I investigated Manus & Open Manus history and here is the result

1. Introduction

Recently, AI agent technology has been receiving major attention. In particular, Manus, developed by the Chinese startup Monica, has made waves as a fully autonomous AI agent platform with the slogan “Turning your thoughts into actions.” Manus demonstrated its potential by achieving the latest State-of-the-Art (SOTA) performance in the GAIA benchmark, which evaluates an AI agent’s ability to solve real-world problems.

However, due to its invite-only preview, accessibility has been limited—invite codes are difficult to acquire and are sold on secondary markets for nearly ₩100,000 (approximately USD 1,000). This exclusivity has prevented many users and developers from directly experiencing Manus’s capabilities.

In response, a new open-source alternative called OpenManus emerged, developed by the MetaGPT team (@mannaandpoem, @XiangJinyu, @MoshiQAQ, @didiforgithub) in just three hours. OpenManus aims to replicate many of Manus’s core features, removing the barrier of invite codes while encouraging community-driven development. This development has sparked a paradigm shift in AI agent technology, bringing attention to the potential of open-source and community-led innovation.

In this document, we will explore what Manus is, delve into the details of OpenManus, compare their features and capabilities, and finally conclude with the broader implications for the AI agent market. In the next post, we plan to investigate OpenManus even more deeply, focusing on technical aspects and hands-on use cases.

2. What is Manus?

Manus is a cloud-based autonomous AI agent platform developed by Monica. Its distinctive feature is that it not only generates ideas but also executes tasks end-to-end. Key capabilities include:

High Performance with GAIA SOTA:
- Manus achieved the latest SOTA performance in the GAIA benchmark, which tests how well an AI agent can handle practical, real-world tasks.
Fully Autonomous Workflow:
- Instead of merely suggesting solutions, Manus plans, executes, and verifies complex tasks on its own. Examples include resume screening, financial transaction analysis, creating and deploying websites, and so on.
Closed, Invite-Only Access:
- Manus is currently in a limited beta testing phase, and only those with official invite codes can access the system. This exclusivity has driven invite code prices exceedingly high.
Black-Box Environment:
- Because Manus is a commercial, closed-source platform, details about its underlying implementation or how exactly it integrates different tools are not transparently disclosed.

Overall, Manus demonstrates how a well-designed AI agent can automate complex tasks that go beyond simple text generation. The downside, however, is the limited access model, which hinders widespread adoption and experimentation.

3. What is OpenManus?

OpenManus is an open-source alternative to Manus AI, introduced by the MetaGPT team. The creators claim to have replicated many of Manus’s core functionalities in just three hours, making it immediately available to a broad user base without requiring invite codes. It features a multi-agent framework that allows developers to build AI agents using various Large Language Models (LLMs) such as GPT-4o, Claude 3.5, Qwen VL Plus, DeepSeek, and more.

Key Goals of OpenManus:

Remove entry barriers imposed by invite-only systems
Enable community-driven feature development and rapid iteration
Provide code transparency and extensibility for real-world AI agent implementations

3.1 Key Advantages of OpenManus

Free to Use: Completely free, with no license fees or subscriptions. Users only pay for the LLM APIs they consume.
Easy API Integration: Simple integration with various APIs, making it easy to add custom functionalities or connect with existing systems.
Community Support: Actively supported by an open-source community. New features and improvements are continuously added via user contributions.
Open Source Code: The entire codebase is accessible, enabling developers to modify or extend it as needed.
Transparent Development Process: Ongoing development is publicly visible; everyone can monitor, participate in, and influence the roadmap.
Multiple LLM Support: Supports various LLMs (Claude 3.5, Qwen VL Plus, DeepSeek, GPT-4o, etc.), offering users the freedom to choose.
Low Technical Barrier: OpenManus can be set up with Python 3.12 in a conda environment (or using uv), making installation straightforward for developers. You only need an LLM API key to start using it.
High Customizability: Users can customize everything from internal workflow to integrated tools, enabling tailor-made solutions for diverse applications.

