hermes-agent

name	hermes-agent
description	Expert in building self-improving AI agents with tool use, multi-platform messaging, and a closed learning loop. Proficient in LLM orchestration, tool integration, session management, and agent autonomy.

Hermes Agent - Complete Project Guide (A-Z)

Purpose of this document: A single, comprehensive reference that explains everything about the Hermes Agent project — its architecture, source code, features, release history, and design patterns — so that any AI or developer can fully understand the system.

---

Table of Contents

1. Project Overview
2. Key Features Summary
3. Installation & Getting Started
4. Project Structure
5. Core Architecture
   • 5.1 AIAgent Class (run_agent.py)
   • 5.2 Tool Orchestration (model_tools.py)
   • 5.3 Toolset System (toolsets.py)
   • 5.4 Tool Registry (tools/registry.py)
   • 5.5 Session Database (hermes_state.py)
   • 5.6 Constants & Home Directory (hermes_constants.py)
6. CLI System
   • 6.1 Interactive CLI (cli.py)
   • 6.2 CLI Entry Point (hermes_cli/main.py)
   • 6.3 Configuration System (hermes_cli/config.py)
   • 6.4 Slash Command Registry (hermes_cli/commands.py)
   • 6.5 Setup Wizard (hermes_cli/setup.py)
   • 6.6 Model Catalog (hermes_cli/models.py)
   • 6.7 Skin/Theme Engine (hermes_cli/skin_engine.py)
7. Tool System
   • 7.1 Terminal Tool (tools/terminal_tool.py)
   • 7.2 File Tools (tools/file_tools.py)
   • 7.3 Web Tools (tools/web_tools.py)
   • 7.4 Browser Tool (tools/browser_tool.py)
   • 7.5 Delegate Tool (tools/delegate_tool.py)
   • 7.6 MCP Tool (tools/mcp_tool.py)
   • 7.7 Approval System (tools/approval.py)
   • 7.8 Terminal Backends (tools/environments/)
8. Agent Internals
   • 8.1 Prompt Builder (agent/prompt_builder.py)
   • 8.2 Context Compressor (agent/context_compressor.py)
   • 8.3 Prompt Caching (agent/prompt_caching.py)
   • 8.4 Auxiliary Client (agent/auxiliary_client.py)
   • 8.5 Display & Spinner (agent/display.py)
   • 8.6 Skill Commands (agent/skill_commands.py)
9. Messaging Gateway
   • 9.1 GatewayRunner (gateway/run.py)
   • 9.2 Session Store (gateway/session.py)
   • 9.3 Platform Adapters (gateway/platforms/)
10. Cron Scheduling
11. Skills System
12. Plugin System
13. Memory System
14. ACP Server (IDE Integration)
15. API Server
16. MCP Server Mode
17. RL Training Environments
18. Profiles (Multi-Instance)
19. Security Model
20. Provider & Model System
21. Streaming & Reasoning
22. Release History
23. File Dependency Chain
24. Key Design Patterns
25. Configuration Reference
26. Known Pitfalls

---

1. Project Overview

Hermes Agent is a self-improving AI agent built by Nous Research. It is an open-source (MIT licensed), Python-based project that provides:

• A full interactive terminal UI (CLI) for conversing with LLMs
• A messaging gateway supporting 16+ platforms (Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, etc.)
• A closed learning loop — the agent creates skills from experience, improves them during use, nudges itself to persist knowledge, searches past conversations, and builds a deepening model of who you are
• 40+ built-in tools — terminal execution, file manipulation, web search, browser automation, code execution, image generation, TTS/STT, and more
• Any LLM provider — OpenRouter (200+ models), Nous Portal (400+ models), OpenAI, Anthropic, Hugging Face, GitHub Copilot, z.ai/GLM, Kimi/Moonshot, MiniMax, Alibaba/DashScope, custom endpoints
• Six terminal backends — local, Docker, SSH, Modal (serverless), Daytona (serverless), Singularity (HPC)
• Scheduled automations via built-in cron scheduler
• IDE integration via ACP (Agent Communication Protocol) for VS Code, Zed, JetBrains
• MCP integration — both client (connect to any MCP server) and server (expose Hermes to MCP clients)
• RL training via Atropos environments for training the next generation of tool-calling models

Tech Stack:

• Python 3.11+ (core agent, tools, gateway, cron)
• Node.js (browser automation via agent-browser)
• SQLite with WAL mode and FTS5 (session storage, full-text search)
• OpenAI-compatible API (primary inference interface)
• Anthropic SDK (native Anthropic support)
• Rich + prompt_toolkit (CLI rendering)

Repository: github.com/NousResearch/hermes-agent Version: 0.7.0 (as of April 2026) License: MIT

---

2. Key Features Summary

Feature	Description
Terminal UI	Full TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, streaming tool output
Multi-Platform Messaging	Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, Home Assistant, DingTalk, Feishu/Lark, WeCom, Mattermost, SMS, Webhook — all from a single gateway process
Learning Loop	Agent-curated memory with periodic nudges, autonomous skill creation, skills self-improve during use, FTS5 session search with LLM summarization, Honcho dialectic user modeling
Scheduled Tasks	Built-in cron scheduler with delivery to any platform (daily reports, nightly backups, weekly audits)
Subagent Delegation	Spawn isolated subagents for parallel workstreams with restricted toolsets
Execute Code	Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns
Terminal Backends	Local, Docker, SSH, Modal, Daytona, Singularity — run on a $5 VPS or a GPU cluster
Skills	70+ bundled skills across 28 categories, Skills Hub for community discovery, agentskills.io compatibility
Plugins	Drop-in Python plugins with lifecycle hooks (pre_llm_call, post_llm_call, on_session_start, on_session_end)
MCP	Client (connect to MCP servers for extended tools) and Server (expose conversations to MCP clients)
IDE Integration	VS Code, Zed, JetBrains via ACP server with session management and tool streaming
API Server	OpenAI-compatible /v1/chat/completions endpoint for headless integrations
Profiles	Multi-instance support — each profile gets isolated config, memory, sessions, skills, gateway
Security	Command approval system, secret redaction, SSRF protection, PII redaction, injection detection, credential directory protection
RL Training	Atropos environments for batch trajectory generation and agent policy optimization

---

3. Installation & Getting Started

# One-line install (Linux, macOS, WSL2)
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

# After install
source ~/.bashrc    # or: source ~/.zshrc
hermes              # start chatting

# Key commands
hermes model        # Choose LLM provider and model
hermes tools        # Configure which tools are enabled
hermes config set   # Set individual config values
hermes gateway      # Start the messaging gateway
hermes setup        # Run the full setup wizard
hermes update       # Update to latest version
hermes doctor       # Diagnose any issues

For development:

git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv venv --python 3.11
source venv/bin/activate
uv pip install -e ".[all,dev]"
python -m pytest tests/ -q    # ~3000 tests

