hermes-agent/AGENTS.md

Hermes Agent - Development Guide

Instructions for AI coding assistants (GitHub Copilot, Cursor, etc.) and human developers.

Hermes-Agent is an AI agent harness with tool-calling capabilities, interactive CLI, messaging integrations, and scheduled tasks.

Development Environment

IMPORTANT: Always use the virtual environment if it exists:

source venv/bin/activate  # Before running any Python commands

Project Structure

hermes-agent/
├── hermes_cli/           # Unified CLI commands
│   ├── main.py           # Entry point, command dispatcher
│   ├── setup.py          # Interactive setup wizard
│   ├── config.py         # Config management & migration
│   ├── status.py         # Status display
│   ├── doctor.py         # Diagnostics
│   ├── gateway.py        # Gateway management
│   └── cron.py           # Cron job management
├── tools/                # Tool implementations
├── gateway/              # Messaging platform adapters
├── cron/                 # Scheduler implementation
├── skills/               # Knowledge documents
├── cli.py                # Interactive CLI (Rich UI)
├── run_agent.py          # Agent runner with AIAgent class
├── model_tools.py        # Tool schemas and handlers
├── toolsets.py           # Tool groupings
├── toolset_distributions.py  # Probability-based tool selection
└── batch_runner.py       # Parallel batch processing

User Configuration (stored in ~/.hermes/):

• ~/.hermes/config.yaml - Settings (model, terminal, toolsets, etc.)
• ~/.hermes/.env - API keys and secrets

File Dependency Chain

tools/*.py → tools/__init__.py → model_tools.py → toolsets.py → toolset_distributions.py
                                       ↑
run_agent.py ──────────────────────────┘
cli.py → run_agent.py (uses AIAgent with quiet_mode=True)
batch_runner.py → run_agent.py + toolset_distributions.py

Always ensure consistency between tools, model_tools.py, and toolsets.py when changing any of them.

---

AIAgent Class

The main agent is implemented in run_agent.py:

class AIAgent:
    def __init__(
        self,
        model: str = "anthropic/claude-sonnet-4",
        api_key: str = None,
        base_url: str = "https://openrouter.ai/api/v1",
        max_turns: int = 20,
        enabled_toolsets: list = None,
        disabled_toolsets: list = None,
        verbose_logging: bool = False,
    ):
        # Initialize OpenAI client, load tools based on toolsets
        ...
    
    def chat(self, user_message: str, task_id: str = None) -> str:
        # Main entry point - runs the agent loop
        ...

Agent Loop

The core loop in _run_agent_loop():

1. Add user message to conversation
2. Call LLM with tools
3. If LLM returns tool calls:
   - Execute each tool
   - Add tool results to conversation
   - Go to step 2
4. If LLM returns text response:
   - Return response to user

while turns < max_turns:
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        tools=tool_schemas,
    )
    
    if response.tool_calls:
        for tool_call in response.tool_calls:
            result = await execute_tool(tool_call)
            messages.append(tool_result_message(result))
        turns += 1
    else:
        return response.content

Conversation Management

Messages are stored as a list of dicts following OpenAI format:

messages = [
    {"role": "system", "content": "You are a helpful assistant..."},
    {"role": "user", "content": "Search for Python tutorials"},
    {"role": "assistant", "content": None, "tool_calls": [...]},
    {"role": "tool", "tool_call_id": "...", "content": "..."},
    {"role": "assistant", "content": "Here's what I found..."},
]

Reasoning Model Support

For models that support chain-of-thought reasoning:

• Extract reasoning_content from API responses
• Store in assistant_msg["reasoning"] for trajectory export
• Pass back via reasoning_content field on subsequent turns

---

CLI Architecture (cli.py)

The interactive CLI uses:

• Rich - For the welcome banner and styled panels
• prompt_toolkit - For fixed input area with history and patch_stdout
• KawaiiSpinner (in run_agent.py) - Animated feedback during API calls and tool execution

Key components:

• HermesCLI class - Main CLI controller with commands and conversation loop
• load_cli_config() - Loads config, sets environment variables for terminal
• build_welcome_banner() - Displays ASCII art logo, tools, and skills summary
• /commands - Process user commands like /help, /clear, /personality, etc.

