NousResearch/hermes-agent
⭐ 141,797 · Python · GitHub Repo
The agent that grows with you
ai ai-agent ai-agents anthropic chatgpt claude claude-code clawdbot
1-Sentence Summary
A self-improving AI agent that learns from experience, persists memory, and deploys anywhere with zero model lock-in.
🔥 Key Capabilities & USP
- Closed Learning Loop with Persistent Memory: The agent autonomously creates skills from complex tasks, uses FTS5 session search with LLM summarization, and builds a dialectic user model (via Honcho) for cross-session recall. This solves the pain point of agents that forget context or require manual retraining after each session.
- Unified Multi-Platform Gateway: A single process enables seamless conversations across CLI, Telegram, Discord, Slack, WhatsApp, and Signal, with voice memo transcription and cross-platform continuity. This eliminates the need to manage separate bots or integrations for each channel.
- Scheduled Automations with Natural Language: Built-in cron scheduler allows users to define tasks like "send a daily summary at 9 AM" in plain English, delivered to any connected platform. This solves the complexity of traditional cron job configuration for non-expert users.
- Flexible Deployment from $5 VPS to Serverless: Supports seven terminal backends (local, Docker, SSH, Singularity, Modal, Daytona, Vercel Sandbox) with serverless persistence that hibernates when idle. This solves the cost and infrastructure overhead of running AI agents 24/7.
- 200+ Model Compatibility with Zero Lock-In: Works with any LLM provider via OpenRouter, NVIDIA NIM, Hugging Face, OpenAI, or custom endpoints. This solves vendor dependency and allows users to switch models freely based on task requirements.
Technical Architecture
| Component | Technology |
|---|---|
| Runtime | Python 3.11, uv package manager |
| Dependencies | Node.js, ripgrep, ffmpeg |
| Memory & Search | FTS5 (Full-Text Search) with LLM summarization |
| User Modeling | Honcho dialectic system for cross-session recall |
| Model Access | OpenRouter (200+ models), NVIDIA NIM, Hugging Face, OpenAI, custom endpoints |
| Deployment | 7 backends: local, Docker, SSH, Singularity, Modal, Daytona, Vercel Sandbox |
| Messaging | Unified gateway: CLI, Telegram, Discord, Slack, WhatsApp, Signal |
| Automation | Built-in cron scheduler with natural language parsing |
| Agent Skills | Compatible with agentskills.io open standard, MCP server integration |
Quick Start Guide
bash
# Linux, macOS, WSL2, Termux installation
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
# Windows (native, PowerShell) installation
irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1 | iex
# After installation
source ~/.bashrc # reload shell
hermes # start chatting!
hermes model # Choose LLM provider and model
hermes tools # Configure enabled tools
hermes config set # Set individual config values
hermes gateway # Start messaging gateway
hermes setup # Run full setup wizard
hermes update # Update to latest version
hermes doctor # Diagnose issuesPros, Cons & Use Cases
Pros
- Model-agnostic architecture with zero vendor lock-in – switch between 200+ models freely
- Runs on minimal infrastructure – a $5 VPS is sufficient for production use
- Serverless options cost nearly nothing when idle, with automatic hibernation
- Extensive platform support – 7 messaging channels, 7 deployment backends
- Built-in learning and memory system that improves over time without manual intervention
- Open standard compatibility via agentskills.io and MCP server integration
Cons
- Native Windows support is early beta with rough edges and limited stability
- Android/Termux support requires manual setup and is not plug-and-play
- Browser-based dashboard chat pane requires WSL2 on Windows, adding complexity
- Learning curve for advanced features like subagent delegation and custom skill creation
Who should NOT use this?
- Users needing a simple, single-purpose chatbot – the learning loop and multi-platform gateway add unnecessary complexity for basic Q&A tasks
- Teams requiring enterprise-grade support and SLAs – this is an open-source project without commercial backing
- Windows-only users who cannot or will not use WSL2 – the native experience is still in early beta
- Users who prefer managed SaaS solutions with zero infrastructure management – Hermes requires some deployment knowledge
Ideal Use Cases
- Personal productivity automation – daily reports, backups, audits, and scheduled tasks across multiple platforms
- AI research and experimentation – batch trajectory generation for training tool-calling models
- Multi-channel customer support – a single agent handling queries across Telegram, Discord, Slack, WhatsApp, and email
- DevOps and system administration – autonomous agents that monitor infrastructure, run diagnostics, and execute fixes
- Knowledge workers needing persistent context – researchers, analysts, and writers who benefit from cross-session memory and user modeling
Community & Activity
With 141,797 stars and a last update on May 10, 2026, Hermes Agent demonstrates exceptional community momentum and active development. The project is clearly resonating with developers and researchers who value self-improving, deployable AI agents. The NousResearch team maintains a steady cadence of updates, and the breadth of topics (ai, ai-agent, anthropic, chatgpt, claude, openai) signals broad compatibility with the entire LLM ecosystem. This is not a side project – it's a rapidly maturing tool with serious adoption and a clear roadmap ahead.