Hermes Agent README

The self-improving AI agent built by Nous Research.

The Hermes Agent README presents an open agent harness with model-provider switching, terminal and messaging interfaces, scheduled automations, isolated subagents, toolsets, persistent memory, session search, and a closed learning loop around skills.

Hermes is an example of the harness conversation moving beyond coding alone: a persistent, multi-surface, model-agnostic agent with memory, skills, automations, and self-improvement loops.

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The Hermes README is valuable as a productized harness inventory. It does not present a single new model capability. It presents the surrounding system: model-provider switching, a terminal UI, messaging gateways, scheduled automations, persistent memory, skills, subagents, session search, toolsets, terminal backends, and research tooling. The distinctive claim is the closed learning loop. Hermes says it can create skills from experience, improve skills during use, nudge itself to persist knowledge, search past conversations, and build a user model across sessions. That is a direct capability-extension claim: the agent becomes more useful not only because the model changes, but because the harness accumulates procedural and contextual memory. Extended Frontier Read Hermes makes the "agent harness" category concrete across several surfaces: • interface harness: CLI/TUI plus Telegram, Discord, Slack, WhatsApp, Signal, and email gateway; • learning harness: skill creation, skill improvement, memory nudges, session search; • execution harness: local, Docker, SSH, Daytona, Singularity, and Modal terminal backends; • social harness: cross-platform continuity, user modeling, scheduled reports; • subagent harness: isolated parallel workstreams and RPC-style tool scripts. This is not just "a chatbot with tools." It is an attempt to make an agent live where the user lives, remember what matters, and turn repeated work into skills. Open Questions • How much of the self-improvement loop is automatic versus user-confirmed? • Which skills improve reliably during use, and which drift? • What validation or audit trail exists when memory and user modeling become part of the harness?

README content verified from GitHub snapshot. Date is access/capture date, not a stable publication date.