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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
AI code-review and context tool that maps a codebase into a knowledge graph so coding agents and PR reviews stay grounded in real context.
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
AI coding assistant with multi-model chat and developer tools to help write, explain and improve code faster.
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
No public pricing
No public pricing
Free trial available
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦AI Architect knowledge graph of the codebase
- ✦Codebase-aware AI code reviews
- ✦Grounded coding and technical design docs
- ✦Impact and feasibility assessment
- ✦Integrations: Cursor, Claude Code, Copilot, Codex, Jira, Slack
- ✦Self-hosted deployment option
- ✦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
- ✦AI code generation
- ✦Multi-model chat
- ✦Developer utility tools
- ✦Code explanation and improvement
- ✦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
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Automated code review
- →Context-grounded AI coding
- →Technical design and scoping
- →Cutting agent token costs
- →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
- →Writing code faster
- →Explaining or debugging code
- →Generating tests and documentation
- →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