3.2 Application Areas of OpenManus

Thanks to its open design and flexible architecture, OpenManus can be applied in numerous fields:

IDE Integration (e.g., VSCode): Automating parts of the development, testing, and deployment process for AI projects.
Chatbots and Virtual Assistants: Creating AI agent-based chatbot solutions that can handle more complex, autonomous tasks.
AI Workflow Automation: Automating repetitive or complex AI workflows, boosting productivity for individuals and teams.
Research and Education: Leveraging open-source code to experiment with multi-agent frameworks and to teach AI concepts.
Community Collaboration: Co-creating new features, expanding tools, and sharing best practices via open-source culture.

3.3 OpenManus's Message to the AI Agent Market

Democratization of AI Technology: Proves that AI agents can be made widely accessible rather than being gated by closed, invite-only systems.
Power of the Open-Source Ecosystem: Accelerates AI innovation through community-driven contributions, focusing on user-centered development.
User-Centered AI Agent Development: Encourages a more collaborative model in which user feedback directly shapes the agent’s features and improvements.
Paradigm Shift: Suggests a move away from closed commercial models toward more transparent open-source platforms in the AI agent sector.

3.4 Future Prospects of OpenManus

Community-Driven Growth: Ongoing feature expansion and stability improvements through global developer collaboration.
Diversified Features: Enhancements for better planning, real-time demos, session replay, etc., are likely to emerge soon.
Reinforcement Learning Fine-Tuning: Potential for training specialized models to improve performance on complex tasks, plus comprehensive benchmarks for evaluation.
Expanded Model & Tool Support: Additional LLM models and integrations for external tools, further extending the range of use cases.

4. Manus vs. OpenManus Comparison

Below is an expanded comparison highlighting the major differences between Manus AI and OpenManus, including technical requirements, community support, and cost structure.

Feature	Manus AI	OpenManus
Accessibility	Invite code required, limited preview	Open source, no invite code needed, immediately usable
Code Disclosure	Closed source, commercial	Fully open source, community-driven
Model Support	Specific internal models (limited info)	Various LLMs (GPT-4o, Claude 3.5, Qwen VL Plus, DeepSeek, etc.)
Execution Process	Runs in a closed environment, user has limited visibility	Provides real-time feedback and process visualization
Cost Structure	Potential future subscription model, enterprise pricing possible	Free platform; users only pay for LLM API usage
User Interface	Polished, user-friendly web UI	Basic interface suitable for technical users; highly customizable
Functionality & Performance	Commercial optimization; GAIA SOTA performance	Core AI agent functions replicated; room for improvement via community contributions
Technical Requirements	Managed cloud-based service	Local setup (Python 3.12, conda or uv), requires some technical knowledge
Community & Support	Official support team, closed roadmap	Open-source community collaboration, transparent development

While Manus currently holds the advantage in proven, benchmark-tested performance (SOTA on GAIA), OpenManus is quickly evolving thanks to its flexible open-source approach. For users who value transparency, cost efficiency, and community-driven improvements, OpenManus represents a compelling alternative.

6. Conclusion

Manus proved the capability of autonomous AI agents to perform complex, real-world tasks, earning top scores in the GAIA benchmark. However, its closed, invite-only environment has limited broad experimentation and community involvement.

On the other hand, OpenManus disrupts that exclusivity by offering a similar AI agent framework under an open-source license. Free access, community contributions, and multi-model support enable faster feature development and broader adoption. While OpenManus has yet to match Manus’s validated performance metrics, its underlying technical structure and community-driven model suggest rapid progress is on the horizon.

Overall, the development of OpenManus is a testament to the increasing demand for open, transparent, and user-centered AI solutions. This trend is likely to push the AI agent industry toward more open-source offerings, potentially accelerating innovations far beyond what closed systems can achieve alone.

In our next post, we will dive deeper into OpenManus’s technical framework, potential real-world use cases, and how its community-driven ecosystem may shape the future of AI agent technology.