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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.
Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
Ai2's family of fully open language models with weights, code, and training data released, built for transparent LLM research and building.
No public pricing
No public pricing
No public pricing
Free trial available
No public pricing
No public pricing
- ✦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
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦Fully open weights, code, and training data
- ✦Base, Think (reasoning), and Instruct variants
- ✦7B and 32B model sizes
- ✦Open model flow across all training stages
- ✦Open-source training/eval tools (OlmoCore, OLMES)
- ✦OlmoTrace to trace outputs to training data
- →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
- →Speeding up coding with AI completions
- →Generating code from plain-language prompts
- →Getting in-editor help and explanations
- →Reviewing pull requests with AI
- →Understanding unfamiliar codebases
- →Researching language-model training and behavior
- →Building and fine-tuning open models
- →Machine-unlearning and clinical-NLP research
- →Deploying transparent open LLMs