AI Agent Index
ByHeather MacAvelia·Last verified Jun 18, 2026
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Hermes Agent

4.6/ 5

by Nous Research

MCP✓ Verified Review
Visit nousresearch.com

Open-source autonomous AI agent by Nous Research with a self-improving learning loop. Runs on your server or desktop app, remembers what it learns, and supports 20+ messaging platforms. {{github_stars}} GitHub stars.

Hermes Agent is an open-source autonomous AI agent built by Nous Research, the lab behind the Hermes and Nomos open-source LLM families, released under the MIT licence in 2026. It is not a coding copilot or a chatbot wrapper. It is a persistent agent that lives on your server, builds a growing knowledge of your projects and preferences across sessions, and gets more capable the longer it runs. The core differentiator is a closed learning loop: after completing complex tasks, the agent autonomously creates SKILL.md files that codify what it learned, improves those skills during future use, and uses full-text search with LLM summarisation to recall relevant context from past sessions. A Honcho dialectic user model builds a structured understanding of your working style and preferences over time. Setup takes minutes using a single curl command on Linux, macOS, or WSL2. A desktop app is also available alongside the terminal CLI. The agent connects through a unified messaging gateway to Telegram, Discord, Slack, WhatsApp, Signal, Email, and 15 plus other platforms simultaneously: start a task from your laptop terminal and check progress on Telegram. Seven terminal backends (local, Docker, SSH, Singularity, Modal, Daytona, and Vercel Sandbox) allow deployment anywhere from a $5 VPS to a GPU cluster, with serverless options that cost nearly nothing when idle. Built-in cron scheduling runs recurring tasks in natural language with delivery to any connected platform: daily briefings, nightly backups, weekly audits. Isolated subagents with their own terminals and Python RPC scripts enable parallel workstreams without sharing context windows. MCP integration connects the agent to any MCP-compatible server for extended tool capabilities. A web portal provides an additional interface for managing the agent. Hermes works with any LLM provider: Anthropic, OpenAI, Google, OpenRouter (200 plus models), Nous Portal, HuggingFace, or a locally hosted model via Ollama. The ecosystem includes 647 community skills across four registries following the agentskills.io open standard. The GitHub repository has 199.2k stars, making it one of the most widely adopted open-source AI agent projects globally. Autonomous rate is approximately 70 to 80 percent: scheduled tasks, skill creation, memory management, and routine workflows run fully without human initiation; complex novel tasks and security-sensitive operations use a command-approval flow. There is no subscription, no telemetry, and no tracking. All data stays on your machine. Named gaps: CLI-first setup requires a server or VPS, terminal familiarity, and an LLM API key (though a desktop app is now available); there is no hosted SaaS version; and the memory system uses character-limited files injected at session start rather than a vector database, requiring manual curation for very large memory contexts. Hermes Agent is not the right fit for non-technical users who cannot configure a Linux server environment: while a desktop app is now available, deployment still requires comfort with terminal commands and API key management with no hosted SaaS version. Teams needing built-in cost controls on LLM API usage should monitor provider spending limits carefully, as the agent runs autonomously and will continue making API calls during scheduled tasks and multi-step workflows. Teams with simpler, single-session needs who do not require cross-session memory or multi-platform messaging may find lighter tools like Claude Chat or ChatGPT sufficient without the server infrastructure overhead. Current state Q2 2026: Hermes Agent has 199.2k GitHub stars, reflecting massive community adoption as one of the most starred open-source AI agent projects globally. Version 0.16.0 shipped June 5, 2026 with 874 commits including batch memory operations and a yolo flag to bypass approval prompts for fully autonomous runs. A desktop app and web portal have been added alongside the original CLI, broadening accessibility. The project is fully open-source under MIT licence with no paid tiers, no telemetry, and no subscription required. The community skills registry has grown to 647 skills. No G2 listing was found; the primary evidence signal is GitHub adoption.

Pricing

free

View pricing ↗

Segment

b2b

Setup

moderate

Verified

Jun 18, 2026

Transparency

Public

Contract

Month-to-month

Data training

Not Trained

Autonomy

Human Optional

Capabilities

autonomousworkflow-builderschedulingweb-searchcode-generationdata-analysisagentic-codingterminal-agentopen-sourcebyok

Pros & Limitations

Editorial assessment

Pros

  • Self-improving learning loop with no manual upkeep: after each complex task the agent automatically creates and refines SKILL.md files so it never forgets how to solve recurring problems, and the 647-skill community ecosystem means most common workflows have a starting point without any user configuration
  • Runs on infrastructure you control with zero telemetry, zero tracking, and zero data leaving your machine: a meaningful security and privacy advantage over SaaS agents for teams handling sensitive data, proprietary research, or regulated information
  • Most widely adopted open-source AI agent with 199.2k GitHub stars: MCP compatible with full cross-session memory across 20 plus platforms, with a desktop app and web portal now available alongside the original CLI

Limitations

  • CLI-first setup with moderate technical requirements: deployment needs a server or VPS, familiarity with a terminal, and an LLM API key; there is no hosted SaaS version or graphical setup wizard, which limits accessibility for non-technical users who cannot configure a Linux environment
  • No built-in cost controls on LLM API usage: the agent runs autonomously and will continue making API calls during scheduled tasks and multi-step workflows, which can generate unexpected token costs without careful monitoring of usage and provider spending limits
  • Memory system uses character-limited files injected as a frozen snapshot at session start rather than a vector database: keeps the system lightweight and predictable but means very large or rapidly growing memory contexts require manual curation to stay within limits

Technical Details

Deployment
cli
Avg setup time15-30 minutes
Autonomous rateApproximately 70-80% autonomous: scheduled tasks, skill creation, memory management, and routine recurring workflows run fully without human initiation; novel complex tasks and security-sensitive operations use a configurable command-approval flow before execution.
MCP compatibleYes
Integrations
TelegramDiscordSlackWhatsAppSignalEmailOpenAIAnthropicOpenRouterHuggingFaceOllamaDocker

Similar agents

Rating

4.6/ 5

Editorial score

How we score this →

Score breakdown

AutCap 4 · IntDepth 5 · PriceTrans 5 · IndEvid 5 · SetupAcc 4 = 4.60

Industries

DevToolsOpen SourceSaaSStartupsB2B

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