clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.

The question to present to the user.

Choices:
Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

To stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), or platform:chat_id:thread_id for a specific destination. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting.

Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.

Provider name (e.g. 'openrouter', 'anthropic', or 'custom:<name>' for a provider defined in custom_providers config — always include the ':<name>' suffix, never pass the bare 'custom'). Omit to use and pin the current provider.

Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')

Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear.

Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured.

REQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored).

DELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently.

WHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content.

Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear.

Optional list of toolset names to restrict the job's agent to (e.g. ["web", "terminal", "file", "delegation"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use "web" if it calls web_search, "terminal" if it runs scripts, "file" if it reads files, "delegation" if it calls delegate_task. On update, pass an empty array to clear.

Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated.

Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. Batch (parallel): provide 'tasks' array with up to delegation.max_concurrent_children items (default 3, configurable via config.yaml, no hard ceiling). All run concurrently and results are returned together. Nested delegation requires role='orchestrator' and delegation.max_spawn_depth >= 2.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify
- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. "respond in Chinese", "return output in Japanese"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.
- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims "uploaded successfully" or "file written" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.
- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by delegation.max_spawn_depth (default 2) and can be disabled globally via delegation.orchestrator_enabled=false.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'vision', 'web', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks.

Task goal

Task-specific context

Toolsets for this specific task. Available: 'browser', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'vision', 'web', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction.

Per-task ACP command override (e.g. 'claude'). Overrides the top-level acp_command for this task only.

Per-task ACP args override.

Per-task role override. See top-level 'role' for semantics.

Batch mode: tasks to run in parallel (limit configurable via delegation.max_concurrent_children, default 3). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored.

Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Requires delegation.max_spawn_depth >= 2 in config; ignored (treated as 'leaf') when the child would exceed max_spawn_depth or when delegation.orchestrator_enabled=false.

Override ACP command for child agents (e.g. 'claude', 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Enables spawning Claude Code (claude --acp --stdio) or other ACP-capable agents from any parent, including Discord/Telegram/CLI.

Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Example: ['--acp', '--stdio', '--model', 'claude-opus-4-6']

Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).

Use normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.

Available via `from hermes_tools import ...`:

  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).

Scripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout.

memory

Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

patch

Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.

Replace mode (default): find a unique string and replace it.
Patch mode: apply V4A multi-file patches for bulk changes.

Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches

File path to edit (required for 'replace' mode)

Text to find in the file (required for 'replace' mode). Must be unique in the file unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'replace' mode). Can be empty string to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

V4A format patch content (required for 'patch' mode). Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).

list poll log wait kill write submit close

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

Send a message to a connected messaging platform, or list available targets.

IMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.
If the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.

Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms.

Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:<account_id>' (DM), 'yuanbao:group:<group_code>' (group chat)

The message text to send. To send an image or file, include MEDIA:<local_path> (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment.

Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.

TWO MODES:
1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.

Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match ("docker networking"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.

Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/hermes/skills/; existing skills can be modified wherever they live.

Actions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.

On delete, pass `absorbed_into=<umbrella>` when you're merging this skill's content into another one, or `absorbed_into=""` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

Pinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin <name>`. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted.

Content for the file. Required for 'write_file'.

For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent.

Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.

The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans').

OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.
Background: Set background=true to get a session_id. Two patterns:
  (1) Long-lived processes that never exit (servers, watchers).
  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.
For servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.
After starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.
Use process(action="poll") for progress checks, process(action="wait") to block until done.
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.

The command to execute on the VM

Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.

Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.

Working directory for this command (absolute path). Defaults to the session working directory.

Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.

When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.

Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both.

Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.<name>), not model-selected.

The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated.

Optional custom file path to save the audio. Defaults to ~/hermes/audio_cache/<timestamp>.mp3

todo

Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

Inspect an image from a URL, file path, or tool output when you need closer detail than what's visible in the conversation. If the user's image is already attached to the conversation and you can see it, just answer directly — only call this tool for images referenced by URL/path, images returned inside other tool results (browser screenshots, search thumbnails), or when you need a deeper look at a specific region the main model's vision may have missed.

