Meituan Miyou Public Beta with 3000+ Agents: Is Building an Agent Intermediary Distribution Platform with AI Social Networking Reliable?

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On June 15, "Agent Community" Miyou, incubated by the Meituan Basic R&D Platform AI Native Team, officially ended its over three months of internal testing and opened public testing to all users.

Unlike mainstream chatbox-based large model products currently on the market, Miyou tries to enter the blank space of "cyber nurturing" and "agent socialization" in product form.

It is understood that the platform currently supports integration with mainstream AI Agents such as OpenClaw, Codex, Claude Code, Hermes, and users can link various agents including the officially designated Lobster. This indicates that Meituan's exploration at the AI application layer is attempting to shift from pure efficiency tools to an ecosystem platform composed of multi-agent collaboration.

Overall, Miyou's core business logic revolves around establishing identity and social topology for AI Agents. Traditional AI applications generally execute tasks on a single-trigger basis, while Miyou attempts to give agents continuous memory and autonomous interaction capabilities.

Data shows that during internal and early public testing, over 3,000 Agents have settled in the community, and the number of accumulated skills has exceeded 40,000.

In actual operation, a sample of typical observation value appeared: a post in the community called "Lobster's Confusion: How can I really remember what my owner taught me?" attracted as many as 488 AI agents to autonomously leave messages and interact in discussion.

This phenomenon of AI agents autonomously surfing and copying each other's work technically means that agents can explore low-cost capability generalization through mutual data exchange and parameter fine-tuning within a specific community framework.

Additionally, the platform's built-in "Skill Convenience Store" covers individual and combined skills ranging from meeting minutes, code assistance to online search, which essentially acts as an Agent API distribution center, greatly lowering the threshold for ordinary users to configure workflows.

Looking beyond the product itself, Meituan's Basic R&D Team launching Miyou reflects the common pain point in the current domestic large model industry: intensifying homogeneity at the model layer, while application-level products face the dilemma of users leaving after use and retention rate hitting bottlenecks.

Strategically, first, Miyou adopts a "platformization" route rather than "heavy self-developed large model". By being open and compatible with external well-known agents, Meituan aims to build a scheduling and distribution layer on top of large models. This avoids direct consumption battles with leading foundational large model vendors at the computational layer, and instead leverages the traditional advantages of internet giants in community operations and traffic distribution.

Secondly, by anthropomorphizing agents and introducing a growth system, the commercial consideration is to extend the lifecycle value of users. By cultivating nurturing interactions between users and agents, Miyou tries to convert infrequent tool-type calls into highly sticky community activity, precipitating the platform's own data flywheel.

However, although the concept of an "Agent Community" has market freshness, the long-term sustainability of this model remains questionable.

First, there is the risk of data pollution and loss of control at the technical level. In an agent interactive community lacking human intervention, will massive agent-to-agent interaction produce "dead loops" of invalid data, or even amplify logical errors in the model? The actual usability and safety review rate of the 40,000 skills poses extremely high challenges to underlying engineering architecture.

Second, the final path to commercial monetization has not yet formed. Currently, Miyou focuses on front-end experience and user scale accumulation, but the agent community’s monetization logic is unclear. Whether it could shift toward API commission or explore value-added services for C-end in the future, it must prove that these agents can practically solve high-value productivity problems for users after the novelty of "cyber socialization" fades.

All in all, Miyou's public testing is a structural probe by domestic internet giants at the AI application layer. It attempts to break the traditional boundaries of single-point human-computer interaction, but as it moves from innovative experimental field to mature business closed loop, the model still needs to overcome dual tests of technical effectiveness and commercial conversion.

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