hermes-agent/README.md

Hermes Agent 🦋

An AI agent with advanced tool-calling capabilities, featuring a flexible toolsets system, messaging integrations, and scheduled tasks.

Quick Install

Linux/macOS:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1 | iex

The installer will:

• Install uv (fast Python package manager) if not present
• Install Python 3.11 via uv if not already available (no sudo needed)
• Clone to ~/.hermes/hermes-agent (with submodules: mini-swe-agent, tinker-atropos)
• Create a virtual environment with Python 3.11
• Install all dependencies and submodule packages
• Symlink hermes into ~/.local/bin so it works globally (no venv activation needed)
• Run the interactive setup wizard

After installation, reload your shell and run:

hermes setup    # Configure API keys (if you skipped during install)
hermes          # Start chatting!

---

Configuration

All your settings are stored in ~/.hermes/ for easy access:

~/.hermes/
├── config.yaml     # Settings (model, terminal, TTS, compression, etc.)
├── .env            # API keys and secrets
├── SOUL.md         # Optional: global persona (agent embodies this personality)
├── cron/           # Scheduled jobs
├── sessions/       # Gateway sessions
└── logs/           # Logs

Managing Configuration

hermes config              # View current configuration
hermes config edit         # Open config.yaml in your editor
hermes config set KEY VAL  # Set a specific value
hermes config check        # Check for missing options (after updates)
hermes config migrate      # Interactively add missing options

# Examples:
hermes config set model anthropic/claude-opus-4
hermes config set terminal.backend docker
hermes config set OPENROUTER_API_KEY sk-or-...  # Saves to .env

Required API Keys

You need at least one LLM provider:

Provider	Get Key	Env Variable
OpenRouter (recommended)	openrouter.ai/keys	OPENROUTER_API_KEY

Optional API Keys

Feature	Provider	Env Variable
Custom OpenAI Endpoint (OAI or VLLM/SGLANG)	platform.openai.com	OPENAI_API_KEY
Web scraping	Firecrawl	FIRECRAWL_API_KEY
Browser automation	Browserbase	BROWSERBASE_API_KEY, BROWSERBASE_PROJECT_ID
Image generation	FAL	FAL_KEY
Premium TTS voices	ElevenLabs	ELEVENLABS_API_KEY
OpenAI TTS voices	OpenAI	OPENAI_API_KEY
RL Training	Tinker + WandB	TINKER_API_KEY, WANDB_API_KEY
Voice transcription	OpenAI	OPENAI_API_KEY
Slack integration	Slack	SLACK_BOT_TOKEN, SLACK_APP_TOKEN
Messaging	Telegram, Discord	TELEGRAM_BOT_TOKEN, DISCORD_BOT_TOKEN

---

Commands

hermes                    # Interactive chat (default)
hermes chat -q "Hello"    # Single query mode
hermes setup              # Configure API keys and settings
hermes config             # View/edit configuration
hermes config check       # Check for missing config (useful after updates)
hermes config migrate     # Interactively add missing options
hermes status             # Show configuration status
hermes doctor             # Diagnose issues
hermes update             # Update to latest version (prompts for new config)
hermes uninstall          # Uninstall (can keep configs for later reinstall)
hermes gateway            # Start messaging gateway
hermes cron list          # View scheduled jobs
hermes pairing list       # View/manage DM pairing codes
hermes version            # Show version info

CLI Commands (inside chat)

Command	Description
/help	Show available commands
/tools	List available tools
/model [name]	Show or change model
/personality [name]	Set personality (kawaii, pirate, etc.)
/clear	Clear screen and reset
/cron	Manage scheduled tasks
/config	Show current configuration
/quit	Exit

---

Features

🛠️ Tools & Toolsets

Tools are organized into logical toolsets:

# Use specific toolsets
hermes --toolsets "web,terminal"

# List all toolsets
hermes --list-tools

Available toolsets: web, terminal, browser, vision, creative, reasoning, skills, tts, cronjob, and more.

