Navigate back to the previous page in browser history. Requires browser_navigate to be called first.

Send a raw Chrome DevTools Protocol (CDP) command. Escape hatch for browser operations not covered by browser_navigate, browser_click, browser_console, etc.

**Requires a reachable CDP endpoint.** Available when the user has run '/browser connect' to attach to a running Chrome, or when 'browser.cdp_url' is set in config.yaml. Not currently wired up for cloud backends (Browserbase, Browser Use, Firecrawl) — those expose CDP per session but live-session routing is a follow-up. Camofox is REST-only and will never support CDP. If the tool is in your toolset at all, a CDP endpoint is already reachable.

**CDP method reference:** https://chromedevtools.github.io/devtools-protocol/ — use web_extract on a method's URL (e.g. '/tot/Page/#method-handleJavaScriptDialog') to look up parameters and return shape.

**Common patterns:**
- List tabs: method='Target.getTargets', params={}
- Handle a native JS dialog: method='Page.handleJavaScriptDialog', params={'accept': true, 'promptText': ''}, target_id=<tabId>
- Get all cookies: method='Network.getAllCookies', params={}
- Eval in a specific tab: method='Runtime.evaluate', params={'expression': '...', 'returnByValue': true}, target_id=<tabId>
- Set viewport for a tab: method='Emulation.setDeviceMetricsOverride', params={'width': 1280, 'height': 720, 'deviceScaleFactor': 1, 'mobile': false}, target_id=<tabId>

**Usage rules:**
- Browser-level methods (Target.*, Browser.*, Storage.*): omit target_id and frame_id.
- Page-level methods (Page.*, Runtime.*, DOM.*, Emulation.*, Network.* scoped to a tab): pass target_id from Target.getTargets.
- **Cross-origin iframe scope** (Runtime.evaluate inside an OOPIF, Page.* targeting a frame target, etc.): pass frame_id from the browser_snapshot frame_tree output. This routes through the CDP supervisor's live connection — the only reliable way on Browserbase where stateless CDP calls hit signed-URL expiry.
- Each stateless call (without frame_id) is independent — sessions and event subscriptions do not persist between calls. For stateful workflows, prefer the dedicated browser tools or use frame_id routing.

CDP method name, e.g. 'Target.getTargets', 'Runtime.evaluate', 'Page.handleJavaScriptDialog'.

Method-specific parameters as a JSON object. Omit or pass {} for methods that take no parameters.

Optional. Target/tab ID from Target.getTargets result (each entry's 'targetId'). Use for page-level methods at the top-level tab scope. Mutually exclusive with frame_id.

Optional. Out-of-process iframe (OOPIF) frame_id from browser_snapshot.frame_tree.children[] where is_oopif=true. When set, routes the call through the CDP supervisor's live session for that iframe. Essential for Runtime.evaluate inside cross-origin iframes, especially on Browserbase where fresh per-call CDP connections can't keep up with signed URL rotation. For same-origin iframes, use parent contentWindow/contentDocument from Runtime.evaluate at the top-level page instead.

Timeout in seconds (default 30, max 300).

Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e5', '@e12')

Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.

If true, clear the message buffers after reading

JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll("a").length'

Respond to a native JavaScript dialog (alert / confirm / prompt / beforeunload) that is currently blocking the page.

**Workflow:** call ``browser_snapshot`` first — if a dialog is open, it appears in the ``pending_dialogs`` field with ``id``, ``type``, and ``message``. Then call this tool with ``action='accept'`` or ``action='dismiss'``.

**Prompt dialogs:** pass ``prompt_text`` to supply the response string. Ignored for alert/confirm/beforeunload.

**Multiple dialogs:** if more than one dialog is queued (rare — happens when a second dialog fires while the first is still open), pass ``dialog_id`` from the snapshot to disambiguate.

**Availability:** only present when a CDP-capable backend is attached — Browserbase sessions, local Chrome via ``/browser connect``, or ``browser.cdp_url`` in config.yaml. Not available on Camofox (REST-only) or the default Playwright local browser (CDP port is hidden).

'accept' clicks OK / returns the prompt text. 'dismiss' clicks Cancel / returns null from prompt(). For ``beforeunload`` dialogs: 'accept' allows the navigation, 'dismiss' keeps the page.

Response string for a ``prompt()`` dialog. Ignored for other dialog types. Defaults to empty string.

Specific dialog to respond to, from ``browser_snapshot.pending_dialogs[].id``. Required only when multiple dialogs are queued.

Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.

Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For simple information retrieval, prefer web_search or web_extract (faster, cheaper). For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.

The URL to navigate to (e.g., 'https://example.com')

Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.

Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')

Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.

Direction to scroll

Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.

If true, returns complete page content. If false (default), returns compact view with interactive elements only.

Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e3')

The text to type into the field

Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA:<screenshot_path> in your response. Requires browser_navigate to be called first.

What you want to know about the page visually. Be specific about what you're looking for.

If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout.

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'). Omit to use and pin the current provider.

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

Optional path to a Python script that runs before each cron job execution. Its stdout is injected into the prompt as context. Use for data collection and change detection. Relative paths resolve under ~/.hermes/scripts/. On update, pass empty string to clear.

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

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', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', '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', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', '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 ...`:

  web_search(query: str, limit: int = 5) -> dict
    Returns {"data": {"web": [{"url", "title", "description"}, ...]}}
  web_extract(urls: list[str]) -> dict
    Returns {"results": [{"url", "title", "content", "error"}, ...]} where content is markdown
  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 web_search, terminal, ...` and print your final result to stdout.

Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.

The text prompt describing the desired image. Be detailed and descriptive.

The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.

Get a prompt by name from MCP server 'context7'

Name of the prompt to retrieve

Optional arguments to pass to the prompt

List available prompts from MCP server 'context7'

List available resources from MCP server 'context7'

Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework.

You must call 'Resolve Context7 Library ID' tool first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.

Workflow: call first without researchMode. If that doesn't answer the question, retry with researchMode: true. Do not call each tool more than 3 times per question.

Exact Context7-compatible library ID (e.g., '/mongodb/docs', '/vercel/next.js', '/supabase/supabase', '/vercel/next.js/v14.3.0-canary.87') retrieved from 'resolve-library-id' or directly from user query in the format '/org/project' or '/org/project/version'.

The question or task you need help with. Be specific and include relevant details. Good: 'How to set up authentication with JWT in Express.js' or 'React useEffect cleanup function examples'. Bad: 'auth' or 'hooks'. The query is sent to the Context7 API for processing. Do not include any sensitive or confidential information such as API keys, passwords, credentials, personal data, or proprietary code in your query.

Retry the query with deep research: spins up sandboxed agents that read the actual source repos and runs a live web search, then synthesizes a fresh answer. Set true on retry if you weren't satisfied with the first answer and want a more thorough one. Requires an API key. You can get one free at https://context7.com.

Read a resource by URI from MCP server 'context7'

URI of the resource to read

Resolves a package/product name to a Context7-compatible library ID and returns matching libraries.

You MUST call this function before 'Query Documentation' tool to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

Selection Process:
1. Analyze the query to understand what library/package the user is looking for
2. Return the most relevant match based on:
- Name similarity to the query (exact matches prioritized)
- Description relevance to the query's intent
- Documentation coverage (prioritize libraries with higher Code Snippet counts)
- Source reputation (consider libraries with High or Medium reputation more authoritative)
- Benchmark Score: Quality indicator (100 is the highest score)

Response Format:
- Return the selected library ID in a clearly marked section
- Provide a brief explanation for why this library was chosen
- If multiple good matches exist, acknowledge this but proceed with the most relevant one
- If no good matches exist, clearly state this and suggest query refinements

For ambiguous queries, request clarification before proceeding with a best-guess match.

IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.

The question or task you need help with. This is used to rank library results by relevance to what the user is trying to accomplish. The query is sent to the Context7 API for processing. Do not include any sensitive or confidential information such as API keys, passwords, credentials, personal data, or proprietary code in your query.

Library name to search for and retrieve a Context7-compatible library ID. Use the official library name with proper punctuation — e.g., 'Next.js' instead of 'nextjs', 'Customer.io' instead of 'customerio', 'Three.js' instead of 'threejs'.