CLI uses quiet_mode=True when creating AIAgent to suppress verbose logging.

Adding CLI Commands

1. Add to COMMANDS dict with description
2. Add handler in process_command() method
3. For persistent settings, use save_config_value() to update config

---

Hermes CLI Commands

The unified hermes command provides all functionality:

Command	Description
hermes	Interactive chat (default)
hermes chat -q "..."	Single query mode
hermes setup	Configure API keys and settings
hermes config	View current configuration
hermes config edit	Open config in editor
hermes config set KEY VAL	Set a specific value
hermes config check	Check for missing config
hermes config migrate	Prompt for missing config interactively
hermes status	Show configuration status
hermes doctor	Diagnose issues
hermes update	Update to latest (checks for new config)
hermes gateway	Start messaging gateway
hermes cron list	View scheduled jobs
hermes version	Show version info

---

Configuration System

Configuration files are stored in ~/.hermes/ for easy user access:

• ~/.hermes/config.yaml - All settings (model, terminal, compression, etc.)
• ~/.hermes/.env - API keys and secrets

Adding New Configuration Options

When adding new configuration variables, you MUST follow this process:

For config.yaml options:

1. Add to DEFAULT_CONFIG in hermes_cli/config.py
2. CRITICAL: Bump _config_version in DEFAULT_CONFIG when adding required fields
3. This triggers migration prompts for existing users on next hermes update or hermes setup

Example:

DEFAULT_CONFIG = {
    # ... existing config ...
    
    "new_feature": {
        "enabled": True,
        "option": "default_value",
    },
    
    # BUMP THIS when adding required fields
    "_config_version": 2,  # Was 1, now 2
}

For .env variables (API keys/secrets):

1. Add to REQUIRED_ENV_VARS or OPTIONAL_ENV_VARS in hermes_cli/config.py
2. Include metadata for the migration system:

OPTIONAL_ENV_VARS = {
    # ... existing vars ...
    "NEW_API_KEY": {
        "description": "What this key is for",
        "prompt": "Display name in prompts",
        "url": "https://where-to-get-it.com/",
        "tools": ["tools_it_enables"],  # What tools need this
        "password": True,  # Mask input
    },
}

Update related files:

• hermes_cli/setup.py - Add prompts in the setup wizard
• cli-config.yaml.example - Add example with comments
• Update README.md if user-facing

Config Version Migration

The system uses _config_version to detect outdated configs:

1. check_for_missing_config() compares user config to DEFAULT_CONFIG
2. migrate_config() interactively prompts for missing values
3. Called automatically by hermes update and optionally by hermes setup

---

Environment Variables

API keys are loaded from ~/.hermes/.env:

• OPENROUTER_API_KEY - Main LLM API access (primary provider)
• FIRECRAWL_API_KEY - Web search/extract tools
• BROWSERBASE_API_KEY / BROWSERBASE_PROJECT_ID - Browser automation
• FAL_KEY - Image generation (FLUX model)
• NOUS_API_KEY - Vision and Mixture-of-Agents tools

Terminal tool configuration (in ~/.hermes/config.yaml):

• terminal.backend - Backend: local, docker, singularity, modal, or ssh
• terminal.cwd - Working directory ("." = current directory)
• terminal.docker_image - Image for Docker backend
• terminal.singularity_image - Image for Singularity backend
• terminal.modal_image - Image for Modal backend
• SSH: TERMINAL_SSH_HOST, TERMINAL_SSH_USER, TERMINAL_SSH_KEY in .env

---

Adding New Tools

Follow this strict order to maintain consistency:

1. Create tools/your_tool.py with:

   • Handler function (sync or async) returning a JSON string via json.dumps()
   • check_*_requirements() function to verify dependencies (e.g., API keys)
   • Schema definition following OpenAI function-calling format

