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General AI agent that executes multi-step tasks end to end — research, slides, design, browsing — instead of only answering questions.
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.
Desktop workspace letting multiple AI coding agents (Claude Code, Codex, Cursor) collaborate on shared context and specs.
No public pricing
No public pricing
Free trial available
No public pricing
- ✦Autonomous multi-step task execution
- ✦Website and app building
- ✦AI slides, design and image generation
- ✦Manus browser operator
- ✦Wide Research mode
- ✦Cross-platform web, desktop and mobile apps
- ✦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
- ✦Visual workflow studio for agents
- ✦RAG knowledge pipelines
- ✦Agent runtime with tools and memory
- ✦Marketplace of models and plugins
- ✦Publish as app, API or MCP tool
- ✦Logging, analytics and monitoring
- ✦Runs multiple coding agents (Claude Code, Codex, OpenCode, Cursor) in one workspace
- ✦Bring-your-own-subscription model for existing agent accounts
- ✦Agent-to-agent communication for questions, reviews and handoffs
- ✦Shared filesystem, decision history and specs per task
- ✦Mid-chat model switching without losing context
- ✦macOS desktop app
- →Automate end-to-end digital tasks
- →Produce websites and presentations
- →Conduct broad research
- →Hand off browser tasks to an agent
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Building AI agents and chatbots
- →Creating RAG-based knowledge apps
- →Deploying LLM apps at enterprise scale
- →Developers coordinating multiple AI coding agents on the same project
- →Teams collaborating around shared agent context and specs
- →Switching between different LLMs mid-task without losing history
- →Reviewing and handing off in-progress coding work between agents