Image URL (http/https) or local file path to analyze.

Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question.

Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file


Du hast ja aktuell ein GMX Postfach. Vergleich es mal damit: https://www.agentmail.to/pricing

Was wäre besser, sollen wir agentmail einrichten?


Explanation:
Der User fragt mich, ob agentmail.to besser wäre als mein aktuelles GMX Postfach, basierend auf deren Pricing-Seite. Ich soll die Seite besuchen und einen Vergleich mit GMX machen.

Lass mich zuerst die Seite besuchen. Da ich im Terminal die URL aufrufen könnte, aber die Skill-Regeln sagen, dass ich skills laden soll, wenn relevant. Hier geht es um einen einfachen Web-Vergleich. Ich kann direkt web_search oder browser verwenden. Da ich keinen "web" tool direkt habe (es gibt "search" und "browser"), schaue ich: ich habe keinen direkten web_search tool. Ich habe search_files, session_search, etc. Aber für Web-Content kann ich terminal mit curl verwenden, oder ich schaue nach, ob es ein "web" toolset gibt. Die Toolsets sind ja oben gelistet.

Ich habe kein dediziertes "web_search" oder "browser" tool in der Tool-Liste. Ich muss also mit curl oder wget arbeiten über terminal.

Warte, lass mich die verfügbaren Tools nochmal prüfen. Die Liste enthält: clarify, cronjob, delegate_task, execute_code, memory, patch, process, read_file, search_files, send_message, session_search, skill_manage, skill_view, skills_list, terminal, text_to_speech, todo, vision_analyze, write_file.

Kein direkter web-Zugriff. Also muss ich curl verwenden im Terminal.

Der User will einen Vergleich zwischen GMX (das ich aktuell für ihn habe) und agentmail.to.

Schritt 1: Besuche https://www.agentmail.to/pricing mit curl.

Dann analysiere ich den Inhalt und vergleiche mit GMX (das ich aus meinem Wissen kenne).

Lass mich das machen.

terminal

curl -sL 'https://www.agentmail.to/pricing' | sed 's/<[^>]*>//g' | tr -s '\n' '\n' | head -n 150


Output:
Pricing | AgentMail((a,b,c,d,e,f,g,h)=>{let i=document.documentElement,j=["light","dark"];function k(b){var c;(Array.isArray(a)?a:[a]).forEach(a=>{let c="class"===a,d=c&&f?e.map(a=>f[a]||a):e;c?(i.classList.remove(...d),i.classList.add(f&&f[b]?f[b]:b)):i.setAttribute(a,b)}),c=b,h&&j.includes(c)&&(i.style.colorScheme=c)}if(d)k(d);else try{let a=localStorage.getItem(b)||c,d=g&&"system"===a?window.matchMedia("(prefers-color-scheme: dark)").matches?"dark":"light":a;k(d)}catch(a){}})("class","theme","dark",null,["light","dark"],null,true,true)We raised $6M in Seed FundingRead more Build Enterprise PricingResources DocsStart for freePricingTransparent pricing that scales with you.Launch offer: Early-stage startups get a free month on our Startup tier.  Apply now FreeFor getting started$0/monthNo credit card required3 inboxes3,000 emails/month3 GB storageGet StartedDeveloperFor individual developers$20/month10 inboxes10,000 emails/month10 GB storage10 custom domainsEmail supportGet StartedStartupFor scaling teams$200/month150 inboxes150,000 emails/month150 GB storage150 custom domainsDedicated IPsSOC 2 reportSlack channel supportGet StartedEnterpriseFor tailored solutionsCustomEverything in Startup tierBulk discountsWhite-label platformUsage-based pricingEU region cloudBYO cloud deploymentOIDC/SAML SSOBook a callCoreFreeDeveloperStartupEnterpriseInboxes310150CustomEmails/month3,00010,000150,000CustomEmails/day100No limitNo limitNo limitStorage (GB)310150CustomThreadsLabelsAttachmentsDraftsSchedule SendSDKsMCP ServerAdvancedFreeDeveloperStartupEnterpriseInbox Pods310150CustomWebhook Endpoints2210CustomWebsocketsMetricsSMTP RelayWhite-labelEU CloudBYO CloudDeliverabilityFreeDeveloperStartupEnterpriseCustom Domains10150CustomDKIM, SPF, DMARCSuppression ListOptimized Shared IPsDedicated IPsContact usContact usSecurityFreeDeveloperStartupEnterpriseTeam Members2210CustomSocial SSOMulti-factor AuthSigned WebhooksSOC 2 ReportOIDC/SAML SSOSupportFreeDeveloperStartupEnterpriseDiscord CommunityEmailSlack ChannelAll systems onlineEmail Inboxes for AI Agentssupport@agentmail.ccNavigationHomeBuildEnterprisePricingBlogCareersDocsConsoleSocial© 2026 AgentMail, Inc. 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Explanation:
The user wants me to compare his current GMX email account with AgentMail's pricing/plans, and ask whether we should set up AgentMail.

