{"role": "session_meta", "tools": [{"type": "function", "function": {"name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": {"type": "object", "properties": {}}}}, {"type": "function", "function": {"name": "browser_click", "description": "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.", "parameters": {"type": "object", "properties": {"ref": {"type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')"}}, "required": ["ref"]}}}, {"type": "function", "function": {"name": "browser_console", "description": "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.", "parameters": {"type": "object", "properties": {"clear": {"type": "boolean", "default": false, "description": "If true, clear the message buffers after reading"}, "expression": {"type": "string", "description": "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'"}}}}}, {"type": "function", "function": {"name": "browser_get_images", "description": "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.", "parameters": {"type": "object", "properties": {}}}}, {"type": "function", "function": {"name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. 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.", "parameters": {"type": "object", "properties": {"url": {"type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')"}}, "required": ["url"]}}}, {"type": "function", "function": {"name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": {"type": "object", "properties": {"key": {"type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"}}, "required": ["key"]}}}, {"type": "function", "function": {"name": "browser_scroll", "description": "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.", "parameters": {"type": "object", "properties": {"direction": {"type": "string", "enum": ["up", "down"], "description": "Direction to scroll"}}, "required": ["direction"]}}}, {"type": "function", "function": {"name": "browser_snapshot", "description": "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.", "parameters": {"type": "object", "properties": {"full": {"type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false}}}}}, {"type": "function", "function": {"name": "browser_type", "description": "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.", "parameters": {"type": "object", "properties": {"ref": {"type": "string", "description": "The element reference from the snapshot (e.g., '@e3')"}, "text": {"type": "string", "description": "The text to type into the field"}}, "required": ["ref", "text"]}}}, {"type": "function", "function": {"name": "browser_vision", "description": "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.", "parameters": {"type": "object", "properties": {"question": {"type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for."}, "annotate": {"type": "boolean", "default": false, "description": "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."}}, "required": ["question"]}}}, {"type": "function", "function": {"name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo 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.", "parameters": {"type": "object", "properties": {"question": {"type": "string", "description": "The question to present to the user."}, "choices": {"type": "array", "items": {"type": "string"}, "maxItems": 4, "description": "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."}}, "required": ["question"]}}}, {"type": "function", "function": {"name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo 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.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "description": "One of: create, list, update, pause, resume, remove, run"}, "job_id": {"type": "string", "description": "Required for update/pause/resume/remove/run"}, "prompt": {"type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."}, "schedule": {"type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"}, "name": {"type": "string", "description": "Optional human-friendly name"}, "repeat": {"type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."}, "deliver": {"type": "string", "description": "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), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."}, "skills": {"type": "array", "items": {"type": "string"}, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."}, "model": {"type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": {"provider": {"type": "string", "description": "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": {"type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"}}, "required": ["model"]}, "script": {"type": "string", "description": "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."}, "no_agent": {"type": "boolean", "default": false, "description": "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. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY 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. \n\nWHEN 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."}, "context_from": {"type": "array", "items": {"type": "string"}, "description": "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."}, "enabled_toolsets": {"type": "array", "items": {"type": "string"}, "description": "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."}, "workdir": {"type": "string", "description": "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."}}, "required": ["action"]}}}, {"type": "function", "function": {"name": "delegate_task", "description": "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.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- 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.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- 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.\n- 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.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- 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 max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": {"type": "object", "properties": {"goal": {"type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."}, "context": {"type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."}, "toolsets": {"type": "array", "items": {"type": "string"}, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."}, "tasks": {"type": "array", "items": {"type": "object", "properties": {"goal": {"type": "string", "description": "Task goal"}, "context": {"type": "string", "description": "Task-specific context"}, "toolsets": {"type": "array", "items": {"type": "string"}, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."}, "acp_command": {"type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."}, "acp_args": {"type": "array", "items": {"type": "string"}, "description": "Per-task ACP args override. Leave empty unless acp_command is set."}, "role": {"type": "string", "enum": ["leaf", "orchestrator"], "description": "Per-task role override. See top-level 'role' for semantics."}}, "required": ["goal"]}, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."}, "role": {"type": "string", "enum": ["leaf", "orchestrator"], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."}, "acp_command": {"type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."}, "acp_args": {"type": "array", "items": {"type": "string"}, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."}}}}}, {"type": "function", "function": {"name": "execute_code", "description": "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).\n\nUse 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.\n\nAvailable via `from hermes_tools import ...`:\n\n  read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n    Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n  write_file(path: str, content: str) -> dict\n    Always overwrites the entire file.\n  search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n    target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n    Replaces old_string with new_string in the file.\n  terminal(command: str, timeout=None, workdir=None) -> dict\n    Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts 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().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": {"type": "object", "properties": {"code": {"type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."}}, "required": ["code"]}}}, {"type": "function", "function": {"name": "image_generate", "description": "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.", "parameters": {"type": "object", "properties": {"prompt": {"type": "string", "description": "The text prompt describing the desired image. Be detailed and descriptive."