clarify

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

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

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

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

The question to present to the user.

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

cronjob

Manage scheduled cron jobs with a single compressed tool.

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

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

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

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

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

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

Required for update/pause/resume/remove/run

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

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

Optional human-friendly name

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Task goal

Task-specific context

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

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

Per-task ACP args override.

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

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

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

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

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

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

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

Available via `from hermes_tools import ...`:

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

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

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

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

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

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

memory

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

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

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

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

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

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

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

The action to perform.

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

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

Short unique substring identifying the entry to replace or remove.

patch

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

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

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

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

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

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

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

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

process

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

list poll log wait kill write submit close

Action to perform on background processes

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

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

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

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

Max lines to return for 'log' action

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

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

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

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

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

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

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

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

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

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

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

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

create patch edit delete write_file remove_file

The action to perform.

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

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

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

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

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

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

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

Content for the file. Required for 'write_file'.

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

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

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

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

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

Optional category filter to narrow results

terminal

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

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

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

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

The command to execute on the VM

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

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

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

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

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

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

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

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

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

todo

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

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

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

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

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

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

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

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

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

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

Complete content to write to the file


Was ist der Unterschied zwischen camofox und playwright Browser?


Explanation:
Der Benutzer fragt nach dem Unterschied zwischen "camofox" und "playwright Browser". Ich habe in meinem Memory einen Eintrag zu "zimaos-camofox-browser" als Skill. Lass mich das laden, um zu verstehen, was Camofox ist. Und ich sollte auch wissen, was Playwright Browser ist.

Lass mich die relevanten Skills laden.

Question: camofox OR playwright browser


zimaos-camofox-browser

---
name: zimaos-camofox-browser
description: "Camofox-Browser als persistenten Docker-Dienst auf ZimaOS für Hermes-Browser-Automation einrichten und betreiben."
version: 1.0.0
author: Kiwi (Ahmed Zeyd Aytac)
tags: [zimaos, camofox, browser, docker, compose, hermes, anti-detection, persistence]
---

# ZimaOS Camofox Browser

Ziel: Auf dem ZimaOS-Server soll Browser-Automation für Hermes **immer** über einen lokalen Camofox-Browser-Container laufen — nicht über Webtop, nicht primär über linuxserver/chromium, nicht primär über Kasm und nicht primär über Browserless.

## Architekturstandard

1. **Camofox local mode** als Docker/Compose-Dienst auf ZimaOS bevorzugen.
2. Hermes verwendet seine nativen Browser-Tools über `CAMOFOX_URL`.
3. Hermes direkt auf ZimaOS-Host → `CAMOFOX_URL=http://127.0.0.1:9377`.
4. Hermes selbst in Docker → gemeinsames Docker-Netzwerk und `CAMOFOX_URL=http://camofox-browser:9377`.
5. **Persistence aktivieren**, damit Cookies, Logins, LocalStorage und Sessions über Neustarts erhalten bleiben.
6. noVNC nur zur Beobachtung oder manuellen MFA/CAPTCHA/Login-Hilfe; noVNC ist **nicht** die primäre Agentensteuerung.
7. Camofox-API nicht öffentlich ins WAN exponieren. Port 9377 an localhost oder nur an internes Docker-Netzwerk binden.
8. **Keinen `CAMOFOX_API_KEY` setzen**, solange Hermes' Camofox-Integration bei gesetztem Key 403-Probleme haben kann. Sicherheit stattdessen über Bind-Adresse, Firewall, Docker-Netzwerk, VPN oder SSH-Tunnel.
9. Für ZimaOS persistente Pfade unter `/DATA/AppData/camofox` verwenden. Keine fragilen Container ohne Volume.
10. Sessions nach Aufgaben: erst `browser.camofox.managed_persistence: true` prüfen, optional extern gemanagte Sessions mit `CAMOFOX_USER_ID`, `CAMOFOX_SESSION_KEY` und `CAMOFOX_ADOPT_EXISTING_TAB=true`.

## Referenz-Compose für ZimaOS

```yaml
services:
  camofox-browser:
    image: camofox-browser:local
    container_name: camofox-browser
    restart: unless-stopped
    ports:
      - "127.0.0.1:9377:9377"
      - "127.0.0.1:6080:6080"
    environment:
      CAMOFOX_PORT: "9377"
      ENABLE_VNC: "1"
      VNC_BIND: "0.0.0.0"
      VNC_RESOLUTION: "1920x1080"
      MAX_OLD_SPACE_SIZE: "2048"
    volumes:
      - /DATA/AppData/camofox:/root/.camofox
```

## Hermes-Konfiguration

### 1. `.env` — CAMOFOX_URL setzen

```bash
# In ~/.hermes/.env:
CAMOFOX_URL=http://127.0.0.1:9377
```

Bei Hermes-in-Docker stattdessen:
```bash
CAMOFOX_URL=http://camofox-browser:9377
```

### 2. `config.yaml` — managed persistence

```yaml
browser:
  camofox:
    managed_persistence: true
```

### 3. Browser-Toolset aktivieren

```bash
hermes tools enable browser
```

### 4. Hermes vollständig neu starten

Nach allen Änderungen muss Hermes komplett neu gestartet werden (Gateway-Restart oder CLI-Neustart).

## Verifikation

### Schritt 1: Camofox-Erreichbarkeit prüfen

```bash
curl -fsS http://127.0.0.1:9377/health
# oder einen anderen API-Endpunkt des Camofox-Servers
```

### Schritt 2: CAMOFOX_URL in .env prüfen

```bash
grep CAMOFOX_URL ~/.hermes/.env
```

### Schritt 3: Browser-Aufgabe + noVNC-Beobachtung

```bash
# noVNC über SSH-Tunnel:
ssh -L 6080:127.0.0.1:6080 user@zimaos
# Dann im Browser: http://localhost:6080
```

Starte eine Browser-Aufgabe in Hermes und beobachte über noVNC.

### Schritt 4: Persistenz-Test

1. Logge dich manuell auf einer Website ein.
2. Beende die Browser-Aufgabe.
3. Starte eine neue Browser-Aufgabe.
4. Prüfe, ob der Login erhalten bleibt.

### Schritt 5: Docker-Netzwerk-Fallback

Wenn Hermes in Docker läuft und `127.0.0.1` nicht funktioniert:
```bash
CAMOFOX_URL=http://camofox-browser:9377
```
im gemeinsamen Docker-Netzwerk verwenden.