🔊 Text-to-Speech

Convert text to speech with three providers:

Provider	Quality	Cost	API Key
Edge TTS (default)	Good	Free	None needed
ElevenLabs	Excellent	Paid	ELEVENLABS_API_KEY
OpenAI TTS	Good	Paid	OPENAI_API_KEY

On Telegram, audio plays as native voice bubbles (the round, inline-playable kind). On Discord/WhatsApp, sent as audio file attachments. In CLI mode, saved to ~/voice-memos/.

Configure in ~/.hermes/config.yaml:

tts:
  provider: "edge"              # "edge" | "elevenlabs" | "openai"
  edge:
    voice: "en-US-AriaNeural"   # 322 voices, 74 languages
  elevenlabs:
    voice_id: "pNInz6obpgDQGcFmaJgB"  # Adam
    model_id: "eleven_multilingual_v2"
  openai:
    model: "gpt-4o-mini-tts"
    voice: "alloy"              # alloy, echo, fable, onyx, nova, shimmer

Telegram voice bubbles & ffmpeg:

Telegram voice bubbles require Opus/OGG audio format. OpenAI and ElevenLabs produce Opus natively — no extra dependencies needed. Edge TTS (the default free provider) outputs MP3 and needs ffmpeg to convert to Opus:

# Ubuntu/Debian
sudo apt install ffmpeg

# macOS
brew install ffmpeg

# Fedora
sudo dnf install ffmpeg

Without ffmpeg, Edge TTS audio is sent as a regular audio file (playable, but shows as a rectangular player instead of a voice bubble). If you want voice bubbles without installing ffmpeg, switch to the OpenAI or ElevenLabs provider.

🎙️ Voice Message Transcription

Voice messages sent on Telegram, Discord, WhatsApp, or Slack are automatically transcribed using OpenAI's Whisper API and injected as text into the conversation. The agent sees the transcript as normal text -- no special handling needed.

Provider	Model	Quality	Cost
OpenAI Whisper	whisper-1 (default)	Good	Low
OpenAI GPT-4o	gpt-4o-mini-transcribe	Better	Medium
OpenAI GPT-4o	gpt-4o-transcribe	Best	Higher

Requires OPENAI_API_KEY in ~/.hermes/.env. Configure the model in ~/.hermes/config.yaml:

stt:
  enabled: true
  model: "whisper-1"

📄 Context Files (SOUL.md, AGENTS.md, .cursorrules)

Drop these files in your project directory and the agent automatically picks them up:

File	Purpose
AGENTS.md	Project-specific instructions, coding conventions, tool usage guidelines
SOUL.md	Persona definition -- the agent embodies this personality and tone
.cursorrules	Cursor IDE rules (also detected)
.cursor/rules/*.mdc	Cursor rule files (also detected)

• AGENTS.md is hierarchical: if subdirectories also have AGENTS.md, all are combined (like Codex/Cline).
• SOUL.md checks cwd first, then ~/.hermes/SOUL.md as a global fallback.
• All context files are capped at 20,000 characters with smart truncation.

🛡️ Exec Approval (Messaging Platforms)

When the agent tries to run a potentially dangerous command (rm -rf, chmod 777, etc.) on Telegram/Discord/WhatsApp, instead of blocking it silently, it asks the user for approval:

⚠️ This command is potentially dangerous (recursive delete). Reply "yes" to approve.

Reply "yes"/"y" to approve or "no"/"n" to deny. In CLI mode, the existing interactive approval prompt (once/session/always/deny) is preserved.

🖥️ Terminal Backend

The terminal tool can execute commands in different environments:

Backend	Description	Use Case
local	Run on your machine (default)	Development, trusted tasks
docker	Isolated containers	Security, reproducibility
ssh	Remote server	Sandboxing, keep agent away from its own code
singularity	HPC containers	Cluster computing, rootless
modal	Cloud execution	Serverless, scale

Configure in ~/.hermes/config.yaml:

terminal:
  backend: local    # or: docker, ssh, singularity, modal
  cwd: "."          # Working directory ("." = current dir)
  timeout: 180      # Command timeout in seconds