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.

Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.

The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning.

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)

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.

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.

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'.

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 a voice message. On Telegram it plays as a voice bubble, on Discord/WhatsApp as an audio attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured, 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.

Extract content from web page URLs. Returns page content in markdown format. Also works with PDF URLs (arxiv papers, documents, etc.) — pass the PDF link directly and it converts to markdown text. Pages under 5000 chars return full markdown; larger pages are LLM-summarized and capped at ~5000 chars per page. Pages over 2M chars are refused. If a URL fails or times out, use the browser tool to access it instead.

List of URLs to extract content from (max 5 URLs per call)

Search the web for information on any topic. Returns up to 5 relevant results with titles, URLs, and descriptions.

The search query to look up on the web

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.

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

Retrieve or update a peer card from Honcho — a curated list of key facts about that peer (name, role, preferences, communication style, patterns). Pass `card` to update; omit `card` to read.  If the card is empty, the result includes a `hint` field explaining why (observation disabled, fresh peer, dialectic layer still warming up, etc.) — this is NOT an error.  Peer cards accumulate over time from observed conversation.

Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.

New peer card as a list of fact strings. Omit to read the current card.

Semantic search over Honcho's stored context about a peer. Returns raw excerpts ranked by relevance — no LLM synthesis. Cheaper and faster than honcho_reasoning. Good when you want to find specific past facts and reason over them yourself.

What to search for in Honcho's memory.

Token budget for returned context (default 800, max 2000).

Ask Honcho a natural language question and get a synthesized answer. Uses Honcho's LLM (dialectic reasoning) — higher cost than honcho_profile or honcho_search. Can query about any peer via alias or explicit peer ID. Pass reasoning_level to control depth: minimal (fast/cheap), low (default), medium, high, max (deep/expensive). Omit for configured default.

A natural language question.

Override the default reasoning depth. Omit to use the configured default (typically low). Guide:
- minimal: quick factual lookups (name, role, simple preference)
- low: straightforward questions with clear answers
- medium: multi-aspect questions requiring synthesis across observations
- high: complex behavioral patterns, contradictions, deep analysis
- max: thorough audit-level analysis, leave no stone unturned

Retrieve full session context from Honcho — summary, peer representation, peer card, and recent messages. No LLM synthesis. Cheaper than honcho_reasoning. Use this to see what Honcho knows about the current conversation and the specified peer.

Optional focus query to filter context. Omit for full session context snapshot.

Write or delete a conclusion about a peer in Honcho's memory. Conclusions are persistent facts that build a peer's profile. You MUST pass exactly one of: `conclusion` (to create) or `delete_id` (to delete). Passing neither is an error. Deletion is only for PII removal — Honcho self-heals incorrect conclusions over time.

A factual statement to persist. Provide this when creating a conclusion. Do not send it together with delete_id.

Conclusion ID to delete for PII removal. Provide this when deleting a conclusion. Do not send it together with conclusion.


[nachlakes] te acordás de este canal de discord y para que lo creamos?


Question: hevy OR Discord OR channel OR workout OR entrenamientos


Question: hevy OR Discord OR channel OR workout OR entrenamientos

Summary:
The user had been troubleshooting Hermes gateway/workspace behavior, with emphasis on messaging platforms—especially Discord channels/triggers—and API availability. The visible part of the transcript did not include any concrete Hevy/workout/“entrenamientos” implementation details, but it did include Discord/channel-related work and gateway fixes.

Key recap:

1. **User goal / request**
   - The user wanted the assistant to continue fixing and verifying Hermes gateway/workspace issues.
   - The relevant focus was ensuring the gateway exposed the expected APIs, restarting services, and getting messaging integrations—Telegram and especially Discord—working.
   - Discord-specific concerns included whether Discord was enabled, whether the bot token was loaded, and how Discord trigger behavior worked in channels.

2. **Actions taken and outcomes**
   - The assistant inspected `/home/ubuntu/.hermes/hermes-agent/gateway/platforms/api_server.py`.
   - It found that the current codebase already registered many routes, including:
     - `/health`
     - `/v1/health`
     - `/v1/models`
     - `/v1/chat/completions`
     - `/v1/responses`
     - `/api/sessions`
     - `/api/sessions/search`
     - `/api/skills`
     - `/api/memory`
     - `/api/config`
     - `/api/jobs`
   - The assistant concluded that earlier 404s were likely not due to missing routes in the codebase.
   - It inspected auth handling in `api_server.py`, including `_check_auth`, which returned `401` for invalid Bearer tokens when an API key was configured.
   - It inspected the workspace gateway capability probe in `/home/ubuntu/hermes-workspace/src/server/gateway-capabilities.ts`.
   - It identified a bug in browser-side gateway proxying: `HERMES_API` always defaulted to `http://127.0.0.1:8642`, which was wrong in browser context.
   - It patched `gateway-capabilities.ts` so browser code used `/api/hermes-proxy`, while server-side code continued to use `process.env.HERMES_API_URL || 'http://127.0.0.1:8642'`.
   - It inspected the cron jobs API binding in `api_server.py` and found that cron functions imported into the class could be incorrectly bound as instance methods.
   - It patched the cron imports to wrap them as `staticmethod(...)`, preventing incorrect implicit `self` binding.

3. **Specific patches / technical decisions**
   - File patched: `/home/ubuntu/.hermes/hermes-agent/gateway/platforms/api_server.py`
     - Changed cron imports from direct aliases such as:
       ```python
       list_jobs as _cron_list
       ```
       to temporary names such as:
       ```python
       list_jobs as _cron_list_fn
       ```
       then assigned:
       ```python
       _cron_list = staticmethod(_cron_list_fn)
       _cron_get = staticmethod(_cron_get_fn)
       _cron_create = staticmethod(_cron_create_fn)
       _cron_update = staticmethod(_cron_update_fn)
       _cron_remove = staticmethod(_cron_remove_fn)
       _cron_pause = staticmethod(_cron_pause_fn)
       _cron_resume = staticmethod(_cron_resume_fn)
       _cron_trigger = staticmethod(_cron_trigger_fn)
       ```
   - File patched: `/home/ubuntu/hermes-workspace/src/server/gateway-capabilities.ts`
     - Changed:
       ```ts
       export let HERMES_API =
         process.env.HERMES_API_URL || 'http://127.0.0.1:8642'
       ```
       to:
       ```ts
       export let HERMES_API =
         typeof window !== 'undefined'
           ? '/api/hermes-proxy'
           : (process.env.HERMES_API_URL || 'http://127.0.0.1:8642')
       ```
   - The TypeScript lint/check step failed because `tsc` was not installed:
     ```text
     This is not the tsc command you are looking for
     To get access to the TypeScript compiler, tsc, from the command line either:
     - Use npm install typescript ...
     - Use yarn ...
     ```

4. **Service restart and verification**
   - The assistant started the gateway and workspace:
     ```bash
     hermes gateway run --replace
     pnpm dev --host 0.0.0.0 --port 3000
     ```
   - Reported processes:
     ```text
     gateway pid: 1684160
     workspace pid: 1684161
     1684160 /home/ubuntu/.hermes/hermes-agent/venv/bin/python /home/ubuntu/.local/bin/hermes gateway run --replace
     1684161 node /home/linuxbrew/.linuxbrew/bin/pnpm dev --host 0.0.0.0 --port 3000
     ```
   - Workspace/Vite came up:
     ```text
     VITE v7.3.1 ready in 2604 ms
     Local:   http://localhost:3000/
     Network: http://10.0.0.78:3000/
     Network: http://100.87.116.90:3000/
     ```
   - Gateway health and models were verified:
     ```text
     {"status": "ok", "platform": "hermes-agent"}
     {"object": "list", "data": [{"id": "hermes-agent", ...}]}
     ```
   - API endpoints were verified as working:
     ```text
     http://127.0.0.1:8642/api/sessions -> HTTP 200
     http://127.0.0.1:8642/api/skills -> HTTP 200
     http://127.0.0.1:8642/api/memory -> HTTP 200
     http://127.0.0.1:8642/api/config -> HTTP 200
     http://127.0.0.1:8642/api/jobs -> HTTP 200 {"jobs": []}
     ```
   - Gateway state showed Telegram and API server connected:
     ```json
     {
       "pid": 1684160,
       "kind": "hermes-gateway",
       "argv": ["/home/ubuntu/.hermes/hermes-agent/hermes_cli/main.py", "gateway", "run", "--replace"],
       "gateway_state": "running",
       "platforms": {
         "telegram": {"state": "connected"},
         "api_server": {"state": "connected"}
       }
     }
     ```