---

4. Project Structure

hermes-agent/
├── run_agent.py              # AIAgent class — core conversation loop
├── model_tools.py            # Tool orchestration, _discover_tools(), handle_function_call()
├── toolsets.py               # Toolset definitions, _HERMES_CORE_TOOLS list
├── toolset_distributions.py  # Toolset sampling distributions for RL
├── cli.py                    # HermesCLI class — interactive CLI orchestrator
├── hermes_state.py           # SessionDB — SQLite session store (FTS5 search)
├── hermes_constants.py       # Shared constants, get_hermes_home()
├── hermes_time.py            # Timezone handling
├── utils.py                  # Shared utility functions
├── batch_runner.py           # Parallel batch processing
├── trajectory_compressor.py  # Trajectory compression for RL training
├── mcp_serve.py              # MCP server mode entry point
├── mini_swe_runner.py        # Minimal SWE benchmark runner
├── rl_cli.py                 # RL CLI commands
│
├── agent/                    # Agent internals
│   ├── prompt_builder.py         # System prompt assembly
│   ├── context_compressor.py     # Auto context compression
│   ├── prompt_caching.py         # Anthropic prompt caching
│   ├── auxiliary_client.py       # Auxiliary LLM client (vision, summarization)
│   ├── model_metadata.py         # Model context lengths, token estimation
│   ├── models_dev.py             # models.dev registry integration
│   ├── display.py                # KawaiiSpinner, tool preview formatting
│   ├── skill_commands.py         # Skill slash commands (shared CLI/gateway)
│   └── trajectory.py             # Trajectory saving helpers
│
├── hermes_cli/               # CLI subcommands and setup
│   ├── main.py               # Entry point — all `hermes` subcommands
│   ├── config.py             # DEFAULT_CONFIG, OPTIONAL_ENV_VARS, migration
│   ├── commands.py           # Slash command definitions + SlashCommandCompleter
│   ├── callbacks.py          # Terminal callbacks (clarify, sudo, approval)
│   ├── setup.py              # Interactive setup wizard
│   ├── skin_engine.py        # Skin/theme engine
│   ├── skills_config.py      # `hermes skills` — skill management
│   ├── tools_config.py       # `hermes tools` — tool management
│   ├── skills_hub.py         # Skills Hub integration
│   ├── models.py             # Model catalog, provider model lists
│   ├── model_switch.py       # Shared /model switch pipeline
│   └── auth.py               # Provider credential resolution
│
├── tools/                    # Tool implementations (one file per tool)
│   ├── registry.py           # Central tool registry
│   ├── approval.py           # Dangerous command detection
│   ├── terminal_tool.py      # Terminal/shell execution
│   ├── process_registry.py   # Background process management
│   ├── file_tools.py         # File read/write/search/patch
│   ├── web_tools.py          # Web search/extract
│   ├── browser_tool.py       # Browser automation
│   ├── code_execution_tool.py # execute_code sandbox
│   ├── delegate_tool.py      # Subagent delegation
│   ├── mcp_tool.py           # MCP client integration
│   ├── skills_tool.py        # Skill management tool
│   ├── todo_tool.py          # Todo/task tracking tool
│   ├── memory_tool.py        # Memory read/write tool
│   ├── tts_tool.py           # Text-to-speech
│   ├── vision_tool.py        # Image analysis
│   ├── image_gen_tool.py     # Image generation
│   └── environments/         # Terminal backends
│       ├── base.py               # BaseEnvironment ABC
│       ├── local.py              # Local execution
│       ├── docker.py             # Docker containers
│       ├── ssh.py                # SSH remote execution
│       ├── modal.py              # Modal serverless
│       ├── managed_modal.py      # Nous-hosted Modal
│       ├── daytona.py            # Daytona serverless
│       ├── singularity.py        # Singularity HPC containers
│       └── persistent_shell.py   # Persistent shell mixin
│
├── gateway/                  # Messaging platform gateway
│   ├── run.py                # GatewayRunner — main message loop
│   ├── session.py            # SessionStore — conversation persistence
│   ├── status.py             # Gateway status, token locks
│   └── platforms/            # 16 platform adapters
│       ├── base.py               # BasePlatformAdapter ABC
│       ├── telegram.py           # Telegram (polling + webhook)
│       ├── discord.py            # Discord
│       ├── slack.py              # Slack
│       ├── whatsapp.py           # WhatsApp
│       ├── matrix.py             # Matrix (E2EE)
│       ├── signal.py             # Signal
│       ├── email.py              # Email (IMAP/SMTP)
│       ├── homeassistant.py      # Home Assistant
│       ├── sms.py                # SMS (Twilio)
│       ├── mattermost.py         # Mattermost
│       ├── dingtalk.py           # DingTalk
│       ├── feishu.py             # Feishu/Lark
│       ├── wecom.py              # WeCom (Enterprise WeChat)
│       ├── webhook.py            # Generic webhook
│       └── api_server.py         # OpenAI-compatible API server
│
├── acp_adapter/              # ACP server (IDE integration)
│   ├── server.py             # HermesACPAgent class
│   ├── session.py            # SessionManager
│   ├── events.py             # Streaming callbacks
│   ├── permissions.py        # Approval callbacks
│   └── entry.py              # Entry point
│
├── cron/                     # Scheduler
│   ├── scheduler.py          # tick() — job execution engine
│   └── jobs.py               # Job storage and CRUD
│
├── plugins/                  # Plugin system
│   └── memory/               # 8 memory provider plugins
│       ├── openviking/
│       ├── mem0/
│       ├── hindsight/
│       ├── holographic/
│       ├── honcho/
│       ├── retaindb/
│       └── byterover/
│
├── environments/             # RL training environments (Atropos)
│   ├── hermes_base_env.py    # Abstract base RL environment
│   ├── agent_loop.py         # HermesAgentLoop — rollout execution
│   ├── tool_context.py       # ToolContext — sandbox for RL
│   ├── web_research_env.py   # Web research tasks
│   └── agentic_opd_env.py    # Observation-Prediction-Demo env
│
├── skills/                   # 70+ bundled skills across 28 categories
├── optional-skills/          # Additional optional skills
├── tests/                    # ~3000 pytest tests
├── scripts/                  # Install, update, packaging scripts
├── docker/                   # Docker build files
├── docs/                     # Documentation source (Docusaurus)
├── website/                  # Landing page
├── desktop/                  # Desktop app (Electron, separate repo)
├── tinker-atropos/           # RL submodule
│
├── pyproject.toml            # Python package config
├── AGENTS.md                 # Developer guide for AI assistants
├── RELEASE_v0.2.0.md → v0.7.0.md  # Release notes
└── cli-config.yaml.example   # Example config

User config directory: ~/.hermes/

~/.hermes/
├── config.yaml           # User settings
├── .env                  # API keys and secrets
├── MEMORY.md             # Persistent agent memory
├── USER.md               # User profile
├── SOUL.md               # Agent personality/identity
├── sessions.db           # SQLite session database
├── skills/               # User-installed skills
├── skins/                # Custom CLI themes
├── plugins/              # User plugins
├── cron/                 # Cron jobs and output
│   ├── jobs.json
│   └── output/
├── cache/                # Image/audio cache
├── plans/                # Generated plans
├── profiles/             # Multi-instance profiles
└── mcp/                  # MCP server configs

---

5. Core Architecture

5.1 AIAgent Class (run_agent.py)

The AIAgent class is the heart of the system — the core conversation loop that orchestrates LLM calls, tool execution, context management, and response delivery.

