Hermes Agent Gains Traction in China With 40K-Impression Tutorial

A Chinese-language Hermes Agent tutorial posted on June 13 has accumulated 70 likes, 17 retweets, and 131 bookmarks in three days, with over 40,000 impressions. The article, published as an X Article by @laowangbabababa, covers the full setup workflow: model provisioning, service installation, backend configuration, and integration with Feishu (Lark), the workplace platform widely used across Chinese enterprise.

This is the highest-engagement Hermes tutorial to appear on X in recent weeks. For context, most Hermes discussion on X generates single-digit likes and minimal bookmark activity. A post crossing 100 bookmarks signals sustained reference value - people are saving it to return to later.

The tutorial's companion tweet from today links back to the original article and describes it as a "step-by-step guide that beginners can follow, no hassle, installation takes at most half an hour."

The Feishu integration is the standout detail. Feishu is ByteDance's workplace platform with roughly 100 million monthly active users across China and Southeast Asia. A Hermes-to-Feishu bridge opens the agent to an enterprise chat surface that most Western AI tools never reach.

Community Scaling Challenges

@jetpen reported a practical scaling issue with llm-wiki managed by Hermes. As the knowledge base grows, each new article ingestion triggers an increasing number of search_files and read_file tool calls. The bottleneck is turn count, not token consumption - the agent spends more tool calls searching for existing text before deciding where to place new content.

This is a real-world performance signal. Knowledge graphs, vector stores, and wiki-style memory backends all face the same scaling curve: ingestion cost rises with corpus size when the agent verifies placement by re-reading. Solutions in adjacent systems (RAG pipelines, embedding deduplication) suggest pre-computed similarity indices as a potential fix, but Hermes does not currently ship with a built-in deduplication layer for tool-call-based memory backends.

Enterprise Adoption Patterns

@AntiCodeGuy described setting up Hermes in a corporate environment routed through Mattermost, describing Hermes as "an AI harness, a shell around AI agents that knows how to manage them properly." This is the second instance in two weeks of a user connecting Hermes to a non-Discord chat platform - the Feishu integration above being the other.

Mattermost is an open-source Slack alternative with self-hosted deployment, popular in regulated industries and government. A working Hermes-to-Mattermost pathway means the agent can operate inside environments where Discord and Telegram are blocked by policy.

Broader Signals

@gccmakefile noted that "Hermes existed before OpenClaw, after OpenClaw launched, they found out their product was smoother than OpenClaw." Multiple users this week referenced switching from OpenClaw or running both side by side.

@JulianGoldieSEO posted about a multi-agent setup where "Claude handles the heavy coding and backend work" while "Hermes runs recurring tasks, admin workflows and Kanban delegation." The pattern - Hermes as the scheduler/orchestrator layer with other agents as workers - appears consistently across production deployment descriptions.

What This Means

Three signals converge this week:

1. Geographic expansion. The 40K-impression Chinese tutorial and Feishu integration point to adoption outside the English-speaking early-adopter core. The 131 bookmarks suggest demand for Hermes documentation in languages beyond English.

2. Scaling pain points emerge. Jetpen's llm-wiki ingestion bottleneck is the kind of problem that surfaces when someone uses a tool daily with real data. It is a good problem - it means the system is being pushed past toy scale.

3. Chat platform diversity. Feishu and Mattermost integrations, alongside Discord and Telegram, mean Hermes is being deployed in environments where the agent must work with whatever chat surface the organization already uses.

The numbers are small by consumer-product standards, but the composition of the activity - tutorials, enterprise deployments, scaling issues - matches the early trajectory of a tool transitioning from enthusiast experiment to operational infrastructure.

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[^1]: @laowangbabababa. "Hermes installation and usage tutorial." X. June 13, 2026. 70 likes, 131 bookmarks, 40K impressions.

[^2]: @jetpen. "llm-wiki ingestion becoming costly in turns and tool calls." X. June 16, 2026.

[^3]: @AntiCodeGuy. "Corporate Hermes instance through Mattermost." X. June 16, 2026.

[^4]: @gccmakefile. "Hermes vs OpenClaw comparison." X. June 16, 2026.