In the early morning of March 6, 2025, Monica.im team released Manus AI, which is called “the world’s first universal AI agent.” In the following days, this product completely ignited the entire AI circle. Manus AI swept the network just two days after its launch, with the number of visits to the official website exceeding ten million and the invitation code speculated to be 50,000 yuan(RMB) on the Chinese second-hand platform.
At the same time, another open-source project called OpenManus quickly emerged on GitHub. OpenManus by MetaGPT four-member team in just three hours to replicate the core functions of Manus, and open source on GitHub, the open source project has received more than 29k stars. Thus, a large number of people who can not get Manus’s invitation code will consider OpenManus as an alternative to Manus.
Many users may wonder how OpenManus performs compared to Manus. What are the advantages of OpenManus? How should I choose an AI Agent? This article will use actual use case scenarios to compare these two models to help you choose the most suitable AI Agent for you.
OpenManus vs Manus: Performance Evaluation
As of today, Manus has not been opened for use, and an invitation code is required. However, almost no netizens have obtained the first-hand invitation code, so we are cautious about Manus. Because Manus only has official demonstrations, no large-scale public tests, and no technologies or products endorsed by experts with real names (such as ChatGPT, NotebookLM, DeepSeek, etc.), its strength is still questionable.
Before comparing and evaluating, let’s look at the technical features of OpenManus.
1. The Rise and Popularity of OpenManus:
On March 6, the MetaGPT four-person team (@mannaandpoem, @XiangJinyu, @MoshiQAQ, @didiforgithub) quickly took action, developing OpenManus in just three hours and open-sourcing it on GitHub. OpenManus focuses on a “no-invitation-code” user experience based on a modular design. It supports multiple language models and toolchains and can execute code, handle files, and search the web.
Its core advantage lies in the real-time feedback mechanism, which allows users to intuitively see the AI’s thought process and execution progress. According to the team, OpenManus was inspired directly by Manus’s popularity, but its technical foundation stems from the multi-agent collaboration framework previously developed by MetaGPT.
One of the team members, @mannaandpoem, said, “We simply integrated the browser toolchain into the existing code, and the core system was up and running within an hour.” OpenManus received significant attention on GitHub on its launch day, with 9 WeChat groups filling up quickly. User @Jenqyanghou commented, “With OpenManus, any idea can be realized without an invitation code, making it too convenient!” Although the initial version of OpenManus has limited features, its open-source nature lowers the barrier to using AI agents. Users only need to configure the environment and API key to interact with the AI through the terminal. Future plans include incorporating reinforcement learning and comprehensive testing, referencing projects like anthropic-computer-use, and demonstrating the potential of open-source community collaboration.
2. The Design Concepts Of OpenManus
When a task is broken down into several subtasks, the system dynamically assigns them to predefined or adapted agents (with their own toolsets and competency tendencies) according to the subtask type. This “temporary allocation + tool collaboration” mechanism can maximize the advantages of multi-model and multi-tool combinations and improve the flexibility to deal with different problem scenarios. The agent is equipped with different toolsets to deal with different types of tasks, improving the system’s flexibility and efficiency.
From the outside, Manus (and a replica of OpenManus) is essentially a multi-agent system. Unlike the one-time “big and complete” answer mode of a single large model, multi-agent systems gradually solve complex real-world problems through the cycle of “planning-execution-feedback.” In the design of OpenManus, the core ideas can be summarized as follows:
1. Minimally Pluggable Frame
The core design of OpenManus is to build a lean Agent framework that emphasizes modularity and extensibility. It uses pluggable tools and prompts to define agent functions and behaviors, lowering the threshold for developing and customizing agents.
- Prompt determines the behavior logic and thinking mode of the Agent;
- Tools provide mobility (computer operations, code execution, search, etc.).
Through the free combination of Prompts and Tools, new Agents can be quickly “assembled” and endowed with the ability to handle different types of tasks.
