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Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
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
Free trial available
No public pricing
Free trial available
No public pricing
No public pricing
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- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
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- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Researching language-model training and behavior
- →Building and fine-tuning open models
- →Machine-unlearning and clinical-NLP research
- →Deploying transparent open LLMs