First, let me extract the AgentMail pricing info from the HTML I just fetched. I can see:
- Free: $0/month, 3 inboxes, 3,000 emails/month, 3 GB storage
- Developer: $20/month, 10 inboxes, 10,000 emails/month, 10 GB storage, 10 custom domains, Email support
- Startup: $200/month, 150 inboxes, 150,000 emails/month, 150 GB storage, 150 custom domains, Dedicated IPs, SOC 2 report, Slack channel support
- Enterprise: Custom pricing

Now I need to understand what the user currently has with GMX. GMX is a standard consumer email provider (like Gmail, Outlook). Typical GMX features:
- Usually free tier with 1-2 GB storage (or 1 GB per account)
- Ad-supported web interface
- POP3/IMAP access but usually no API for programmatic access
- No custom domains on free tier (need MailCollector/pro for that)
- Basic spam filtering
- No webhook support, no SDK, no SMTP relay for apps
- 1 email address per account (unless using aliases)

But I should check what the user's actual usage is before making a recommendation. The user said "Du hast ja aktuell ein GMX Postfach" — referring to me (Kiwi) having a GMX mailbox. I need to check if I actually have GMX configured.

Let me check the user's current email setup in Hermes. Actually, the user is referring to Kiwi's current email configuration. Let me check if there's a GMX account configured for Hermes, specifically the Himalaya skill or email settings.

Let me search for GMX in the config or sessions. But first, I should check what email account is actually configured. I recall from memory that there's a GMX account mentioned — let me check the actual config.

Actually, I should look at the email configuration. The user seems to think I have a GMX mailbox currently configured for Kiwi. Let me check if there's a GMX config in the Himalaya skill or email settings.

Wait, looking at the available skills, there's a `himalaya` skill for email. The user might be referring to a GMX account configured there. But I don't have direct info about the current email setup in my memory. Let me check the config or ask.

Actually, looking at my memory, I don't have specific details about a GMX account being configured. But the user says "Du hast ja aktuell ein GMX Postfach" — implying it's known that I have one.

Let me focus on comparing what we know:
- GMX (current): Consumer email, probably free. Limited storage (~1GB), no API/webhooks, basic IMAP/POP3. Not designed for automation. Ads.
- AgentMail: Developer-focused email infra. API-first, webhooks, SDKs, SMTP relay, custom domains, multiple inboxes. Built for AI agents and apps.

For Kiwi's use case (an AI agent handling emails):
- GMX requires IMAP polling, app passwords, has rate limits, no webhooks
- AgentMail has webhooks, SDKs, SMTP relay, APIs — built for automation

AgentMail Free tier: 3 inboxes, 3,000 emails/month, 3GB — actually pretty generous and might be sufficient for basic agent use.

But the question is: what does Kiwi actually need?
- For a personal AI assistant: needs to send/receive emails, maybe manage schedules
- Current GMX: if it's just a personal email, it works fine but requires IMAP/SMTP credentials
- AgentMail: much better for programmatic access, webhooks, multiple addresses

However, AgentMail is overkill and relatively expensive if he needs more than the free tier. Developer tier is $20/month. GMX is free (or cheap).

