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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

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✓ verifiedFreemium

Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.

👁 775K/mo

Thin 'Lingbot-map' agent listing on github.com with zero traffic; too thin to tell.

5.2K
Aide Dev
✓ verifiedPaid

Aide helps developers code faster with parallel agents and automated workflows.

👁 7.6K/mo
Sherpa Coder
✓ verifiedFree

VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.

Cody
✓ verifiedPaid

Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.

👁 245K/mo
Pricing

No public pricing

No public pricing

Standard: $49 per month

No public pricing

Enterprise: starting at $16K (includes AI feature credits, scales with team size)
Core features
  • 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)
  • Parallel Agents for faster coding
  • GitHub native integration
  • Automated PR workflow
  • Smart PR suggestions
  • Automatic code reviews
  • Real-time progress tracking
  • in-editor chat with OpenAI assistants
  • workspace source-code context sharing
  • support for custom, user-defined assistants
  • secure management of the user's OpenAI account
  • 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
Use cases
  • 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
  • Automating code reviews
  • Generating PRs automatically
  • Improving code quality through continuous improvements
  • getting coding help without switching out of VS Code
  • using a personalized OpenAI assistant tuned to a project
  • quick in-editor Q&A while writing code
  • 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
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