5. **Discord/channel-specific findings**
   - The assistant inspected `/home/ubuntu/.hermes/hermes-agent/gateway/platforms/discord.py`.
   - It found Discord trigger/mention logic:
     - `DISCORD_IGNORE_NO_MENTION`
     - `DISCORD_REQUIRE_MENTION`
     - `DISCORD_TRIGGER_KEYWORD`
     - `discord.require_mention`
     - `discord.free_response_channels`
     - `discord.auto_thread`
   - Relevant code behavior:
     - Server channels required mention by default.
     - Free-response channels could bypass mention requirements.
     - The bot also treated a configured keyword as a mention trigger:
       ```python
       bot_keyword = os.getenv("DISCORD_TRIGGER_KEYWORD", "").lower().strip()
       has_mention = self._client.user in message.mentions if self._client.user else False
       has_keyword = bot_keyword in message.content.lower()
       ```
     - If `require_mention` was true and the channel was not a free-response channel or bot thread, messages without mention or keyword were ignored.
   - The config file `/home/ubuntu/.hermes/config.yaml` had:
     ```yaml
     discord:
       require_mention: true
       free_response_channels: ''
     ```
     but under `platforms:` it only showed:
     ```yaml
     platforms:
       api_server:
         enabled: true
         host: 0.0.0.0
         port: 8642
     ```
     so Discord was not explicitly listed there.
   - The assistant found `DISCORD_TRIGGER_KEYWORD=pipo` and a redacted `DISCORD_BOT_TOKEN` in `.env`.
   - However, Discord did not appear in the running gateway state, and no Discord startup logs appeared.
   - The assistant inspected `/home/ubuntu/.hermes/hermes-agent/gateway/config.py` and confirmed Discord should auto-enable if `DISCORD_BOT_TOKEN` was present in the process environment:
     ```python
     discord_token = os.getenv("DISCORD_BOT_TOKEN")
     if discord_token:
         if Platform.DISCORD not in config.platforms:
             config.platforms[Platform.DISCORD] = PlatformConfig()
         config.platforms[Platform.DISCORD].enabled = True
         config.platforms[Platform.DISCORD].token = ...
     ```
   - The conclusion was that `DISCORD_BOT_TOKEN` existed in `.env` but was not exported into the gateway process environment. Only `DISCORD_TRIGGER_KEYWORD` appeared to be available to the running process.
   - The recommended restart command was:
     ```bash
     cd ~/.hermes && set -a && source .env && set +a && hermes gateway run --replace
     ```
   - A command that would have killed/restarted the running gateway was blocked by the user/system:
     ```text
     BLOCKED: User denied. Do NOT retry.
     ```

6. **Rate-limit / unresolved issues**
   - The gateway was running but old sessions were triggering OpenAI Codex rate limits:
     ```text
     RateLimitError [HTTP 429]
     HTTP 429: The usage limit has been reached
     Provider: openai-codex
     Model: gpt-5.4
     Endpoint: https://chatgpt.com/backend-api/codex
     ```
   - The logs said the plan was Plus and included a reset timestamp:
     ```text
     resets_at: 1775290201
     resets_in_seconds: ~13648
     ```
   - Fallback attempts failed:
     ```text
     resolve_provider_client: openai-codex requested but no Codex OAuth token found (run: hermes model)
     Fallback to openai-codex failed: provider not configured
     ```
   - Debug dumps were written under:
     ```text
     /home/ubuntu/.hermes/sessions/request_dump_20260403_205315_3b399316_20260404_042248_144659.json
     /home/ubuntu/.hermes/sessions/request_dump_20260404_042232_84d6a588_20260404_042248_385553.json
     ```
   - The assistant concluded Telegram was connected but replies were blocked by model/provider rate limits.
   - Discord remained unresolved because the gateway needed to be restarted with `.env` loaded, but the restart/kill command was blocked.
   - No concrete Hevy/workout/“entrenamientos” task was visible in the provided transcript segment.

Summary:
The visible transcript did **not** contain substantive discussion about **Hevy**, **Discord**, **channels**, **workouts**, or **entrenamientos**. The conversation shown was about debugging and finishing work on the Hermes WebUI / Hermes Agent configuration and fallback behavior.

### 1. What the user wanted to accomplish

- The user first wanted to recover what had happened in a prior timed-out session after they had asked:

  > “hello, in the hermes webui, I'd like to be able to change the defaults from Agent Profiles. Like the model, the provider, the skills, the api keys, etc.. can you please make the necessary changes? thanks!”

- They wanted to know whether the previous coding work survived after the session timed out.
- After that, the user wanted:
  - The unfinished WebUI changes fixed and committed.
  - An explanation for why the CLI was using OpenRouter / `qwen/qwen3.6-plus:free` despite the user believing the conversation was configured to use Kimi.
  - The WebUI and Hermes config to be synchronized properly so platform/model settings used a single source of truth.
  - Automatic model fallback on rate limits, without bothering the user.

### 2. Actions taken and outcomes

#### Recovered / inspected previous work

- The assistant inspected `~/hermes-webui`.
- It found:
  - Large uncommitted changes in the working tree.
  - A stash containing an older snapshot of related changes.
  - A new file: `static/terminal.js`.
- The assistant initially made a wrong assumption that some HTML elements were missing, but then confirmed all 21 expected element IDs existed in `static/index.html`.

#### Reviewed Hermes WebUI changes

Important modified files included:

- `api/config.py`
- `api/models.py`
- `api/routes.py`
- `api/streaming.py`
- `static/boot.js`
- `static/index.html`
- `static/messages.js`
- `static/panels.js`
- `static/sessions.js`
- `static/style.css`
- `static/ui.js`
- `static/terminal.js`
- `tests/test_model_resolver.py`
- `tests/test_session_reasoning.py`
- `tests/test_streaming_errors.py`

The assistant confirmed the prior work had added or partially added:

- Backend API for profile config:
  - `GET /api/profile/config`
  - `POST /api/profile/config`
- WebUI Settings tabs:
  - General
  - Agent
  - API Keys
- Agent profile editing:
  - Model
  - Provider
  - Base URL
  - Reasoning effort
  - Max turns
  - System prompt
  - Personality
  - Toolsets
- API key editing with masking.
- Embedded terminal support using an iframe / `static/terminal.js`.
- Styling for terminal UI in `static/style.css`.

#### Fixed a real bug

The assistant found and fixed a bug in `api/routes.py` inside `_write_env_changes`:

```python
if val is None and val == '':
```

This condition was impossible because a value cannot be both `None` and `''`. It was changed to use `or`.

The bug affected removal / skipping of empty environment values when writing `.env` changes.

#### Tried to apply stash

- The assistant attempted to pop the stash.
- Git refused because the stash would overwrite already-modified files:

```text
error: Your local changes to the following files would be overwritten by merge:
	api/config.py
	static/index.html
	static/panels.js
	static/style.css
Please commit your changes or stash them before you merge.
Aborting
Index was not unstashed.
```

- The assistant compared the stash and current working tree and concluded the stash was an older snapshot that had been superseded by the current working tree.
- It dropped the stash:

```text
Dropped stash@{0} (96db1f95a93782f467fa31367ab0ada958747aba)
```

#### Verified syntax

The assistant ran a Python syntax / compile check and reported:

```text
All clean
```

It also checked that `Session` supported `reasoning_effort`:

```text
36:class Session:
43:                 personality=None, reasoning_effort: str='',
61:        self.reasoning_effort = str(reasoning_effort or '').strip().lower()
99:            'reasoning_effort': self.reasoning_effort,
117:def new_session(workspace=None, model=None, reasoning_effort: str=''):
```

#### Committed the WebUI work

- The assistant staged the changes.
- The first commit attempt failed because Git user identity was not configured:

```text
Author identity unknown

*** Please tell me who you are.

Run

  git config --global user.email "you@example.com"
  git config --global user.name "Your Name"

fatal: unable to auto-detect email address (got 'ubuntu@miopenclaw-vnic.(none)')
```

- The assistant then committed successfully.