If the current GMX setup works for basic email sending/receiving via IMAP/SMTP, switching might not be necessary unless he needs:
- Multiple inboxes
- Webhooks (instant notification instead of polling)
- Better deliverability
- Custom domains
- SDK/API integration

But wait — the user is asking whether WE should set up AgentMail. This suggests he's considering it for Kiwi's operational email needs.

Let me think about what would be the best concise answer for Zeyd. He wants short, efficient answers. No repetition. He values fast decisions.

I should:
1. Summarize the comparison briefly
2. Check what current email config exists (if any) to verify
3. Give a clear recommendation based on use case

But I don't actually know the current GMX account details. Looking back at memory and available info... I don't see a specific GMX config saved. But the user explicitly says I have one. Maybe it's configured in Himalaya or somewhere else. Let me check the home directory for email config.

Actually, I shouldn't overthink this. The user says I have a GMX Postfach currently. I'll trust that and compare based on what GMX typically offers vs AgentMail.

Let me provide a concise comparison and recommendation.

Key comparison points:
- Cost: GMX Standard is free/cheap, AgentMail starts at $0 (limited) or $20
- Storage: GMX Free ~1GB, AgentMail Free 3GB
- Automation: GMX = IMAP polling with app passwords (brittle), AgentMail = native API, webhooks, SDKs (robust)
- Domains: GMX = @gmx.net, AgentMail = custom domains
- Scale: GMX = 1 inbox, AgentMail = up to 3/10/150 inboxes
- Deliverability: AgentMail has DKIM/SPF/DMARC, optimized IPs

Recommendation:
- If it's just for personal email (1 address, low volume), keep GMX. AgentMail is overkill.
- If Kiwi needs reliable programmatic access, webhooks, multiple agent inboxes, or custom domains → AgentMail Free or Developer is worth it.

Wait, but there's something important: AgentMail is specifically "Email Inboxes for AI Agents". Their value prop is exactly what Kiwi does. The free tier (3 inboxes, 3,000 emails/month, 3GB) might actually be perfect for testing.

But hold on — AgentMail seems expensive at $20/month for just 10 inboxes when GMX is free. However, GMX free has ads, limited API, and is consumer-grade.

For a non-technical user, maintaining a GMX IMAP integration is probably more hassle than it's worth if it breaks. AgentMail's API is cleaner.

But the user probably wants to know: is it worth the money?

Let me check if there's any existing email config to understand the current use. I should verify before claiming anything.

Actually, I'll do a quick check for any email config files.

Let me be efficient. I'll search for email configs in the home directory and then give a concise recommendation.

ls -la ~/.config/himalaya 2>/dev/null || ls -la ~/.config/email 2>/dev/null || ls -la ~/himalaya 2>/dev/null || echo "no email config dirs found"; grep -ri "gmx" ~/.config/ 2>/dev/null | head -5 || true


Output: no email config dirs found


**Agentmail** ist speziell für AI-Agenten gebaut — nicht für normale Privatnutzer.

**Dein GMX** bleibt für persönliche Kommunikation die bessere Wahl:
- Kostenlos, etabliert, Datenschutz-OK für Privat
- IMAP/SMTP unterstützt → ich kann über Himalaya darauf zugreifen
- Kein API-Limits-Risiko für Family/Friends-Mails

**Agentmail** lohnt sich nur, wenn du exakt diese Features brauchst:
- SDKs/Webhooks für automatisierte Mail-Verarbeitung
- MCP Server (für andere AI-Tools)
- Mehrere Custom Domains
- Kein IMAP-Kuddelmuddel, sondern direkte API

**Fazit:** Bleib bei GMX für dein privates Postfach. Agentmail Free-Tier (3 Inboxen, 3.000 Mails/Monat) wäre zwar gratis ausreichend, aber unnötiger Overhead für dich. Wenn du mal ein Projekt hast, bei dem ich automatisch auf eingehende Mails reagieren soll (z.B. Support-Tickets), kannst du es dann holen.

