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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Open-source framework for automated end-to-end UI testing of mobile and web apps, with a paid cloud for parallel device runs.
Enterprise data-annotation and evaluation platform pairing a labeling tool with a managed expert annotator workforce.
No public pricing
No public pricing
No public pricing
Free trial available
Free trial available
No public pricing
- ✦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
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Human-readable YAML test flows
- ✦Local CLI and Studio testing for free
- ✦Open-source, CI-friendly design
- ✦Cloud device farm for parallel runs
- ✦AI-agent integration through MCP
- ✦Self-healing tests with local agents
- ✦Customizable multimodal annotation editors for image, video, text and audio
- ✦Support for RLHF preference data, SFT datasets, RAG and agent evaluation workflows
- ✦Managed expert annotator workforce option
- ✦Data curation, exploration and analytics tools
- ✦Team and project management with SSO on higher tiers
- ✦Integrations with AWS, GCP, Databricks, Snowflake and others
- →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
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Automate mobile app UI regression tests
- →Run tests in parallel across many devices
- →Integrate UI testing into CI pipelines
- →Let AI agents generate and run app tests
- →Building large-scale labeled datasets to train computer vision or NLP models
- →Running human evaluation and RLHF pipelines for LLM fine-tuning
- →Auditing and scoring AI agent decisions with human review