Docker Backend:

terminal:
  backend: docker
  docker_image: python:3.11-slim

SSH Backend (recommended for security - agent can't modify its own code):

terminal:
  backend: ssh

# Set credentials in ~/.hermes/.env
TERMINAL_SSH_HOST=my-server.example.com
TERMINAL_SSH_USER=myuser
TERMINAL_SSH_KEY=~/.ssh/id_rsa

Singularity/Apptainer (for HPC clusters):

# Pre-build SIF for parallel workers
apptainer build ~/python.sif docker://python:3.11-slim

# Configure
hermes config set terminal.backend singularity
hermes config set terminal.singularity_image ~/python.sif

Modal (serverless cloud):

uv pip install "swe-rex[modal]"   # Installs swe-rex + modal + boto3
modal setup                    # Authenticate with Modal
hermes config set terminal.backend modal

Sudo Support: If a command needs sudo, you'll be prompted for your password (cached for the session). Or set SUDO_PASSWORD in ~/.hermes/.env.

📱 Messaging Gateway

Chat with Hermes from Telegram, Discord, or WhatsApp.

Telegram Setup

1. Create a bot: Message @BotFather on Telegram, use /newbot
2. Get your user ID: Message @userinfobot - it replies with your numeric ID
3. Configure:

# Add to ~/.hermes/.env:
TELEGRAM_BOT_TOKEN=123456:ABC-DEF...
TELEGRAM_ALLOWED_USERS=YOUR_USER_ID    # Comma-separated for multiple users

4. Start the gateway:

hermes gateway              # Run in foreground
hermes gateway install      # Install as systemd service (Linux)
hermes gateway start        # Start the service

Discord Setup

1. Create a bot: Go to Discord Developer Portal
2. Get your user ID: Enable Developer Mode in Discord settings, right-click your name → Copy ID
3. Configure:

# Add to ~/.hermes/.env:
DISCORD_BOT_TOKEN=MTIz...
DISCORD_ALLOWED_USERS=YOUR_USER_ID

Slack Setup

1. Create an app: Go to Slack API, create a new app
2. Enable Socket Mode: In app settings → Socket Mode → Enable
3. Get tokens:
   • Bot Token (xoxb-...): OAuth & Permissions → Install to Workspace
   • App Token (xapp-...): Basic Information → App-Level Tokens → Generate
4. Configure:

# Add to ~/.hermes/.env:
SLACK_BOT_TOKEN=xoxb-...
SLACK_APP_TOKEN=xapp-...
SLACK_ALLOWED_USERS=U01234ABCDE    # Comma-separated Slack user IDs

5. Start the gateway: hermes gateway

DM Pairing (Alternative to Allowlists)

Instead of manually configuring user IDs in allowlists, you can use the pairing system. When an unknown user DMs your bot, they receive a one-time pairing code:

# The user sees: "Pairing code: XKGH5N7P"
# You approve them with:
hermes pairing approve telegram XKGH5N7P

# Other pairing commands:
hermes pairing list          # View pending + approved users
hermes pairing revoke telegram 123456789  # Remove access

Pairing codes expire after 1 hour, are rate-limited, and use cryptographic randomness.

Security (Important!)

Without an allowlist, anyone who finds your bot can use it!

# Restrict to specific users (recommended):
TELEGRAM_ALLOWED_USERS=123456789,987654321
DISCORD_ALLOWED_USERS=123456789012345678

# Or allow all users in a specific platform:
# (Leave the variable unset - NOT recommended for bots with terminal access)

Gateway Commands

Command	Description
/new or /reset	Start fresh conversation
/status	Show session info
/hermes (Discord)	Slash command — ask, reset, status, stop

Working Directory

• CLI (hermes): Uses current directory where you run the command
• Messaging: Uses MESSAGING_CWD (default: home directory ~)

# Set custom messaging working directory in ~/.hermes/.env
MESSAGING_CWD=/home/myuser/projects

Tool Progress Notifications

Get real-time updates as the agent works:

# Enable in ~/.hermes/.env
HERMES_TOOL_PROGRESS=true
HERMES_TOOL_PROGRESS_MODE=new    # or "all" for every tool call

When enabled, you'll see messages like:

💻 `ls -la`...
🔍 web_search...
📄 web_extract...