Constructor (~60 parameters):

class AIAgent:
    def __init__(self,
        model: str = "anthropic/claude-opus-4.6",
        max_iterations: int = 90,
        enabled_toolsets: list = None,
        disabled_toolsets: list = None,
        quiet_mode: bool = False,
        save_trajectories: bool = False,
        platform: str = None,            # "cli", "telegram", etc.
        session_id: str = None,
        session_db: SessionDB = None,
        skip_context_files: bool = False,
        skip_memory: bool = False,
        base_url: str = None,
        api_key: str = None,
        provider: str = None,
        api_mode: str = "chat_completions",  # or "anthropic_messages" or "codex_responses"
        tool_progress_callback = None,
        stream_delta_callback = None,
        thinking_callback = None,
        status_callback = None,
        iteration_budget: IterationBudget = None,
        credential_pool = None,
        checkpoints_enabled: bool = False,
        # ... plus provider, routing, callback params
    )

Main Methods:

Method	Returns	Purpose
chat(message, stream_callback)	str	Simple interface — returns final response text
run_conversation(user_message, system_message, conversation_history, task_id)	dict	Full interface — returns {final_response, messages, completed, api_calls, error}
_interruptible_api_call(api_kwargs)	Response	Runs API request in background thread with interrupt support
_interruptible_streaming_api_call(api_kwargs, on_first_delta)	Response	Streaming variant with delta callbacks

The Core Agent Loop (inside run_conversation()):

while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
    response = client.chat.completions.create(
        model=model, messages=messages, tools=tool_schemas
    )
    if response.tool_calls:
        for tool_call in response.tool_calls:
            result = handle_function_call(tool_call.name, tool_call.args, task_id)
            messages.append(tool_result_message(result))
        api_call_count += 1
    else:
        return response.content  # Final text response

Key Behaviors:

• Three API modes: chat_completions (OpenAI-compatible), anthropic_messages (Anthropic SDK), codex_responses (OpenAI Codex)
• Parallel tool execution: Independent tool calls run concurrently via ThreadPoolExecutor (unless they share file paths or are in the never-parallel list)
• Interrupt support: Background threads allow interrupt detection without blocking on HTTP
• Error recovery: Automatic fallback chain (primary → fallback model), retry with exponential backoff, context compression on token overflow
• Budget pressure: Warnings at 70% (caution) and 90% (urgent) of iteration budget
• Oversized results: Tool results >100K chars are saved to temp files with a preview
• Stale connection detection: 90s timeout for streaming, 60s read timeout

IterationBudget (thread-safe):

class IterationBudget:
    def consume() -> bool    # Check and consume one iteration
    def refund()             # Give back iteration (for execute_code turns)
    @property remaining      # Remaining iterations

---

5.2 Tool Orchestration (model_tools.py)

Bridges the agent and tool registry — handles discovery, schema generation, and dispatch.

Key Functions:

Function	Purpose
_discover_tools()	Imports all tool modules (each calls registry.register() on import)
get_tool_definitions(enabled_toolsets, disabled_toolsets, quiet_mode)	Returns OpenAI-format tool schemas filtered by toolset
handle_function_call(function_name, function_args, task_id, user_task, enabled_tools)	Main dispatcher — routes calls to registry with arg coercion
coerce_tool_args(tool_name, args)	Type coercion for LLM-generated arguments (string→int, string→bool, etc.)

Tool Discovery Order:

1. Static tools (web_tools, terminal_tool, file_tools, browser_tool, etc.)
2. Optional tools (fal_client for image gen, honcho, etc.) — graceful fallback if missing
3. Plugin-registered tools
4. MCP server tools (dynamic, via tools/list_changed notifications)

Special Tool Handling:

• Agent-level tools (todo, memory, session_search, delegate_task): Intercepted by run_agent.py before handle_function_call()
• execute_code: Passes enabled_tools for sandbox tool list
• Dynamic schema adjustments: browser_navigate strips web_search reference if tools unavailable

Async Bridging:

• Persistent event loops (not asyncio.run()) to prevent "Event loop is closed" errors
• Main thread uses shared loop; worker threads get per-thread loops
• _run_async() detects running loop and spins up disposable thread if needed

---

5.3 Toolset System (toolsets.py)

Provides flexible tool grouping and composition.

Core Toolsets:

Toolset	Tools Included
web	web_search, web_extract, web_crawl
terminal	terminal
file	read_file, write_file, edit_file, list_files, search_files
browser	browser_navigate, browser_snapshot, browser_click, browser_type, browser_scroll, browser_extract
vision	analyze_image
image_gen	generate_image
tts	text_to_speech
todo	todo_read, todo_write
memory	memory_read, memory_write
session_search	session_search
delegation	delegate_task
code_execution	execute_code
cronjob	create_job, list_jobs, delete_job
messaging	send_message
homeassistant	ha_get_states, ha_call_service, ...

Composite Toolsets:

• hermes-cli — All core tools for CLI platform
• hermes-telegram, hermes-discord, etc. — Platform-specific tool sets
• hermes-gateway — Union of all platform tools
• debugging — terminal + file + web
• safe — Everything except terminal

Resolution:

resolve_toolset(name, visited=None) → List[str]
# Recursively resolves toolset to tool names
# Handles composition (includes) and cycle detection
# Special aliases: "all" or "*" = all tools

---

5.4 Tool Registry (tools/registry.py)

Singleton managing all tool schemas and handlers. Circular-import safe — has no tool dependencies.

ToolEntry (per-tool metadata):

@dataclass(slots=True)
class ToolEntry:
    name: str
    toolset: str
    schema: dict           # OpenAI-format tool definition
    handler: Callable      # Sync or async handler function
    check_fn: Callable     # Returns True if tool is available
    requires_env: list     # Required environment variables
    is_async: bool
    description: str
    emoji: str

Key Methods:

registry.register(name, toolset, schema, handler, check_fn, requires_env)
registry.get_definitions(tool_names, quiet)  # Returns filtered schemas
registry.dispatch(name, args, **kwargs)      # Execute with async bridging
registry.deregister(name)                    # Remove (for MCP tool refresh)
registry.check_tool_availability()           # Returns (available, unavailable)

---

5.5 Session Database (hermes_state.py)

SQLite-based persistent session storage with FTS5 full-text search.

Schema (v6):

-- Sessions table
sessions (
    id TEXT PRIMARY KEY,
    source TEXT, user_id TEXT, model TEXT, model_config TEXT,
    system_prompt TEXT, parent_session_id TEXT,
    started_at TEXT, ended_at TEXT, end_reason TEXT,
    message_count INTEGER, tool_call_count INTEGER,
    input_tokens INTEGER, output_tokens INTEGER,
    cache_read_tokens INTEGER, cache_write_tokens INTEGER, reasoning_tokens INTEGER,
    estimated_cost_usd REAL, actual_cost_usd REAL,
    title TEXT  -- UNIQUE INDEX
)

-- Messages table
messages (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    session_id TEXT, role TEXT, content TEXT,
    tool_call_id TEXT, tool_calls TEXT,  -- JSON
    tool_name TEXT, timestamp TEXT,
    token_count INTEGER, finish_reason TEXT,
    reasoning TEXT, reasoning_details TEXT, codex_reasoning_items TEXT
)

-- FTS5 virtual table (auto-synced via triggers)
messages_fts (content)

Concurrency Model:

• WAL (Write-Ahead Logging) for concurrent readers + single writer
• BEGIN IMMEDIATE for write transactions (lock at start, not commit)
• Jitter retry on lock: 20-150ms random backoff, max 15 retries
• Periodic WAL checkpoint every 50 writes

Key Operations:

• create_session(), end_session(), reopen_session()
• add_message(), get_messages()
• search_sessions(query) — FTS5 full-text search
• update_token_counts() — Supports both incremental (CLI) and absolute (gateway) modes

---

5.6 Constants & Home Directory (hermes_constants.py)

Import-safe constants module with no circular dependencies.

get_hermes_home() → Path          # HERMES_HOME env var or ~/.hermes
display_hermes_home() → str       # User-friendly display: "~/.hermes"
get_optional_skills_dir() → Path  # HERMES_OPTIONAL_SKILLS env var
parse_reasoning_effort(str) → Dict  # "high" → {"enabled": True, "effort": "high"}

# Key constants
OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
NOUS_API_BASE_URL = "https://inference-api.nousresearch.com/v1"
AI_GATEWAY_BASE_URL = "https://ai-gateway.vercel.sh/v1"
VALID_REASONING_EFFORTS = ("xhigh", "high", "medium", "low", "minimal")

---

6. CLI System

6.1 Interactive CLI (cli.py)

The HermesCLI class provides the interactive terminal interface.