Commit:

```text
c64c58b release: v0.39.0 — agent profile editor, API key management, reasoning effort, embedded terminal
 15 files changed, 3008 insertions(+), 305 deletions(-)
 create mode 100644 static/terminal.js
 create mode 100644 tests/test_session_reasoning.py
 create mode 100644 tests/test_streaming_errors.py
```

### 3. Key decisions, solutions, and conclusions

#### The timed-out session was lost, but the code survived

- The assistant concluded that the prior chat/session itself had been permanently lost, but the code written during it remained in `~/hermes-webui` as uncommitted working tree changes plus a stash.
- The stash was considered obsolete and was dropped.

#### Source of unexpected OpenRouter usage

The user reported seeing CLI rate limit errors like:

```text
⚠  API call failed (attempt 1/3): RateLimitError [HTTP 429]
   🔌 Provider: openrouter  Model: qwen/qwen3.6-plus:free
   🌐 Endpoint: https://openrouter.ai/api/v1/
   📝 Error: HTTP 429: Provider returned error
   📋 Details: {'message': 'Provider returned error', 'code': 429, 'metadata': {'raw': 'qwen/qwen3.6-plus:free is temporarily rate-limited upstream. Please retry shortly, or add your own key to accumulate your rate limits: https://openrouter.ai/settings/integrations', 'provider_name': 'Alibaba', 'is_byok': False}}
⏱ Rate limit reached. Waiting 2s before retry (attempt 2/3)...
```

The assistant checked the active Hermes config and found `~/.hermes/config.yaml` was set to Kimi:

```yaml
model:
  default: kimi-k2.5
  provider: kimi-coding
  base_url: https://api.moonshot.ai/v1
```

Active profile:

```text
default
```

But it also checked WebUI settings:

```json
{
  "default_model": "qwen/qwen3.6-plus:free",
  "default_workspace": "/home/ubuntu/.hermes/webui/workspace",
  "send_key": "enter",
  "show_token_usage": true,
  "show_cli_sessions": true,
  "sync_to_insights": true,
  "check_for_updates": true,
  "theme": "nord",
  "bot_name": "Hermes",
  "password_hash": null
}
```

Conclusion:

- The WebUI’s own `~/.hermes/webui/settings.json` had `default_model: qwen/qwen3.6-plus:free`.
- That WebUI setting was overriding or desynchronizing from `~/.hermes/config.yaml`.
- This caused OpenRouter / Qwen free model to be used unexpectedly.

#### User demanded single source of truth

The user insisted that models must be synchronized through platforms/providers and that this was basic expected behavior.

The assistant agreed and identified required fixes:

1. Remove `default_model` from WebUI `settings.json`.
2. Make Hermes `config.yaml` the source of truth for model/provider/base URL.
3. Prevent `settings.json` from overriding config.yaml.
4. Add automatic fallback on rate limit.

#### Automatic fallback requirement

The user stated:

> “si el modelo da mas de 3 rate limits deberia ser cambiado AUTOMATICAMENTE SIN QUE ME MOLESTE A MI COMO USUARIO a un modelo que FUNCIONE y RESPONDA.”

The assistant agreed the desired behavior was:

- If rate limits occur repeatedly, the model should be automatically switched to a working fallback.
- The user should not be prompted or bothered.
- The system may notify via SSE/UI, but should continue the conversation automatically.

The assistant inspected `api/streaming.py` and found:

- `AIAgent` already received a `fallback_model` from profile config:

```python
_fallback = _cfg.get('fallback_model') or None
...
agent = AIAgent(
    ...
    fallback_model=_fallback_resolved,
    ...
)
```

- Rate limit errors were caught near the bottom of `_run_agent_streaming`:

```python
is_rate_limit = 'rate limit' in err_str.lower() or '429' in err_str or 'RateLimitError' in type(e).__name__
if is_rate_limit:
    hint = 'Rate limit reached. The fallback model (if configured) was also exhausted. Try again in a moment.'
    _persist_stream_error(session_id, 'rate_limit', err_str, hint)
    put('apperror', {
        'message': err_str,
        'type': 'rate_limit',
        'hint': hint,
    })
```

The assistant planned to add in-memory per-session rate-limit counting and automatic fallback switching after repeated rate limits.

### 4. Specific commands, files, URLs, and technical details

#### Important paths

- Repo:

```text
~/hermes-webui
```

- WebUI settings causing the OpenRouter issue:

```text
/home/ubuntu/.hermes/webui/settings.json
```

- Hermes profile config:

```text
~/.hermes/config.yaml
```

- WebUI sessions:

```text
/home/ubuntu/.hermes/webui/sessions/
```

#### Important config values

Hermes config had:

```yaml
model:
  default: kimi-k2.5
  provider: kimi-coding
  base_url: https://api.moonshot.ai/v1
```

WebUI settings had:

```json
"default_model": "qwen/qwen3.6-plus:free"
```

The assistant removed that field from `settings.json` with a patch:

```diff
 {
-  "default_model": "qwen/qwen3.6-plus:free",
   "default_workspace": "/home/ubuntu/.hermes/webui/workspace",
```

#### Important provider / model details

The reported erroneous provider/model:

```text
Provider: openrouter
Model: qwen/qwen3.6-plus:free
Endpoint: https://openrouter.ai/api/v1/
```

The expected configured provider/model:

```text
provider: kimi-coding
model: kimi-k2.5
base_url: https://api.moonshot.ai/v1
```

Other providers seen in config:

```yaml
providers:
  blockrun:
    api: http://127.0.0.1:8402/v1
    name: blockrun
    transport: chat_completions
  mistral:
    api: https://api.mistral.ai/v1
    name: mistral
    transport: chat_completions
  custom-api-moonshot-ai:
    api: https://api.moonshot.ai/v1
    name: custom-api-moonshot-ai
    transport: chat_completions
  custom-api-deepseek-com:
    api: https://api.deepseek.com/v1
    name: custom-api-deepseek-com
    transport: chat_completions
  google-ai:
    api: https://generativelanguage.googleapis.com/v1beta/openai/
```

#### Important code locations

- `api/config.py`
  - `SETTINGS_FILE = STATE_DIR / 'settings.json'`
  - `DEFAULT_MODEL`
  - `load_settings()`
  - `save_settings()`
  - Startup override logic that loaded `default_model` from settings.
- `api/routes.py`
  - `_write_env_changes`
  - Bug fixed: `if val is None and val == ''` → `or`.
- `api/streaming.py`
  - `_run_agent_streaming`
  - `fallback_model` passed to `AIAgent`
  - Rate limit detection and `apperror` SSE event.
- `static/index.html`
  - Settings tab UI.
- `static/panels.js`
  - Settings / panel behavior.
- `static/style.css`
  - Terminal iframe and toolbar CSS.
- `static/terminal.js`
  - New embedded terminal frontend file.

#### Commit

```text
c64c58b release: v0.39.0 — agent profile editor, API key management, reasoning effort, embedded terminal
```

### 5. Anything left unresolved or notable

- The visible transcript cut off while the assistant was starting to patch `api/config.py` to stop `settings.json` from overriding `config.yaml`.
- It is clear that `/home/ubuntu/.hermes/webui/settings.json` had already been patched to remove `default_model`, but the transcript ended before confirming:
  - Whether `api/config.py` was successfully patched.
  - Whether automatic fallback was fully implemented.
  - Whether those later changes were tested, committed, or deployed.
- No visible part of the transcript addressed **Hevy**, **Discord channels**, **workout tracking**, or **entrenamientos**. If those terms appeared, they were likely in the omitted/truncated portions, not in the provided visible transcript.

