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Connects Git repos to answer plain-English questions about your code with file references and dependency context.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
AI tool for engineering teams that automates code review, status updates, and answers questions about what's changing in code.
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.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Natural-language search across a codebase
- ✦Architecture explanations and dependency graphs
- ✦Bug hunter that traces issues across files
- ✦AI code review before opening a PR
- ✦Automatic documentation generation
- ✦Multi-repo support via OAuth
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦AI code review
- ✦Automatic engineering status updates
- ✦Agent that answers questions and takes action
- ✦Metrics on coding time and project focus
- ✦Pushed vs landed tracking
- ✦Commit and contributor insights
- ✦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
- →Onboarding new engineers faster
- →Answering questions about a codebase
- →Understanding how components connect
- →Finding and diagnosing bugs
- →Generating documentation from code
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Automating code reviews
- →Keeping stakeholders updated on engineering progress
- →Understanding what's changing in a codebase
- →Tracking team productivity metrics
- →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