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Agentic terminal and cloud agent platform (Warp Terminal, Warp Agent, Oz) for developers orchestrating Claude Code, Codex, and other agents.
Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.
AI-powered code editor with agentic workflows for developers.
IDE coding assistant for VS Code and JetBrains that uses your own API keys across 15+ model providers, with agentic mode and autocomplete.
Design-system platform that packages tokens, code components, and rules into scoped context for AI coding agents.
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No public pricing
- ✦Modern terminal rebuilt for agentic coding workflows
- ✦Warp Agent with multi-agent orchestration and model routing
- ✦Oz platform for launching agents into the cloud via SDK, CLI, or terminal
- ✦Codebase indexing and granular permission controls
- ✦Team-wide usage visibility and spend/credit caps
- ✦Open-source terminal core
- ✦Git repository hosting
- ✦Bitbucket Pipelines CI/CD
- ✦Pull requests and code review
- ✦Native Jira integration
- ✦Branch permissions and access controls
- ✦IP allowlisting and security features
- ✦AI-powered code completion and suggestions
- ✦Automated lint fixing
- ✦Cascade agent for advanced coding assistance
- ✦Integrated app building and deployment
- ✦MCP server support for custom tools
- ✦Terminal command integration
- ✦Memory of codebase structure and workflow
- ✦BYOK access to 15+ model providers
- ✦Agentic planning-then-build mode
- ✦AI autocomplete
- ✦MCP connections to external systems
- ✦Custom rules and live context tracking
- ✦Local models via Ollama/LM Studio
- ✦VS Code and JetBrains plugins
- ✦Scoped MCP context distribution to multiple AI coding tools
- ✦Design token and component API management
- ✦Collaborative documentation with analytics
- ✦Figma and Storybook data source integration
- ✦Feedback loop for improving AI context quality
- ✦Skill and exporter management for agent capabilities
- →Developers who want an AI-assisted terminal for daily coding
- →Teams orchestrating multiple coding agents (Claude Code, Codex) together
- →Engineering orgs needing governance over agent-driven development
- →Companies moving agent workflows from local machines to the cloud
- →Source code management
- →CI/CD automation
- →Team code review
- →DevOps for Jira-based teams
- →Accelerating software development by automating repetitive tasks
- →Reducing onboarding time for new developers
- →Improving code quality and reducing tech debt
- →Streamlining the app building and deployment process
- →Enhancing developer productivity by keeping them in a state of flow
- →Code generation, refactoring and debugging
- →Control AI spend with your own keys
- →Switch between frontier models per task
- →Keep code private and data-sovereign
- →Product teams giving AI coding agents accurate design-system context
- →Design system managers publishing a single source of truth
- →Engineering teams reducing token usage by scoping agent context per team