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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Google's cloud-based, AI-assisted development environment, now rebranded and merged into Firebase Studio.
Open-source AI search and vector database platform for building large-scale search, RAG, and recommendation systems.
No public pricing
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Codebase-aware developer chat
- ✦AI code completions and inline edits
- ✦Customizable and shareable prompts
- ✦Automatic bug identification and debugging help
- ✦Context filters to exclude sensitive repos
- ✦Integrates with major code hosts and IDEs
- ✦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
- ✦Cloud-based IDE accessible from the browser
- ✦AI-assisted coding
- ✦Cross-platform app development
- ✦Preconfigured workspaces and templates
- ✦Now part of Firebase Studio
- ✦Combined vector, text, and structured search
- ✦Distributed machine-learned ranking at query time
- ✦Streaming search mode for cost-efficient personal/private data
- ✦Support for retrieval-augmented generation pipelines
- ✦Continuous deployment and automated scaling
- ✦Open-source core with a managed cloud option
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Engineers asking questions about an unfamiliar large codebase
- →Teams standardizing common coding tasks with shared prompts
- →Developers debugging errors faster with AI-assisted context
- →Enterprises running large-scale code migrations
- →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
- →Building apps from anywhere in the browser
- →Prototyping with AI assistance
- →Developing cross-platform applications
- →Building large-scale enterprise search engines
- →Powering RAG pipelines that need strong retrieval relevance
- →Building recommendation and ad-targeting systems
- →Search over personal/private data at lower indexing cost