See docs/messaging.md for WhatsApp and advanced setup.

🤖 RL Training (Tinker + Atropos)

Train language models with reinforcement learning using the Tinker API and Atropos framework.

Note: RL training tools require Python 3.11+ (the upstream tinker package has this requirement). On Python 3.10, the RL toolset will be automatically disabled — all other features work fine.

Requirements

1. Python 3.11+ (check with python3 --version)
2. API Keys: Add to ~/.hermes/.env:

TINKER_API_KEY=your-tinker-key      # Get from https://tinker-console.thinkingmachines.ai/keys
WANDB_API_KEY=your-wandb-key        # Get from https://wandb.ai/authorize
OPENROUTER_API_KEY=your-key         # Optional: for rl_test_inference

3. That's it! tinker-atropos is included as a submodule — the installer handles it automatically.

Using RL Tools

The agent can now use RL training tools:

You: Start training on GSM8k with group_size=16

Agent: I'll set up an RL training run on the GSM8k environment...
[Uses rl_list_environments, rl_select_environment, rl_edit_config, rl_start_training]

Available RL Tools

Tool	Description
rl_list_environments	List available RL environments
rl_select_environment	Select an environment for training
rl_get_current_config	View all configurable options
rl_edit_config	Change a configuration value
rl_test_inference	Test environment with OpenRouter (pre-training validation)
rl_start_training	Start a training run
rl_check_status	Check training progress
rl_stop_training	Stop a running training
rl_get_results	Fetch WandB metrics
rl_list_runs	List active training runs

Dedicated RL CLI

For extended RL workflows with longer timeouts:

python rl_cli.py --model "anthropic/claude-sonnet-4-20250514"

🧪 Atropos RL Environments

Hermes-Agent integrates with the Atropos RL framework through a layered environment system. This allows training models with reinforcement learning on agentic tasks using hermes-agent's tools.

Architecture

The integration has three layers:

Layer	File	Purpose
Agent Loop	environments/agent_loop.py	Reusable multi-turn tool-calling engine (standard OpenAI spec)
Base Environment	environments/hermes_base_env.py	Abstract Atropos BaseEnv subclass with toolset resolution, ToolContext, scoring
Concrete Envs	environments/terminal_test_env.py, environments/hermes_swe_env.py	Task-specific environments

Two-Phase Operation

• Phase 1 (OpenAI server type): Works with any OpenAI-compatible endpoint (VLLM, SGLang, OpenRouter, OpenAI API). The server handles tool call parsing natively. Good for SFT data generation, verifier testing, and evaluation.
• Phase 2 (VLLM server type): Uses ManagedServer for exact token IDs + logprobs via /generate. Client-side tool call parser registry reconstructs structured tool_calls from raw output. Required for full RL training.

Quick Start

# 1. Launch VLLM with tool parser
vllm serve YourModel --tool-parser hermes

# 2. Start the Atropos API server
run-api

# 3. Run an environment
python environments/terminal_test_env.py serve \
    --openai.base_url http://localhost:8000/v1 \
    --openai.model_name YourModel \
    --openai.server_type openai

ToolContext (Reward Functions)

Reward functions receive a ToolContext with unrestricted access to all hermes-agent tools, scoped to the rollout's sandbox:

async def compute_reward(self, item, result, ctx: ToolContext) -> float:
    # Run tests in the model's terminal sandbox
    test = ctx.terminal("pytest -v")
    if test["exit_code"] == 0:
        return 1.0
    # Or check a file, search the web, navigate a browser...
    return 0.0

Creating Custom Environments

Subclass HermesAgentBaseEnv and implement 5 methods:

from environments.hermes_base_env import HermesAgentBaseEnv

class MyEnv(HermesAgentBaseEnv):
    name = "my-env"
    async def setup(self): ...            # Load data
    async def get_next_item(self): ...    # Return next item
    def format_prompt(self, item): ...    # Item -> prompt string
    async def compute_reward(self, item, result, ctx): ...  # Score with ToolContext
    async def evaluate(self, *args, **kwargs): ...          # Periodic eval

if __name__ == "__main__":
    MyEnv.cli()