## Build-Prozess (ZimaOS)

Da `jo-inc/camofox-browser` kein öffentliches Docker-Image hat, muss **lokal gebaut** werden.
ZimaOS hat kein `make` — die Schritte sind manuell:

### 1. Repo clonen + Assets downloaden

```bash
cd /tmp
git clone https://github.com/jo-inc/camofox-browser.git
cd camofox-browser
mkdir -p dist
curl -fSL "https://github.com/daijro/camoufox/releases/download/v135.0.1-beta.24/camoufox-135.0.1-beta.24-lin.x86_64.zip" -o dist/camoufox-x86_64.zip
curl -fSL "https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp_linux" -o dist/yt-dlp-x86_64
```

### 2. Image bauen (mit VNC-Support)

Der Standard-Dockerfile hat **kein** `x11vnc`, `noVNC` oder `websockify`.
Ein angepasster Dockerfile wird benötigt. Siehe `references/build-dockerfile.md` für das vollständige Dockerfile.

```bash
DOCKER_BUILDKIT=1 docker build --no-cache \
  --build-arg ARCH=x86_64 \
  --build-arg CAMOUFOX_VERSION=135.0.1 \
  --build-arg CAMOUFOX_RELEASE=beta.24 \
  -t camofox-browser:135.0.1-x86_64 \
  -f /path/to/custom.Dockerfile .
```

### 3. VNC-Config für Camofox

VNC ist im Standard-Image **deaktiviert**. Config mit aktiviertem VNC als Volume mounten:

```json
{
  "id": "camofox-browser",
  "name": "Camofox Browser",
  "version": "1.6.0",
  "plugins": {
    "youtube": { "enabled": true },
    "persistence": { "enabled": true },
    "vnc": { "enabled": true, "resolution": "1920x1080" }
  }
}
```

Die Datei ins Compose-File einbinden (siehe Referenz-Compose).

### 4. Websockify nachinstallieren (im laufenden Container)

`websockify` fehlt im Python-Path des Containers und muss nachinstalliert werden:

```bash
docker exec camofox-browser pip3 install --break-system-packages websockify
# Oder im Dockerfile ergänzen: RUN pip3 install --break-system-packages websockify
```

### Compose-File starten

```bash
docker --config /tmp/docker-config compose -f /DATA/AppData/camofox-compose/docker-compose.yml up -d
```

**Wichtig:** Auf ZimaOS immer `DOCKER_CONFIG=/tmp/docker-config` setzen — `/root/.docker` ist read-only.

## Web-Research Patterns (new in this session)

When doing ad-hoc web research through Camofox, use this ordered fallback chain:

1. **Search with DuckDuckGo first.** Google frequently blocks Camofox/jina.ai with 429/CAPTCHA. DuckDuckGo (`https://duckduckgo.com/?q=...`) is far more tolerant of automated text extraction and usually returns enough link text via the `/tabs/:tabId/links` endpoint.
2. **Extract article text with r.jina.ai.** `curl -sL "https://r.jina.ai/<URL>" -H "Accept: text/plain"` renders JS and returns clean markdown. Many news sites and blogs work; some block it (403). Use it before falling back to screenshots.
3. **Use Camofox only when the above is insufficient.** Good for: JavaScript-heavy sites, pages that block jina.ai, consent popups, or when you need a screenshot for visual confirmation.
4. **Avoid Fandom, Reddit, and X for anonymous extraction.** All three actively block anonymous/automated access with Cloudflare, JS challenges, or login walls. Use them only as a last resort and expect failures.

### Content Extraction from SPAs (JS-heavy sites)

Many modern sites are React/SPA — `curl` gets empty `<div id="root">`. This includes Austrian government sites: **RIS** (Rechtsinformationssystem), **Findok** (BMF Steuerdokumente), WU Wien, ÖH WU. For legal research on RIS, see `references/ris-legal-research.md` for the proven workflow.

General extraction options for SPAs:

1. **links endpoint** (fast, no JS needed): `GET /tabs/:tabId/links?userId=kiwi` — returns link texts, but hrefs may be empty (JS-routed).
2. **screenshot + vision** (reliable): Take screenshot, `vision_analyze` it for visual content.
3. **jina.ai reader** (best for text extraction): `curl -sL "https://r.jina.ai/<URL>" -H "Accept: text/plain"` — renders JS and returns clean markdown. Use when browser-extracted content is insufficient.
4. **Hidden JSON APIs** (fastest, no browser needed): Many JS SPAs have backend API endpoints. Look for them in page source (`grep -oP 'api|endpoint|json|type=\d+'`), or try common patterns like `?type=INTEGER` on the page URL. Example: `ihr-notariat.at/notarfinder/?type=1602231006` returned the full Austrian notary database as JSON — avoiding the entire SPA. Always try this before launching browser automation for content extraction.
5. **evaluate endpoint**: `POST /tabs/:tabId/evaluate` — requires `CAMOFOX_API_KEY` unless loopback. For localhost calls without API key, prefer links + screenshot.

### Cookie / Consent Popups

Many EU sites (WU, ÖH, Herzstiftung, Deutsches Ärzteblatt) show cookie banners that overlay content and often resist automated dismissal. Dismiss via:
- `POST /tabs/:tabId/click` with selector `"button:has-text('Alle akzeptieren')"` or similar
- If clicking times out (popup blocks interaction), try waiting 2-3s first, then click
- Take screenshot after dismissal to verify
- For medical/government sites specifically, cookie walls and paywalls frequently defeat both `jina.ai` and Camofox `/click`. See `references/research-patterns.md` Pattern 5 for the full fallback chain.

### Prayer Times API

For Islamic prayer times, use the free Aladhan API. See `references/aladhan-prayer-times-api.md` for endpoints, parameters, and the high-latitude adjustment method needed for cities like Vienna.

### Gold & Precious Metals Valuation

For valuing gold coins, bars, and silver: combine live spot-price APIs (gold-api.com, exchangerate-api.com) with dealer-specific Camofox screenshots. See `references/gold-precious-metals-research.md` for the full two-layer workflow, Turkish coin reference table, and conversion formulas.

### Auth / sessionKey Convention

- **POST endpoints**: `userId` and `sessionKey` go in the **JSON body**, NOT as query params. Query params are ignored.
  ```bash
  # ✅ CORRECT
  curl -X POST "http://127.0.0.1:9377/tabs" \
    -d '{"userId":"kiwi","sessionKey":"default","url":"https://example.com"}'

  # ❌ WRONG — query params ignored
  curl -X POST "http://127.0.0.1:9377/tabs?userId=kiwi&sessionKey=default" \
    -d '{"url":"https://example.com"}'
  ```
- **GET endpoints** (screenshot, links): use **query params**.
  ```bash
  curl "http://127.0.0.1:9377/tabs/$TAB/screenshot?userId=kiwi&sessionKey=default"
  ```
- The `sessionKey` value is arbitrary — `"default"`, `"tuya"`, etc. all work as long as it's present.

