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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
AI software testing platform whose autonomous agent Aximo generates and runs end-to-end tests across web, mobile, and desktop apps.
No public pricing
Free trial available
No public pricing
Free trial available
No public pricing
Free trial available
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Cross-session project memory recalling prior decisions and state
- ✦Autonomous execution of long, multi-step notebook tasks
- ✦Reads cell outputs (plots, tables, metrics), not just code
- ✦In-notebook cell-level assistance and error fixing
- ✦Installs directly into existing JupyterLab via pip, no new editor
- ✦Concept explanations with runnable example cells
- ✦Aximo autonomous AI testing agent
- ✦Natural-language and visual test generation
- ✦End-to-end, regression, and visual testing
- ✦Web, mobile, and desktop coverage
- ✦Credit-based, concurrency-tiered plans
- ✦Managed QA and on-prem options
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Data scientists running multi-week model iteration projects
- →Domain experts (e.g. risk/fintech) who know the problem but not deep Python
- →Researchers wanting an agent that remembers project context across days
- →Analysts needing help understanding unfamiliar algorithms or libraries
- →Automating regression testing without scripting
- →Replacing manual QA workflows
- →Testing Salesforce, Canvas/WebGL, and mobile apps
- →Scaling test coverage for engineering teams