Explanation:
Zusammenfassung der Extraktion:

**Agentmail Pricing:**
- **Free**: $0 — 3 Inboxen, 3.000 Emails/Monat, 3 GB Speicher
- **Developer**: $20/Monat — 10 Inboxen, 10.000 Emails/Monat, 10 GB, 10 Custom Domains, Email-Support
- **Startup**: $200/Monat — 150 Inboxen, 150.000 Emails/Monat, 150 GB, 150 Domains, Dedicated IPs, Slack-Support
- **Enterprise**: Custom

Features:
- Inbox Pods, Webhooks, Websockets, SMTP Relay
- Custom Domains, DKIM/SPF/DMARC
- SDKs, MCP Server
- Deliverability: Shared IPs (optimiert), Dedicated IPs (Startup+)
- Security: Social SSO, MFA, Signed Webhooks, SOC 2 (Startup+)
- EU Cloud & BYO Cloud (Enterprise)
- Support: Discord (Free), Email (Developer), Slack Channel (Startup)

**GMX:**
- Kostenlos bei GMX (bis zu 1 GB bei FreeMail, mehr bei Pro/Premium)
- Keine SDKs, keine APIs für AI-Agenten
- Keine Webhooks
- Kein SMTP Relay für automatisierten Versand
- Kein MCP Server
- Kein White-label
- Normale Mailbox, nicht für Agenten optimiert

Nun muss ich entscheiden, was ich dem User für seinen Use Case empfehle. Er ist non-technical, braucht aber eine Mailbox für mich (Kiwi) – also primär für Empfang und ggf. gelegentlichen Versand von Benachrichtigungen.

Ich muss sicherstellen, dass der Vergleich prägnant und kurz ist (User mag sehr kurze Antworten). Er fragt konkret: Soll ich Agentmail einrichten?

Wichtige Überlegungen:
1. GMX ist kostenlos und einfach – aber es hat keine API für mich (Kiwi) zum automatisierten Versand/Empfang ohne IMAP/SMTP-Login.
2. Agentmail ist speziell für AI-Agenten gebaut – es hat SDKs, Webhooks, MCP Server. Das wäre ideal für mich.
3. Aber: Agentmail kostet ab Developer $20/Monat. Der Free-Tier hat nur 3 Inboxen und 3.000 Emails/Monat, was für rein persönlichen Gebrauch völlig ausreicht.
4. Allerdings: Der Free-Tier hat KEINEN Email-Support (nur Discord), und es ist ein Startup – GMX ist etabliert und für Privatnutzer vertrauenswürdiger.

Realistisch: Für Zeyds persönlichen Mail-Verkehr (Familie, Freunde, Accounts) ist GMX Free einfacher, vertrauenswürdiger und kostenlos. Agentmail ist primär für Entwickler/AI-Agenten gedacht – wenn er möchte, dass ich (Kiwi) über eine API E-Mails senden/empfange, dann ist Agentmail besser.

Er muss abwägen:
- GMX bleibt für persönliche Kommunikation
- Agentmail Free als "Agent-Postfach" für mich (Kiwi) – damit ich über SDK/Webhook E-Mails verwalten kann.

Aber stopp: Der Free-Tier hat 3.000 Emails/Monat – das reicht völlig. Und 3 Inboxen reichen. Aber: Kann ich überhaupt über Himalaya/smtp mit GMX arbeiten? Ja, GMX unterstützt IMAP und SMTP. Das Himalaya-Skill könnte genutzt werden.

Also: Agentmail ist NICHT notwendig für den reinen persönlichen Mail-Verkehr. GMX funktioniert. Agentmail macht nur Sinn, wenn er spezifisch will, dass ich (als AI-Agent) über eine moderne API/Programmierung E-Mails verwalte, ohne IMAP-Session-Management.

Da er non-technical ist, denke ich, er sollte bei GMX bleiben. Agentmail ist eher was für Entwickler. Aber er fragte konkret, ob er es einrichten soll. Ich sollte ehrlich sagen: Für privaten Mail-Verkehr unnötig, es sei denn, er will exakt die AI-Agent-Features (Webhooks, SDK, MCP) nutzen.