### API Endpoint Quirks

| Endpoint | Method | Notes |
|----------|--------|-------|
| `/tabs` | POST | Create tab — `{userId, sessionKey, url}` in body → returns `tabId` |
| `/tabs?userId=kiwi` | GET | List all tabs for user — works |
| `/tabs/:tabId?userId=kiwi` | GET | May return 404 with current version — use listing instead |
| `/tabs/:tabId/links?userId=kiwi&sessionKey=X` | GET | Link extraction — JS-routed sites have empty hrefs (React SPA). Returns `{links, pagination}`. Buttons/submit inputs inside forms are NOT listed (they're not `<a>` tags) |
| `/tabs/:tabId/screenshot?userId=kiwi&sessionKey=X` | GET | Screenshot — works, reliable. Returns PNG via stdout |
| `/tabs/:tabId/evaluate` | POST | **BLOCKED without CAMOFOX_API_KEY** even on loopback. Returns `{"error":"This endpoint requires CAMOFOX_API_KEY except for loopback requests in non-production environments."}`. Workaround: use `/type` for form filling + `/click` for interaction |
| `/tabs/:tabId/type` | POST | Type text into an input field. Body: `{userId, sessionKey, selector, text}`. Returns `{"ok":true}`. Works for both text and password fields. Chain `/type` + `/click` as a substitute for `/evaluate` when API key is absent |
| `/tabs/:tabId/click` | POST | CSS selector click. Body: `{userId, sessionKey, selector}`. May return `{"error":"Internal server error"}` on submit buttons inside forms — `/type` + form interaction may be needed instead |
| `/tabs/:tabId/navigate` | POST | Set URL. Body: `{userId, sessionKey, url}`. **Cannot navigate to `javascript:` URLs** — returns "Bad Request". Only works with `http(s)://` URLs |
| `/health` | GET | Health check — `browserConnected` goes true lazily on first tab |

## Troubleshooting

- **Google returns 429/CAPTCHA during research:** Switch to DuckDuckGo or use r.jina.ai on the target URL directly. Do not keep retrying Google from the same IP.
- **Camofox nicht erreichbar:** `docker logs camofox-browser` prüfen.
- **403 bei gesetztem CAMOFOX_API_KEY:** Key entfernen, Sicherheit über Netzwerk-Binding sicherstellen.
- **Cookies/Logins nicht persistent:** Prüfen ob `/DATA/AppData/camofox` gemountet ist und `managed_persistence: true` in config.yaml steht.
- **noVNC nicht erreichbar:** Port 6080 nur an localhost gebunden? SSH-Tunnel nutzen: `ssh -L 6080:127.0.0.1:6080 user@zimaos`
- **`/root/.docker` read-only:** Auf ZimaOS `DOCKER_CONFIG=/tmp/docker-config` vor allen Docker-Befehlen setzen.
- **`make: command not found`:** ZimaOS hat kein make. Build-Schritte manuell ausführen (curl für Assets, docker build direkt).
- **`websockify: not found`:** Im Container `pip3 install --break-system-packages websockify` ausführen, dann `websockify --web /usr/share/novnc 0.0.0.0:6080 127.0.0.1:5900` starten.
- **`browserConnected: false` in Health:** Normal — Camoufox startet lazily erst beim ersten Tab-Request. Einen Tab öffnen, dann prüft Health auf `true`.
- **VNC-Plugin deaktiviert:** In `camofox.config.json` muss `"vnc": { "enabled": true, "resolution": "1920x1080" }` stehen. Per Volume-Mount überschreiben.

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'


Question: camofox OR playwright browser

Summary:
The user wanted to establish a permanent browser automation setup for the Hermes AI agent on a ZimaOS server. The primary goal was to use a local **Camofox** (Firefox-based anti-detection browser) container rather than cloud-based solutions or standard Chromium.

### Actions Taken and Outcomes
*   **Skill Creation:** Created a persistent skill named `zimaos-camofox-browser` located at `/DATA/.hermes/skills/devops/zimaos-camofox-browser/SKILL.md`. This skill documents the architecture, configuration, and verification steps for the local Camofox setup.
*   **Hermes Configuration:**
    *   Modified `~/.hermes/.env` to set `CAMOFOX_URL=http://127.0.0.1:9377`.
    *   Updated `~/.hermes/config.yaml` to activate the `browser` toolset and enable `browser.camofox.managed_persistence: true`.
*   **Docker Deployment:**
    *   Cloned the `camofox-browser` repository to `/tmp/camofox-browser`.
    *   Since `make` was missing on the ZimaOS host, the assistant manually downloaded the required binaries (`camoufox` and `yt-dlp`) into the `dist/` folder.
    *   Built a custom Docker image: `camofox-browser:135.0.1-x86_64`.
    *   Deployed the container using a `docker-compose.yml` located at `/DATA/AppData/camofox-compose/`.
*   **Verification:** Confirmed the Camofox API was responsive via the `/health` endpoint and successfully performed a smoke test by creating a tab and navigating to `example.com`.

### Key Decisions and Technical Details
*   **Architecture:** Hermes uses its native browser tools routed through the `CAMOFOX_URL`. Persistence is enabled to maintain sessions, cookies, and logins across restarts.
*   **VNC Support:** Enabled the `vnc` plugin and `noVNC` for manual observation and MFA handling. The assistant had to rebuild the image multiple times to include missing dependencies (`noVNC` and `x11vnc`) required for the VNC watcher to function.
*   **Security:** The API and VNC ports (9377 and 6080) were bound to `127.0.0.1` to prevent public exposure, requiring an SSH tunnel for remote access.
*   **Paths:**
    *   **Config:** `/DATA/AppData/hermes/config.yaml`
    *   **Compose:** `/DATA/AppData/camofox-compose/docker-compose.yml`
    *   **Persistence:** `/DATA/AppData/camofox` mapped to `/root/.camofox` in the container.

### Unresolved / Notable
*   **VNC Plugin Stability:** The final part of the conversation involved troubleshooting the `vnc-watcher.sh` script, which initially failed due to `x11vnc` being missing from the container image. A rebuild was initiated to resolve this.
*   **Gateway Restart:** A restart of the Hermes gateway was required to pick up the new toolset and environment variables.

Summary:
This conversation from June 01, 2026, focused on a career evaluation regarding a job opening at the **Wirtschaftsuniversität Wien (WU)** and the management of a digital media library.

### 1. User Goals and Accomplishments
The user wanted to determine if applying for a **Teamassistenz** (Administrative Assistant) position at WU Wien would be beneficial for their career. Additionally, the user requested that the Assistant correct and supplement the metadata for a collection of **books** stored on their local server.

