Manus and Claude address similar developer needs on the surface, but they are designed around very different workflows, which is the most important distinction to understand. Manus is built as a goal-driven AI agent that focuses on executing multi-step tasks end to end, while Claude is built as a conversational AI that excels at interactive reasoning, writing, and analysis under human guidance. In practical terms, Manus is optimized for situations where you want to hand over a task and let the system plan, act, and iterate toward completion, whereas Claude is optimized for situations where you want to stay in the loop, refining ideas through dialogue. This difference in workflow orientation—execution-first versus conversation-first—is why developers often compare the two, and it is also why Meta’s recent acquisition of Manus drew attention: Meta is signaling that autonomous, task-executing agents are becoming strategically important alongside traditional conversational assistants.
Manus is best understood as an agent runtime rather than a chat interface. Its core responsibility is to take a high-level objective, decompose it into steps, execute those steps using tools or environments, and manage state across the entire lifecycle of the task. This implies several concrete system design choices. Manus-style systems need persistent task state so they can track what has already been done, what remains, and what artifacts have been produced. They also need orchestration logic to decide when to call tools, how to sequence actions, and how to retry or adjust when something fails. Failure handling is especially important: if a web request times out or a code generation step produces an error, the agent must detect that failure, update its internal state, and attempt a recovery rather than simply stopping. Over longer tasks, memory becomes a bottleneck if it is not designed carefully. Instead of keeping all prior context in a single prompt, an agent typically stores intermediate outputs, notes, and extracted facts externally and retrieves them when needed. This is where a vector database such as Milvus or Zilliz Cloud fits naturally. By embedding task artifacts and querying them semantically, Manus can pull in only the most relevant context for each step, keeping prompts smaller, costs predictable, and behavior more stable. From an engineering perspective, this makes Manus closer to a distributed workflow system powered by models, rather than a single conversational loop. Meta’s interest in Manus aligns with this view: at large scale, the hard problem is not generating text, but reliably coordinating actions, memory, and recovery across many concurrent tasks.
Claude, by contrast, is designed around a dialogue-centric workflow where the user remains actively involved. Its strength lies in understanding complex instructions, producing clear and structured language, and reasoning through problems interactively. In a Claude-based workflow, the user typically drives the process: you ask a question, receive an answer, adjust constraints, ask for revisions, and gradually converge on a result. Tool use and structured outputs can be layered on top, but orchestration usually lives outside the model. If you want to run a multi-step process, your application code decides the steps, calls Claude at each stage, validates outputs, and determines what to do next. This design gives developers fine-grained control and makes it easier to enforce rules, schemas, or approval gates, which is valuable in regulated or high-risk environments. Memory and retrieval are still important, but they are often implemented as explicit components of the surrounding system rather than as part of an autonomous agent loop. For example, a developer might build a retrieval-augmented generation pipeline where documents are embedded, stored in Milvus or Zilliz Cloud, and selectively retrieved to provide context for Claude during a conversation. The key difference is responsibility: with Claude, the human or the application orchestrates the workflow; with Manus, the system itself is expected to do that orchestration. This is why the comparison matters. Choosing between Manus and Claude is less about which model writes better prose and more about whether you want an execution-oriented agent that runs tasks independently or a conversational assistant that supports careful, human-guided problem solving.