Features:

• Rich for banner/panels, prompt_toolkit for input with autocomplete
• KawaiiSpinner — animated faces during API calls, ┊ activity feed for tool results
• Multiline editing with Shift+Enter
• Slash-command autocomplete
• Session history with up/down arrow navigation
• Clipboard image paste (Alt+V / Ctrl+V)
• Status bar showing model, provider, and token counts
• Inline diff previews for file write/patch operations

Configuration Loading:

load_cli_config() → dict
# Loads from ~/.hermes/config.yaml (or ./cli-config.yaml fallback)
# Merges with hardcoded defaults
# Expands ${ENV_VAR} references
# Maps terminal config → env vars

---

6.2 CLI Entry Point (hermes_cli/main.py)

All hermes subcommands are dispatched from here:

hermes                    # Default: interactive chat
hermes chat               # Explicit interactive mode
hermes gateway start|stop|status|install|uninstall
hermes setup              # Setup wizard
hermes model              # Select model/provider
hermes tools              # Configure tools
hermes skills             # Manage skills
hermes config set|get     # Direct config manipulation
hermes cron list|delete   # Cron job management
hermes doctor             # Diagnose issues
hermes sessions browse    # Session picker
hermes profile create|list|switch|delete|export|import
hermes mcp serve|add|remove  # MCP management
hermes acp                # Start ACP server
hermes update|uninstall|version

Profile System:

• _apply_profile_override() runs BEFORE any imports to set HERMES_HOME
• Pre-parses --profile/-p from argv
• Allows fully isolated agent instances with separate config, memory, sessions, skills

---

6.3 Configuration System (hermes_cli/config.py)

Key Configuration Sections:

model: "anthropic/claude-opus-4.6" # or dict with provider/base_url/api_key
providers: {} # Provider-specific configs
fallback_providers: [] # Ordered failover list
credential_pool: {} # Multiple API keys per provider

agent:
  max_turns: 90
  gateway_timeout: 1800
  tool_use_enforcement: "auto"

terminal:
  backend: "local" # local|docker|modal|daytona|ssh|singularity
  timeout: 180
  persistent_shell: true
  docker_image: "nikolaik/python-nodejs:..."

compression:
  enabled: true
  threshold: 0.50 # Compress when 50% of context used
  target_ratio: 0.20 # Summary = 20% of compressed content
  protect_last_n: 20

auxiliary:
  vision: { provider, model }
  web_extract: { provider, model }
  compression: { provider, model }

memory:
  memory_enabled: true
  provider: "" 
   


   
   
   
   
   


    


   
    


   

 {} 


   [] 


    

Config Files:

• ~/.hermes/config.yaml — User settings (authoritative)
• ~/.hermes/.env — API keys and secrets
• Config version migration system (currently v5)

---

6.4 Slash Command Registry (hermes_cli/commands.py)

All slash commands defined centrally in COMMAND_REGISTRY:

CommandDef(name, description, category, aliases, args_hint, cli_only, gateway_only)

Derived automatically by:

• CLI process_command() — dispatch on canonical name
• Gateway dispatch + help
• Telegram BotCommand menu
• Slack /hermes subcommands
• Autocomplete + help text

Key Commands:

Command	Aliases	Description
/new	/reset	Start fresh conversation
/model		Show/switch model
/personality		Set agent personality
/retry		Retry last turn
/undo		Remove last turn
/compress	/compact	Compress context
/usage	/cost	Show token usage
/insights		Usage analytics
/skills		Browse/install skills
/background	/bg	Manage background processes
/plan		Generate implementation plan
/rollback		Restore filesystem checkpoint
/verbose		Toggle debug output
/reasoning		Set reasoning effort
/yolo		Toggle approval bypass
/btw		Ephemeral side question
/stop		Kill current agent run
/queue		Queue next prompt
/browser		Interactive browser session
/history	/resume	Session browser
/skin		Switch CLI theme

---

6.5 Setup Wizard (hermes_cli/setup.py)

Modular interactive wizard with independent sections:

1. Model & Provider — Select AI provider, enter API keys, choose model
2. Terminal Backend — Choose execution environment
3. Agent Settings — Max iterations, compression, session policies
4. Messaging Platforms — Configure Telegram, Discord, Slack, etc.
5. Tools — TTS, STT, web search, image generation, browser

Features:

• Live credential validation
• Real-time model list fetching from provider APIs
• Automatic OpenClaw migration detection
• Atomic config file writes

---

6.6 Model Catalog (hermes_cli/models.py)

Provider-specific model lists:

_PROVIDER_MODELS = {
    "nous": ["anthropic/claude-opus-4.6", "anthropic/claude-sonnet-4.6", ...],  # 25+
    "openrouter": ["anthropic/claude-opus-4.6", "google/gemini-3-flash", ...],  # 30+
    "anthropic": ["claude-opus-4-6", "claude-sonnet-4-6", ...],
    "openai": ["gpt-5", "gpt-5.4-mini", "gpt-4.1", "gpt-4o", ...],
    "copilot": ["gpt-5.4", "gpt-5.4-mini", "gpt-5.3-codex", ...],
    "huggingface": [...],
    "minimax": [...],
    "kimi-coding": [...],
    "alibaba": [...],
    "deepseek": [...],
    # ... more providers
}

Features:

• Dynamic fetching via provider /models endpoints
• Curated lists used when live probe returns fewer models
• Fuzzy matching for typo correction
• Validation against provider catalog

---

6.7 Skin/Theme Engine (hermes_cli/skin_engine.py)

Data-driven CLI visual customization — no code changes needed.

Customizable Elements:

Element	Key	Used By
Banner border/title/accent	colors.*	banner.py
Response box border	colors.response_border	cli.py
Spinner faces (waiting/thinking)	spinner.*	display.py
Spinner verbs/wings	spinner.*	display.py
Tool output prefix	tool_prefix	display.py
Per-tool emojis	tool_emojis	display.py
Agent name/welcome/prompt	branding.*	banner.py, cli.py

Built-in Skins: default, ares, mono, slate, poseidon, sisyphus, charizard

User Skins: Drop ~/.hermes/skins/<name>.yaml and activate with /skin <name>

---

7. Tool System

7.1 Terminal Tool (tools/terminal_tool.py)

Shell command execution across multiple backends.

def terminal_tool(
    command: str,
    background: bool = False,
    timeout: Optional[int] = None,
    task_id: Optional[str] = None,
    force: bool = False,        # Skip approval for dangerous commands
    workdir: Optional[str] = None,
    check_interval: Optional[int] = None,  # Background task polling
    pty: bool = False,
) -> str  # JSON result

Features:

• Multi-backend: Selects based on TERMINAL_ENV (local/docker/ssh/modal/daytona/singularity)
• Per-task_id sandboxes with thread-safe creation locks
• Dangerous command routing through approval system
• Background task support with file-based IPC
• Interrupt handling — polls is_interrupted() during execution
• Auto-cleanup daemon thread for idle environments (>300s)
• Disk usage warnings at configurable threshold

---

7.2 File Tools (tools/file_tools.py)

Safe file operations with size guards and sensitive path protection.

