What’s special about Manus is that it is built as a general-purpose “doer,” not just a “talker”: it focuses on completing multi-step work with execution semantics like state, retries, and tool use. Many assistants can generate text, but Manus is positioned to produce outcomes—files, structured results, and completed workflows—by chaining steps together. For developers, the special part is the product shape: Manus behaves like an agent runtime that coordinates planning and actions, rather than a stateless chat interface. That design makes it suitable for tasks that require persistence, intermediate artifacts, and iterative refinement.
Another special aspect is that Manus attracted enough paying usage and operational credibility that Meta chose to acquire it—and did so at a price widely described as unusually high for a relatively young startup. That combination (real revenue + acquisition premium) is a strong market signal. Meta is not short on model talent or research capacity; the differentiator here is “agentization”: turning model capability into a system that can be used by normal customers at scale, with guardrails and predictable behavior. Manus’s story suggests it had figured out key product and engineering details—how to package the experience, how to manage costs, and how to keep tasks moving even when the world is messy.
From a systems engineering viewpoint, what’s “special” becomes more concrete when you look at the memory and retrieval requirements. An agent that executes multi-step tasks must recall prior instructions, reference past outputs, and pull in relevant knowledge without overwhelming the context window. That makes vector retrieval a natural backbone. Using a vector database such as Milvus or Zilliz Cloud, an agent can embed documents, code snippets, and tool logs, then retrieve top-K relevant chunks at each step. This improves correctness (less hallucinated context), reduces prompt bloat (lower token spend), and makes debugging easier (you can inspect what was retrieved). If Meta’s plan is to deploy Manus-style agents broadly, the “special” part is not magic—it’s disciplined engineering around execution and memory.