### 2. Actions Taken and Outcomes
*   **Job Research:** The Assistant used a browser-based tool to scrape the job description from a Rexx-systems portal. It identified the role as a part-time (20–30h) position in the **Department for Privatrecht**, specifically supporting **Univ.-Prof. Dr. Martin Spitzer**.
*   **Comparative Analysis:** The Assistant compared the WU position to the user's current role as a **Rechtsberater (Legal Advisor)** at **ÖAMTC** (18h for ~€1,040 net).
*   **Historical Data Retrieval:** The Assistant performed a `session_search` to find previous employment details, successfully recalling the existence of ÖAMTC pay slips in a document management system (**DocMaster**).
*   **Media Management Preparation:** The Assistant accessed the `media-file-management` skill to review naming conventions and metadata tagging procedures (using Python/mutagen and the Audible API) for the user's book library.

### 3. Key Decisions and Conclusions
*   **Strategic Value:** The Assistant concluded that the WU role is a "networking turbocompressor" for anyone seeking an **academic career** (PhD/Dissertation), as Prof. Spitzer leads the *ÖJZ* (Austrian Jurists' Newspaper) and major legal commentaries.
*   **Practical Trade-off:** For a career in **legal practice** (attorney/corporate), the Assistant advised against the move, noting that the WU role is purely administrative (SAP, travel billing) and represents a "content regression" compared to the user's current legal advisory work at ÖAMTC.
*   **Career Advice:** The decision was left to the user based on their long-term goal: "Wissenschaft" (Science/Academia) vs. "Praxis" (Legal Practice).

### 4. Technical Details
*   **Browser/Scraping Issues:** While researching, the Assistant encountered a **`SecurityCompromiseError`** when attempting to access Google. The error stated that anonymous access was blocked due to "previous abuse" and a "suspected DDoS attack."
*   **Credentials:** The Assistant recalled credentials for the **DocMaster** app: `ahmed` / `Uskudar1453!`.
*   **Paths and URLs:**
    *   **Job URL:** `https://wirtschaftsuniversitaet-wien-portal.rexx-systems.com/Teamassistenz-de-j2797.html`
    *   **Department URL:** `https://www.wu.ac.at/privatrecht/structure/`
    *   **Media Path:** `/media/HDD_1TB/Medien/Bücher/`
*   **Tools:** The Assistant utilized `terminal` for web scraping and `session_search` for historical context.

### 5. Unresolved or Notable
*   **Job Decision:** The user had not yet decided whether to apply by the end of the transcript.
*   **Metadata Execution:** The actual processing of the book metadata remained pending; the Assistant had only reviewed the necessary skills and scripts.
*   **Google Block:** The automated browser access to Google remained blocked at the time of the session.

Summary:
The conversation on May 11, 2026, touched upon the browser automation capabilities of the Hermes Agent (Kiwi) in response to the user sharing a documentation link regarding browser features.

### 1. User Intent
The user wanted to verify if the agent was utilizing the browser features described in the Hermes documentation (`https://hermes-agent.nousresearch.com/docs/user-guide/features/browser`) and how they were implemented.

### 2. Actions Taken and Outcomes
*   **Status Verification:** The assistant confirmed that browser automation was enabled and active on two distinct levels.
*   **Native Toolset Check:** The assistant verified that the `browser` and `web` toolsets were enabled in the Hermes configuration. It explained that the native `browser` tool typically utilizes **Camofox**, Browserbase, or a local **Playwright** installation.
*   **Docker Server Health Check:** The assistant performed a health check on a standalone "Kiwi Browser Server v2" running in a Docker container.
*   **Outcome:** The health check was successful, confirming the server was reachable and operational.

### 3. Key Decisions and Conclusions
*   **Dual Implementation:** It was concluded that the system uses both native toolsets for general web tasks and a dedicated Playwright-based server for more complex interactions involving mouse movements and clicks.
*   **Operational Status:** The assistant confirmed the browser environment was ready for use, though the conversation quickly shifted to other topics like Reddit AMA insights and Honcho memory.

### 4. Technical Details
*   **URL Referenced:** `https://hermes-agent.nousresearch.com/docs/user-guide/features/browser`
*   **Toolsets:** `browser` and `web` (both confirmed enabled).
*   **Kiwi Browser Server v2:**
    *   **Port:** `30000`
    *   **Version:** `v2-playwright`
    *   **Health Check Response:** `{"status": "ok", "version": "v2-playwright"}`
*   **Technologies Mentioned:** Camofox, Playwright, Browserbase, and Docker.

### 5. Unresolved or Notable Items
*   There were no unresolved issues regarding the browser itself; however, the assistant noted that while the browser tools were active, the system was facing other unrelated challenges, such as a full `/tmp` directory (RAM-disk) and issues with the **Honcho** memory plugin following a gateway restart.
*   The assistant later clarified that **Camofox** or local **Playwright** are the standard backends for the native `browser` toolset in this environment.


Explanation:
The user is asking about the difference between Camofox and Playwright browser. Let me synthesize what I know from the skill and session search.

Camofox is a Firefox-based anti-detection browser (based on Camoufox, a fork of Firefox) that runs as a persistent Docker container. It has its own HTTP API for browser automation (create tabs, navigate, click, type, screenshot, etc.).

Playwright is a Node.js/Python library for browser automation that can control Chromium, Firefox, and WebKit. In our setup, there's a "Kiwi Browser Server v2" running on port 30000 that uses Playwright.

Let me check the current config to see what's actually configured now.

terminal

grep -nE 'browser|camofox|playwright|CAMOFOX' /DATA/.hermes/config.yaml 2>/dev/null; echo "---"; grep CAMOFOX /DATA/.hermes/.env 2>/dev/null; echo "---"; curl -s --max-time 3 http://127.0.0.1:9377/health 2>/dev/null || echo "camofox not reachable"; echo "---"; curl -s --max-time 3 http://127.0.0.1:30000/ 2>/dev/null || echo "playwright server not reachable"


Output:
51:browser:
61:  camofox:
---
---
{"ok":true,"engine":"camoufox","browserConnected":false,"browserRunning":false,"activeTabs":0,"activeSessions":0,"consecutiveFailures":0,"memory":{"rssMb":132,"heapUsedMb":76,"nativeMemMb":56}}---
playwright server not reachable


**Camofox** und **Playwright** sind zwei verschiedene Browser-Automation-Backends — hier die Unterschiede:

---

### 🔥 Camofox (aktiv, Port 9377)
- **Engine:** Firefox-basiert — genauer: **Camoufox**, ein speziell gehärteter Firefox-Fork
- **Anti-Detection:** Eingebaut. Spooft Fingerprints, umgeht Bot-Erkennung (Cloudflare, Google-CAPTCHAs etc.)
- **Architektur:** Persistenter Docker-Container mit **REST-API** — Tabs per `curl` steuern
- **Persistenz:** Cookies, Logins, LocalStorage überleben Container-Neustarts (`managed_persistence: true`)
- **Status bei dir:** ✅ Läuft, Health-Check OK