2. Tool-driven ReAct Agent
OpenManus is based on the ReAct (Reason + Act) model and uses tools as the core to drive Agent actions. Prompt guides the agent’s reasoning and logic, while Tools give the Agent the ability to act. The introduction of the ToolCall Agent further improves the efficiency and standardization of tool use.
3. Planning capability for complex tasks
OpenManus continues Manus’s multi-agent planning strengths by using PlanningTool for high-level planning of user needs. This “plan first, execute later” approach works better on complex, long-chain tasks. PlanningTool breaks down complex user requirements into linear sub-task plans, and this planning ability is key to dealing with complex real-world problems. Past studies have shown that, with the same model capabilities, the success rate of many real-world problems is greatly reduced without systematic decomposition and planning and significantly improved with planning.
3. Features Of OpenManus – Pros and Cons
- OpenManus is an open-source project that does not require an invitation code and can be downloaded and used immediately by anyone (the full code is available on GitHub). In contrast, Manus is currently invitation-only, with access codes speculated at thousands of dollars on platforms such as Idle Fish, and the bar is extremely high.
- OpenManus is completely free, and users only need to bear the cost of calling external LLM APIs by themselves (e.g., the cost of OpenAI or Qwen’s key). And Manus invitation code is costly, the future may introduce a subscription system.
- Modular design (agent layer, tool layer, memory layer) allows developers to adjust the functionality according to the specific task, such as adding new tools or optimize the task flow
- OpenManus is rapidly developed and continuously iterated by the MetaGPT community, and developers around the world can contribute code, fix bugs, or add new features.
- OpenManus requires some technical skills (e.g., configuring Python environment, API keys, etc.), and is not as “out-of-the-box” friendly to install and run as Manus.
- The performance of OpenManus relies on external LLM and user configuration, and may not be as stable as Manus. Manus claims to outperform OpenAI’s DeepResearch in GAIA benchmarks, while OpenManus has not yet reached the same level of polish, and there are bugs or incomplete functions.
- Manus comes with 29 built-in tools (e.g., browser navigation, file manipulation, etc.) that have been optimized by the Monica team to work right out of the box, while OpenManus supports similar functionality, but the integration of the tools and the smoothness of the task execution may vary depending on the community’s development progress.
- While Manus is supported by an official team, OpenManus relies on self-maintenance by the community, and the documentation and tutorials may not be complete enough for novice users to encounter difficulties.
The MetaGPT team stunned users with the feat of replicating Manus and open-sourcing it within 3 hours. People are interested in the technical basis behind this development speed (based on MetaGPT’s multi-agent framework) and whether it can really rival Manus.
The OpenManus open-source model has users enthusiastic about its modular design and customization potential. For example, could the code be tweaked to achieve features beyond Manus (e.g., support for more LLMs or the addition of unique tools)?
In addition, many users are excited about OpenManus’ free “no invitation required” model, which breaks the monopoly of AI agent technology and allows ordinary developers to participate in AI innovation.
4. OpenManus Demonstration
In the project’s demo video, enter the prompt, “Conduct a comprehensive SEO audit of Karpathy’s website (https://karpathy.ai/) and provide a detailed optimization report, including actionable recommendations for improvement.”
Next, OpenManus thinks through and breaks down the execution steps:
- Examine the website and gather basic information;
- Analyzing key SEO elements;
- Examining the technical aspects of SEO;
- Collating optimization recommendations;
What follows is a step-by-step execution of the tasks.
