  • Speeding up pull-request reviews
  • Implementing features from task descriptions
  • Debugging with AI-proposed solutions
  • Answering questions about a repo
  • Boosting a solo developer's output
  • Delegating complex software development tasks to AI agents for autonomous completion.
  • Performing multi-file code edits and refactoring through natural language chat.
  • Gaining deep architectural understanding of a codebase to resolve issues with precision.
  • Generating unit tests, code explanations, and uncovering codebase architecture.
  • Systematically tackling software development tasks from planning to testing.
  • Automating developer performance reviews
  • Spotting delivery bottlenecks
  • Generating retrospective insights
  • Motivating teams via gamification
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