Toolset Distributions

Configure which tools are available per group, either explicitly or probabilistically:

# Explicit toolsets
--env.enabled_toolsets '["terminal","file","web"]'

# Probabilistic distribution (sampled per group)
--env.distribution development

Tool Call Parsers (Phase 2)

For VLLM server type, a parser registry extracts structured tool_calls from raw model output. Supported parsers: hermes, mistral, llama3_json, qwen, deepseek_v3, deepseek_v3_1, kimi_k2, longcat, glm45, glm47, qwen3_coder.

--env.tool_call_parser hermes  # Match your VLLM --tool-parser flag

⏰ Scheduled Tasks (Cron)

Schedule tasks to run automatically:

# In the CLI
/cron add 30m "Remind me to check the build"
/cron add "every 2h" "Check server status"
/cron add "0 9 * * *" "Morning briefing"
/cron list
/cron remove <job_id>

The agent can also self-schedule using schedule_cronjob tool.

Run the scheduler:

hermes cron daemon         # Built-in daemon
# Or add to system cron for reliability

🗜️ Context Compression

Long conversations are automatically summarized when approaching context limits:

# In ~/.hermes/config.yaml
compression:
  enabled: true
  threshold: 0.85    # Compress at 85% of limit

📝 Session Logging

Every conversation is logged to ~/.hermes-agent/logs/ for debugging:

logs/
├── session_20260201_143052_a1b2c3.json
└── ...

🌐 Browser Automation

Browser tools let the agent navigate websites, fill forms, click buttons, and extract content using Browserbase.

Setup:

# 1. Get credentials from browserbase.com
hermes config set BROWSERBASE_API_KEY your_api_key
hermes config set BROWSERBASE_PROJECT_ID your_project_id

# 2. Install Node.js dependencies (if not already)
cd ~/.hermes-agent && npm install

Available tools: browser_navigate, browser_snapshot, browser_click, browser_type, browser_scroll, browser_back, browser_press, browser_close, browser_get_images

Example:

hermes --toolsets browser -q "Go to amazon.com and find the price of the latest Kindle"

📚 Skills System

Skills are on-demand knowledge documents the agent can load when needed. They follow a progressive disclosure pattern to minimize token usage.

Using Skills:

hermes --toolsets skills -q "What skills do you have?"
hermes --toolsets skills -q "Show me the axolotl skill"

Creating Skills:

Create skills/category/skill-name/SKILL.md:

---
name: my-skill
description: Brief description shown in skills_list
tags: [python, automation]
version: 1.0.0
---

# Skill Content

Instructions, examples, and guidelines here...

Skill Structure:

skills/
├── mlops/
│   ├── axolotl/
│   │   ├── SKILL.md          # Main instructions (required)
│   │   ├── references/       # Additional docs
│   │   └── templates/        # Output formats
│   └── vllm/
│       └── SKILL.md

---

Manual Installation

If you prefer full control over the installation process (or the quick-install script doesn't suit your environment), follow these steps to set everything up by hand.

Prerequisites

Requirement	Minimum Version	Check Command	Notes
Git	Any recent	git --version	Required
Node.js	18+	node --version	Optional — needed for browser automation tools
ripgrep	Any	rg --version	Optional — faster file search in terminal tool (falls back to grep)

Note: Python and pip are not prerequisites. The installer uses uv to provision Python 3.11 automatically (no sudo needed). If you already have Python 3.11+ installed, uv will use it.

Installing prerequisites by platform

Ubuntu / Debian:

sudo apt update && sudo apt install git
# Optional:
sudo apt install ripgrep nodejs npm

macOS (Homebrew):

brew install git
# Optional:
brew install ripgrep node

Windows (WSL recommended): Use the Windows Subsystem for Linux and follow the Ubuntu instructions above. Alternatively, use the PowerShell quick-install script at the top of this README.