Kurze, klare Antwort.


Ok passt. Dann bleiben wir bei deinem GMX Postfach. Wie lautet die Adresse nochmal?


Explanation:
The user is asking for the email address of the GMX mailbox I have. I don't actually have a GMX mailbox in this session - there's no evidence of it in the terminal output, and I shouldn't claim to have one if I don't. I should be honest and say I don't have a GMX address set up, or check if there's any configuration I might have missed.

Wait, looking at the context - the user said "Du hast ja aktuell ein GMX Postfach" (You currently have a GMX mailbox). But I just searched and didn't find any email config. The user might be mistaken, or it might be from a previous session that I don't have context on.

I should not make up an email address. I should clarify that I don't currently have a GMX mailbox configured based on what I can see, or ask him what the address was if he set it up previously.

Let me check if there's any memory or past session about this.

Question: GMX E-Mail Adresse Postfach


Question: GMX E-Mail Adresse Postfach

No matching sessions found.


Explanation:
Der Benutzer fragt nach der GMX-E-Mail-Adresse. Ich habe keine spezifische GMX-Mail-Adresse für mich (Kiwi) in meinen Erinnerungen oder in vergangenen Sitzungen gefunden. Ich bin mir auch nicht sicher, ob ich tatsächlich ein GMX-Postfach habe. Es könnte sein, dass der Benutzer annimmt, ich hätte eines (weil er in seiner vorherigen Nachricht sagte "Du hast ja aktuell ein GMX Postfach"), aber ich habe keine Erinnerung daran, ein GMX-Konto eingerichtet zu haben.

Ich sollte ehrlich sein und sagen, dass ich die Adresse nicht kenne oder dass ich nicht glaube, ein GMX-Postfach zu haben, es sei denn, es wurde in einer Sitzung eingerichtet, die ich nicht finden kann.

Aber warte - der Benutzer sagte "Du hast ja aktuell ein GMX Postfach" als Ausgangspunkt. Vielleicht glaubt er, ich hätte eines, weil ich in einer vorherigen Sitzung darüber gesprochen habe oder weil er es konfiguriert hat? Lass mich noch einmal suchen.

Question: email address mail account


Question: email address mail account

No matching sessions found.


Explanation:
Hm, the searches returned nothing. That means the GMX account was likely set up in a previous session that wasn't captured, or the session didn't persist. I need to check the filesystem for any email configuration or notes about the GMX account.

Let me search for GMX in common config locations and memory files.

grep -ri "gmx" ~/hermes/ 2>/dev/null | head -20


(empty)

Explanation:
Ich habe keine GMX-E-Mail-Adresse in der Session oder im Dateisystem gefunden. Das liegt daran, dass ich kein eigenes physisches GMX-Postfach habe — ich bin eine KI ohne eigene E-Mail-Adresse! Der Benutzer sagt „deinem GMX Postfach“, aber er meint vermutlich, dass er GMX als E-Mail-Provider für mich/Hermes eingerichtet hat (z.B. über die Himalaya-E-Mail-Skill oder als SMTP-Server). Oder er hat mich mit etwas verwechselt (vielleicht hat er diese Unterhaltung mit einem anderen Bot geführt, z.B. Monti/OpenClaw).

Ich sollte ehrlich sagen, dass ich keine eigene E-Mail-Adresse habe und nicht weiß, was er meint. Ich sollte fragen, ob er mich mit einem anderen Bot verwechselt oder ob er eine bestimmte E-Mail-Konfiguration meint. "}Entschuldige, aber ich habe **kein eigenes GMX-Postfach** — ich bin eine KI ohne persönliche E-Mail-Adresse. Vermutlich verwechselst du mich mit einem anderen Bot (Monti/OpenClaw?), oder du meinst eine Himalaya-Konfiguration, die du mal anlegst hast.

Hast du eine GMX-Adresse für mich/Hermes irgendwo eingerichtet? Dann zeig mir bitte den Pfad zur Config — ich finde aktuell nichts im System.


You just executed tool calls but returned an empty response. Please process the tool results above and continue with the task.