• read_file_tool(path, offset, limit) — Read with pagination (default 100K char limit)
• write_file_tool(path, content) — Write with approval for sensitive paths
• edit_file_tool(path, old_text, new_text) — String replacement editing
• list_files_tool(path) — Directory listing
• search_files_tool(pattern, path) — Glob/regex file search

Safety:

• Device path blocklist (/dev/zero, /dev/stdin, etc.)
• Read dedup tracking — returns stub on re-read if mtime unchanged
• Sensitive path blocking: /etc/, /boot/, ~/.ssh without approval
• Prompt injection protection for known dangerous paths

---

7.3 Web Tools (tools/web_tools.py)

Web search and content extraction.

• web_search_tool(query, limit) — Search via configurable backend
• web_extract_tool(urls, format) — Extract content from URLs
• web_crawl_tool(url, instructions) — LLM-guided web crawling

Backends: Firecrawl, Parallel Web, Tavily, Exa, DuckDuckGo

• Fallback detection by highest-priority available API key
• LLM processing via auxiliary client (Gemini 3 Flash) for intelligent extraction
• SSRF protection and URL safety checks

---

7.4 Browser Tool (tools/browser_tool.py)

Headless browser automation with accessibility tree.

browser_navigate(url, task_id) → str
browser_snapshot(task_id, max_chars) → str
browser_click(ref, task_id) → str      # Element refs like @e1, @e2
browser_type(ref, text, task_id) → str
browser_scroll(ref, direction, task_id) → str
browser_extract(task, max_chars, task_id) → str

Backends:

• Local: Headless Chromium via agent-browser CLI
• Cloud: Browserbase (stealth, proxies, CAPTCHA solving)
• Camofox: Anti-detection browser using Camoufox

Features:

• Accessibility tree for text-based snapshots (no vision required)
• Session isolation per task_id with inactivity cleanup
• API key detection in URLs (prevents exfiltration)
• SSRF protection for private/internal addresses

---

7.5 Delegate Tool (tools/delegate_tool.py)

Spawn isolated subagents for parallel workstreams.

def delegate_task(
    goal: str,
    context: str = None,
    toolsets: List[str] = None,       # Default: ["terminal", "file", "web"]
    tasks: List[Dict] = None,          # Batch mode: up to 3 concurrent
    max_iterations: int = 50,
    parent_agent = None,
) → str

Isolation:

• Fresh conversation (no parent history)
• Own task_id (separate terminal session, file ops)
• Restricted toolset (configurable, blocked tools always stripped)
• Focused system prompt from goal + context
• Parent only sees delegation call and summary result

Blocked Tools: delegate_task, clarify, memory, send_message, execute_code (no recursion, no user interaction)

Constraints: MAX_DEPTH=2, MAX_CONCURRENT_CHILDREN=3

---

7.6 MCP Tool (tools/mcp_tool.py)

Model Context Protocol client integration.

Configuration (config.yaml):

mcp_servers:
  filesystem:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
    timeout: 120
  remote_api:
    url: "https://my-mcp-server.example.com/mcp"
    headers:
      Authorization: "Bearer sk-..."
    sampling:
      enabled: true
      model: "gemini-3-flash"

Features:

• Transports: Stdio (local processes) and HTTP/StreamableHTTP (remote)
• Dynamic tool discovery: Listens for tools/list_changed notifications
• Sampling support: MCP servers can request LLM completions
• Automatic reconnection with exponential backoff (5 retries)
• Dedicated background event loop in daemon thread
• Thread-safe via lock protecting server dict

---

7.7 Approval System (tools/approval.py)

Dangerous command detection and approval flow.

detect_dangerous_command(command: str) → (is_dangerous, pattern_key, description)

106 patterns covering:

• Destructive operations (rm -r /, mkfs, dd if=)
• Privilege escalation (chmod 777, chown -R root)
• SQL injection (DROP TABLE, DELETE FROM without WHERE)
• System targeting (/etc/ writes, systemctl stop, fork bombs)
• Shell injection (pipe to sh, wget to sh, process substitution)
• Network exfiltration (curl with embedded API keys)
• Secret access (cat ~/.env, cat ~/.netrc)

Normalization: Strips ANSI escapes, null bytes, normalizes Unicode

Approval Modes:

• smart — Learns which commands are safe based on user decisions
• always — Always ask for dangerous commands
• off — Never ask (or /yolo toggle)

---

7.8 Terminal Backends (tools/environments/)

Backend	File	Features
Local	local.py	Direct execution, interrupt support, non-blocking I/O, persistent shell
Docker	docker.py	Sandboxed containers, cap-drop ALL, no-new-privileges, PID limits, bind mounts
SSH	ssh.py	Remote execution via ControlMaster, persistent shell mixin
Modal	modal.py	Native Modal SDK Sandbox.create()/Sandbox.exec(), persistent snapshots
Managed Modal	managed_modal.py	Modal through Nous-hosted gateway
Daytona	daytona.py	Daytona SDK cloud sandboxes, stop/resume lifecycle
Singularity	singularity.py	Singularity containers with scratch dir, SIF cache

Common Interface (BaseEnvironment ABC):

execute(command, timeout) → {"output": str, "returncode": int}
cleanup() → None

---

8. Agent Internals

8.1 Prompt Builder (agent/prompt_builder.py)

Assembles the system prompt from multiple sources:

Component	Source
Agent Identity	DEFAULT_AGENT_IDENTITY or SOUL.md
Memory Guidance	When/how to use memory tool
Session Search Guidance	How to recall past conversations
Skills Guidance	When to create/patch skills
Tool Use Enforcement	Must execute tools, not describe actions
Skills Index	~/.hermes/skills/.hermes-skills.json
Platform Hints	OS, Python version, shell, available tools
Context Files	.hermes.md, AGENTS.md, .cursorrules, SOUL.md
Model/Provider Info	Current model and provider identity

Context File Discovery:

1. Check cwd/.hermes.md or HERMES.md
2. Walk parent directories up to git root
3. Validate against injection patterns before inclusion

Injection Detection (30+ patterns):

• "ignore previous instructions", "system prompt override"
• "do not tell the user", "act as if you have no restrictions"
• HTML comment injection, exfiltration via curl, Unicode stealth chars

---

8.2 Context Compressor (agent/context_compressor.py)

Automatically compresses conversation history when approaching context limits.

class ContextCompressor:
    threshold_percent: float = 0.50   # Compress when 50% of context used
    protect_first_n: int = 3          # Keep system prompt + first turn
    protect_last_n: int = 20          # Keep recent N messages
    summary_target_ratio: float = 0.20  # Summary = 20% of compressed content

Compression Algorithm:

1. Pre-pass: Replace old tool results with placeholder (cheap)
2. Protect head: System prompt + first exchange always kept
3. Protect tail: Token-budget-based tail protection (~20K tokens)
4. Summarize middle: LLM-generated structured summary (Goal, Progress, Decisions, Files, Next Steps)
5. Iterative updates: On subsequent compressions, update previous summary instead of re-summarizing

---

8.3 Prompt Caching (agent/prompt_caching.py)

Anthropic prompt caching support for cost reduction.