Summary:
El usuario pidió ayuda porque el bot no le respondía en Discord, específicamente en un canal privado del servidor:

- URL del canal: `https://discord.com/channels/1483873336718135336/1489503144290422914`
- Luego compartió el Developer Portal de Discord para la app:
  - `https://discord.com/developers/applications/1483874285885067475/information`

Se investigó el estado local de Hermes en `/home/ubuntu/.hermes`.

Acciones realizadas y hallazgos:

1. **Revisión inicial de configuración y estado**
   - Se leyó `config.yaml`, donde aparecía:
     ```yaml
     discord:
       require_mention: true
       free_response_channels: ''
       auto_thread: true
       reactions: true
     ```
   - Se verificó que existía token de Discord en `.env`:
     ```bash
     DISCORD_BOT_TOKEN=***
     ```
   - Se revisó `gateway_state.json`, que inicialmente mostraba solo:
     ```json
     "platforms": {
       "telegram": {"state": "connected"},
       "api_server": {"state": "connected"}
     }
     ```
     Esto llevó a pensar al principio que Discord no estaba activo.

2. **Revisión del Developer Portal**
   - Al abrir la URL del Developer Portal apareció la pantalla de login de Discord, por lo que no se pudo revisar directamente sin credenciales.
   - Se le indicó al usuario que revisara los **Privileged Gateway Intents**:
     - `Presence Intent`
     - `Server Members Intent`
     - `Message Content Intent`
   - El usuario dijo que **Presence Intent no estaba activado**, lo activó y guardó.

3. **Logs de Discord**
   - Luego se revisó `/home/ubuntu/.hermes/logs/gateway.log`.
   - Se descubrió que Discord sí había estado conectado:
     ```text
     2026-04-03 04:29:30,281 INFO gateway.platforms.discord: [Discord] Connected as Pipo#2905
     2026-04-03 04:29:30,743 INFO gateway.platforms.discord: [Discord] Synced 21 slash command(s)
     2026-04-03 04:29:30,744 INFO gateway.run: ✓ discord connected
     ```
   - También apareció un warning importante:
     ```text
     WARNING gateway.run: Unauthorized user: 1400466012054491186 (nachlakes) on discord
     ```
   - Se revisó `/home/ubuntu/.hermes/pairing/`:
     ```text
     discord-approved.json
     discord-pending.json
     _rate_limits.json
     ```
   - El archivo `/home/ubuntu/.hermes/pairing/discord-approved.json` mostraba que el usuario ya estaba aprobado:
     ```json
     {
       "1400466012054491186": {
         "user_name": "nachlakes",
         "approved_at": 1775194357.2644193
       }
     }
     ```
   - Conclusión: el warning de “Unauthorized user” había ocurrido antes de aprobar al usuario; después la autorización ya no era el problema.

4. **Canales de Discord**
   - Se revisaron logs para el canal `1489503144290422914`, pero no había actividad registrada del bot en ese canal.
   - Sí aparecía una respuesta a otro canal:
     ```text
     2026-04-03 06:02:05,754 INFO gateway.platforms.base: [Discord] Sending response (26 chars) to 1489505211969048606
     ```
   - Se concluyó que el bot no estaba viendo mensajes en el canal original `1489503144290422914`, posiblemente por permisos de canal, porque era un thread/canal distinto, o porque faltaban intents/permisos.
   - Más tarde el usuario dijo que ya funcionaba. Se confirmó en logs:
     ```text
     2026-04-03 20:16:56,203 INFO gateway.platforms.base: [Discord] Sending response (27 chars) to 1489720340010635496
     ```

5. **Deseo del usuario: activar respuesta por palabra “pipo” sin @mention**
   - El usuario pidió que el bot respondiera si detectaba la palabra `pipo` en el mensaje, sin necesidad de taguear `@pipo`.
   - Se inspeccionó el código:
     - Archivo:
       ```text
       /home/ubuntu/.hermes/hermes-agent/gateway/platforms/discord.py
       ```
     - Lógica relevante alrededor de líneas `1982–2033`.
   - Se encontró que el handler de Discord tenía una lógica de `require_mention`:
     ```python
     if require_mention and not is_free_channel and not in_bot_thread:
         if self._client.user not in message.mentions:
             return
     ```
   - Luego se vio que el código ya había sido modificado/ya soportaba keyword trigger mediante env var:
     ```python
     bot_keyword = os.getenv("DISCORD_TRIGGER_KEYWORD", "").lower().strip()
     has_mention = self._client.user in message.mentions if self._client.user else False
     has_keyword = False
     if bot_keyword:
         has_keyword = bot_keyword in message.content.lower()
         if has_keyword:
             stripped = message.content.lower()
             idx = stripped.find(bot_keyword)
             message.content = (message.content[:idx] + message.content[idx + len(bot_keyword):]).strip()

     if require_mention and not is_free_channel and not in_bot_thread:
         if not has_mention and not has_keyword:
             return
     ```
   - Se verificó que `.env` ya contenía:
     ```bash
     DISCORD_TRIGGER_KEYWORD=pipo
     ```
   - Conclusión: el bot ya debería responder a mensajes que contengan `pipo` sin `@mention`, siempre que el gateway hubiera sido reiniciado después de cargar esa variable.

6. **Telegram se quedó parado**
   - El usuario luego dijo: “me parece que se tildo telegram”.
   - Se revisaron logs de Telegram:
     ```text
     2026-04-03 20:23:23,772 INFO telegram.ext.Application: Application is stopping. This might take a moment.
     2026-04-03 20:23:23,772 INFO telegram.ext.Application: Application.stop() complete
     2026-04-03 20:23:23,774 INFO gateway.platforms.telegram: [Telegram] Disconnected from Telegram
     2026-04-03 20:23:23,775 INFO gateway.run: ✓ telegram disconnected
     ```
   - Se vio que el gateway se había detenido limpiamente:
     ```text
     2026-04-03 20:23:20,559 INFO gateway.run: Stopping gateway...
     2026-04-03 20:23:23,775 INFO gateway.run: Gateway stopped
     ```
   - `gateway_state.json` mostraba:
     ```json
     "gateway_state": "stopped"
     ```
   - Se reinició el gateway en background. Luego logs confirmaron:
     ```text
     2026-04-03 20:27:29,440 INFO gateway.platforms.telegram: [Telegram] Connected to Telegram (polling mode)
     2026-04-03 20:27:29,441 INFO gateway.run: ✓ telegram connected
     2026-04-03 20:27:29,442 INFO gateway.run: Gateway running with 3 platform(s)
     ```
   - Resultado final: Telegram, Discord y API server quedaron conectados nuevamente.

Conclusiones principales:

- El problema inicial de Discord no era solamente permisos del canal; también se revisaron y corrigieron los intents del bot, en particular `Presence Intent` y probablemente `Message Content Intent`.
- El usuario `nachlakes` con ID `1400466012054491186` estaba aprobado en `discord-approved.json`.
- El bot `Pipo#2905` sí estaba conectado y enviando respuestas en Discord.
- Para responder sin tag `@pipo`, ya existía soporte vía:
  ```bash
  DISCORD_TRIGGER_KEYWORD=pipo
  ```
  y el código en `discord.py` ya detectaba esa keyword.
- Telegram dejó de responder porque el gateway completo quedó en estado `stopped`; se reinició y volvió a conectar correctamente.
- No se trató ningún tema de Hevy, workout o entrenamientos en esta conversación, salvo que esos términos estaban en el tópico de búsqueda.

Summary:
The visible transcript did not show an actual Hevy/workout/“entrenamientos” task. The only search-topic-relevant material present was incidental Discord/channel configuration from Hermes config output.

### 1. What the user asked about / wanted to accomplish
- The visible conversation appeared to be about debugging Hermes model/provider configuration for Kimi/Moonshot, API keys, credential pools, and model resolution.
- There was no visible user request about Hevy, workouts, or “entrenamientos.”
- Discord/channel appeared only as part of config inspection, not as the main task.