As you can see, the results shown in the demo video are far less detailed and rich than those of Manus. OpenManus is still very basic in its functionality. Still, the team has also made public the subsequent development route, according to which, basically, it’s not a problem to fully replicate Manus:
- Better planning system
- Real-time demo function
- Real-time demos
- Reinforcement learning fine-tuning model
- Comprehensive performance benchmarking
OpenManus Performance Against Manus
| Use Case Scenarios | Manus | OpenManus |
| Tesla Stock Analysis | – Interactive dashboard showing financials (revenue, net profit) – DCF valuation model with stock price comparison – Investment recommendation matrix (growth/value/income/speculative) | – Analyzing Musk’s regulatory issues, sales challenges, charging network expansion plans – Impact of legal disputes on stock price – Manually extracted CNBC News |
| Amazon Online Store Operations | – Sales metrics correlation matrix – Customized sales strategies (e.g., discount optimization, order value enhancement) – Real-time data-driven recommendations | – Generate visualization reports based on Excel data – Step-by-step execution of Python code (e,.g. importing Pandas) |
| Travel Insurance Policy Comparison | – Structured comparison tables (vendors, cancellation clauses, delay compensation) – Key insights labeling (e.g., “cancel for any reason” benefits) | – Manually create HTML tables to compare policy dimensions (health measures, destinations, etc.) |
| B2B Rubber Mats Sourcing | – Interactive price comparison dashboard – Filter by type/thickness/retailer Best value option highlighting | – Recommend price comparison websites (Amazon, HKTVMall, etc.) – Manually navigate web searches |
| Apparel Industry AI Research | – Solution comparison matrix (technology, value chain, pricing) – Covers Lily AI, ThredUp, Syte., ai, and many other competitors | – Manually visit official websites (SaffronEdge, Lily.ai) – Gather product information and pricing models |
| Momentum Theorem Interactive Course | – Interactive animation controls (speed adjustment, reset) – Real-time momentum conservation equation display – Realistic use cases (rocket propulsion, skaters) | – HTML presentation with animations (elastic/inelastic collisions, explosion cases) – Manual browser opening of files |
| YC Company List Extraction | – Visualization Startup Directory – Filter by industry/batch – Display funding history and valuation data | – Python script crawl W25 B2B business information (name, description) – Export structured tables |
| April Japan Trip | – Integrated maps and key location labeling – Cultural experience activities (tea ceremony, kendo) – Dynamic Budget Adjustment Functionality | – Generate a 7-day itinerary + HTML travel brochure (maps, Japanese phrases) – Wedding Proposal Location Recommendations |
As you can see, OpenManus is not to be underestimated, and there are reports that OpenManus is approaching the performance of some proprietary models (e.g., Qwen-32B). However, it should be noted that although OpenManus is free and open source, it still relies on external LLM support.
Comparison of Manus, OpenManus with OpenAI’s New Tools
In addition to the hotly anticipated OpenManus, OpenAI also released new developer tools on March 11, 2025, including the Responses API and SDK designed to help organizations build AI agents. These tools replace the Assistants API, which will be discontinued in 2026, and support the GPT-4o search and GPT-4o mini-search models, enabling networked searches and generating citation answers.
In SimpleQA benchmark tests, the GPT-4o search scored 90%, and the GPT-4o mini search scored 88%, emphasizing high factual accuracy.
Below is a detailed comparison of the three models.
| Comparison Dimension | Manus | OpenManus | OpenAI New Tools |
| Access method | Invitation-based, paid access code | Open source, free, no threshold | Developer API, OpenAI account, and key required |
| Autonomy | High, can complete multi-step tasks independently | Medium, relies on external LLM and user configuration | Medium, requires developer-defined task flow |
| Performance | GAIA benchmarks outperform DeepResearch | Nearly proprietary model, depending on the configuration | SimpleQA score 90%, high accuracy |
| Functionality | Complex tasks such as trip planning, stock analysis, etc. | Modular support for code execution, search, etc. | Networked search, report generation, and custom development required |
| Openness Partially | Partially planned open-source | Fully open source, community-driven | Proprietary, dependent on the OpenAI ecosystem |
| Cost | High (invitation code can cost thousands of dollars) | Free (but you need to bear the LLM cost) | Billing per API call |
| Development Difficulty | No development is required, and it is straightforward to use | Requires technical skills to configure and optimize | Requires programming skills, API friendly |
| Current status | Preview version, with bugs | Rapid iteration, feature expansion in progress | Official release, stable but needs integration |
Analysis and Summary: Manus is more autonomous but unstable, OpenManus is more open but requires configuration, and OpenAI is more mature but dependent on its ecosystem.