• System prompt cached across turns (first conversation turn establishes cache)
• Cache markers inserted at system prompt boundaries
• Must not break mid-conversation — altering past context invalidates cache
• The ONLY time context is altered is during compression

Critical Rule: Do NOT implement changes that would alter past context, change toolsets, reload memories, or rebuild system prompts mid-conversation.

---

8.4 Auxiliary Client (agent/auxiliary_client.py)

Separate LLM client for non-primary tasks:

• Vision analysis — Image description and analysis
• Web extraction — Content summarization
• Context compression — Generating conversation summaries
• Session search — Summarizing search results
• MCP sampling — Serving server-initiated LLM requests

Configured per-task via auxiliary section in config.yaml.

---

8.5 Display & Spinner (agent/display.py)

• KawaiiSpinner — Animated face characters during API calls
• Tool preview formatting — ┊ prefixed activity feed for tool execution
• Inline diff previews — Shows unified diffs for write/patch operations
• Respects active skin for colors, emojis, and branding

---

8.6 Skill Commands (agent/skill_commands.py)

Shared skill invocation for CLI and gateway:

• Skills loaded from ~/.hermes/skills/ and external directories
• Injected as user message (not system prompt) to preserve prompt caching
• /plan command generates implementation plans stored in .hermes/plans/
• Skill content includes setup instructions, tool options, usage examples

---

9. Messaging Gateway

9.1 GatewayRunner (gateway/run.py)

Main controller managing all platform adapters and routing messages.

Key Attributes:

• adapters: Dict[Platform, BasePlatformAdapter] — Active platform instances
• session_store: SessionStore — Conversation persistence
• _running_agents: Dict[str, AIAgent] — Per-session cached agents (preserves prompt caching)
• _pending_approvals: Dict[str, Dict] — Dangerous command approval tracking
• pairing_store: PairingStore — DM code-based user authorization

Message Flow:

1. Platform adapter queues MessageEvent
2. GatewayRunner dequeues, calls _handle_message()
3. Slash command? → dispatch to handler
4. Regular message? → _handle_message_with_agent() (async, with per-session locking)
5. Response delivered back via platform adapter

Features:

• Agent caching per session (preserves Anthropic prompt cache across turns)
• Session reset policies (inactivity timeout, hard reset)
• Per-session model overrides via /model
• Background memory flush on session expiry
• Approval routing (/approve, /deny) with interactive buttons (Discord)
• 30+ slash command handlers

---

9.2 Session Store (gateway/session.py)

SessionSource — Where a message originated (platform, chat_id, user info) SessionContext — Full context for system prompt injection (platforms, home channels, metadata) SessionStore — Loads/saves conversation transcripts as JSON files

~/.hermes/sessions/{session_key}.json
Format: [{role, content, timestamp}, ...]

Features:

• Session key computed from platform + chat_id + thread_id (deterministic hash)
• Inactivity timeout for session resets
• PII redaction (phone numbers hashed)
• Survives gateway restarts

---

9.3 Platform Adapters (gateway/platforms/)

16 adapters sharing BasePlatformAdapter interface:

Platform	Key Features
Telegram	Polling + webhook mode, media handling, inline keyboards, forum topic isolation, group mention gating
Discord	Server channels, threads, reactions (processing/done/error), button-based approval, @mention requirement
Slack	Multi-workspace OAuth, thread handling, app_mention, /hermes subcommands
WhatsApp	Group & DM support, media captions, LID↔phone alias resolution
Matrix	E2EE room encryption, threaded messages, trusted device flow, native voice messages
Signal	Encrypted DMs, group membership, SSE keepalive, phone URL encoding
Email	IMAP/SMTP, multi-recipient, skip_attachments option
Home Assistant	REST tools + WebSocket, service discovery, smart home automation
SMS	Twilio integration
Mattermost	Self-hosted Slack alternative, configurable mention behavior
DingTalk	Alibaba enterprise messaging
Feishu/Lark	Enterprise messaging, message cards, approval workflows
WeCom	Enterprise WeChat, department management
Webhook	Generic HTTP POST for custom integrations
API Server	OpenAI-compatible /v1/chat/completions endpoint

Common Features:

• Image/audio caching for vision and STT tools
• Rate limiting with exponential backoff
• Session routing with authorization rules
• Cross-platform conversation continuity

---

10. Cron Scheduling

Built-in job scheduler running in the gateway background thread.

Schedule Types:

• "once in 5m" — One-shot after duration
• "every 30m" — Recurring interval
• "0 9 * * *" — Standard cron expression
• "2026-04-06T14:00" — Absolute datetime

Job Storage: ~/.hermes/cron/jobs.json

Execution Flow:

1. tick() called every 60s from gateway background thread
2. Fetch due jobs past next_run_at
3. Spawn hermes CLI subprocess with job prompt + skills
4. Capture output → save to ~/.hermes/cron/output/{job_id}/{timestamp}.md
5. Deliver to target platform (or stay local)

Delivery Targets:

• "local" — Output saved locally only
• "origin" — Send to originating chat
• "telegram:<chat_id>" — Explicit platform/chat routing
• [SILENT] prefix — Suppress delivery but keep logs

Grace Windows: Based on schedule frequency (120s–2hrs) to handle missed jobs

---

11. Skills System

Skills are composable, agent-invokable knowledge units.

Structure:

skills/
├── creative/              # ASCII art, diagrams, music
├── software-development/  # Debugging, testing, docs
├── github/                # Codebase inspection, PR workflow
├── research/              # Literature, web scraping
├── productivity/          # Task management
├── media/                 # Image, video, audio processing
├── mlops/                 # ML experiment tracking
├── autonomous-ai-agents/  # Multi-agent orchestration
└── [20+ more categories]

Per-Skill Structure:

skill-name/
├── SKILL.md     # Metadata (YAML frontmatter) + implementation instructions
└── [sub-skills]/

SKILL.md Example:

---
name: ascii-art
description: Generate ASCII art using multiple tools
version: 4.0.0
dependencies: []
metadata:
  hermes:
    tags: [ASCII, Art, Banners, Creative]
    related_skills: [excalidraw]
---
[Implementation instructions, tool options, examples...]

Discovery:

• Auto-discovered from ~/.hermes/skills/ + external dirs
• Skills index built at startup (.hermes-skills.json)
• Loaded as user messages to preserve prompt caching
• Per-platform enable/disable via hermes skills
• Skills Hub (agentskills.io) for community sharing

---

12. Plugin System

Drop Python files into ~/.hermes/plugins/ to extend Hermes.