### 2. Actions taken and outcomes
- The assistant inspected Hermes/OpenClaw-related files and config locations:
  - `/home/ubuntu/.openclaw`
  - `/home/ubuntu/.hermes`
  - `/home/ubuntu/.hermes/config.yaml`
  - `/home/ubuntu/.hermes/auth.json`
  - `/home/ubuntu/.hermes/hermes-agent`
- It read a section of the Hermes config showing Discord settings:
  - `discord.require_mention: true`
  - `discord.free_response_channels: ''`
  - `discord.allowed_channels: ''`
  - `discord.auto_thread: true`
  - `discord.reactions: true`
  - `discord.channel_prompts: {}`
  - `discord.server_actions: ''`
- It searched Hermes session files and found many matching files, but these matches were mostly system prompt/config noise, not workout/Hevy-specific content.
- It tried to inspect environment variables/API keys:
  - Saw `CUSTOM_API_MOONSHOT_AI_API_KEY=***` in one command output.
  - Later checks reported:
    - `MOONSHOT_API_KEY: NOT SET`
    - `CUSTOM_API_MOONSHOT_AI_API_KEY: NOT SET`
    - `KIMI_API_KEY: NOT SET`
    - `KIMI_CODING_API_KEY: NOT SET`
- It queried Bitwarden, but Bitwarden was locked:
  - `bw status` returned `"status":"locked"`
  - `BW_SESSION: NOT SET`
  - A Bitwarden command failed with `error: unknown option '--output'`
- It inspected Hermes agent source code related to credentials and providers:
  - `/home/ubuntu/.hermes/hermes-agent/agent/credential_pool.py`
  - `/home/ubuntu/.hermes/hermes-agent/agent/auxiliary_client.py`
  - `/home/ubuntu/.hermes/hermes-agent/agent/moonshot_schema.py`
  - `/home/ubuntu/.hermes/hermes-agent/hermes_cli/auth.py`
- It found Kimi/Moonshot-related implementation details:
  - Kimi provider aliasing existed in `auxiliary_client.py`.
  - Kimi temperature should be omitted because Moonshot/Kimi manages it server-side.
  - Moonshot tool schemas were sanitized in `moonshot_schema.py`.
  - `_resolve_kimi_base_url()` routed `sk-kimi-` keys to `https://api.kimi.com/coding/v1` unless `KIMI_BASE_URL` was explicitly set.
- It investigated credential-pool behavior:
  - Strategies included:
    - `fill_first`
    - `round_robin`
    - `random`
    - `least_used`
  - Config showed:
    - `credential_pool_strategies.kimi-coding: fill_first`
  - Exhausted credentials were cooled down for 1 hour on `429` or default errors:
    - `EXHAUSTED_TTL_429_SECONDS = 60 * 60`
    - `EXHAUSTED_TTL_DEFAULT_SECONDS = 60 * 60`
- Some code-search attempts errored:
  - A `grep` over `/home/ubuntu/.hermes/hermes-agent` timed out after 10 seconds.
  - A Python regex search failed with:
    - `re.error: global flags not at the start of the expression at position 8`

### 3. Key decisions / solutions / conclusions
- No concrete Hevy/workout/entrenamientos conclusion was reached in the visible transcript.
- For Discord/channel, the only conclusion was the current Hermes Discord config state:
  - Discord required mentions.
  - No free-response channels were configured.
  - No allowed-channel restriction was configured.
  - Auto-threading and reactions were enabled.
  - No channel-specific prompts existed.
- For the actual debugging topic shown, the assistant appeared to conclude that Kimi/Moonshot credential availability was inconsistent or missing from the current process environment, and Bitwarden could not be used because it was locked.

### 4. Important commands, files, URLs, and technical details
Important files/paths:
- `/home/ubuntu/.hermes/config.yaml`
- `/home/ubuntu/.hermes/auth.json`
- `/home/ubuntu/.hermes/sessions/`
- `/home/ubuntu/.hermes/hermes-agent/AGENTS.md`
- `/home/ubuntu/.hermes/hermes-agent/agent/credential_pool.py`
- `/home/ubuntu/.hermes/hermes-agent/agent/auxiliary_client.py`
- `/home/ubuntu/.hermes/hermes-agent/agent/moonshot_schema.py`
- `/home/ubuntu/.hermes/hermes-agent/hermes_cli/auth.py`

Important config details:
```yaml
discord:
  require_mention: true
  free_response_channels: ''
  allowed_channels: ''
  auto_thread: true
  reactions: true
  channel_prompts: {}
  server_actions: ''
```

Important model/provider config snippets:
```yaml
model:
  default: kimi-k2.5
  provider: kimi-coding
  base_url: https://api.moonshot.ai/v1
credential_pool_strategies:
  kimi-coding: fill_first
```

Important URLs:
- `https://api.moonshot.ai/v1`
- `https://api.kimi.com/coding/v1`
- `https://portal.nousresearch.com`
- `https://inference-api.nousresearch.com/v1`
- Moonshot schema discussion URL referenced in code:
  - `https://forum.moonshot.ai/t/tool-calling-specification-violation-on-moonshot-api/102`

Important errors:
- Bitwarden:
  - `error: unknown option '--output'`
  - `"status":"locked"`
  - `BW_SESSION: NOT SET`
- Search/code errors:
  - `subprocess.TimeoutExpired: Command '['grep', '-r', '--include=*.py', '-l', 'kimi', '/home/ubuntu/.hermes/hermes-agent']' timed out after 10 seconds`
  - `re.error: global flags not at the start of the expression at position 8`

### 5. Unresolved / notable
- The visible transcript did not contain enough information to reconstruct any actual Hevy/workout/entrenamientos work.
- Discord/channel information was only incidental config inspection; no Discord channel setup, Hevy integration, workout automation, or “entrenamientos” flow was shown.
- Bitwarden remained locked, so secrets could not be fetched from it.
- Kimi/Moonshot API-key state remained unclear because one command showed a redacted key while later environment checks showed all expected Kimi/Moonshot vars unset.

Summary:
The conversation was mainly about configuring Hermes/Pipo’s model routing and multi-model setup, with only minor relevant details around Discord/Telegram channels.

1. **What the user wanted**
   - The user first asked to review Hermes’ current model/routing configuration after discussing whether Claude, GPT-5, or Gemini would be best for Hermes.
   - They then asked whether multiple models could be added to the “roster” because they had “varios amigos.”
   - They wanted to understand whether Hermes profiles with separate gateways would work together or as isolated agents.
   - The user ultimately clarified they wanted **one single stronger Pipo** that automatically chose the best model depending on context.
   - At the end, they asked whether they could install **ClawRouter** too.
   - No concrete work was done around **Hevy**, **workouts**, or **entrenamientos** in the visible transcript. Discord/channel details only appeared as part of Hermes config and documentation.

2. **Actions taken and outcomes**
   - The assistant searched for Hermes config files and found:
     - `/home/ubuntu/.hermes/config.yaml`
     - `/home/ubuntu/.hermes/hermes-agent/skills/mlops/inference/obliteratus/templates/abliteration-config.yaml`
     - `/home/ubuntu/.hermes/skills/mlops/inference/obliteratus/templates/abliteration-config.yaml`
   - The assistant read `/home/ubuntu/.hermes/config.yaml`.
   - The current Hermes default model was found to be:
     ```yaml
     model:
       default: claude-sonnet-4-6
       provider: anthropic
     ```
   - `fallback_providers` was empty:
     ```yaml
     fallback_providers: []
     ```
   - `smart_model_routing` existed but was disabled:
     ```yaml
     smart_model_routing:
       enabled: false
       max_simple_chars: 160
       max_simple_words: 28
       cheap_model: {}
     ```
   - A commented-out example existed at the bottom of the config for enabling smart routing:
     ```yaml
     # smart_model_routing:
     #   enabled: true
     #   max_simple_chars: 160
     #   max_simple_words: 28
     #   cheap_model:
     #     provider: openrouter
     #     model: google/gemini-2.5-flash
     ```
   - Context compression was already configured to use Gemini:
     ```yaml
     compression:
       summary_model: google/gemini-3-flash-preview
       summary_provider: auto
     ```
   - The assistant inspected Hermes CLI help and confirmed available commands included:
     ```bash
     hermes profile ...
     hermes gateway ...
     hermes model ...
     hermes config ...
     ```
   - `hermes profile list` showed only the default profile:
     ```text
     Profile          Model                        Gateway      Alias
     ◆default         claude-sonnet-4-6            running      —
     ```

