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AI Agents Experiments

What 'grows with you' means for Hermes Agent

· 5 min read

Most agents reset personality every session. You re-explain the stack, the preferences, the last failure mode — then watch the same agent rediscover decisions you already paid for. When a product says it “grows with you,” that claim is worth unpacking before you pipe an install script into bash.

Hermes Agent, built by Nous Research, leads with that line. This is not a benchmark post and not a bake-off scorecard. It is what “grows with you” means in the README — memory, skills, session search, user modeling — and where that sits next to harness layers like ECC and thin skills packs.

Growth here is persistence and procedural memory, not a magic IQ bump between turns.

What “grows with you” means concretely

Hermes positions itself as a self-improving agent with a built-in learning loop. From the public docs, that loop is concrete machinery, not vibes:

  • Skills from experience. After complex tasks, it can create skills; those skills are meant to improve during use. Compatible with the agentskills.io open standard.
  • Persistent knowledge. Agent-curated memory with periodic nudges to write things down instead of hoping the context window remembers.
  • Past-conversation search. FTS5 session search plus LLM summarization for cross-session recall — so “what did we decide about the deploy path?” is a query, not a re-prompt.
  • Deepening user model. Honcho dialectic user modeling across sessions — preferences and patterns that accumulate, not a blank slate each morning.

None of that is “the model got smarter overnight.” It is procedural memory, searchable history, and a user model that compounds if you keep using the same runtime. That is a real product shape. It is also a different shape from “install 200 skills into Claude Code.”

Where it sits vs ECC and thin skills packs

I have been writing about layers in this stack separately on purpose, because they solve different jobs:

  • Thin skills packs — things like Humanizer or Diagram Design — are focused Markdown (or plugin) workflows you drop into whatever agent host you already use. Sharp tools. Small surface.
  • Harness OS / skills pack layer — ECC is closer to an engineering process layer across Claude Code and friends: skills, hooks, review loops, memory/instincts, AgentShield. It optimizes the harness you already live in.
  • Hermes — a full agent runtime and messaging gateway with its own learning loop, terminal backends, cron, subagents, MCP, and toolsets. Not “a skill for Claude.” An always-on agent you talk to from Telegram, Discord, Slack, WhatsApp, Signal, or CLI.

Complementary framing, not replacement. ECC does not become Hermes, and Hermes does not erase the need for harness-side process when you are deep in an IDE-native coding agent. If your bottleneck is “Claude forgot our TDD gate again,” that is harness/skills territory. If your bottleneck is “I want one agent that lives on a VPS, remembers last week, and texts me on Telegram,” that is closer to Hermes.

The runtime shape that makes growth useful

Memory only matters if the agent is reachable when you need it. Hermes is explicitly not laptop-tied: one gateway process, messaging surfaces plus CLI. Model choice is swappable — Nous Portal, OpenRouter, OpenAI, custom endpoints — via hermes model. Terminal backends include local, Docker, SSH, Singularity, Modal, Daytona, and Vercel Sandbox, with serverless persistence options that hibernate when idle.

Add cron scheduling, subagents, MCP, and tools/toolsets, and you get a picture of an always-on agent host — not a one-shot chat in an IDE sidebar. That is why the learning loop pitch lands differently here than in a session-scoped coding agent: the unit of growth is days and channels, not a single context window.

When I would try it vs when I would stick to IDE + ECC

I would try Hermes when:

  • Messaging-first is the interface I actually want — ping the agent from Telegram while it works on a cloud VM or VPS.
  • I care about skill accumulation and cross-session recall more than IDE-native diffs and inline review.
  • I want cron + gateway delivery for recurring jobs without bolting that onto a coding harness.

I would stick to IDE-native agents + ECC when:

  • The work is mostly in-repo coding with tight edit/test/review loops inside Claude Code (or similar).
  • I already own harness process — skills, hooks, memory vault — and the missing piece is process durability, not a second agent personality on Slack.
  • I am not ready to own another always-on runtime, gateway, and supply-chain surface.

Coming from another messaging agent? Hermes documents an OpenClaw migration path (hermes claw migrate) that can import settings, memories, skills, and keys. Useful if you already have that footprint; irrelevant if you are starting clean.

Install caveats (no fake evals)

Official Linux/macOS/WSL install from the project site:

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

That is a trust decision: you are executing remote script content as your user. Read the script, prefer a pinned/reviewed path if your threat model demands it, then proceed deliberately. After install, the documented flow is hermes, hermes setup, and hermes doctor. Docs live at hermes-agent.nousresearch.com/docs.

I am not inventing latency numbers, win rates, or “beats X by N%” claims. Point-in-time read of the public README and docs is enough to evaluate fit. Try it if the runtime shape matches how you work. Skip it if you only needed a skill file.

Bottom line

“Grows with you” for Hermes means a learning loop wired into a multi-surface agent runtime: create and improve skills, persist knowledge, search past sessions, deepen a user model. That is complementary to harness OS layers like ECC and to thin skills packs — different layer, different job. Pick the layer that matches the bottleneck you actually have.

Sources: github.com/NousResearch/hermes-agent, hermes-agent.nousresearch.com.

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