2. Export in tools/__init__.py:

   • Import the handler and check function
   • Add to __all__ list

3. Register in model_tools.py:

   • Add to TOOLSET_REQUIREMENTS if it needs API keys
   • Create get_*_tool_definitions() function or add to existing
   • Add routing in handle_function_call() dispatcher
   • Update get_all_tool_names() with the tool name
   • Update get_toolset_for_tool() mapping
   • Update get_available_toolsets() and check_toolset_requirements()

4. Add to toolset in toolsets.py:

   • Add to existing toolset or create new one in TOOLSETS dict

5. If the tool requires an API key:

   • Add to OPTIONAL_ENV_VARS in hermes_cli/config.py
   • The tool will be auto-disabled if the key is missing

6. Optionally add to toolset_distributions.py for batch processing

Tool Implementation Pattern

# tools/example_tool.py
import json
import os

def check_example_requirements() -> bool:
    """Check if required API keys/dependencies are available."""
    return bool(os.getenv("EXAMPLE_API_KEY"))

def example_tool(param: str, task_id: str = None) -> str:
    """Execute the tool and return JSON string result."""
    try:
        result = {"success": True, "data": "..."}
        return json.dumps(result, ensure_ascii=False)
    except Exception as e:
        return json.dumps({"error": str(e)}, ensure_ascii=False)

All tool handlers MUST return a JSON string. Never return raw dicts.

Dynamic Tool Availability

Tools are automatically disabled when their API keys are missing:

# In model_tools.py
TOOLSET_REQUIREMENTS = {
    "web": {"env_vars": ["FIRECRAWL_API_KEY"]},
    "browser": {"env_vars": ["BROWSERBASE_API_KEY", "BROWSERBASE_PROJECT_ID"]},
    "creative": {"env_vars": ["FAL_KEY"]},
}

The check_tool_availability() function determines which tools to include.

Stateful Tools

Tools that maintain state (terminal, browser) require:

• task_id parameter for session isolation between concurrent tasks
• cleanup_*() function to release resources
• Cleanup is called automatically in run_agent.py after conversation completes

---

Trajectory Format

Conversations are saved in ShareGPT format for training:

{"from": "system", "value": "System prompt with <tools>...</tools>"}
{"from": "human", "value": "User message"}
{"from": "gpt", "value": "<think>reasoning</think>\n<tool_call>{...}</tool_call>"}
{"from": "tool", "value": "<tool_response>{...}</tool_response>"}
{"from": "gpt", "value": "Final response"}

Tool calls use <tool_call> XML tags, responses use <tool_response> tags, reasoning uses <think> tags.

Trajectory Export

agent = AIAgent(save_trajectories=True)
agent.chat("Do something")
# Saves to trajectories/*.jsonl in ShareGPT format

---

Batch Processing (batch_runner.py)

For processing multiple prompts:

• Parallel execution with multiprocessing
• Content-based resume for fault tolerance (matches on prompt text, not indices)
• Toolset distributions control probabilistic tool availability per prompt
• Output: data/<run_name>/trajectories.jsonl (combined) + individual batch files

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

---

Skills System

Skills are on-demand knowledge documents the agent can load. Located in skills/ directory:

skills/
├── mlops/                    # Category folder
│   ├── axolotl/             # Skill folder
│   │   ├── SKILL.md         # Main instructions (required)
│   │   ├── references/      # Additional docs, API specs
│   │   └── templates/       # Output formats, configs
│   └── vllm/
│       └── SKILL.md
└── example-skill/
    └── SKILL.md

Progressive disclosure (token-efficient):

1. skills_categories() - List category names (~50 tokens)
2. skills_list(category) - Name + description per skill (~3k tokens)
3. skill_view(name) - Full content + tags + linked files

SKILL.md files use YAML frontmatter:

---
name: skill-name
description: Brief description for listing
tags: [tag1, tag2]
related_skills: [other-skill]
version: 1.0.0
---
# Skill Content...

Tool files: tools/skills_tool.py → model_tools.py → toolsets.py

---

Testing Changes

After making changes:

1. Run hermes doctor to check setup
2. Run hermes config check to verify config
3. Test with hermes chat -q "test message"
4. For new config options, test fresh install: rm -rf ~/.hermes && hermes setup