- Autonomy and Ease of Use: Manus wins in high autonomy out-of-the-box, suitable for non-technical users; OpenAI New Tools requires developer intervention, flexible but complex; OpenManus is in between, with a high degree of freedom but requires technical support.
- Cost and accessibility: OpenManus free open source is the most advantageous; Manus is costly and difficult to obtain; OpenAI is in the middle.
- Performance and Stability: OpenAI’s tools are currently more stable and accurate, Manus has great potential but has bugs, and OpenManus’s performance varies by configuration.
- Ecology and Future: OpenAI is backed by a mature ecosystem and updated frequently; Manus and Qwen cooperation may bring surprises; OpenManus is driven by the community, and development potential is unknown but flexible.
Who Are OpenManus and Manus Suitable?
Choosing OpenManus, Manus (or another AI agent) depends on your needs, skills, and budget.
If you can get the invitation code for Manus, try it first to see if it meets the demand; meanwhile, download OpenManus and run a few test tasks to compare the effect. Practical experience is more important than theory.
User groups for which OpenManus is suitable
1. Technology enthusiast & developer:
- Characteristics: Basic programming (familiar with Python, API calls, etc.), like to tweak and optimize tools on their own.
- Reason: OpenManus is an open-source project that allows users to freely configure LLM (e.g., using Qwen, LLaMA, or other models), add tools, or modify code to meet individual needs.
- Example: A developer wants to use OpenManus to integrate local GPU resources and run open-source models to analyze data.
2. A user with a limited budget:
- Characteristics: Don’t want to spend money on an invitation code or subscription, only willing to pay for a minimal external API.
- Reason: OpenManus is free; the only cost is calling external LLMs (you can even run it at zero cost if you use free models).
- Example: A student or small entrepreneur who wants to try out AI agent functionality but doesn’t want to spend thousands of dollars on Manus.
3. Open source community supporter:
- Characteristics: Values technical transparency and decentralization, willing to participate in community contributions or rely on community support.
- Reason: OpenManus is driven by the MetaGPT community; the code is publicly available, and users can fix bugs or extend functionality independently.
- Example: An AI researcher wants to study the internal mechanisms of agent-based AI and contribute code.
4. Experimental user:
- Characteristics: Don’t mind system instability, willing to try new features and accept potential bugs.
- Reason: OpenManus is still undergoing rapid iteration and is suitable for people who like to try new things and explore their potential.
- Example: A geek wants to test whether OpenManus can do complex tasks (e.g., automate social media management).
Users for whom Manus is suitable:
1. Non-technical general users:
- Characteristics: Don’t know how to program, just want an easy to use tool to get things done.
- Reason: Manus offers a cloud-based interface that works out-of-the-box with no configuration and is suitable for people with zero technical background.
- Example: A busy professional who needs to quickly plan a trip or generate a market report.
2. A high-end user willing to pay:
- Characteristics: Budget-minded, able to accept hefty invite code fees or future subscriptions, and looking for stability and official support.
- Reason: Manus is developed by Monica and is commercially optimized for more reliable performance and better support.
- Example: A business owner wants to use an AI agent to handle financial analysis and is willing to spend money in exchange for efficiency.
3. Users seeking high performance:
- Characteristics: Need top-notch AI agent capabilities and value benchmarking results (e.g., GAIA beyond DeepResearch).
- Reason: Manus has 29 built-in tools that are professionally tuned for smoother task execution and suitable for complex scenarios.
- Example: A quantitative trader needs AI to analyze stocks and automate strategy execution.
4. Users who rely on brand trust:
- Characteristics: More trusting of products with company endorsement, unwilling to take risks with community programs.
- Reason: Manus is backed by Monica and Qwen, so updates and maintenance are more guaranteed.
- Example: A medium-sized company wants to integrate AI agents into its workflow and prefers an “official” product.
Overall, if you’re an average person who wants to save money and can afford to spend it, go with Manus.
You’re a developer; you’re on a budget; you’re a tinkerer: go with OpenManus.
If you’re not sure yet, try OpenManus first (it’s free and has no threshold). If you’re not satisfied, consider investing in Manus.