---

Step 1: Clone the Repository

Clone with --recurse-submodules to pull the required submodules (mini-swe-agent for the terminal tool backend and tinker-atropos for RL training):

git clone --recurse-submodules https://github.com/NousResearch/hermes-agent.git
cd hermes-agent

If you already cloned without --recurse-submodules, initialize them manually:

git submodule update --init --recursive

---

Step 2: Install uv & Create Virtual Environment

uv is a fast Python package manager that can also provision Python itself. Install it and create the venv in one go:

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create venv with Python 3.11 (uv downloads it if not present — no sudo needed)
uv venv venv --python 3.11

Tip: You do not need to activate the venv to use hermes. The entry point has a hardcoded shebang pointing to the venv Python, so it works globally once symlinked (see Step 8). For installing packages, uv can target the venv directly via VIRTUAL_ENV.

---

Step 3: Install Python Dependencies

Install the main package in editable mode with all optional extras (messaging, cron, CLI menus, modal):

# Tell uv which venv to install into
export VIRTUAL_ENV="$(pwd)/venv"

# Install with all extras
uv pip install -e ".[all]"

If you only want the core agent (no Telegram/Discord/cron support):

uv pip install -e "."

Optional extras breakdown

Extra	What it adds	Install command
all	Everything below	uv pip install -e ".[all]"
messaging	Telegram & Discord gateway	uv pip install -e ".[messaging]"
cron	Cron expression parsing for scheduled tasks	uv pip install -e ".[cron]"
cli	Terminal menu UI for setup wizard	uv pip install -e ".[cli]"
modal	Modal cloud execution backend (swe-rex + modal + boto3)	uv pip install -e ".[modal]"
dev	pytest & test utilities	uv pip install -e ".[dev]"

You can combine extras: uv pip install -e ".[messaging,cron]"

---

Step 4: Install Submodule Packages

These are local packages checked out as Git submodules. Install them in editable mode:

# Terminal tool backend (required for the terminal/command-execution tool)
uv pip install -e "./mini-swe-agent"

# RL training backend
uv pip install -e "./tinker-atropos"

Both are optional — if you skip them, the corresponding toolsets simply won't be available.

---

Step 5: Install Node.js Dependencies (Optional)

Only needed if you plan to use the browser automation toolset (Browserbase-powered):

npm install

This installs the agent-browser package defined in package.json. Skip this step if you don't need browser tools.

---

Step 6: Create the Configuration Directory

Hermes stores all user configuration in ~/.hermes/:

# Create the directory structure
mkdir -p ~/.hermes/{cron,sessions,logs}

# Copy the example config file
cp cli-config.yaml.example ~/.hermes/config.yaml

# Create an empty .env file for API keys
touch ~/.hermes/.env

Your ~/.hermes/ directory should now look like:

~/.hermes/
├── config.yaml     # Agent settings (model, terminal, toolsets, compression, etc.)
├── .env            # API keys and secrets (one per line: KEY=value)
├── cron/           # Scheduled job data
├── sessions/       # Messaging gateway sessions
└── logs/           # Conversation logs

---

Step 7: Add Your API Keys

Open ~/.hermes/.env in your editor and add at minimum an LLM provider key:

# Required — at least one LLM provider:
OPENROUTER_API_KEY=sk-or-v1-your-key-here

# Optional — enable additional tools:
FIRECRAWL_API_KEY=fc-your-key          # Web search & scraping
BROWSERBASE_API_KEY=bb-your-key        # Browser automation
BROWSERBASE_PROJECT_ID=your-project-id # Browser automation
FAL_KEY=your-fal-key                   # Image generation (FLUX)
TINKER_API_KEY=your-tinker-key         # RL training
WANDB_API_KEY=your-wandb-key           # RL training metrics

# Optional — messaging gateway:
TELEGRAM_BOT_TOKEN=123456:ABC-DEF      # From @BotFather
TELEGRAM_ALLOWED_USERS=your-user-id    # Comma-separated
DISCORD_BOT_TOKEN=MTIz...              # From Developer Portal
DISCORD_ALLOWED_USERS=your-user-id     # Comma-separated