3. **Key decisions, solutions, and conclusions**
   - The assistant concluded that the current main/default Hermes model was **Claude Sonnet 4.6**, not Opus, though the active session seemed to be running Opus via override.
   - The assistant recommended:
     - Keeping `claude-sonnet-4-6` as the practical default, or upgrading to `claude-opus-4-6` for more complex/agentic work.
     - Enabling `smart_model_routing` to route short/simple messages to a cheaper model like Gemini Flash.
     - Adding a fallback model/provider for Anthropic downtime or rate limits.
   - For multiple models, the assistant explained three options:
     - **Profiles:** separate agents with separate config/memory/gateways.
     - **Smart model routing:** one Pipo that routes simple vs complex tasks automatically.
     - **Fallback model:** automatic failover when the main model errors.
   - The assistant clarified that **profiles are isolated silos by default**. Each profile has its own config, `.env`, memory, skills, sessions, and gateway process. They do **not natively work together** unless manually coordinated or connected via delegation.
   - The user chose the concept of **one single Pipo that chooses the model automatically**.
   - The assistant found/loaded an existing `model-router` skill and explained it already matched this goal for delegated/sub-agent tasks.

4. **Important technical details**
   - Hermes config path:
     ```text
     /home/ubuntu/.hermes/config.yaml
     ```
   - Relevant Discord config in `config.yaml`:
     ```yaml
     discord:
       require_mention: true
       free_response_channels: ''
       auto_thread: true
       reactions: true
     ```
     This meant Discord was configured to require mentions, had no free-response channels set, and auto-threading/reactions were enabled.
   - Telegram-related config:
     ```yaml
     TELEGRAM_HOME_CHANNEL: '842987156'
     platform_toolsets:
       telegram:
       - hermes-telegram
     ```
   - Discord platform toolset:
     ```yaml
     platform_toolsets:
       discord:
       - hermes-discord
     ```
   - The assistant searched Hermes docs and found the “Profiles: Running Multiple Agents” documentation:
     - URL: `https://hermes-agent.nousresearch.com/docs/user-guide/profiles/`
   - Important profile commands discussed:
     ```bash
     hermes profile create <name>
     hermes profile create <name> --clone
     hermes profile create <name> --clone-all
     hermes profile create <name> --clone --clone-from <source>
     hermes profile list
     hermes profile use <name>
     hermes profile show <name>
     hermes profile rename <old> <new>
     hermes profile export <name>
     hermes profile import <file>.tar.gz
     hermes profile delete <name>
     ```
   - Profile aliases were described as wrapper scripts under:
     ```text
     ~/.local/bin/<name>
     ```
     which set:
     ```bash
     HERMES_HOME=~/.hermes/profiles/<profile>
     ```
   - Gateway service examples from docs:
     ```bash
     coder gateway start
     coder gateway install
     ```
   - The `model-router` skill classified tasks into tiers:
     - Tier 1/simple → Gemini 2.0 Flash or similar cheap model.
     - Tier 2/moderate → Claude Haiku 4.5 or Gemini 2.5 Flash.
     - Tier 3/complex → Claude Sonnet 4.5 / Gemini 2.5 Pro / GPT-5.
     - Tier 4/reasoning → Claude Opus 4.6 / o3 / DeepSeek R1.
   - The assistant noted that this `model-router` skill mainly applied when spawning/delegating sub-tasks, not necessarily the main conversation. For the main conversation, native Hermes `smart_model_routing` was only a simpler two-model style route.
   - ClawRouter search found the GitHub repo:
     - URL: `https://github.com/BlockRunAI/ClawRouter`
   - ClawRouter was described as an OpenAI-compatible/local proxy/router using smart model selection and USDC/x402 payments.
   - ClawRouter quick-start command found:
     ```bash
     curl -fsSL https://blockrun.ai/ClawRouter-update | bash
     openclaw gateway restart
     ```
   - Standalone command found:
     ```bash
     npx @blockrun/clawrouter
     ```
   - ClawRouter endpoint mentioned in search/docs:
     ```text
     http://localhost:8402/v1
     ```
   - Existing Hermes custom provider already included a local `blockrun` endpoint:
     ```yaml
     custom_providers:
     - api_key: ''
       api_mode: chat_completions
       base_url: http://127.0.0.1:8402/v1
       name: blockrun
     ```

5. **Unresolved / notable**
   - No actual config changes were applied in the visible transcript.
   - `smart_model_routing` remained disabled.
   - No fallback model/provider was configured yet.
   - ClawRouter was discussed and researched, but installation was not completed in the visible portion.
   - It remained unresolved whether the user wanted the assistant to actually enable smart routing, configure the `blockrun`/ClawRouter provider, or install ClawRouter.
   - No Hevy/workout/entrenamientos integration or Discord workout channel automation was addressed in the visible transcript.


Sí: este es el canal/thread **#hevy**.

Lo creamos para usarlo como espacio dedicado a **Hevy / entrenamientos / tracking de workouts**: registrar o consultar rutinas, progreso, ejercicios, historial, métricas, etc., sin mezclarlo con el resto de cosas de Pipo/Hermes.

Lo que sí tengo que aclarar: en mi memoria no aparece todavía una implementación concreta de integración con Hevy en este canal. Veo bastante historial de Discord/gateway/permisos, pero no una automatización Hevy ya terminada. O sea: el propósito era ese, pero si llegamos a armar algo más específico —sync con API, resúmenes semanales, logs de fuerza, PRs, etc.— no me quedó guardado como contexto recuperable.


[nachlakes] No pasa nada. Te fijas porfa si podes conectarte a Hevy si encontrás la api key?


hevy

---
name: hevy
description: Query workout data from Hevy including workouts, routines, exercises, and history. Use when user asks about their workouts, gym sessions, exercise progress, or fitness routines.
homepage: https://hevy.com
metadata:
  clawdbot:
    emoji: "🏋"
    requires:
      bins: ["hevy"]
      env: ["HEVY_API_KEY"]
---

# Hevy CLI

CLI for the Hevy workout tracking API. Query workouts, routines, exercises, and track progress.

## Setup

Requires Hevy Pro subscription for API access.

1. Get API key from https://hevy.com/settings?developer
2. Set environment variable: `export HEVY_API_KEY="your-key"`

## Commands

### Status

```bash
# Check configuration and connection
hevy status
```

### Workouts

```bash
# List recent workouts (default 5)
hevy workouts
hevy workouts --limit 10

# Fetch all workouts
hevy workouts --all

# Show detailed workout
hevy workout <workout-id>

# JSON output
hevy workouts --json
hevy workout <id> --json

# Show weights in kg (default is lbs)
hevy workouts --kg
```

### Routines

```bash
# List all routines
hevy routines

# Show detailed routine
hevy routine <routine-id>

# JSON output
hevy routines --json
```

### Exercises

```bash
# List all exercise templates
hevy exercises

# Search by name
hevy exercises --search "bench press"

# Filter by muscle group
hevy exercises --muscle chest

# Show only custom exercises
hevy exercises --custom

# JSON output
hevy exercises --json
```

### Exercise History

```bash
# Show history for specific exercise
hevy history <exercise-template-id>
hevy history <exercise-template-id> --limit 50

# JSON output
hevy history <exercise-template-id> --json
```

### Creating Routines

```bash
# Create routine from JSON (stdin)
echo '{"routine": {...}}' | hevy create-routine

# Create routine from file
hevy create-routine --file routine.json

# Create a routine folder
hevy create-folder "Push Pull Legs"

# Update existing routine
echo '{"routine": {...}}' | hevy update-routine <routine-id>

# Create custom exercise (checks for duplicates first!)
hevy create-exercise --title "My Exercise" --muscle chest --type weight_reps

# Force create even if duplicate exists
hevy create-exercise --title "My Exercise" --muscle chest --force
```

**⚠ Duplicate Prevention:** `create-exercise` checks if an exercise with the same name already exists and will error if found. Use `--force` to create anyway (not recommended).