### 🎭 Playwright (inaktiv, Port 30000)
- **Engine:** Microsofts Standard-Automation-Library — kann **Chromium, Firefox, WebKit** steuern
- **Anti-Detection:** Nicht eingebaut. Normales Playwright wird von vielen Seiten als Bot erkannt (es sei denn, man nutzt `playwright-stealth` o.Ä.)
- **Architektur:** Normalerweise eine **Code-Bibliothek** (`import playwright`), kein Standalone-Server. Der "Kiwi Browser Server v2" war ein Wrapper, der Playwright als HTTP-Service exposed hat
- **Status bei dir:** ❌ Server auf Port 30000 nicht erreichbar — vermutlich nicht mehr deployed

---

**Kurz gesagt:** Camofox = getarnter Fuchs (Firefox) für unerkannte Automation. Playwright = Schweizer Taschenmesser für allgemeine Browser-Steuerung, aber ohne Tarnkappe. Bei dir ist aktuell nur Camofox live.

Explanation:
The user is asking about the difference between Camofox and Playwright browser. Let me synthesize what I know from the skill, session history, and live config.

From the skill and session history:

**Camofox** is a Firefox-based anti-detection browser (based on Camoufox, a Firefox fork). It runs as a persistent Docker container on ZimaOS, providing a REST API for browser automation. It's designed to evade bot detection — it spoofs fingerprints, has anti-detection features built in. It's the primary browser backend for Hermes on this setup.

**Playwright** is a standard browser automation library (Microsoft's). It can drive Chromium, Firefox, or WebKit browsers programmatically. In this environment, there was a "Kiwi Browser Server v2" running on port 30000 that used Playwright. But from the live check, that server is NOT reachable right now.

Key differences:
1. **Engine**: Camofox = Firefox-based (Camoufox fork). Playwright = can drive Chromium/Firefox/WebKit.
2. **Anti-detection**: Camofox is specifically built for stealth/anti-detection. Playwright is a general automation tool — sites can detect it unless you use stealth plugins.
3. **Architecture**: Camofox runs as a persistent Docker service with REST API. Playwright is typically a library you import in code, though it can also be wrapped in a server (like the Kiwi Browser Server v2 that was on port 30000).
4. **Current status**: Camofox is live and configured (port 9377, health check OK). Playwright server (port 30000) is not reachable.
5. **Persistence**: Camofox has built-in session persistence (cookies, logins survive restarts). Playwright's persistence depends on how you configure browser contexts.

Let me give a clear, concise answer in German since the user asked in German.


Arbeitet die Türkei gerade an einem LLM?


Explanation: Der Nutzer fragt, ob die Türkei derzeit an einem LLM (Large Language Model) arbeitet. Ich sollte aktuelle Informationen dazu recherchieren. Ich werde eine Websuche durchführen, um herauszufinden, ob es türkische LLM-Projekte gibt.

Question: Turkey LLM large language model 2025 2026 Turkish AI model development


Tool 'web_search' does not exist. Available tools: clarify, cronjob, delegate_task, execute_code, memory, patch, process, read_file, search_files, send_message, session_search, skill_manage, skill_view, skills_list, terminal, text_to_speech, todo, vision_analyze, write_file


Explanation:
The user is asking whether Turkey is currently working on an LLM. I don't have web_search available, but I can use the terminal to curl search engines or use Camofox browser. Let me try DuckDuckGo via terminal first, and also check if there's any relevant info in my memory or past sessions.

Let me try a few approaches in parallel: terminal curl to DuckDuckGo, and session_search for any past discussions about Turkish AI/LLM.

curl -sL "https://lite.duckduckgo.com/lite/?q=Turkey+LLM+large+language+model+Turkish+AI+2025+2026" | sed -n 's/.*<a[^>]*href="\([^"]*\)"[^>]*>\(.*\)<\/a>.*/\1 | \2/p' | head -20

Question: Turkey OR Turkish OR LLM OR AI model OR yapay zeka


Output: https://duckduckgo.com/ | here


Question: Turkey OR Turkish OR LLM OR AI model OR yapay zeka

Summary:
The conversation, which took place on June 05, 2026, focused on the tax implications of a **Turkish pension** for an individual residing in Austria and the legal pathways for **Turkish investors** to obtain an Austrian residency permit.

### 1. Tax Treatment of Turkish Pensions in Austria
The user sought advice for their grandfather ("Opa"), an Austrian resident receiving a pension from Turkey. The assistant conducted a detailed search of the **Double Taxation Agreement (DBA) between Austria and Turkey** (BGBl III 2009/96, Law No. 20006481).

*   **Key Findings from the DBA:**
    *   **Article 18 (Private Pensions):** These are taxed exclusively in the country of residence. Since the recipient lives in Austria, Austria has the sole right to tax the Turkish private pension.
    *   **Article 19 (Public Service Pensions):** These are generally taxed in the source state (Turkey) unless the recipient is a national of the residence state (Austria).
    *   **Article 23 (Methods):** Austria uses the "Exemption Method with Progression" (Progressionsvorbehalt) for income taxed in Turkey under the DBA, meaning the income is tax-free in Austria but increases the tax rate on other Austrian income.
    *   **Article 24 (Mutual Agreement Procedure/Verständigungsverfahren):** Provides a diplomatic mechanism to resolve cases where an individual is being taxed contrary to the DBA (e.g., if Turkey is incorrectly withholding tax on a private pension).

### 2. Legal Risks and Solutions
The assistant identified that failing to declare the Turkish pension in Austria constitutes a violation of the Federal Tax Code (BAO § 119) and the Financial Crimes Act (FinStrG).
*   **Solutions Found:**
    *   **Voluntary Disclosure (Selbstanzeige):** Recommended under FinStrG § 29 to avoid penalties, provided it is done before the authorities discover the omission.
    *   **Statute of Limitations:** Noted a 10-year period for tax evasion.
    *   **Tax Recovery:** To recover tax wrongly paid in Turkey, the recipient must apply for a refund in Turkey using an Austrian residence certificate (Form ZS-QU1) or initiate a Mutual Agreement Procedure via the Austrian Ministry of Finance (BMF).

### 3. Red-White-Red (RWR) Card for Turkish Investors
The user inquired about how **Turkish investors** can obtain a Red-White-Red Card for self-employed key managers.
*   **Actions Taken:** The assistant attempted to access official migration and labor ministry portals (`migration.gv.at`, `bma.gv.at`).
*   **Outcomes:** The search was hindered by several technical issues, including **404 errors** on the WKO (Chamber of Commerce) and migration websites, and a **certificate error** on the Ministry of Labor site.
*   **Unresolved:** Specific, up-to-date investment thresholds and point-calculator details for Turkish investors remained partially unresolved due to these site failures.

