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IDE coding assistant for VS Code and JetBrains that uses your own API keys across 15+ model providers, with agentic mode and autocomplete.
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
Open-source terminal AI pair programmer that edits code in your local git repo and auto-commits, working with most LLMs.
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
Open-source, self-hostable AI coding agent with autocomplete, in-IDE chat and autonomous task execution for teams needing data control.
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- ✦BYOK access to 15+ model providers
- ✦Agentic planning-then-build mode
- ✦AI autocomplete
- ✦MCP connections to external systems
- ✦Custom rules and live context tracking
- ✦Local models via Ollama/LM Studio
- ✦VS Code and JetBrains plugins
- ✦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
- ✦Terminal-based AI pair programming
- ✦Edits code in your local git repo
- ✦Automatic git commits with messages
- ✦Codebase mapping for large projects
- ✦Works with cloud and local LLMs
- ✦Voice-to-code, image/web context, lint and test
- ✦Automatic context-gathering from connected engineering sources
- ✦AI-generated code, tests and pull requests from tickets
- ✦Task planning that breaks complex work into subtasks
- ✦Auto-updating engineering documentation
- ✦Vector search over embedded project data
- ✦Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
- ✦Engineering productivity analytics dashboard
- ✦Autonomous AI agent that plans and executes multi-step coding tasks
- ✦In-IDE chat for asking, editing, debugging and generating code
- ✦Real-time code autocompletion using retrieval-augmented generation
- ✦Repository search and analysis for context-aware execution
- ✦Integrations with GitHub, databases and CI/CD pipelines
- ✦Self-hosted/on-premise deployment option for data privacy
- ✦Support for choosing among different underlying LLMs
- →Code generation, refactoring and debugging
- →Control AI spend with your own keys
- →Switch between frontier models per task
- →Keep code private and data-sovereign
- →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
- →Building features and fixing bugs via AI in the terminal
- →Working on large existing codebases
- →Automating git commits
- →Using local LLMs for private coding
- →Engineering teams automating ticket-to-PR workflows
- →Developers wanting AI-assisted debugging and test generation
- →Engineering managers tracking AI-driven productivity gains
- →Teams centralizing documentation from scattered sources
- →Developer teams wanting an in-IDE autonomous coding agent
- →Organizations requiring on-premise/self-hosted AI coding tools for data control
- →Individuals doing 'vibe coding' with minimal manual coding
- →Teams fine-tuning an AI assistant to their own codebase