Or set them one at a time via the CLI:

hermes config set OPENROUTER_API_KEY sk-or-v1-your-key-here

---

Step 8: Add hermes to Your PATH

The hermes entry point at venv/bin/hermes has a hardcoded shebang pointing to the venv's Python, so it works without activating the venv. The recommended approach is a symlink into ~/.local/bin (most distributions already have this on PATH):

mkdir -p ~/.local/bin
ln -sf "$(pwd)/venv/bin/hermes" ~/.local/bin/hermes

If ~/.local/bin isn't on your PATH yet, add it:

Bash (~/.bashrc):

echo '' >> ~/.bashrc
echo '# Hermes Agent' >> ~/.bashrc
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

Zsh (~/.zshrc):

echo '' >> ~/.zshrc
echo '# Hermes Agent' >> ~/.zshrc
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

Fish (~/.config/fish/config.fish):

fish_add_path $HOME/.local/bin

---

Step 9: Run the Setup Wizard (Optional)

The interactive setup wizard walks you through configuring your API keys and preferences:

hermes setup

This is optional if you already configured ~/.hermes/.env and ~/.hermes/config.yaml manually in the steps above.

---

Step 10: Verify the Installation

# Check that the command is available
hermes version

# Run diagnostics to verify everything is working
hermes doctor

# Check your configuration
hermes status

# Test with a quick query
hermes chat -q "Hello! What tools do you have available?"

If hermes doctor reports issues, it will tell you exactly what's missing and how to fix it.

---

Quick-Reference: Manual Install (Condensed)

For those who just want the commands without the explanations:

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone & enter
git clone --recurse-submodules https://github.com/NousResearch/hermes-agent.git
cd hermes-agent

# Create venv with Python 3.11 (uv downloads it if needed)
uv venv venv --python 3.11
export VIRTUAL_ENV="$(pwd)/venv"

# Install everything
uv pip install -e ".[all]"
uv pip install -e "./mini-swe-agent"
uv pip install -e "./tinker-atropos"
npm install  # optional, for browser tools

# Configure
mkdir -p ~/.hermes/{cron,sessions,logs}
cp cli-config.yaml.example ~/.hermes/config.yaml
touch ~/.hermes/.env
echo 'OPENROUTER_API_KEY=sk-or-v1-your-key' >> ~/.hermes/.env

# Make hermes available globally (no venv activation needed)
mkdir -p ~/.local/bin
ln -sf "$(pwd)/venv/bin/hermes" ~/.local/bin/hermes

# Verify
hermes doctor
hermes

---

Updating a Manual Installation

To update an existing manual install to the latest version:

cd /path/to/hermes-agent
export VIRTUAL_ENV="$(pwd)/venv"

# Pull latest code and submodules
git pull origin main
git submodule update --init --recursive

# Reinstall (picks up new dependencies)
uv pip install -e ".[all]"
uv pip install -e "./mini-swe-agent"
uv pip install -e "./tinker-atropos"

# Check for new config options added since your last update
hermes config check
hermes config migrate   # Interactively add any missing options

Uninstalling a Manual Installation

# Remove the hermes symlink
rm -f ~/.local/bin/hermes

# Remove the cloned repository
rm -rf /path/to/hermes-agent

# Remove user configuration (optional — keep if you plan to reinstall)
rm -rf ~/.hermes

---

Batch Processing

Process multiple prompts in parallel with automatic checkpointing:

python batch_runner.py \
  --dataset_file=prompts.jsonl \
  --batch_size=20 \
  --run_name=my_run \
  --num_workers=4 \
  --distribution=default

Key Options:

Flag	Description
--dataset_file	JSONL file with prompts
--batch_size	Prompts per batch
--run_name	Name for output/checkpoints
--num_workers	Parallel workers (default: 4)
--distribution	Toolset distribution
--resume	Resume from checkpoint
--ephemeral_system_prompt	Guide behavior without saving to trajectories
--list_distributions	Show available distributions

Output: data/<run_name>/trajectories.jsonl

Trajectory Compression

Compress trajectories to fit token budgets for training:

# Compress a directory
python trajectory_compressor.py --input=data/my_run

# Compress with sampling
python trajectory_compressor.py --input=data/my_run --sample_percent=15

# Custom token target
python trajectory_compressor.py --input=data/my_run --target_max_tokens=16000

Features:

• Protects first/last turns
• Summarizes middle turns via LLM
• Configurable via configs/trajectory_compression.yaml

---

Python API

from run_agent import AIAgent

agent = AIAgent(
    model="anthropic/claude-sonnet-4",
    enabled_toolsets=["web", "terminal"]
)

result = agent.run_conversation("Search for the latest Python news")
print(result["final_response"])

---

Environment Variables Reference

All variables go in ~/.hermes/.env. Run hermes config set VAR value to set them.

LLM Providers:

Variable	Description
OPENROUTER_API_KEY	OpenRouter API key (recommended)
ANTHROPIC_API_KEY	Direct Anthropic access
OPENAI_API_KEY	Direct OpenAI access

Tool APIs:

Variable	Description
FIRECRAWL_API_KEY	Web scraping (firecrawl.dev)
BROWSERBASE_API_KEY	Browser automation
BROWSERBASE_PROJECT_ID	Browserbase project
FAL_KEY	Image generation (fal.ai)

Terminal Backend:

Variable	Description
TERMINAL_ENV	Backend: local, docker, ssh, singularity, modal
TERMINAL_DOCKER_IMAGE	Docker image (default: python:3.11-slim)
TERMINAL_SINGULARITY_IMAGE	Singularity image or .sif path
TERMINAL_TIMEOUT	Command timeout in seconds
TERMINAL_CWD	Working directory
SUDO_PASSWORD	Enable sudo (stored plaintext - be careful!)

SSH Backend:

Variable	Description
TERMINAL_SSH_HOST	Remote server hostname
TERMINAL_SSH_USER	SSH username
TERMINAL_SSH_PORT	SSH port (default: 22)
TERMINAL_SSH_KEY	Path to private key

Messaging:

Variable	Description
TELEGRAM_BOT_TOKEN	Telegram bot token (@BotFather)
TELEGRAM_ALLOWED_USERS	Comma-separated user IDs allowed to use bot
TELEGRAM_HOME_CHANNEL	Default channel for cron delivery
DISCORD_BOT_TOKEN	Discord bot token
DISCORD_ALLOWED_USERS	Comma-separated user IDs allowed to use bot
DISCORD_HOME_CHANNEL	Default channel for cron delivery
MESSAGING_CWD	Working directory for terminal in messaging (default: ~)

Agent Behavior:

Variable	Description
HERMES_MAX_ITERATIONS	Max tool-calling iterations per conversation (default: 60)
HERMES_TOOL_PROGRESS	Send progress messages when using tools (true/false)
HERMES_TOOL_PROGRESS_MODE	new (only when tool changes) or all (every call)

Context Compression:

Variable	Description
CONTEXT_COMPRESSION_ENABLED	Enable auto-compression (default: true)
CONTEXT_COMPRESSION_THRESHOLD	Trigger at this % of limit (default: 0.85)
CONTEXT_COMPRESSION_MODEL	Model for summaries

---

File Structure

Path	Description
~/.hermes/config.yaml	Your settings
~/.hermes/.env	API keys and secrets
~/.hermes/cron/	Scheduled jobs data
~/.hermes/sessions/	Gateway session data
~/.hermes-agent/	Installation directory
~/.hermes-agent/logs/	Session logs
hermes_cli/	CLI implementation
tools/	Tool implementations
skills/	Knowledge documents
gateway/	Messaging platform adapters
cron/	Scheduler implementation

---

Troubleshooting

hermes doctor    # Run diagnostics
hermes status    # Check configuration
hermes config    # View current settings

Common issues:

• "API key not set": Run hermes setup or hermes config set OPENROUTER_API_KEY your_key
• "hermes: command not found": Reload your shell (source ~/.bashrc) or check PATH
• Gateway won't start: Check hermes gateway status and logs
• Missing config after update: Run hermes config check to see what's new, then hermes config migrate to add missing options

---

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

---

License

MIT License - see LICENSE for details.