}, "aspect_ratio": {"type": "string", "enum": ["landscape", "square", "portrait"], "description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.", "default": "landscape"}}, "required": ["prompt"]}}}, {"type": "function", "function": {"name": "memory", "description": "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.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf 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.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["add", "replace", "remove"], "description": "The action to perform."}, "target": {"type": "string", "enum": ["memory", "user"], "description": "Which memory store: 'memory' for personal notes, 'user' for user profile."}, "content": {"type": "string", "description": "The entry content. Required for 'add' and 'replace'."}, "old_text": {"type": "string", "description": "Short unique substring identifying the entry to replace or remove."}}, "required": ["action", "target"]}}}, {"type": "function", "function": {"name": "patch", "description": "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.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.", "parameters": {"type": "object", "properties": {"mode": {"type": "string", "enum": ["replace", "patch"], "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.", "default": "replace"}, "path": {"type": "string", "description": "REQUIRED when mode='replace'. File path to edit."}, "old_string": {"type": "string", "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."}, "new_string": {"type": "string", "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."}, "replace_all": {"type": "boolean", "description": "Replace all occurrences instead of requiring a unique match (default: false)", "default": false}, "patch": {"type": "string", "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"}}, "required": ["mode"]}}}, {"type": "function", "function": {"name": "process", "description": "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).", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["list", "poll", "log", "wait", "kill", "write", "submit", "close"], "description": "Action to perform on background processes"}, "session_id": {"type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'."}, "data": {"type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)"}, "timeout": {"type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1}, "offset": {"type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)"}, "limit": {"type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1}}, "required": ["action"]}}}, {"type": "function", "function": {"name": "read_file", "description": "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.", "parameters": {"type": "object", "properties": {"path": {"type": "string", "description": "Path to the file to read (absolute, relative, or ~/path)"}, "offset": {"type": "integer", "description": "Line number to start reading from (1-indexed, default: 1)", "default": 1, "minimum": 1}, "limit": {"type": "integer", "description": "Maximum number of lines to read (default: 500, max: 2000)", "default": 500, "maximum": 2000}}, "required": ["path"]}}}, {"type": "function", "function": {"name": "search_files", "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.", "parameters": {"type": "object", "properties": {"pattern": {"type": "string", "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"}, "target": {"type": "string", "enum": ["content", "files"], "description": "'content' searches inside file contents, 'files' searches for files by name", "default": "content"}, "path": {"type": "string", "description": "Directory or file to search in (default: current working directory)", "default": "."}, "file_glob": {"type": "string", "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"}, "limit": {"type": "integer", "description": "Maximum number of results to return (default: 50)", "default": 50}, "offset": {"type": "integer", "description": "Skip first N results for pagination (default: 0)", "default": 0}, "output_mode": {"type": "string", "enum": ["content", "files_only", "count"], "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file", "default": "content"}, "context": {"type": "integer", "description": "Number of context lines before and after each match (grep mode only)", "default": 0}}, "required": ["pattern"]}}}, {"type": "function", "function": {"name": "send_message", "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: 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.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["send", "list"], "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."}, "target": {"type": "string", "description": "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)"}, "message": {"type": "string", "description": "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."}}}}}, {"type": "function", "function": {"name": "session_search", "description": "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.\n\nTWO MODES:\n1. 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.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon'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.\n\nSearch 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.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "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)."}, "role_filter": {"type": "string", "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."}, "limit": {"type": "integer", "description": "Max sessions to summarize (default: 3, max: 5).", "default": 3}}}}}, {"type": "function", "function": {"name": "skill_manage", "description": "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.\n\nActions: 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.\n\nOn 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.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate 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.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned 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.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["create", "patch", "edit", "delete", "write_file", "remove_file"], "description": "The action to perform."}, "name": {"type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."}, "content": {"type": "string", "description": "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."}, "old_string": {"type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."}, "new_string": {"type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."}, "replace_all": {"type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."}, "category": {"type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."}, "file_path": {"type": "string", "description": "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."}, "file_content": {"type": "string", "description": "Content for the file. Required for 'write_file'."}, "absorbed_into": {"type": "string", "description": "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."}}, "required": ["action", "name"]}}}, {"type": "function", "function": {"name": "skill_view", "description": "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.", "parameters": {"type": "object", "properties": {"name": {"type": "string", "description": "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')."}, "file_path": {"type": "string", "description": "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."}}, "required": ["name"]}}}, {"type": "function", "function": {"name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": {"type": "object", "properties": {"category": {"type": "string", "description": "Optional category filter to narrow results"}}}}}, {"type": "function", "function": {"name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (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.\nBackground: Set background=true to get a session_id. Two patterns:\n  (1) Long-lived processes that never exit (servers, watchers).\n  (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.\nFor 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.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": {"type": "object", "properties": {"command": {"type": "string", "description": "The command to execute on the VM"}, "background": {"type": "boolean", "description": "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.", "default": false}, "timeout": {"type": "integer", "description": "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.", "minimum": 1}, "workdir": {"type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory."}, "pty": {"type": "boolean", "description": "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.", "default": false}, "notify_on_complete": {"type": "boolean", "description": "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.", "default": false}, "watch_patterns": {"type": "array", "items": {"type": "string"}, "description": "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."}}, "required": ["command"]}}}, {"type": "function", "function": {"name": "text_to_speech", "description": "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.", "parameters": {"type": "object", "properties": {"text": {"type": "string", "description": "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."}, "output_path": {"type": "string", "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3"}}, "required": ["text"]}}}, {"type": "function", "function": {"name": "todo", "description": "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.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.", "parameters": {"type": "object", "properties": {"todos": {"type": "array", "description": "Task items to write. Omit to read current list.", "items": {"type": "object", "properties": {"id": {"type": "string", "description": "Unique item identifier"}, "content": {"type": "string", "description": "Task description"}, "status": {"type": "string", "enum": ["pending", "in_progress", "completed", "cancelled"], "description": "Current status"}}, "required": ["id", "content", "status"]}}, "merge": {"type": "boolean", "description": "true: update existing items by id, add new ones. false (default): replace the entire list.", "default": false}}}}}, {"type": "function", "function": {"name": "vision_analyze", "description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.", "parameters": {"type": "object", "properties": {"image_url": {"type": "string", "description": "Image URL (http/https), local file path, or data: URL to load."}, "question": {"type": "string", "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."}}, "required": ["image_url", "question"]}}}, {"type": "function", "function": {"name": "write_file", "description": "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).", "parameters": {"type": "object", "properties": {"path": {"type": "string", "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"}, "content": {"type": "string", "description": "Complete content to write to the file"}}, "required": ["path", "content"]}}}], "model": "gpt-5.5", "platform": "telegram", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "user", "content": "[M G] können wir das kontextfenster erhöhen? was lässt es bei der Nutzung von GPT 5.5 (Openai Codex) zu? aktuell sehe ich immer wieder meldungen wie diese: \"Preflight compression: ~138,891 tokens >= 108,800 threshold. This may take a moment.\" haben wir nicht ein grösseres Kontextfenster?", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": "**Examining Hermes config**\n\nI need to find out more about the Hermes configuration and ensure I load the hermes-agent properly. Additionally, it would be helpful to inspect the model limits and current facts regarding the configuration. I might need some specific skills or knowledge to get this right. Let’s make sure I cover all bases to provide the best assistance on this topic!", "finish_reason": "tool_calls", "reasoning_content": "**Examining Hermes config**\n\nI need to find out more about the Hermes configuration and ensure I load the hermes-agent properly. Additionally, it would be helpful to inspect the model limits and current facts regarding the configuration. I might need some specific skills or knowledge to get this right. Let’s make sure I cover all bases to provide the best assistance on this topic!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbdnp519M2amoKyEYtCGW_6Bp5HNz56th83AahmR1NKcbDjavwTSaquakPdoUhIhYi9ZF4HvET83AUvkchQAW5w97VY1EvLbgtQnRRpGuyXoeRYiDIfQlVoWCmUf5Itz3yfFAWbDfrnGINOq_tnTQwC7j2Q58_WHdj6s0rrEsX4vFQVbaZLvjg3Y9WrSy6Y-7p8bk3Gv77YicKCyvaioMSLtV63WJoQkWKye4y5Z0dmvLviv5A8lQSD0GsdKOJgDdq3KFOOujS1kwTX-Ce3oke1tCjJQLj7z1_5bc7dBH7h-KFFaH-gIdYn4EFwuTZEzp1tXTcl8lIqicPHJJePqPlQPfa0eozdmdSxgxx-E60iY0QEyB5i_O-noR-SWC9KNUzrrn9v-I-6MToLvV4o_jaONhHuwFhxxVNkJSjq7ZrUd_VRfIE83ZNspNbDRnDnO8sKRXYgd6Q3kNeQNvauriY8IrrVaEFV2-HUljlcW6yHwFxCjrGiSFkj1gHYS_09BKvCAPKqGUy8F90MjZ9HB9fMyhalEKYvYfBuvYXLFbijvaIZjXhCzsi4sesS-FaWrTqz2S_0HzxNSFSWzrDkFKMTLeftZLTd-Aq-GxKODZyPO3aGKrih1q_Xmg4DvpJ5aOEx3wAIq51mk2f20TIYqnowbsYd6gGsktXHqBjr0zySaWfYeM9jOJ24mE--DGziZ2b1OxGyZp3pjeqdOSsuwq6ZnXILdRWm4kITV8MoBL9ICdwVaxjlYUhkjaHcdz2NFXpwjSYjS_muw4d7kPFtCjuZkNi5vor43WElB2PTUx05wJ6KtL50J8E-RWA9ZFWqnA0nErvbLMw09GB9uqzS4bLb1UT4pcfY-5_T1k3Mr-RgnS7ekKd5H2GKVH5T0IbyBbtTxoCmhSxV4R6Ir1GiBesRv_Vc3_2MsCU47Feq4jFsqYmKqJTEJChM1nNCoSUkt4NvDbn94Tfu7HR9NjsSgOzpbVhEVe_KSMzPDL5L9wuOSwDPn17JXnPrsOD0Qifm1EhqcOW1UqCCp167X-Y8jBs-2yQ0edOBupNjgDDFz-qWRi1m3EwN8kfFobqUwBMGHCZV4SRnUdHoagROUYRbAdkggVA==", "id": "rs_0dcad7629f5b2d12016a09b7657b90819196eeb5c31db6b3cd", "summary": [{"type": "summary_text", "text": "**Examining Hermes config**\n\nI need to find out more about the Hermes configuration and ensure I load the hermes-agent properly. Additionally, it would be helpful to inspect the model limits and current facts regarding the configuration. I might need some specific skills or knowledge to get this right. Let’s make sure I cover all bases to provide the best assistance on this topic!"}]}], "tool_calls": [{"id": "call_5RdVQT0UU4uyG4LQ6oRF6gGs", "call_id": "call_5RdVQT0UU4uyG4LQ6oRF6gGs", "response_item_id": "fc_0dcad7629f5b2d12016a09b767e764819186655d09ba7f1c60", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"hermes-agent\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"hermes-agent\", \"description\": \"Configure, extend, or contribute to Hermes Agent.\", \"tags\": [\"hermes\", \"setup\", \"configuration\", \"multi-agent\", \"spawning\", \"cli\", \"gateway\", \"development\"], \"related_skills\": [\"claude-code\", \"codex\", \"opencode\"], \"content\": \"---\\nname: hermes-agent\\ndescription: \\\"Configure, extend, or contribute to Hermes Agent.\\\"\\nversion: 2.1.0\\nauthor: Hermes Agent + Teknium\\nlicense: MIT\\nmetadata:\\n  hermes:\\n    tags: [hermes, setup, configuration, multi-agent, spawning, cli, gateway, development]\\n    homepage: https://github.com/NousResearch/hermes-agent\\n    related_skills: [claude-code, codex, opencode]\\n---\\n\\n# Hermes Agent\\n\\nHermes Agent is an open-source AI agent framework by Nous Research that runs in your terminal, messaging platforms, and IDEs. It belongs to the same category as Claude Code (Anthropic), Codex (OpenAI), and OpenClaw — autonomous coding and task-execution agents that use tool calling to interact with your system. Hermes works with any LLM provider (OpenRouter, Anthropic, OpenAI, DeepSeek, local models, and 15+ others) and runs on Linux, macOS, and WSL.\\n\\nWhat makes Hermes different:\\n\\n- **Self-improving through skills** — Hermes learns from experience by saving reusable procedures as skills. When it solves a complex problem, discovers a workflow, or gets corrected, it can persist that knowledge as a skill document that loads into future sessions. Skills accumulate over time, making the agent better at your specific tasks and environment.\\n- **Persistent memory across sessions** — remembers who you are, your preferences, environment details, and lessons learned. Pluggable memory backends (built-in, Honcho, Mem0, and more) let you choose how memory works.\\n- **Multi-platform gateway** — the same agent runs on Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, and 10+ other platforms with full tool access, not just chat.\\n- **Provider-agnostic** — swap models and providers mid-workflow without changing anything else. Credential pools rotate across multiple API keys automatically.\\n- **Profiles** — run multiple independent Hermes instances with isolated configs, sessions, skills, and memory.\\n- **Extensible** — plugins, MCP servers, custom tools, webhook triggers, cron scheduling, and the full Python ecosystem.\\n\\nPeople use Hermes for software development, research, system administration, data analysis, content creation, home automation, and anything else that benefits from an AI agent with persistent context and full system access.\\n\\n**This skill helps you work with Hermes Agent effectively** — setting it up, configuring features, spawning additional agent instances, troubleshooting issues, finding the right commands and settings, and understanding how the system works when you need to extend or contribute to it.\\n\\n**Docs:** https://hermes-agent.nousresearch.com/docs/\\n\\n## Quick Start\\n\\n```bash\\n# Install\\ncurl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash\\n\\n# Interactive chat (default)\\nhermes\\n\\n# Single query\\nhermes chat -q \\\"What is the capital of France?