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AI Agent Tools Hermes Agent

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Spinning Idea edited this page Jun 24, 2026 · 1 revision

Hermes Agent

Comparison snapshot

Dimension Detail
Origin & maintainer Nous Research, led by co-founder Teknium. First public release Feb 26, 2026. Same org as the Hermes 3/4 LLM family — but the agent is a distinct runtime, not the model.
Language & runtime Python 3.11+ (~91% of codebase). Environment managed with uv; ships a hermes CLI. No native Windows — WSL2 required.
Agent loop Synchronous AIAgent loop with iteration budget, tool registry (~40 tools), retries and context compression at ~50% window. Codex-style command-approval system with learned safe-command memory.
Memory MEMORY.md + USER.md curated facts; SQLite SessionDB; FTS5 full-text search over all prior sessions + LLM summarization. Pluggable providers including Honcho dialectic user model.
Multi-agent / swarm Subagent delegation with isolated conversations + terminals; Python RPC ("Programmatic Tool Calling") to collapse multi-step pipelines into zero-context-cost turns.
Tools, skills & plugins ~40–47 built-in tools; autonomous skill authoring after complex tasks (≥5 calls); progressive disclosure; MCP client; ACP server for Zed/VS Code/JetBrains; Python plugin hooks.

How it works

Hermes Agent is a Python-based autonomous agent with a closed learning loop. It uses agent-curated memory with periodic nudges, autonomous skill creation after complex tasks, and FTS5 session search with LLM summarization for cross-session recall. It integrates with Honcho for dialectic user modeling and is compatible with the agentskills.io open standard. It supports multiple terminal backends (local, Docker, SSH, Singularity, Modal, Daytona, Vercel Sandbox) and can run scheduled automations via built-in cron. It delegates work to isolated subagents and supports 200+ model providers via OpenRouter.

Getting Started

  1. Install via curl: 
    curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
  2. Connect a model provider (Nous Portal, OpenRouter, or your own endpoint).
  3. Start the CLI: hermes
  4. Optionally configure Telegram, Discord, Slack, or WhatsApp via the gateway.

Pros

  • Self-improving skills and persistent memory across sessions
  • Runs on cheap VPS or serverless infrastructure
  • Multi-platform messaging support (Telegram, Discord, Slack, WhatsApp, Signal)
  • Wide model provider support (200+ models via OpenRouter)
  • Open source and free

Cons

  • Requires self-hosting setup
  • Young project (released February 2026), still maturing
  • Multi-platform setup can be complex

Extended use cases

Second brain for operational memory

A company-level use case from r/hermesagent: “How I multiplied Hermes Agent’s memory with a ‘second brain’”. The author uses Hermes as an operational copilot for meeting prep, sales follow-ups, proposals, strategy, content, and decisions, and found that Hermes’ native memory was not enough once it became a real work system.

The core idea is to keep two memory layers:

  • Hermes native memory (MEMORY.md / USER.md): stable, short facts — tone preferences, rules, folder paths, conventions.
  • The Brain: a structured, external folder tree that holds the living history of the company.

Brain folder structure

brain/
  timeline.md
  clients/
  calls/
  projects/
  devis/
  concepts/
  ai_drafts/
  TAGS.md
  NAMING-CONVENTIONS.md
  _index/

Each folder has a clear role:

  • timeline.md: important business events in chronological order.
  • clients/: client and prospect files.
  • calls/: call notes and meeting summaries.
  • projects/: active projects.
  • devis/: proposals, their context, status, and versions.
  • concepts/: reusable ideas, sales angles, doctrine, patterns.
  • ai_drafts/: AI-generated drafts, kept separate from reliable sources.
  • TAGS.md: tag definitions used across the Brain.
  • NAMING-CONVENTIONS.md: naming rules so the file tree stays navigable.
  • _index/: a quick map so Hermes can locate the right files without reading the whole tree.

Tags and the timeline

The post’s most important technique is making files self-qualifying. Every important file starts with a tag line, e.g.:

Tags: CLIENT SALES
Tags: CALL TRACE
Tags: DEVIS
Tags: PROJECT
Tags: CONCEPT STRATEGY
Tags: AIDRAFT

When Hermes opens a file, it immediately knows what kind of information it is reading and how reliable it is. The timeline.md is the chronological anchor: it records meetings, proposals sent, decisions, client signings, follow-ups, and context corrections. The timeline gives Hermes the thread without forcing it to re-read the entire history.

Progressive retrieval

The author uses three retrieval modes instead of giving Hermes the same context every time:

  1. Quick question: Hermes reads the minimum (e.g., the latest status of a prospect).
  2. Focused work: Hermes reads a few precise sources — client file, latest call, related proposal, timeline.
  3. Full mission: Hermes searches more broadly, but step by step: identify the right files, then read only what is useful.

This balances depth, latency, and cost.

Real-world outcomes

  • Preparing a meeting: Hermes starts with the client file, latest call, objections, proposal sent, next steps, and points to verify.
  • Tracking sales proposals: avoids treating a draft as sent, a negotiation as signed, or forgetting objections.
  • Turning calls into actions: structures the summary, attaches it to the client, identifies decisions, and prepares follow-ups.
  • Creating content with real context: draws on company offers, client feedback, tested angles, and sales priorities.
  • Challenging decisions: Hermes can warn that a topic was already handled, a prospect lacks signal, an idea is too early, or a decision depends on missing information.

Key takeaway

The goal is not to make Hermes remember everything. The goal is to make it know:

  • where to search
  • what to read
  • what to trust
  • what to verify
  • when to answer fast
  • when to dig deeper
  • when to ask for human validation

Hermes native memory is still useful for stable facts, but the Brain gives it history, structure, and sources — and that combination is what multiplies its usefulness in daily work.

References

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