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Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Headless, open-source rich-text editor framework with paid add-ons for collaboration, comments, AI editing agents and document conversion.
No public pricing
No public pricing
Free trial available
Free trial available
- ✦CSV to API conversion
- ✦Data parsing (CSV to JSON)
- ✦Filtering capabilities
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
- ✦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
- ✦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
- ✦Headless, extensible core editor with 100+ extensions
- ✦Real-time collaborative editing with live cursors
- ✦Inline and document comments
- ✦DOCX, ODT and Markdown import/export
- ✦AI Toolkit for building document-editing AI agents
- ✦Prebuilt UI components and editor templates
- →Sharing CSV data with a team via an API
- →Creating a public API from CSV data
- →Filtering and accessing specific data within a CSV file programmatically
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →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
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Building a custom rich-text editor for a SaaS product
- →Adding real-time collaboration to a document app
- →Letting an AI agent edit documents with tracked changes
- →Importing or exporting Word or Markdown content in-app