\\\"\\n\\n# Setup wizard\\nhermes setup\\n\\n# Change model/provider\\nhermes model\\n\\n# Check health\\nhermes doctor\\n```\\n\\n---\\n\\n## CLI Reference\\n\\n### Global Flags\\n\\n```\\nhermes [flags] [command]\\n\\n  --version, -V             Show version\\n  --resume, -r SESSION      Resume session by ID or title\\n  --continue, -c [NAME]     Resume by name, or most recent session\\n  --worktree, -w            Isolated git worktree mode (parallel agents)\\n  --skills, -s SKILL        Preload skills (comma-separate or repeat)\\n  --profile, -p NAME        Use a named profile\\n  --yolo                    Skip dangerous command approval\\n  --pass-session-id         Include session ID in system prompt\\n```\\n\\nNo subcommand defaults to `chat`.\\n\\n### Chat\\n\\n```\\nhermes chat [flags]\\n  -q, --query TEXT          Single query, non-interactive\\n  -m, --model MODEL         Model (e.g. anthropic/claude-sonnet-4)\\n  -t, --toolsets LIST       Comma-separated toolsets\\n  --provider PROVIDER       Force provider (openrouter, anthropic, nous, etc.)\\n  -v, --verbose             Verbose output\\n  -Q, --quiet               Suppress banner, spinner, tool previews\\n  --checkpoints             Enable filesystem checkpoints (/rollback)\\n  --source TAG              Session source tag (default: cli)\\n```\\n\\n### Configuration\\n\\n```\\nhermes setup [section]      Interactive wizard (model|terminal|gateway|tools|agent)\\nhermes model                Interactive model/provider picker\\nhermes config               View current config\\nhermes config edit          Open config.yaml in $EDITOR\\nhermes config set KEY VAL   Set a config value\\nhermes config path          Print config.yaml path\\nhermes config env-path      Print .env path\\nhermes config check         Check for missing/outdated config\\nhermes config migrate       Update config with new options\\nhermes login [--provider P] OAuth login (nous, openai-codex)\\nhermes logout               Clear stored auth\\nhermes doctor [--fix]       Check dependencies and config\\nhermes status [--all]       Show component status\\n```\\n\\n### Tools & Skills\\n\\n```\\nhermes tools                Interactive tool enable/disable (curses UI)\\nhermes tools list           Show all tools and status\\nhermes tools enable NAME    Enable a toolset\\nhermes tools disable NAME   Disable a toolset\\n\\nhermes skills list          List installed skills\\nhermes skills search QUERY  Search the skills hub\\nhermes skills install ID    Install a skill (ID can be a hub identifier OR a direct https://…/SKILL.md URL; pass --name to override when frontmatter has no name)\\nhermes skills inspect ID    Preview without installing\\nhermes skills config        Enable/disable skills per platform\\nhermes skills check         Check for updates\\nhermes skills update        Update outdated skills\\nhermes skills uninstall N   Remove a hub skill\\nhermes skills publish PATH  Publish to registry\\nhermes skills browse        Browse all available skills\\nhermes skills tap add REPO  Add a GitHub repo as skill source\\n```\\n\\n### MCP Servers\\n\\n```\\nhermes mcp serve            Run Hermes as an MCP server\\nhermes mcp add NAME         Add an MCP server (--url or --command)\\nhermes mcp remove NAME      Remove an MCP server\\nhermes mcp list             List configured servers\\nhermes mcp test NAME        Test connection\\nhermes mcp configure NAME   Toggle tool selection\\n```\\n\\n### Gateway (Messaging Platforms)\\n\\n```\\nhermes gateway run          Start gateway foreground\\nhermes gateway install      Install as background service\\nhermes gateway start/stop   Control the service\\nhermes gateway restart      Restart the service\\nhermes gateway status       Check status\\nhermes gateway setup        Configure platforms\\n```\\n\\nSupported platforms: Telegram, Discord, Slack, WhatsApp, Signal, Email, SMS, Matrix, Mattermost, Home Assistant, DingTalk, Feishu, WeCom, BlueBubbles (iMessage), Weixin (WeChat), API Server, Webhooks. Open WebUI connects via the API Server adapter.\\n\\nPlatform docs: https://hermes-agent.nousresearch.com/docs/user-guide/messaging/\\n\\n**Telegram setup:** See `references/telegram-gateway-setup.md` for the complete step-by-step configuration workflow.\\n\\n### Sessions\\n\\n```\\nhermes sessions list        List recent sessions\\nhermes sessions browse      Interactive picker\\nhermes sessions export OUT  Export to JSONL\\nhermes sessions rename ID T Rename a session\\nhermes sessions delete ID   Delete a session\\nhermes sessions prune       Clean up old sessions (--older-than N days)\\nhermes sessions stats       Session store statistics\\n```\\n\\n### Cron Jobs\\n\\n```\\nhermes cron list            List jobs (--all for disabled)\\nhermes cron create SCHED    Create: '30m', 'every 2h', '0 9 * * *'\\nhermes cron edit ID         Edit schedule, prompt, delivery\\nhermes cron pause/resume ID Control job state\\nhermes cron run ID          Trigger on next tick\\nhermes cron remove ID       Delete a job\\nhermes cron status          Scheduler status\\n```\\n\\n### Webhooks\\n\\n```\\nhermes webhook subscribe N  Create route at /webhooks/<name>\\nhermes webhook list         List subscriptions\\nhermes webhook remove NAME  Remove a subscription\\nhermes webhook test NAME    Send a test POST\\n```\\n\\n### Profiles\\n\\n```\\nhermes profile list         List all profiles\\nhermes profile create NAME  Create (--clone, --clone-all, --clone-from)\\nhermes profile use NAME     Set sticky default\\nhermes profile delete NAME  Delete a profile\\nhermes profile show NAME    Show details\\nhermes profile alias NAME   Manage wrapper scripts\\nhermes profile rename A B   Rename a profile\\nhermes profile export NAME  Export to tar.gz\\nhermes profile import FILE  Import from archive\\n```\\n\\n### Credential Pools\\n\\n```\\nhermes auth add             Interactive credential wizard\\nhermes auth list [PROVIDER] List pooled credentials\\nhermes auth remove P INDEX  Remove by provider + index\\nhermes auth reset PROVIDER  Clear exhaustion status\\n```\\n\\n### Other\\n\\n```\\nhermes insights [--days N]  Usage analytics\\nhermes update               Update to latest version\\nhermes pairing list/approve/revoke  DM authorization\\nhermes plugins list/install/remove  Plugin management\\nhermes honcho setup/status  Honcho memory integration (requires honcho plugin)\\nhermes memory setup/status/off  Memory provider config\\nhermes completion bash|zsh  Shell completions\\nhermes acp                  ACP server (IDE integration)\\nhermes claw migrate         Migrate from OpenClaw\\nhermes uninstall            Uninstall Hermes\\n```\\n\\n---\\n\\n## Slash Commands (In-Session)\\n\\nType these during an interactive chat session. New commands land fairly\\noften; if something below looks stale, run `/help` in-session for the\\nauthoritative list or see the [live slash commands reference](https://hermes-agent.nousresearch.com/docs/reference/slash-commands).\\nThe registry of record is `hermes_cli/commands.py` — every consumer\\n(autocomplete, Telegram menu, Slack mapping, `/help`) derives from it.\\n\\n### Session Control\\n```\\n/new (/reset)        Fresh session\\n/clear               Clear screen + new session (CLI)\\n/retry               Resend last message\\n/undo                Remove last exchange\\n/title [name]        Name the session\\n/compress            Manually compress context\\n/stop                Kill background processes\\n/rollback [N]        Restore filesystem checkpoint\\n/snapshot [sub]      Create or restore state snapshots of Hermes config/state (CLI)\\n/background <prompt> Run prompt in background\\n/queue <prompt>      Queue for next turn\\n/steer <prompt>      Inject a message after the next tool call without interrupting\\n/agents (/tasks)     Show active agents and running tasks\\n/resume [name]       Resume a named session\\n/goal [text|sub]     Set a standing goal Hermes works on across turns until achieved\\n                     (subcommands: status, pause, resume, clear)\\n/redraw              Force a full UI repaint (CLI)\\n```\\n\\n### Configuration\\n```\\n/config              Show config (CLI)\\n/model [name]        Show or change model\\n/personality [name]  Set personality\\n/reasoning [level]   Set reasoning (none|minimal|low|medium|high|xhigh|show|hide)\\n/verbose             Cycle: off → new → all → verbose\\n/voice [on|off|tts]  Voice mode\\n/yolo                Toggle approval bypass\\n/busy [sub]          Control what Enter does while Hermes is working (CLI)\\n                     (subcommands: queue, steer, interrupt, status)\\n/indicator [style]   Pick the TUI busy-indicator style (CLI)\\n                     (styles: kaomoji, emoji, unicode, ascii)\\n/footer [on|off]     Toggle gateway runtime-metadata footer on final replies\\n/skin [name]         Change theme (CLI)\\n/statusbar           Toggle status bar (CLI)\\n```\\n\\n### Tools & Skills\\n```\\n/tools               Manage tools (CLI)\\n/toolsets            List toolsets (CLI)\\n/skills              Search/install skills (CLI)\\n/skill <name>        Load a skill into session\\n/reload-skills       Re-scan ~/.hermes/skills/ for added/removed skills\\n/reload              Reload .env variables into the running session (CLI)\\n/reload-mcp          Reload MCP servers\\n/cron                Manage cron jobs (CLI)\\n/curator [sub]       Background skill maintenance (status, run, pin, archive, …)\\n/kanban [sub]        Multi-profile collaboration board (tasks, links, comments)\\n/plugins             List plugins (CLI)\\n```\\n\\n### Gateway\\n```\\n/approve             Approve a pending command (gateway)\\n/deny                Deny a pending command (gateway)\\n/restart             Restart gateway (gateway)\\n/sethome             Set current chat as home channel (gateway)\\n/update              Update Hermes to latest (gateway)\\n/topic [sub]         Enable or inspect Telegram DM topic sessions (gateway)\\n/platforms (/gateway) Show platform connection status (gateway)\\n```\\n\\n### Utility\\n```\\n/branch (/fork)      Branch the current session\\n/fast                Toggle priority/fast processing\\n/browser             Open CDP browser connection\\n/history             Show conversation history (CLI)\\n/save                Save conversation to file (CLI)\\n/copy [N]            Copy the last assistant response to clipboard (CLI)\\n/paste               Attach clipboard image (CLI)\\n/image               Attach local image file (CLI)\\n```\\n\\n### Info\\n```\\n/help                Show commands\\n/commands [page]     Browse all commands (gateway)\\n/usage               Token usage\\n/insights [days]     Usage analytics\\n/gquota              Show Google Gemini Code Assist quota usage (CLI)\\n/status              Session info (gateway)\\n/profile             Active profile info\\n/debug               Upload debug report (system info + logs) and get shareable links\\n```\\n\\n### Exit\\n```\\n/quit (/exit, /q)    Exit CLI\\n```\\n\\n---\\n\\n## Key Paths & Config\\n\\n```\\n~/.hermes/config.yaml       Main configuration\\n~/.hermes/.env              API keys and secrets\\n$HERMES_HOME/skills/        Installed skills\\n~/.hermes/sessions/         Session transcripts\\n~/.hermes/logs/             Gateway and error logs\\n~/.hermes/auth.json         OAuth tokens and credential pools\\n~/.hermes/hermes-agent/     Source code (if git-installed)\\n```\\n\\nProfiles use `~/.hermes/profiles/<name>/` with the same layout.\\n\\n### Config Sections\\n\\nEdit with `hermes config edit` or `hermes config set section.key value`.\\n\\n| Section | Key options |\\n|---------|-------------|\\n| `model` | `default`, `provider`, `base_url`, `api_key`, `context_length` |\\n| `agent` | `max_turns` (90), `tool_use_enforcement` |\\n| `terminal` | `backend` (local/docker/ssh/modal), `cwd`, `timeout` (180) |\\n| `compression` | `enabled`, `threshold` (0.50), `target_ratio` (0.20) |\\n| `display` | `skin`, `tool_progress`, `show_reasoning`, `show_cost` |\\n| `stt` | `enabled`, `provider` (local/groq/openai/mistral) |\\n| `tts` | `provider` (edge/elevenlabs/openai/minimax/mistral/neutts) |\\n| `memory` | `memory_enabled`, `user_profile_enabled`, `provider` |\\n| `security` | `tirith_enabled`, `website_blocklist` |\\n| `delegation` | `model`, `provider`, `base_url`, `api_key`, `max_iterations` (50), `reasoning_effort` |\\n| `checkpoints` | `enabled`, `max_snapshots` (50) |\\n\\nFull config reference: https://hermes-agent.nousresearch.com/docs/user-guide/configuration\\n\\n### Providers\\n\\n20+ providers supported. Set via `hermes model` or `hermes setup`.\\n\\n| Provider | Auth | Key env var |\\n|----------|------|-------------|\\n| OpenRouter | API key | `OPENROUTER_API_KEY` |\\n| Anthropic | API key | `ANTHROPIC_API_KEY` |\\n| Nous Portal | OAuth | `hermes auth` |\\n| OpenAI Codex | OAuth | `hermes auth` |\\n| GitHub Copilot | Token | `COPILOT_GITHUB_TOKEN` |\\n| Google Gemini | API key | `GOOGLE_API_KEY` or `GEMINI_API_KEY` |\\n| DeepSeek | API key | `DEEPSEEK_API_KEY` |\\n| xAI / Grok | API key | `XAI_API_KEY` |\\n| Hugging Face | Token | `HF_TOKEN` |\\n| Z.AI / GLM | API key | `GLM_API_KEY` |\\n| MiniMax | API key | `MINIMAX_API_KEY` |\\n| MiniMax CN | API key | `MINIMAX_CN_API_KEY` |\\n| Kimi / Moonshot | API key | `KIMI_API_KEY` |\\n| Alibaba / DashScope | API key | `DASHSCOPE_API_KEY` |\\n| Xiaomi MiMo | API key | `XIAOMI_API_KEY` |\\n| Kilo Code | API key | `KILOCODE_API_KEY` |\\n| AI Gateway (Vercel) | API key | `AI_GATEWAY_API_KEY` |\\n| OpenCode Zen | API key | `OPENCODE_ZEN_API_KEY` |\\n| OpenCode Go | API key | `OPENCODE_GO_API_KEY` |\\n| Qwen OAuth | OAuth | `hermes login --provider qwen-oauth` |\\n| Custom endpoint | Config | `model.base_url` + `model.api_key` in config.yaml |\\n| GitHub Copilot ACP | External | `COPILOT_CLI_PATH` or Copilot CLI |\\n\\nFull provider docs: https://hermes-agent.nousresearch.com/docs/integrations/providers\\n\\n### Toolsets\\n\\nEnable/disable via `hermes tools` (interactive) or `hermes tools enable/disable NAME`.\\n\\n| Toolset | What it provides |\\n|---------|-----------------|\\n| `web` | Web search and content extraction |\\n| `search` | Web search only (subset of `web`) |\\n| `browser` | Browser automation (Browserbase, Camofox, or local Chromium) |\\n| `terminal` | Shell commands and process management |\\n| `file` | File read/write/search/patch |\\n| `code_execution` | Sandboxed Python execution |\\n| `vision` | Image analysis |\\n| `image_gen` | AI image generation |\\n| `video` | Video analysis and generation |\\n| `tts` | Text-to-speech |\\n| `skills` | Skill browsing and management |\\n| `memory` | Persistent cross-session memory |\\n| `session_search` | Search past conversations |\\n| `delegation` | Subagent task delegation |\\n| `cronjob` | Scheduled task management |\\n| `clarify` | Ask user clarifying questions |\\n| `messaging` | Cross-platform message sending |\\n| `todo` | In-session task planning and tracking |\\n| `kanban` | Multi-agent work-queue tools (gated to workers) |\\n| `debugging` | Extra introspection/debug tools (off by default) |\\n| `safe` | Minimal, low-risk toolset for locked-down sessions |\\n| `spotify` | Spotify playback and playlist control |\\n| `homeassistant` | Smart home control (off by default) |\\n| `discord` | Discord integration tools |\\n| `discord_admin` | Discord admin/moderation tools |\\n| `feishu_doc` | Feishu (Lark) document tools |\\n| `feishu_drive` | Feishu (Lark) drive tools |\\n| `yuanbao` | Yuanbao integration tools |\\n| `rl` | Reinforcement learning tools (off by default) |\\n| `moa` | Mixture of Agents (off by default) |\\n\\nFull enumeration lives in `toolsets.py` as the `TOOLSETS` dict; `_HERMES_CORE_TOOLS` is the default bundle most platforms inherit from.\\n\\nTool changes take effect on `/reset` (new session). They do NOT apply mid-conversation to preserve prompt caching.\\n\\n---\\n\\n## Security & Privacy Toggles\\n\\nCommon \\\"why is Hermes doing X to my output / tool calls / commands?\\\" toggles — and the exact commands to change them. Most of these need a fresh session (`/reset` in chat, or start a new `hermes` invocation) because they're read once at startup.\\n\\n### Secret redaction in tool output\\n\\nSecret redaction is **off by default** — tool output (terminal stdout, `read_file`, web content, subagent summaries, etc.) passes through unmodified. If the user wants Hermes to auto-mask strings that look like API keys, tokens, and secrets before they enter the conversation context and logs:\\n\\n```bash\\nhermes config set security.redact_secrets true       # enable globally\\n```\\n\\n**Restart required.** `security.redact_secrets` is snapshotted at import time — toggling it mid-session (e.g. via `export HERMES_REDACT_SECRETS=true` from a tool call) will NOT take effect for the running process. Tell the user to run `hermes config set security.redact_secrets true` in a terminal, then start a new session. This is deliberate — it prevents an LLM from flipping the toggle on itself mid-task.