### 4. Technical Details & AI Performance
*   **Files/URLs:** The assistant heavily utilized the **RIS (Rechtsinformationssystem)** for Austrian federal laws and the **FINDOK** database for Ministry of Finance guidelines.
*   **AI Model Discussion:** The user critiqued the assistant's speed. The assistant explained that navigating JavaScript-heavy legal databases like RIS via browser tools and vision analysis (screenshots) created significant latency. The assistant suggested that using the **Kimi k2.6 model** might be more efficient for future deep-web research tasks.

### 5. Notable Details
*   **Specific Commands/Paths:** The assistant used `vision_analyze` to interpret complex legal tables from RIS when text extraction failed.
*   **Key Conclusion:** For the Turkish pension, the assistant emphasized that the "Opa" must declare the income in Austria regardless of whether Turkey has already taxed it, as the DBA grants Austria the primary taxing right for residents.

Summary:
The conversation focused on the technical configuration and troubleshooting of the **Hermes Agent** (an autonomous AI framework) and the integration of the **Honcho** memory/social cognition layer, specifically regarding the selection and deployment of various **AI models (LLMs)**.

### 1. User Goals
*   **Self-host Honcho:** The user wanted to activate the Honcho memory plugin but lacked a cloud API key, necessitating a local/self-hosted setup.
*   **Fix Gateway Crash:** The user reported a "restart loop" in the Hermes Gateway and provided a diagnosis report for the assistant to review.
*   **Optimize Performance:** The user sought to reduce timeouts and "context compression" interruptions during AI interactions.

### 2. Actions Taken and Outcomes
*   **Honcho Deployment:** The assistant researched Honcho’s architecture (FastAPI, PostgreSQL/pgvector, Redis) and attempted a Docker Compose setup on **ZimaOS**. This failed due to multiple environment restrictions (read-only filesystem, missing `buildx`, and permission errors).
*   **Pivot to Native Setup:** The assistant successfully started PostgreSQL and Redis containers but pivoted to a native Python installation for the Honcho API and Deriver using `uv`.
*   **Gateway Recovery:** The assistant identified that a previous attempt to "clean up" unused providers had accidentally deleted `codex_models.py` and other files related to the **OpenAI Codex** model integration. This caused a `ModuleNotFoundError` that crashed the gateway. The user resolved this by using `git restore .`.
*   **Model Reconfiguration:** To prevent timeouts, the assistant remapped auxiliary tasks (title generation, vision, etc.) from the heavy main model to smaller, faster models.

### 3. Key Decisions and AI Model Details
*   **Main Chat Model:** The system was configured to use **`deepseek-v4-pro`** via a custom Ollama Cloud Pro endpoint.
*   **Auxiliary & Coding Model:** **`kimi-k2.6`** was designated for coding tasks and context compression (due to its 64k+ context window requirement).
*   **Vision Model:** After `llava-phi3` failed (404), the assistant switched to **`gemma3:4b`** for image analysis.
*   **Efficiency Models:** **`qwen2.5:1.5b`** was selected for low-latency tasks like title generation and session search.
*   **Provider Constraints:** The user utilizes **Ollama Cloud Pro** ($20/mo), which has a limit of **3 concurrent models**. The assistant documented a warning never to add a 4th model to avoid 404 errors.

### 4. Technical Details
*   **Endpoint:** `https://ollama.com/v1`
*   **Environment:** ZimaOS (v1.6.1) on a ZimaCube.
*   **Critical Files:** `/DATA/AppData/hermes/config.yaml` (main config) and `/DATA/AppData/hermes-agent/` (source code).
*   **Honcho Ports:** API on `8000`, PostgreSQL on `5433`, Redis on `6380`.

### 5. Unresolved or Notable
*   **Honcho Finalization:** The native Python startup of Honcho was in progress but not fully verified as functional by the end of the transcript.
*   **ZimaOS Limitations:** The assistant noted that Docker-based builds are a "dead end" on ZimaOS due to the read-only root filesystem and outdated Docker components, requiring native Python workarounds for future agent skills.
*   **Turkish/Turkey:** While the search topic included "Turkey" and "yapay zeka" (Turkish for AI), the conversation did not specifically address Turkish language models or regional topics, focusing instead on the global LLM landscape (DeepSeek, Kimi, Qwen, Google).

Summary:
The conversation focused on two primary technical areas: **Loop Engineering for AI Coding Agents (LLMs)** and the technical documentation/usage of the **Camofox browser automation tool**, specifically its application in researching **Turkish-Austrian legal treaties**.

### 1. What the user asked about or wanted to accomplish
*   **AI Agent Strategy:** The user shared an article regarding "Loop Engineering" to correct the Assistant’s initial misunderstanding that the topic was about social media marketing. The goal was to define how developers should move from manual prompting to designing autonomous systems (loops) for LLMs.
*   **Medical Information:** The user requested information on the risks of heart ablation surgery for a family member.
*   **Camofox Retrieval:** The user commanded the Assistant to "read everything with Camofox," seeking a comprehensive recap of all stored documentation, skills, and research patterns related to the Camofox browser tool.

### 2. Actions taken and outcomes
*   **Web Access Failure:** The Assistant attempted to access an X (Twitter) article by `mikenevermiss` but was blocked by X’s security measures (451/429 errors).
*   **Information Synthesis:** After the user provided the article text, the Assistant summarized the "Loop Engineering" framework, contrasting "Promoting" (manual prompting) with "Looping" (autonomous agent systems).
*   **Skill Retrieval:** The Assistant executed `skill_view` and `search_files` to extract detailed technical documentation for **Camofox**, a persistent Docker-based browser used for anti-detection and automation.
*   **Legal Research Recap:** The Assistant retrieved specific research patterns used for the **Austrian Rechtsinformationssystem (RIS)**, specifically regarding a past project on the **Double Taxation Treaty (DBA) between Austria and Turkey**.

### 3. Key decisions, solutions found, or conclusions reached
*   **Loop Engineering Framework:** A loop is only viable if: (1) the task repeats weekly, (2) verification is automated (tests/linters), (3) the token budget allows for exploration, and (4) the agent has senior-level tools (logs/env).
*   **Maker-vs-Checker Split:** A key conclusion was that the model writing the code should not be the one grading it; a second "checker" agent or automated gate is required to prevent "Ralph Wiggum loops" (where agents agree on incorrect work).
*   **Camofox Architecture:** For ZimaOS, Camofox should run as a persistent Docker service (`CAMOFOX_URL=http://127.0.0.1:9377`) with `managed_persistence: true` to maintain sessions and logins.

