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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.
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
Low-code integration platform for connecting thousands of APIs into workflows and AI agents, including an MCP tool server.
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
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦Data labeling across modalities
- ✦RL environments and reward signals
- ✦Custom model evaluations and benchmarks
- ✦Human preference/annotation from an expert network
- ✦Recursion RL platform for enterprise agents
- ✦Robotics data (video, trajectories)
- ✦Visual and code-based workflow builder
- ✦Prebuilt AI agent builder and deployment
- ✦Managed authentication across thousands of apps
- ✦MCP server exposing integrations as agent tools
- ✦Scheduled and event-triggered workflows
- ✦Connect SDK for embedding integrations into other products
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents
- →Building AI agents that call external APIs and tools
- →Automating cross-app workflows such as Slack, Gmail, or Sheets notifications
- →Embedding third-party integrations into a SaaS product
- →Prototyping event-driven automations without heavy infrastructure