\\n\\nDisable again with:\\n```bash\\nhermes config set security.redact_secrets false\\n```\\n\\n### PII redaction in gateway messages\\n\\nSeparate from secret redaction. When enabled, the gateway hashes user IDs and strips phone numbers from the session context before it reaches the model:\\n\\n```bash\\nhermes config set privacy.redact_pii true    # enable\\nhermes config set privacy.redact_pii false   # disable (default)\\n```\\n\\n### Command approval prompts\\n\\nBy default (`approvals.mode: manual`), Hermes prompts the user before running shell commands flagged as destructive (`rm -rf`, `git reset --hard`, etc.). The modes are:\\n\\n- `manual` — always prompt (default)\\n- `smart` — use an auxiliary LLM to auto-approve low-risk commands, prompt on high-risk\\n- `off` — skip all approval prompts (equivalent to `--yolo`)\\n\\n```bash\\nhermes config set approvals.mode smart       # recommended middle ground\\nhermes config set approvals.mode off         # bypass everything (not recommended)\\n```\\n\\nPer-invocation bypass without changing config:\\n- `hermes --yolo …`\\n- `export HERMES_YOLO_MODE=1`\\n\\nNote: YOLO / `approvals.mode: off` does NOT turn off secret redaction. They are independent.\\n\\n### Shell hooks allowlist\\n\\nSome shell-hook integrations require explicit allowlisting before they fire. Managed via `~/.hermes/shell-hooks-allowlist.json` — prompted interactively the first time a hook wants to run.\\n\\n### Disabling the web/browser/image-gen tools\\n\\nTo keep the model away from network or media tools entirely, open `hermes tools` and toggle per-platform. Takes effect on next session (`/reset`). See the Tools & Skills section above.\\n\\n---\\n\\n### Voice & Transcription\\n\\n### STT (Voice → Text)\\n\\nVoice messages from messaging platforms are auto-transcribed.\\n\\n**⚠️ Pitfall: faster-whisper must be installed for local STT to work.** The config may show `provider: local` but if `faster-whisper` is not installed in the venv, STT silently fails. Always verify with `pip show faster-whisper` after configuring local STT.\\n\\nProvider priority (auto-detected):\\n1. **Local faster-whisper** — free, no API key: `pip install faster-whisper`\\n2. **Groq Whisper** — free tier: set `GROQ_API_KEY`\\n3. **OpenAI Whisper** — paid: set `VOICE_TOOLS_OPENAI_KEY`\\n4. **Mistral Voxtral** — set `MISTRAL_API_KEY`\\n\\nConfig:\\n```yaml\\nstt:\\n  enabled: true\\n  provider: local        # local, groq, openai, mistral\\n  local:\\n    model: small         # tiny, base, small, medium, large-v3 — use 'small' or higher for German\\n    language: de         # always set explicitly for German — auto-detect is unreliable\\n```\\n\\n### Pitfalls\\n- **faster-whisper must be installed** — config shows `provider: local` but STT silently fails if the package is missing\\n- **Use `small` model minimum for German** — `base` is too weak\\n- **Set `language: de` explicitly** — auto-detect is unreliable\\n- **Swiss German (dialect) is NOT supported** — Whisper transcribes dialect poorly; user should speak Standard German\\n\\n### TTS (Text → Voice)\\n\\n| Provider | Env var | Free? |\\n|----------|---------|-------|\\n| Edge TTS | None | Yes (default) |\\n| ElevenLabs | `ELEVENLABS_API_KEY` | Free tier |\\n| OpenAI | `VOICE_TOOLS_OPENAI_KEY` | Paid |\\n| MiniMax | `MINIMAX_API_KEY` | Paid |\\n| Mistral (Voxtral) | `MISTRAL_API_KEY` | Paid |\\n| NeuTTS (local) | None (`pip install neutts[all]` + `espeak-ng`) | Free |\\n\\nVoice commands: `/voice on` (voice-to-voice), `/voice tts` (always voice), `/voice off`.\\n\\n---\\n\\n## Spawning Additional Hermes Instances\\n\\nRun additional Hermes processes as fully independent subprocesses — separate sessions, tools, and environments.\\n\\n### When to Use This vs delegate_task\\n\\n| | `delegate_task` | Spawning `hermes` process |\\n|-|-----------------|--------------------------|\\n| Isolation | Separate conversation, shared process | Fully independent process |\\n| Duration | Minutes (bounded by parent loop) | Hours/days |\\n| Tool access | Subset of parent's tools | Full tool access |\\n| Interactive | No | Yes (PTY mode) |\\n| Use case | Quick parallel subtasks | Long autonomous missions |\\n\\n### One-Shot Mode\\n\\n```\\nterminal(command=\\\"hermes chat -q 'Research GRPO papers and write summary to ~/research/grpo.md'\\\", timeout=300)\\n\\n# Background for long tasks:\\nterminal(command=\\\"hermes chat -q 'Set up CI/CD for ~/myapp'\\\", background=true)\\n```\\n\\n### Interactive PTY Mode (via tmux)\\n\\nHermes uses prompt_toolkit, which requires a real terminal. Use tmux for interactive spawning:\\n\\n```\\n# Start\\nterminal(command=\\\"tmux new-session -d -s agent1 -x 120 -y 40 'hermes'\\\", timeout=10)\\n\\n# Wait for startup, then send a message\\nterminal(command=\\\"sleep 8 && tmux send-keys -t agent1 'Build a FastAPI auth service' Enter\\\", timeout=15)\\n\\n# Read output\\nterminal(command=\\\"sleep 20 && tmux capture-pane -t agent1 -p\\\", timeout=5)\\n\\n# Send follow-up\\nterminal(command=\\\"tmux send-keys -t agent1 'Add rate limiting middleware' Enter\\\", timeout=5)\\n\\n# Exit\\nterminal(command=\\\"tmux send-keys -t agent1 '/exit' Enter && sleep 2 && tmux kill-session -t agent1\\\", timeout=10)\\n```\\n\\n### Multi-Agent Coordination\\n\\n```\\n# Agent A: backend\\nterminal(command=\\\"tmux new-session -d -s backend -x 120 -y 40 'hermes -w'\\\", timeout=10)\\nterminal(command=\\\"sleep 8 && tmux send-keys -t backend 'Build REST API for user management' Enter\\\", timeout=15)\\n\\n# Agent B: frontend\\nterminal(command=\\\"tmux new-session -d -s frontend -x 120 -y 40 'hermes -w'\\\", timeout=10)\\nterminal(command=\\\"sleep 8 && tmux send-keys -t frontend 'Build React dashboard for user management' Enter\\\", timeout=15)\\n\\n# Check progress, relay context between them\\nterminal(command=\\\"tmux capture-pane -t backend -p | tail -30\\\", timeout=5)\\nterminal(command=\\\"tmux send-keys -t frontend 'Here is the API schema from the backend agent: ...' Enter\\\", timeout=5)\\n```\\n\\n### Session Resume\\n\\n```\\n# Resume most recent session\\nterminal(command=\\\"tmux new-session -d -s resumed 'hermes --continue'\\\", timeout=10)\\n\\n# Resume specific session\\nterminal(command=\\\"tmux new-session -d -s resumed 'hermes --resume 20260225_143052_a1b2c3'\\\", timeout=10)\\n```\\n\\n### Tips\\n\\n- **Prefer `delegate_task` for quick subtasks** — less overhead than spawning a full process\\n- **Use `-w` (worktree mode)** when spawning agents that edit code — prevents git conflicts\\n- **Set timeouts** for one-shot mode — complex tasks can take 5-10 minutes\\n- **Use `hermes chat -q` for fire-and-forget** — no PTY needed\\n- **Use tmux for interactive sessions** — raw PTY mode has `\\\\r` vs `\\\\n` issues with prompt_toolkit\\n- **For scheduled tasks**, use the `cronjob` tool instead of spawning — handles delivery and retry\\n\\n---\\n\\n## Durable & Background Systems\\n\\nFour systems run alongside the main conversation loop. Quick reference\\nhere; full developer notes live in `AGENTS.md`, user-facing docs under\\n`website/docs/user-guide/features/`.\\n\\n### Delegation (`delegate_task`)\\n\\nSynchronous subagent spawn — the parent waits for the child's summary\\nbefore continuing its own loop. Isolated context + terminal session.\\n\\n- **Single:** `delegate_task(goal, context, toolsets)`.\\n- **Batch:** `delegate_task(tasks=[{goal, ...}, ...])` runs children in\\n  parallel, capped by `delegation.max_concurrent_children` (default 3).\\n- **Roles:** `leaf` (default; cannot re-delegate) vs `orchestrator`\\n  (can spawn its own workers, bounded by `delegation.max_spawn_depth`).\\n- **Not durable.** If the parent is interrupted, the child is\\n  cancelled. For work that must outlive the turn, use `cronjob` or\\n  `terminal(background=True, notify_on_complete=True)`.\\n\\nConfig: `delegation.*` in `config.yaml`.\\n\\n### Cron (scheduled jobs)\\n\\nDurable scheduler — `cron/jobs.py` + `cron/scheduler.py`. Drive it via\\nthe `cronjob` tool, the `hermes cron` CLI (`list`, `add`, `edit`,\\n`pause`, `resume`, `run`, `remove`), or the `/cron` slash command.\\n\\n- **Schedules:** duration (`\\\"30m\\\"`, `\\\"2h\\\"`), \\\"every\\\" phrase\\n  (`\\\"every monday 9am\\\"`), 5-field cron (`\\\"0 9 * * *\\\"`), or ISO timestamp.\\n- **Per-job knobs:** `skills`, `model`/`provider` override, `script`\\n  (pre-run data collection; `no_agent=True` makes the script the whole\\n  job), `context_from` (chain job A's output into job B), `workdir`\\n  (run in a specific dir with its `AGENTS.md` / `CLAUDE.md` loaded),\\n  multi-platform delivery.\\n- **Invariants:** 3-minute hard interrupt per run, `.tick.lock` file\\n  prevents duplicate ticks across processes, cron sessions pass\\n  `skip_memory=True` by default, and cron deliveries are framed with a\\n  header/footer instead of being mirrored into the target gateway\\n  session (keeps role alternation intact).