### 4. Key technical details
*   **Tools/Models:** Mentioned **Codex**, **Claude Code**, and the **Model Context Protocol (MCP)** for connecting agents to external tools like GitHub and Slack.
*   **Camofox Endpoints:**
    *   `POST /tabs`: Create a tab with `userId` and `sessionKey`.
    *   `GET /tabs/:tabId/links`: Extract links (noting that React SPAs often return empty hrefs).
    *   `GET /tabs/:tabId/screenshot`: Reliable visual verification.
    *   `r.jina.ai`: Used as a high-speed markdown extractor for JS-heavy sites.
*   **Specific Legal Reference:** The documentation highlighted a successful research workflow for **DBA Österreich-Türkei Art 18 (Pensions)**, using NOR number patterns (e.g., Art 18 = `NOR40110822`) to bypass difficult navigation on the RIS website.

### 5. Anything left unresolved or notable
*   **X Access:** Direct access to X remains blocked for the Assistant; it must rely on user-provided text or third-party readers like Jina.
*   **Medical Disclaimer:** The Assistant noted it cannot provide real-time verified medical research and directed the user to official German cardiology sources (DGK, Herzstiftung).
*   **Comprehension Debt:** A notable warning was issued regarding "cognitive surrender"—the risk of developers accepting agent-generated code without understanding it, leading to unmanageable technical debt.


Explanation: DuckDuckGo Lite didn't return useful results. Let me try a different approach - use Camofox browser to search, or try curl with a different search engine. Let me try searching via Google or use r.jina.ai to search. Actually, let me try a more direct approach - search for specific Turkish AIinitiatives.

curl -sL "https://html.duckduckgo.com/html/?q=Turkey+LLM+large+language+model+Turkish+AI+development+2025+2026" -H "User-Agent: Mozilla/5.0" 2>/dev/null | python3 -c "
import sys, re
html = sys.stdin.read()
# Extract result links and snippets
results = re.findall(r']*class=\"result__a\"[^>]*href=\"([^\"]+)\"[^>]*>(.*?)', html)
snippets = re.findall(r']*class=\"result__snippet\"[^>]*>(.*?)', html)
for i, (url, title) in enumerate(results[:10]):
    snippet = snippets[i] if i < len(snippets) else ''
    clean_title = re.sub(r'<[^>]+>', '', title)
    clean_snippet = re.sub(r'<[^>]+>', '', snippet)
    print(f'{i+1}. {clean_title}')
    print(f'   URL: {url}')
    print(f'   {clean_snippet}')
    print()
"


Output:
1. Bridging the Bosphorus | Advancing Turkish Large Language Models ...
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Femrecanacikgoz.github.io%2FBridging%2Dthe%2DBosphorus%2F&rut=58fa860edb0d216bece5cc7dd925158aef4252413c7b4b4afe9396677f89143b
   Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations, with a special focus on Turkish. We conduct an in-depth analysis to evaluate the ...

2. Türkiye to launch first national LLM enriched with Turkish data
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.turkiyetoday.com%2Fbusiness%2Fturkiye%2Dto%2Dlaunch%2Dfirst%2Dnational%2Dllm%2Denriched%2Dwith%2Dturkish%2Ddata%2D149162%2F&rut=96c0c1521f790924af3a2452ec14c63534ad3092ebc529e15c2368f2a79a481e
   Türkiye plans to unveil its first indigenous large language model (LLM) enriched with Turkish data as part of the country's push to become a leader in artificial intelligence technology, Industry and Technology Minister Mehmet Fatih Kacir announced Wednesday.

3. Türkiye Unveils Kumru AI: First Home-Grown LLM Aimed at Tech ...
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Feuropeans24.com%2F2025%2F10%2Fturkiye%2Dunveils%2Dkumru%2Dai%2Dfirst%2Dhome%2Dgrown%2Dllm%2Daimed%2Dat%2Dtech%2Dsovereignty%2F&rut=27b773bda0f8d55fa86e4ff6dbc74a98e3a88377e5088d4122d5d7e91184523f
   Turkish engineers on Sunday lifted the wraps on Kumru AI, the country's first domestically developed large language model, in a move Ankara hopes will end dependence on Silicon-Valley giants and anchor Türkiye's National AI Strategy of turning artificial intelligence into a 5%-of-GDP engine by 2025. Model size: 7.4 B parameters (decoder-only)

4. Beta version of 1st Turkish large language model T3AI launched
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.dailysabah.com%2Fbusiness%2Ftech%2Fbeta%2Dversion%2Dof%2D1st%2Dturkish%2Dlarge%2Dlanguage%2Dmodel%2Dt3ai%2Dlaunched&rut=f4f06be31ab58f4e6959e68129447e9d42ff0dde4d4fedcc35e0cc902d634152
   The first Turkish large language model (LLM) T3AI, developed in collaboration with the Turkish Technology Team (T3) Foundation and defense giant Baykar, launched its beta version ...

5. Turkish large language model T3 AI launches beta version
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.anews.com.tr%2Fturkey%2F2025%2F07%2F12%2Fturkish%2Dlarge%2Dlanguage%2Dmodel%2Dt3%2Dai%2Dlaunches%2Dbeta%2Dversion&rut=b996700034caeab1bab9bb5683604e51ca56cf3e125084dabcbb7e0e852a8c36
   The first Turkish large language model (LLM) T3AI, developed in collaboration with the Turkish Technology Team (T3) Foundation and defense firm Baykar, launched its beta version Friday on the TEKNOFEST social platform. "Hello world, I am T3AI," it said in its first post.

6. Turkish large language model T3 AI launches beta version
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.aa.com.tr%2Fen%2Fturkiye%2Fturkish%2Dlarge%2Dlanguage%2Dmodel%2Dt3%2Dai%2Dlaunches%2Dbeta%2Dversion%2F3628696&rut=31cbc47af6b02422ea3793a52f0269b863bd38f783101205394b76089c8f9966
   The first Turkish large language model (LLM), T3AI, developed in collaboration with the Turkish Technology Team (T3) Foundation and defense firm Baykar, launched its beta version Friday on the ...

7. Kumru AI: How Turkey's National AI is the Future - LinkedIn
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.linkedin.com%2Fpulse%2Fkumru%2Dhas%2Dlanded%2Dwhat%2Dt%25C3%25BCrkiyes%2Dnational%2Dai%2Dmeans%2Dhealth%2Domar%2Dghouat%2Dlcpaf%2F&rut=163907cb3f9e919b26291ce240472813685139eba51a1f5891b11732ae27d6a0
   Turkey's Kumru AI, a groundbreaking Turkish-language LLM, is reshaping healthcare and patient acquisition in the medical tourism sector.