**Routine JSON format:**
```json
{
  "routine": {
    "title": "Push Day 💪",
    "folder_id": null,
    "notes": "Chest, shoulders, triceps",
    "exercises": [
      {
        "exercise_template_id": "79D0BB3A",
        "notes": "Focus on form",
        "rest_seconds": 90,
        "sets": [
          { "type": "warmup", "weight_kg": 20, "reps": 15 },
          { "type": "normal", "weight_kg": 60, "reps": 8 }
        ]
      }
    ]
  }
}
```

### Other

```bash
# Total workout count
hevy count

# List routine folders
hevy folders
```

## Usage Examples

**User asks "What did I do at the gym?"**
```bash
hevy workouts
```

**User asks "Show me my last chest workout"**
```bash
hevy workouts --limit 10  # Find relevant workout ID
hevy workout <id>         # Get details
```

**User asks "How am I progressing on bench press?"**
```bash
hevy exercises --search "bench press"  # Get exercise template ID
hevy history <exercise-id>              # View progression
```

**User asks "What routines do I have?"**
```bash
hevy routines
hevy routine <id>  # For details
```

**User asks "Find leg exercises"**
```bash
hevy exercises --muscle quadriceps
hevy exercises --muscle hamstrings
hevy exercises --muscle glutes
```

**User asks "Create a push day routine"**
```bash
# 1. Find exercise IDs
hevy exercises --search "bench press"
hevy exercises --search "shoulder press"
# 2. Create routine JSON with those IDs and pipe to create-routine
```

## Notes

- **Duplicate Prevention:** `create-exercise` checks for existing exercises with the same name before creating. Use `--force` to override (not recommended).
- **API Limitations:** Hevy API does NOT support deleting or editing exercise templates - only creating. Delete exercises manually in the app.
- **API Rate Limits:** Be mindful when fetching all data (--all flag)
- **Weights:** Defaults to lbs, use --kg for kilograms
- **Pagination:** Most commands auto-paginate, but limit flags help reduce API calls
- **IDs:** Workout/routine/exercise IDs are UUIDs, shown in detailed views

## API Reference

Full API docs: https://api.hevyapp.com/docs/

### Available Endpoints
- `GET /v1/workouts` - List workouts (paginated)
- `GET /v1/workouts/{id}` - Get single workout
- `GET /v1/workouts/count` - Total workout count
- `GET /v1/routines` - List routines
- `GET /v1/routines/{id}` - Get single routine
- `GET /v1/exercise_templates` - List exercises
- `GET /v1/exercise_templates/{id}` - Get single exercise
- `GET /v1/exercise_history/{id}` - Exercise history
- `GET /v1/routine_folders` - List folders

### Write Operations (supported but use carefully)
- `POST /v1/workouts` - Create workout
- `PUT /v1/workouts/{id}` - Update workout
- `POST /v1/routines` - Create routine
- `PUT /v1/routines/{id}` - Update routine
- `POST /v1/exercise_templates` - Create custom exercise
- `POST /v1/routine_folders` - Create folder

The CLI focuses on read operations. Write operations are available via the API client for programmatic use.


Explanation:
**Searching for API key**

I need to find the API key. I should consider using the terminal environment or check the skills available, like looking into binaries. I'll check the commands, environment variables, or files. The user mentioned finding it in a specific file, like a .env file, which seems like a good place to start. I'll also think about checking Bitwarden if that could help me retrieve the key I need. Let's get started!

terminal

command -v hevy || true; python3 - <<'PY'
import os
for k in os.environ:
    if 'HEVY' in k.upper():
        print(k+'='+('SET' if os.environ[k] else 'EMPTY'))
PY


{"total_count": 0}

[Subdirectory context discovered: ~/CLAUDE.md]
# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Environment

Ubuntu 24.04 LTS, ARM64 (Oracle Cloud VPS). This is the home directory of an AI agent named **Pipo**, serving user **Ignacio/Chicho**.

- Node.js v22, npm 10, Python 3.12, Git 2.43, Bitwarden CLI, Tailscale
- Tailscale IP: `100.87.116.90`, hostname: `miopenclaw-vnic.tail9799d2.ts.net`

## Key Directories

| Path | Purpose |
|------|---------|
| `~/.openclaw/workspace/` | Main agent workspace (canonical) |
| `~/openclaw-workspace/` | Secondary git repo with project artifacts |
| `~/.openclaw/openclaw.json` | OpenClaw platform config (gateway, channels, skills) |
| `~/.bw/` | Bitwarden session token |

## Shell Helpers (from `~/.bash_aliases`)

```bash
bw-sync           # Load saved Bitwarden session
bw-unlock         # Unlock vault and save session
g "prompt"        # Gemini CLI wrapper
c "prompt"        # Codex safe mode (read-only, no internet)
cn "prompt"       # Codex network mode (full access)
pipo-deploy <port> [name]   # Expose port to Tailscale at /preview/<name>/
pipo-undeploy <name>        # Remove Tailscale preview route
pj                # Job controller (~/.openclaw/workspace/ops/cli-jobs/jobctl.sh)
```

## Critical Constraints

**OpenClaw gateway** (port 18789): Restarting severs any active agent session. Prefer config hot-reload when possible (`openclaw config set ...`). If a restart is truly needed (e.g. fixing broken Telegram delivery), it is allowed — Ignacio authorizes it. Use `openclaw gateway restart` and verify logs afterward.

**Chromium browser tool**: ALWAYS use profile `openclaw` (connected to headless Chromium on port 9222). NEVER use profile `chrome` — that profile requires a manual extension that does not exist on this VPS.

**Tailscale serve**: Port 443 root (`/`) is reserved for the OpenClaw dashboard. Use preview ports 3001–3010 for apps and expose them via `pipo-deploy`.

## Deployment Pattern

1. Start your app on a port in range 3001–3010
2. Run `pipo-deploy <port> <name>`
3. App is accessible at `https://miopenclaw-vnic.tail9799d2.ts.net/preview/<name>/`

## Agent Workspace Structure (`~/.openclaw/workspace/`)

- `SOUL.md` / `AGENTS.md` — agent persona and session protocol
- `MEMORIES.md` — long-term curated memory (canonical; `MEMORY.md` is a compat alias)
- `memory/YYYY-MM-DD.md` — daily session logs
- `HEARTBEAT.md` — periodic task checklist (empty = skip heartbeat)
- `skills/` — installed agent skills (bitwarden, openclaw-bitwarden, parallel-preview-orchestrator)
- `ops/` — operational scripts (cli-jobs, docker-cli-stack)
- `node_modules/` — Playwright installed here

## Secrets

Retrieved from Bitwarden vault. After `bw-sync`, use:
```bash
bw get username "item-name"
bw get password "item-name"
```

API keys (Gemini, OpenAI/Codex, GitHub) are auto-loaded from `~/.openclaw/openclaw.json` into the shell environment via `~/.bash_aliases`.


"content": "{\"success\": true, \"name\": \"hevy\", \"description\": \"Query workout data from Hevy including workouts, routines, exercises, and history. Use when user asks about their workouts, gym sessions, exercise progress, or fitness routines.\", \"tags\": [], \"related_skills\": [], \"content\": \"---\\nname: hevy\\ndescription: Query workout data from Hevy including workouts, routines, exercises, and history. Use when user asks about their workouts, gym sessions, exercise progress,

console.log("❌ Not configured - HEVY_API_KEY environment variable not set");

console.log("Then set: export HEVY_API_KEY=***

console.log("✓ HEVY_API_KEY is set");

* API key is read from HEVY_API_KEY environment variable.

const ENV_VAR_NAME = "HEVY_API_KEY";

* Docs: https://api.hevyapp.com/docs/

const BASE_URL = "https://api.hevyapp.com";

* Docs: https://api.hevyapp.com/docs/

env: ["HEVY_API_KEY"]

2. Set environment variable: `export HEVY_API_KEY="***"`

Full API docs: https://api.hevyapp.com/docs/

* Docs: https://api.hevyapp.com/docs/

const ENV_VAR_NAME = "HEVY_API_KEY";

console.log("✓ HEVY_API_KEY is set");