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Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
Vibe-coding builder creating full-stack apps by chatting with AI.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Spreadsheet-style Python data tool inside Jupyter that generates code via AI so analysts and data scientists can skip hand-coding.
AI chatbot builder that trains a support agent on your website content in minutes to deflect tickets and capture leads with cited answers.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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
- ✦CodeFlying enables full-stack app creation via chat in minutes
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦Spreadsheet interface inside Jupyter notebooks
- ✦AI-generated Python code from spreadsheet actions
- ✦Runs on customer infrastructure (no data sent to Mito)
- ✦Bring-your-own LLM API keys
- ✦Excel-to-Python conversion
- ✦Compatible with existing Jupyter extensions
- ✦Fast setup trained directly on website content
- ✦Answers cited back to source content
- ✦Inbound lead capture with automated follow-up
- ✦Integrations with existing support/marketing stack
- ✦Self-improving responses over time
- →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
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- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Analysts automating Excel-style reports without hand-writing code
- →Data scientists speeding up exploratory data analysis
- →ML engineers iterating on feature engineering in notebooks
- →Enterprises needing private, self-hosted AI coding assistance
- →SaaS companies deflecting repetitive support questions
- →Marketing teams capturing and qualifying leads via chat
- →Support teams wanting AI answers backed by verifiable sources