8. A deep look at the TURNA LLM from Türkiye - Erkan's Field Diary
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Ferkansaka.net%2F2025%2F10%2F14%2Fturna%2Dai%2Dkumru%2D2b%2Dlaunch%2Dreactions%2Dturkey%2Dllm%2F&rut=a4d9a61403711b458305afe18154553485d12eb8e8ae37b87c4d44fcc4464b7e
   Turna AI's Kumru-2B model - a 2-billion-parameter Turkish large language model - was launched open source in October 2025, along with the extensive web corpus (500 GB of Turkish text) used to train it.

9. Türkiye makes dual push with large language model, mapping initiatives
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.dailysabah.com%2Fbusiness%2Ftech%2Fturkiye%2Dmakes%2Ddual%2Dpush%2Dwith%2Dlarge%2Dlanguage%2Dmodel%2Dmapping%2Dinitiatives&rut=1b4a842c37ce47b53a54010cd53269738090bf411cff60c21fde742c4c45ca31
   In a strategic move to boost digital sovereignty and reduce foreign dependency, Türkiye this week launched two major initiatives focused on artificial intelligence and location-based services. ...

10. Turkey - National AI Strategy (2024-2025)
   URL: //duckduckgo.com/l/?uddg=https%3A%2F%2Fregulations.ai%2Fregulations%2Fturkey%2D2024%2D7%2Dnational%2Dai%2Daction%2Dplan%2D2024%2D2025&rut=53f0c3ad1ae260b2879c50e377bb90c77ffceecbcb98921e54d1ff3745217e63
   The National Artificial Intelligence Strategy 2024-2025 Action Plan is a government strategy update assigning 71 priority actions to accelerate Turkey's AI ecosystem, strengthen infrastructure, increase skilled workforce, develop Turkish-language large models, and coordinate cross‑ministerial implementation. The plan is an update to the 2021-2025 National AI Strategy and is coordinated ...


Explanation: The search results are very informative. Let me now fetch a couple of the key articles to get more details about the specific models andtheir status.

curl -sL "https://r.jina.ai/https://www.turkiyetoday.com/business/turkiye-to-launch-first-national-llm-enriched-with-turkish-data-149162/" -H "Accept: text/plain" 2>/dev/null | head -80

curl -sL "https://r.jina.ai/https://erkansaka.net/2025/10/14/turna-ai-kumru-2b-launch-reactions-turkey-llm/" -H "Accept: text/plain" 2>/dev/null | head -80


Output:
Title: Türkiye to launch first national LLM enriched with Turkish data

URL Source: https://www.turkiyetoday.com/business/turkiye-to-launch-first-national-llm-enriched-with-turkish-data-149162/

Published Time: 2025-04-24T13:59:34+03:00

Markdown Content:
Most Read

![Image 1: newsletters](https://www.turkiyetoday.com/s/i/newsletter.png)

T ürkiye plans to unveil its first indigenous large language model (LLM) enriched with Turkish data as part of the country's push to become a leader in artificial intelligence technology, Industry and Technology Minister Mehmet Fatih Kacir announced Wednesday.

"We will implement applications that will pave the way for the development of domestic and original artificial intelligence models," Kacir said at the opening of the 7th Productivity and Technology Fair in Ankara, which runs until April 27.

The fair, themed "Artificial Intelligence Technologies" this year, was inaugurated with the participation of Turkish Parliament Speaker Numan Kurtulmuş at the ATO Congresium.

![Image 2: Minister of Industry and Technology Mehmet Fatih Kacir delivers a speech at the](https://img.turkiyetoday.com/images/2025/04/AA-20250424-37734612-37734611-BAKAN_KACIR_7_VERIMLILIK_VE_TEKNOLOJI_FUARINDA_KONUSTU-1600x1067.jpg)

![Image 3](https://www.turkiyetoday.com/s/i/i-fotobuyut.svg)

Minister of Industry and Technology Mehmet Fatih Kacir delivers a speech at the

In his address, Kacir highlighted Türkiye's industrial achievements over the past 22 years under President Recep Tayyip Erdogan's leadership, noting that the country has become a world leader in unmanned aerial vehicle production and now manufactures its own training aircraft, helicopters, naval platforms, and satellites.

"Türkiye has taken its place at the heart of European value chains in many areas, from solar panels to commercial vehicles, from white goods to iron and steel," Kacir said, adding that Togg, Türkiye's domestically produced electric vehicle, is now on the roads.

The minister emphasized Türkiye's export growth, which has risen from $36 billion to over $263 billion annually, reaching the highest level in the republic's history. He noted that after China, Türkiye is the country that can export the most diverse range of products competitively to the most countries in the region extending to Central Europe.

## Türkiye's AI development strategy

Addressing the global AI landscape, Kacir expressed concern that AI development is primarily driven by profit-maximizing companies rather than entities with direct social responsibility like governments or academia.

He pointed out that the United States and China are steadily widening their lead in this field, which he described as "a new front in technology wars."

"The inability of global actors to adopt a common stance even in the face of major humanitarian crises like Gaza weakens hopes for reaching consensus on universal principles for AI use in the near future," he said.

Kacir stressed that Türkiye, as a country pursuing the National Technology Initiative, cannot afford to fall behind in AI development. "With the widespread adoption of artificial intelligence, it's possible to achieve significant additional growth in our economy in the coming years," he said.

"The key to Türkiye's best utilization of the AI revolution is shaping our production systems, education policies, and technological infrastructure with a holistic strategy," Kacir explained. "This means preparing our entrepreneurship ecosystem, industry, and workforce for the digital economy—that's the essence of the National Technology Initiative."

### Türkiye invests 25 billion liras in 4,800 projects, 6,275 individuals

The minister revealed that Türkiye has provided more than 25 billion liras ($653 million) in support to 4,800 projects and 6,275 individuals in the field of artificial intelligence over the past 22 years through TUBITAK scholarship and support programs.

"More than 2,900 companies in our technoparks are implementing innovative projects in artificial intelligence," Kacir said, adding that 218 R&D centers are working on AI projects.

Türkiye has also established a supercomputer called ARF with the computing power of 35,000 laptops to support big data analysis and artificial intelligence research. Additionally, as a member of the EuroHPC Joint Undertaking, Türkiye has direct access to MareNostrum 5, one of the world's most powerful supercomputers.

"In the coming period, we will make available Türkiye's first large language model enriched with Turkish data," Kacir said. "We will implement applications that will pave the way for the development of domestic and original artificial intelligence models."

The minister concluded by inviting Turkish Cypriots and all citizens to TEKNOFEST, which will be held in the Turkish Republic of Northern Cyprus from May 1-4.