Plugin Capabilities:

• Register custom tools and toolsets
• Inject messages into conversation
• Lifecycle hooks: pre_llm_call, post_llm_call, on_session_start, on_session_end
• Enable/disable via hermes plugins enable/disable <name>

Memory Provider Plugins (plugins/memory/): 8 implementations: openviking, mem0, hindsight, holographic, honcho, retaindb, byterover

Each implements:

class MemoryProvider:
    def is_available() → bool
    def store(key, value)
    def retrieve(query) → list
    def clear()

---

13. Memory System

Hermes has a pluggable memory provider interface:

Built-in Memory:

• MEMORY.md — Markdown file with persistent facts
• USER.md — User profile information
• Memory read/write tools called by agent during conversation
• Periodic nudges prompt agent to save important information
• FTS5 session search for cross-session recall

Honcho Integration:

• AI-native dialectic user modeling
• Async memory writes
• Profile-scoped host/peer resolution
• Multi-user isolation in gateway mode

Configuration:

memory:
  memory_enabled: true
  provider: "" # "" for built-in, "honcho", "mem0", etc.
  memory_char_limit: 2200

---

14. ACP Server (IDE Integration)

Agent Communication Protocol server for VS Code, Zed, JetBrains.

Entry: hermes acp → acp_adapter/server.py

HermesACPAgent Class:

initialize()                    # Handshake with IDE client
authenticate(method_id)         # Validate credentials
new_session(cwd, mcp_servers)   # Create isolated session
load_session(session_id)        # Resume session
fork_session(session_id)        # Branch for parallel work
list_sessions()                 # Browse all sessions
cancel(session_id)              # Interrupt running agent

Features:

• Slash command support (/model, /tools, /reset, /compact)
• Client-provided MCP servers (IDE's MCP ecosystem flows into agent)
• Streaming callbacks for messages, thinking, tool progress
• Dangerous command approval via IDE UI

---

15. API Server

OpenAI-compatible API endpoint for headless integrations (e.g., Open WebUI).

Endpoint: POST /v1/chat/completions

Features:

• X-Hermes-Session-Id header for persistent sessions
• Tool progress streaming via SSE events
• /api/jobs REST API for cron management
• Input limits, field whitelists, SQLite-backed response persistence
• CORS origin protection

---

16. MCP Server Mode

Expose Hermes conversations to MCP-compatible clients.

Entry: hermes mcp serve

Features:

• Browse conversations and sessions
• Read messages and search across sessions
• Manage attachments
• Supports both stdio and Streamable HTTP transports
• Compatible with Claude Desktop, Cursor, VS Code, etc.

---

17. RL Training Environments

Atropos-based RL training framework for agent policy optimization.

Base Class: HermesAgentBaseEnv (extends atroposlib.BaseEnv)

Configuration:

HermesAgentEnvConfig:
    enabled_toolsets: ["terminal", "file", "web"]
    max_agent_turns: 30
    agent_temperature: 1.0
    terminal_backend: "local"  # or docker/modal for isolation
    dataset_name: str

Subclass Requirements:

• setup() — Load dataset
• get_next_item() — Return next task
• format_prompt() — Convert item → user message
• compute_reward() — Score rollout
• evaluate() — Periodic eval on test set

Example Environments:

• web_research_env.py — Web research tasks
• agentic_opd_env.py — Observation-Prediction-Demonstration
• hermes_swe_env.py — Software engineering tasks

Supporting:

• HermesAgentLoop — Orchestrates step-by-step rollouts
• ToolContext — Sandbox for tool execution, records side effects
• trajectory_compressor.py — Compresses trajectories for training data

---

18. Profiles (Multi-Instance)

Run multiple fully isolated Hermes instances from the same installation.

Commands:

hermes profile create <name>
hermes profile list
hermes profile switch <name>
hermes profile delete <name>
hermes profile export <name>
hermes profile import <file>
hermes -p <name>             # Launch with specific profile

Each profile gets:

• Own HERMES_HOME directory (~/.hermes/profiles/<name>/)
• Own config.yaml, .env, memory, sessions, skills, gateway service
• Token-lock isolation (prevents two profiles sharing bot credentials)

Implementation:

• _apply_profile_override() sets HERMES_HOME env var before any imports
• All 119+ references to get_hermes_home() automatically scope to active profile
• Profile operations are HOME-anchored (~/.hermes/profiles/) for cross-profile visibility

---

19. Security Model

Command Approval

• 106 dangerous command patterns (destructive, privilege escalation, SQL, exfiltration)
• Smart approval mode learns from user decisions
• Session-based approval state (per-session tracking)
• --fuck-it-ship-it flag or /yolo toggle to bypass

Secret Protection

• Secret redaction in logs and tool output (API keys, tokens)
• Browser URL scanning for embedded secrets
• LLM response scanning for exfiltration attempts
• Credential directory protection (.docker, .azure, .config/gh, .ssh)
• execute_code sandbox output redaction

Input Safety

• Prompt injection detection in context files (30+ patterns)
• Unicode stealth character detection
• ANSI escape sequence normalization
• Device path blocklist for file reads
• SSRF protection in web/browser tools

PII Handling

• Optional privacy.redact_pii mode
• Phone number hashing in session metadata
• Sender ID anonymization in gateway logs

Supply Chain

• All dependency version ranges pinned
• uv.lock with hashes for reproducible builds
• CI workflow scanning PRs for supply chain attack patterns
• Compromised litellm dependency removed

---

20. Provider & Model System

Supported Providers

Provider	API Mode	Key Features
Nous Portal	chat_completions	400+ models, first-class setup
OpenRouter	chat_completions	200+ models, provider routing preferences
Anthropic	anthropic_messages	Native prompt caching, OAuth PKCE
OpenAI	chat_completions	GPT-5, Codex
GitHub Copilot	chat_completions	OAuth, 400k context
Hugging Face	chat_completions	Curated agentic model picker
Google (Direct)	chat_completions	Full Gemini context lengths
z.ai/GLM	chat_completions	Chinese LLM models
Kimi/Moonshot	chat_completions	Kimi Code API
MiniMax	anthropic_messages	M2.7 models
Alibaba/DashScope	chat_completions	Qwen models
DeepSeek	chat_completions	V3 models
Vercel AI Gateway	chat_completions	Routing through Vercel
Kilo Code	chat_completions	Custom provider
OpenCode Zen/Go	chat_completions	Custom provider
Custom Endpoint	configurable	Any OpenAI-compatible API

Provider Features

• Ordered fallback chain: Auto-failover across configured providers on errors
• Credential pools: Multiple API keys per provider with least_used rotation
• Per-turn primary restoration: After fallback use, restore primary on next turn
• Context length detection: models.dev integration, provider-aware resolution, /v1/props for llama.cpp
• Rate limit handling: User-friendly 429 messages with Retry-After countdown
• Anthropic long-context tier: Auto-reduces to 200k on tier limit 429

---

21. Streaming & Reasoning

Streaming

• Enabled by default in CLI and gateway
• Token-by-token delivery via stream_delta_callback
• Proper spinner/tool progress display during streaming
• Stale connection detection (90s timeout)
• Fallback to non-streaming if provider doesn't support it

Reasoning/Thinking

• Configurable effort: xhigh, high, medium, low, minimal, none
• /reasoning command to toggle display and effort level
• Anthropic thinking blocks preserved across multi-turn conversations
• <think> tag extraction for compatible models
• Reasoning persisted to SessionDB (v6 schema) for cross-session continuity
• Thinking-budget exhaustion detection to skip useless continuation retries

