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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
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
No public pricing
Free trial available
Free trial available
Free trial available
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦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
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →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