Manus and Gemini solve overlapping problems for developers, but they approach those problems from different system and workflow assumptions. Manus is designed as a goal-driven AI agent that executes multi-step tasks with minimal human involvement, while Gemini is designed as a general-purpose large model and assistant that excels at interactive reasoning, multimodal understanding, and tight integration with surrounding platforms. In practice, this means Manus is oriented toward “hand the system a task and let it run,” whereas Gemini is oriented toward “work with the model through prompts, tools, and iteration.” This distinction is not about which system is more capable in isolation, but about where responsibility for planning, execution, and error recovery lives. That difference is also why Meta’s acquisition of Manus is relevant industry context: Meta is signaling that autonomous execution layers matter, which naturally invites comparisons with established model-centric systems like Gemini.
Manus treats task execution as its primary concern, and that shapes its system design. A Manus-style workflow starts with a goal, not a prompt, and the system is expected to break that goal into steps, track progress, and decide what to do next without constant user input. This requires persistent task state, so the agent knows which steps have succeeded, which have failed, and which artifacts have already been produced. It also requires orchestration logic that can call tools, schedule actions, and handle failures in a structured way. For example, if a data collection step fails due to a transient network issue, the agent should retry or switch strategy rather than terminate. Over longer tasks, memory management becomes critical. Instead of pushing all prior context into each model call, Manus-style systems externalize memory and retrieve only what is relevant for the current step. A vector database such as Milvus or Zilliz Cloud fits naturally into this design, storing embeddings of intermediate notes, extracted facts, and task artifacts, and enabling semantic retrieval as the agent progresses. This architecture makes the agent more predictable and cost-efficient, which is essential if the system is expected to run unattended. Meta’s interest in Manus aligns with this execution-first approach: at scale, the challenge is not generating responses, but coordinating work reliably across many steps and users.
Gemini, by contrast, is best understood as a model-centric assistant that developers and users actively steer. Its typical workflow is prompt-driven: the user asks a question or gives an instruction, receives a response, and then refines the request based on that output. Tool use and structured actions can be layered on, but orchestration usually lives in the surrounding application or in the user’s head. This makes Gemini well suited for exploratory tasks, multimodal reasoning, and situations where the human wants to remain in control of each step. If you want Gemini to participate in a multi-step workflow, you typically implement the workflow yourself: your code decides the sequence, calls the model at each stage, validates outputs, and handles retries or fallbacks. Memory and retrieval are still important, but they are often implemented as explicit components outside the model. For example, an application might retrieve relevant documents from Milvus or Zilliz Cloud and include them in the prompt for Gemini to reason over. The key difference is responsibility: with Gemini, the developer or user orchestrates the process; with Manus, the system itself is expected to orchestrate. Choosing between them is therefore a question of workflow ownership rather than raw model capability.