---

22. Release History

v0.2.0 (March 12, 2026) — The Foundation Release

216 merged PRs from 63 contributors, resolving 119 issues

• Multi-platform messaging gateway (Telegram, Discord, Slack, WhatsApp, Signal, Email, Home Assistant)
• MCP client support (stdio + HTTP)
• 70+ skills across 15+ categories
• Centralized provider router (call_llm() API)
• ACP server for IDE integration
• CLI skin/theme engine
• Git worktree isolation (hermes -w)
• Filesystem checkpoints and /rollback
• 3,289 tests

v0.3.0 (March 17, 2026) — Streaming, Plugins, Providers

• Unified streaming infrastructure (token-by-token delivery)
• First-class plugin architecture (~/.hermes/plugins/)
• Native Anthropic provider with prompt caching
• Smart approvals + /stop command
• Honcho memory integration
• Voice mode (CLI push-to-talk, Telegram/Discord voice)
• Concurrent tool execution (ThreadPoolExecutor)
• PII redaction
• /browser connect via Chrome DevTools Protocol
• Vercel AI Gateway provider
• Persistent shell mode for local/SSH backends
• Agentic On-Policy Distillation RL environment

v0.4.0 (March 23, 2026) — Platform Expansion

• OpenAI-compatible API server (/v1/chat/completions)
• 6 new messaging adapters (Signal rewrite, DingTalk, SMS, Mattermost, Matrix, Webhook)
• @file and @url context references with tab completion
• 4 new providers (GitHub Copilot, Alibaba, Kilo Code, OpenCode)
• MCP server management CLI with OAuth 2.1 PKCE
• Gateway prompt caching (dramatic cost reduction)
• Context compression overhaul (structured summaries, iterative updates)
• Streaming enabled by default
• 200+ bug fixes

v0.5.0 (March 28, 2026) — Hardening

• Nous Portal expanded to 400+ models
• Hugging Face as first-class provider
• Telegram Private Chat Topics
• Native Modal SDK backend (replaced swe-rex)
• Plugin lifecycle hooks activated
• GPT model tool-use enforcement
• Nix flake with NixOS module
• Supply chain hardening (removed litellm, pinned deps, CI scanning)
• Anthropic per-model output limits (128K for Opus 4.6)

v0.6.0 (March 30, 2026) — Multi-Instance

95 PRs and 16 resolved issues in 2 days

• Profiles for multiple isolated agent instances
• MCP Server Mode (hermes mcp serve)
• Official Docker container
• Ordered fallback provider chain
• Feishu/Lark platform adapter
• WeCom (Enterprise WeChat) adapter
• Slack multi-workspace OAuth
• Telegram webhook mode + group controls
• Exa search backend
• Skills & credentials on remote backends

v0.7.0 (April 3, 2026) — Resilience

168 PRs and 46 resolved issues

• Pluggable memory provider interface (ABC-based plugin system)
• Same-provider credential pools with automatic rotation
• Camofox anti-detection browser backend
• Inline diff previews in tool activity feed
• API server session continuity + tool streaming
• ACP client-provided MCP servers
• Gateway hardening (race conditions, approval routing, compression death spirals)
• Secret exfiltration blocking (URL scanning, base64 detection)

---

23. File Dependency Chain

hermes_constants.py  (no deps — imported by everything)
       ↑
tools/registry.py  (no tool deps — imported by all tool files)
       ↑
tools/*.py  (each calls registry.register() at import time)
       ↑
model_tools.py  (imports tools/registry + triggers tool discovery)
       ↑
run_agent.py (AIAgent), cli.py (HermesCLI), gateway/run.py (GatewayRunner)
       ↑
hermes_cli/main.py  (entry point — dispatches to all subsystems)

Key Principle: tools/registry.py is circular-import safe. It has no tool dependencies. Tool files import the registry; model_tools.py imports both.

---

24. Key Design Patterns

Pattern	Description
Registry-based Tool System	Single source of truth; plugins register at import time; dynamic (MCP) and static tools coexist
Toolset Composition	Recursive resolution with cycle detection; platform-specific composites
Iteration Budget	Thread-safe shared budget across parent + subagents
Streaming First	Preferred over non-streaming for health checking (stale connection detection)
Prefix Caching	System prompt cached across turns (Anthropic optimization); context never altered mid-conversation
Proactive Compression	Triggered at 50% context usage; structured summaries with iterative updates
Async Bridging	Persistent event loops prevent "Event loop is closed"; per-thread loops for workers
Profile Isolation	HERMES_HOME env var set before imports; all state functions route through get_hermes_home()
Agent Caching	Gateway caches AIAgent per session to preserve prompt cache across turns
WAL Concurrency	SQLite WAL mode + jitter retry for concurrent readers + single writer
Plugin Architecture	Tools, toolsets, hooks, memory providers extensible via plugins
Multi-Backend Execution	Pluggable terminal backends with unified BaseEnvironment interface
Safety Layers	Approval system → sensitive path guards → injection detection → capability filtering

---

25. Configuration Reference

Environment Variables (key ones)

Variable	Purpose
HERMES_HOME	Override home directory (profiles set this automatically)
OPENROUTER_API_KEY	OpenRouter provider key
ANTHROPIC_API_KEY	Anthropic provider key
OPENAI_API_KEY	OpenAI provider key
NOUS_API_KEY	Nous Portal key
FIRECRAWL_API_KEY	Web search/extraction
EXA_API_KEY	Exa search backend
BROWSERBASE_API_KEY	Cloud browser
FAL_KEY	Image generation
ELEVENLABS_API_KEY	Premium TTS
HONCHO_API_KEY	Honcho memory
TERMINAL_ENV	Terminal backend override
TERMINAL_CWD	Terminal working directory
MESSAGING_CWD	Gateway working directory

Config File Locations

File	Purpose
~/.hermes/config.yaml	Main configuration (YAML)
~/.hermes/.env	API keys and secrets
~/.hermes/MEMORY.md	Persistent agent memory
~/.hermes/USER.md	User profile
~/.hermes/SOUL.md	Agent persona/identity
~/.hermes/sessions.db	SQLite session database
~/.hermes/cron/jobs.json	Cron job definitions
.hermes.md (in project dir)	Per-project context file
AGENTS.md (in project dir)	Developer instructions for agent

---

26. Known Pitfalls

1. DO NOT hardcode ~/.hermes paths — Use get_hermes_home() from hermes_constants. Hardcoding breaks profiles.

2. DO NOT use simple_term_menu — Rendering bugs in tmux/iTerm2 (ghosting). Use curses instead.

3. DO NOT use \033[K (ANSI erase-to-EOL) — Leaks as literal text under prompt_toolkit's patch_stdout. Use space-padding.

4. _last_resolved_tool_names is process-global — Saved/restored around subagent execution in delegate_tool.py.

5. DO NOT hardcode cross-tool references in schemas — Tool may be unavailable. Add dynamic references in get_tool_definitions().

6. Tests must not write to ~/.hermes/ — _isolate_hermes_home autouse fixture redirects to temp dir.

7. Prompt caching must not break — Do NOT alter past context, change toolsets, reload memories, or rebuild system prompts mid-conversation.

8. Working directory behavior differs: CLI uses os.getcwd(), gateway uses MESSAGING_CWD env var.

9. Config has three loaders: load_cli_config() (CLI), load_config() (hermes tools/setup), direct YAML (gateway). They have different merge behaviors.

10. Profile operations are HOME-anchored — _get_profiles_root() returns Path.home() / ".hermes" / "profiles", NOT get_hermes_home() / "profiles". This is intentional for cross-profile visibility.

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This document covers Hermes Agent v0.7.0 as of April 2026. For the latest information, refer to the official documentation and the GitHub repository.