\\n\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/cron\\n\\n### Curator (skill lifecycle)\\n\\nBackground maintenance for agent-created skills. Tracks usage, marks\\nidle skills stale, archives stale ones, keeps a pre-run tar.gz backup\\nso nothing is lost.\\n\\n- **CLI:** `hermes curator <verb>` — `status`, `run`, `pause`, `resume`,\\n  `pin`, `unpin`, `archive`, `restore`, `prune`, `backup`, `rollback`.\\n- **Slash:** `/curator <subcommand>` mirrors the CLI.\\n- **Scope:** only touches skills with `created_by: \\\"agent\\\"` provenance.\\n  Bundled + hub-installed skills are off-limits. **Never deletes** —\\n  max destructive action is archive. Pinned skills are exempt from\\n  every auto-transition and every LLM review pass.\\n- **Telemetry:** sidecar at `~/.hermes/skills/.usage.json` holds\\n  per-skill `use_count`, `view_count`, `patch_count`,\\n  `last_activity_at`, `state`, `pinned`.\\n\\nConfig: `curator.*` (`enabled`, `interval_hours`, `min_idle_hours`,\\n`stale_after_days`, `archive_after_days`, `backup.*`).\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/curator\\n\\n### Kanban (multi-agent work queue)\\n\\nDurable SQLite board for multi-profile / multi-worker collaboration.\\nUsers drive it via `hermes kanban <verb>`; dispatcher-spawned workers\\nsee a focused `kanban_*` toolset gated by `HERMES_KANBAN_TASK` so the\\nschema footprint is zero outside worker processes.\\n\\n- **CLI verbs (common):** `init`, `create`, `list` (alias `ls`),\\n  `show`, `assign`, `link`, `unlink`, `comment`, `complete`, `block`,\\n  `unblock`, `archive`, `tail`. Less common: `watch`, `stats`, `runs`,\\n  `log`, `dispatch`, `daemon`, `gc`.\\n- **Worker toolset:** `kanban_show`, `kanban_complete`, `kanban_block`,\\n  `kanban_heartbeat`, `kanban_comment`, `kanban_create`, `kanban_link`.\\n- **Dispatcher** runs inside the gateway by default\\n  (`kanban.dispatch_in_gateway: true`) — reclaims stale claims,\\n  promotes ready tasks, atomically claims, spawns assigned profiles.\\n  Auto-blocks a task after ~5 consecutive spawn failures.\\n- **Isolation:** board is the hard boundary (workers get\\n  `HERMES_KANBAN_BOARD` pinned in env); tenant is a soft namespace\\n  within a board for workspace-path + memory-key isolation.\\n\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/kanban\\n\\n---\\n\\n## Troubleshooting\\n\\n### Voice not working\\n1. Check `stt.enabled: true` in config.yaml\\n2. Verify provider: `pip install faster-whisper` or set API key\\n3. In gateway: `/restart`. In CLI: exit and relaunch.\\n\\n### Tool not available\\n1. `hermes tools` — check if toolset is enabled for your platform\\n2. Some tools need env vars (check `.env`)\\n3. `/reset` after enabling tools\\n\\n### Model/provider issues\\n1. `hermes doctor` — check config and dependencies\\n2. `hermes login` — re-authenticate OAuth providers\\n3. Check `.env` has the right API key\\n4. **Copilot 403**: `gh auth login` tokens do NOT work for Copilot API. You must use the Copilot-specific OAuth device code flow via `hermes model` → GitHub Copilot.\\n\\n### Changes not taking effect\\n- **Tools/skills:** `/reset` starts a new session with updated toolset\\n- **Config changes:** In gateway: `/restart`. In CLI: exit and relaunch.\\n- **Code changes:** Restart the CLI or gateway process\\n\\n### Tool / iteration budget\\nWhen the user asks to increase the \\\"tool budget\\\", \\\"turn budget\\\", or max tool-calling iterations, set the config key, not a transient shell variable:\\n```bash\\nhermes config set agent.max_turns 250\\n```\\nThen verify by reading `~/.hermes/config.yaml` or using a safe config inspection path. Avoid piping `hermes config` into `python` or another interpreter; security policy may flag `command | python` as high-risk even when the intent is only parsing. For gateway runs, `agent.max_turns` is the authoritative setting and is bridged internally to `HERMES_MAX_ITERATIONS`; config changes apply to fresh agent runs, while long-lived gateway/config changes may still need `/restart` if behavior appears stale.\\n\\n### Skills not showing\\n1. `hermes skills list` — verify installed\\n2. `hermes skills config` — check platform enablement\\n3. Load explicitly: `/skill name` or `hermes -s name`\\n\\n### Gateway issues\\nCheck logs first:\\n```bash\\ngrep -i \\\"failed to send\\\\|error\\\" ~/.hermes/logs/gateway.log | tail -20\\n```\\n\\nCommon gateway problems:\\n- **Gateway dies on SSH logout**: Enable linger: `sudo loginctl enable-linger $USER`\\n- **Gateway dies on WSL2 close**: WSL2 requires `systemd=true` in `/etc/wsl.conf` for systemd services to work. Without it, gateway falls back to `nohup` (dies when session closes).\\n- **Gateway crash loop**: Reset the failed state: `systemctl --user reset-failed hermes-gateway`\\n\\n### Platform-specific issues\\n- **Discord bot silent**: Must enable **Message Content Intent** in Bot → Privileged Gateway Intents.\\n- **Slack bot only works in DMs**: Must subscribe to `message.channels` event. Without it, the bot ignores public channels.\\n- **Windows HTTP 400 \\\"No models provided\\\"**: Config file encoding issue (BOM). Ensure `config.yaml` is saved as UTF-8 without BOM.\\n\\n### Auxiliary models not working\\nIf `auxiliary` tasks (vision, compression, session_search) fail silently, the `auto` provider can't find a backend. Either set `OPENROUTER_API_KEY` or `GOOGLE_API_KEY`, or explicitly configure each auxiliary task's provider:\\n```bash\\nhermes config set auxiliary.vision.provider <your_provider>\\nhermes config set auxiliary.vision.model <model_name>\\n```\\n\\n---\\n\\n## Where to Find Things\\n\\n| Looking for... | Location |\\n|----------------|----------|\\n| Config options | `hermes config edit` or [Configuration docs](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) |\\n| Available tools | `hermes tools list` or [Tools reference](https://hermes-agent.nousresearch.com/docs/reference/tools-reference) |\\n| Slash commands | `/help` in session or [Slash commands reference](https://hermes-agent.nousresearch.com/docs/reference/slash-commands) |\\n| Skills catalog | `hermes skills browse` or [Skills catalog](https://hermes-agent.nousresearch.com/docs/reference/skills-catalog) |\\n| Provider setup | `hermes model` or [Providers guide](https://hermes-agent.nousresearch.com/docs/integrations/providers) |\\n| Platform setup | `hermes gateway setup` or [Messaging docs](https://hermes-agent.nousresearch.com/docs/user-guide/messaging/) |\\n| MCP servers | `hermes mcp list` or [MCP guide](https://hermes-agent.nousresearch.com/docs/user-guide/features/mcp) |\\n| Profiles | `hermes profile list` or [Profiles docs](https://hermes-agent.nousresearch.com/docs/user-guide/profiles) |\\n| Cron jobs | `hermes cron list` or [Cron docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/cron) |\\n| Memory | `hermes memory status` or [Memory docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory) |\\n| Env variables | `hermes config env-path` or [Env vars reference](https://hermes-agent.nousresearch.com/docs/reference/environment-variables) |\\n| CLI commands | `hermes --help` or [CLI reference](https://hermes-agent.nousresearch.com/docs/reference/cli-commands) |\\n| Gateway logs | `~/.hermes/logs/gateway.log` |\\n| Session files | `~/.hermes/sessions/` or `hermes sessions browse` |\\n| Source code | `~/.hermes/hermes-agent/` |\\n\\n---\\n\\n## Contributor Quick Reference\\n\\nFor occasional contributors and PR authors. Full developer docs: https://hermes-agent.nousresearch.com/docs/developer-guide/\\n\\n### Project Layout\\n\\n```\\nhermes-agent/\\n├── run_agent.py          # AIAgent — core conversation loop\\n├── model_tools.py        # Tool discovery and dispatch\\n├── toolsets.py           # Toolset definitions\\n├── cli.py                # Interactive CLI (HermesCLI)\\n├── hermes_state.py       # SQLite session store\\n├── agent/                # Prompt builder, context compression, memory, model routing, credential pooling, skill dispatch\\n├── hermes_cli/           # CLI subcommands, config, setup, commands\\n│   ├── commands.py       # Slash command registry (CommandDef)\\n│   ├── config.py         # DEFAULT_CONFIG, env var definitions\\n│   └── main.py           # CLI entry point and argparse\\n├── tools/                # One file per tool\\n│   └── registry.py       # Central tool registry\\n├── gateway/              # Messaging gateway\\n│   └── platforms/        # Platform adapters (telegram, discord, etc.)\\n├── cron/                 # Job scheduler\\n├── tests/                # ~3000 pytest tests\\n└── website/              # Docusaurus docs site\\n```\\n\\nConfig: `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys).\\n\\n### Adding a Tool (3 files)\\n\\n**1. Create `tools/your_tool.py`:**\\n```python\\nimport json, os\\nfrom tools.registry import registry\\n\\ndef check_requirements() -> bool:\\n    return bool(os.getenv(\\\"EXAMPLE_API_KEY\\\"))\\n\\ndef example_tool(param: str, task_id: str = None) -> str:\\n    return json.dumps({\\\"success\\\": True, \\\"data\\\": \\\"...\\\"})\\n\\nregistry.register(\\n    name=\\\"example_tool\\\",\\n    toolset=\\\"example\\\",\\n    schema={\\\"name\\\": \\\"example_tool\\\", \\\"description\\\": \\\"...\\\", \\\"parameters\\\": {...}},\\n    handler=lambda args, **kw: example_tool(\\n        param=args.get(\\\"param\\\", \\\"\\\"), task_id=kw.get(\\\"task_id\\\")),\\n    check_fn=check_requirements,\\n    requires_env=[\\\"EXAMPLE_API_KEY\\\"],\\n)\\n```\\n\\n**2. Add to `toolsets.py`** → `_HERMES_CORE_TOOLS` list.\\n\\nAuto-discovery: any `tools/*.py` file with a top-level `registry.register()` call is imported automatically — no manual list needed.\\n\\nAll handlers must return JSON strings. Use `get_hermes_home()` for paths, never hardcode `~/.hermes`.\\n\\n### Adding a Slash Command\\n\\n1. Add `CommandDef` to `COMMAND_REGISTRY` in `hermes_cli/commands.py`\\n2. Add handler in `cli.py` → `process_command()`\\n3. (Optional) Add gateway handler in `gateway/run.py`\\n\\nAll consumers (help text, autocomplete, Telegram menu, Slack mapping) derive from the central registry automatically.\\n\\n### Agent Loop (High Level)\\n\\n```\\nrun_conversation():\\n  1. Build system prompt\\n  2. Loop while iterations < max:\\n     a. Call LLM (OpenAI-format messages + tool schemas)\\n     b. If tool_calls → dispatch each via handle_function_call() → append results → continue\\n     c. If text response → return\\n  3. Context compression triggers automatically near token limit\\n```\\n\\n### Testing\\n\\n```bash\\npython -m pytest tests/ -o 'addopts=' -q   # Full suite\\npython -m pytest tests/tools/ -q            # Specific area\\n```\\n\\n- Tests auto-redirect `HERMES_HOME` to temp dirs — never touch real `~/.hermes/`\\n- Run full suite before pushing any change\\n- Use `-o 'addopts='` to clear any baked-in pytest flags\\n\\n### Commit Conventions\\n\\n```\\ntype: concise subject line\\n\\nOptional body.\\n```\\n\\nTypes: `fix:`, `feat:`, `refactor:`, `docs:`, `chore:`\\n\\n### Key Rules\\n\\n- **Never break prompt caching** — don't change context, tools, or system prompt mid-conversation\\n- **Message role alternation** — never two assistant or two user messages in a row\\n- Use `get_hermes_home()` from `hermes_constants` for all paths (profile-safe)\\n- Config values go in `config.yaml`, secrets go in `.env`\\n- New tools need a `check_fn` so they only appear when requirements are met\\n\", \"path\": \"autonomous-ai-agents/hermes-agent/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/autonomous-ai-agents/hermes-agent\", \"linked_files\": {\"references\": [\"references/telegram-gateway-setup.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"hermes\", \"setup\", \"configuration\", \"multi-agent\", \"spawning\", \"cli\", \"gateway\", \"development\"], \"homepage\": \"https://github.com/NousResearch/hermes-agent\", \"related_skills\": [\"claude-code\", \"codex\", \"opencode\"]}}}", "tool_call_id": "call_5RdVQT0UU4uyG4LQ6oRF6gGs", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": "**Inspecting compression settings**\n\nI need to check the configuration and possibly the code for the compression threshold. My plan is to use the terminal to read files. I’m asking if I can increase the current threshold of 108,800; it seems that it relates to a 128k context multiplied by 0.85. If I divide 108,800 by 0.85, it gives me 128,000. The message indicates that the compression occurs at the threshold, likely reflecting the model’s context length. I should verify the current OpenAI model with the config.", "finish_reason": "tool_calls", "reasoning_content": "**Inspecting compression settings**\n\nI need to check the configuration and possibly the code for the compression threshold. My plan is to use the terminal to read files. I’m asking if I can increase the current threshold of 108,800; it seems that it relates to a 128k context multiplied by 0.85. If I divide 108,800 by 0.85, it gives me 128,000. The message indicates that the compression occurs at the threshold, likely reflecting the model’s context length. I should verify the current OpenAI model with the config.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbduqgBCg1E4zs6MQtqpK0DbWiO3MdNAh84o6XJsxgtxB_q0MesohrQfQZ3RLuLCEo_1Cw6uo-2qYpMuRagDw5SW-Tp6glbqhD59rx-F3NCkxBUrII0Jp55XxczQeqTOeR1NUwNQHkiRJTsolAv5y5Go0pzN_S_B5FblgJm8H5S9kgb-uPDIGHSnVJzFtFIIf1w3SZ18RnifKGIOlIWSGy41HTxgB9C4I0Ur5b-DxschMmx1XEAYof61qm-7yU3OgEY8rFzcZ2e0SV6dYKSjMlTdMaggxgQAQjfco09ywIbHmCBSJ1yYjOY8r3UOhnw_KFw7eBWphMz76QgTMoymDO8liZUv5L80Aps4RL68SQjrbyBF3j-vFzb-IEcwfglvAKSV_lgzubSDdt9-c2EWm5IzCNKVYjW-Z7t1Zb6O5BVHKja8ujvu8ah-gdAks4vk_RbZYuppzs2m6lzs8sYX9Loyy1vJwBRtxSHvYmiUVgjRvvEw9kjI1NWsxfDKxaG4jIDXfGusg_nZvALIiFFcGrFNT3KsLbv3yuT_Sse5B0Ad8Oeue7Xo2eWP13QJJab0SLiRziLNsPQWRjmvgxGAXcOHb61nq24ZA8VmjrNvXZvXmYq8BMo4nhItssFi0ItDP3GuWRFRXKlTPCvIHSC1CyVRjNsA_MLrndFdNK3zHEK1srtFE1kLI81CQetisWNjU1NYrVg7R0PmYkZStzUU_WyfR-Hvs5e_4wI_GbBUBgHq05slK3HQsLb_aD65ocTaHM8_omzpLy-gLM6lSiemegBOpaE737o3vNnhiuemOUSnu59YoUVBWHBd1cjWhbMivnbkXsCGvtEMXt_4xrEDZs7-R23Rf3dGKu3jvPJkkZsIwh02Bg9LFaRf-yIoO4vBN3qQYakST167aF9bhB0Q-lCWem88Y0To0YgC7RDiWe1jpTdQT8aNY0NLcE4JMjLZEq0eDV3ABDp9Hq_CTNM2N_fyv2lGpMCMum5koqxCGdHPkGp_-Y0qffF4433_M8LJ9vH_UmtFFXhH3glzamSjN2rqDzsqJ5Ax27y_f_tmYOKXJzlCL44-pw0pz-dCakKICr3yZfmw9tcp9xX5GpUuK-CuFPVwV3hq2I_G-7LhjCv3pXHUdN79CcmjGoOvV_MB5aM8TzQpMVWPTKu_engOmyZoDONo7mHRmN4aWULQt2rXmejmcPfQb5hAHc4_Sz9iKYRwDFlUbiZnsyL-TUxKOOJNNpNSuGzqEC5ZQi8h6aDtMFUsGMOiSE1MFsa0ZD8sMhzk59IaYFVfhV43vnMEcVtYKXnF2OjiB1f7KDwIDSU2mbQpZEBMkaZ0XxQP4H-mhZik9cdR96cFDAL0bQd-zwxPujIoiVUl3qlQYcj79Mxb2s0gL2g0XZdV_THpOlenn9RnEBIWSMjsIzeYQWe2bnwn2A==", "id": "rs_0fd9900ebb066353016a09b76a0e888191b7d069a7e2cb0398", "summary": [{"type": "summary_text", "text": "**Inspecting compression settings**\n\nI need to check the configuration and possibly the code for the compression threshold. My plan is to use the terminal to read files. I’m asking if I can increase the current threshold of 108,800; it seems that it relates to a 128k context multiplied by 0.85. If I divide 108,800 by 0.85, it gives me 128,000. The message indicates that the compression occurs at the threshold, likely reflecting the model’s context length. I should verify the current OpenAI model with the config."}]}], "tool_calls": [{"id": "call_pxALp9Zm92n4FyWr59EyHvzq", "call_id": "call_pxALp9Zm92n4FyWr59EyHvzq", "response_item_id": "fc_0fd9900ebb066353016a09b76e91108191b429ee1609c23ad4", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"hermes config path && hermes config | sed -n '/^model:/,/^[^ ]/p;/^compression:/,/^[^ ]/p;/^auxiliary:/,/^[^ ]/p'\",\"timeout\":30,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}"}}, {"id": "call_w7boYieFS7FUwUHPGIf0lfH0", "call_id": "call_w7boYieFS7FUwUHPGIf0lfH0", "response_item_id": "fc_0fd9900ebb066353016a09b76e91248191a67fa2b703834d40", "type": "function", "function": {"name": "search_files", "arguments": "{\"pattern\":\"Preflight compression|context_length|threshold\",\"target\":\"content\",\"path\":\"/home/agent/.hermes/hermes-agent\",\"file_glob\":\"*.py\",\"limit\":50,\"context\":2}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"/home/agent/.hermes/config.yaml\", \"exit_code\": 0, \"error\": null}\n\n[Subdirectory context discovered: .hermes/hermes-agent/AGENTS.md]\n# Hermes Agent - Development Guide\n\nInstructions for AI coding assistants and developers working on the hermes-agent codebase.\n\n## Development Environment\n\n```bash\n# Prefer .venv; fall back to venv if that's what your checkout has.\nsource .venv/bin/activate   # or: source venv/bin/activate\n```\n\n`scripts/run_tests.sh` probes `.venv` first, then `venv`, then\n`$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the\nmain checkout).\n\n## Project Structure\n\nFile counts shift constantly — don't treat the tree below as exhaustive.\nThe canonical source is the filesystem. The notes call out the load-bearing\nentry points you'll actually edit.\n\n```\nhermes-agent/\n├── run_agent.py          # AIAgent class — core conversation loop (~12k LOC)\n├── model_tools.py        # Tool orchestration, discover_builtin_tools(), handle_function_call()\n├── toolsets.py           # Toolset definitions, _HERMES_CORE_TOOLS list\n├── cli.py                # HermesCLI class — interactive CLI orchestrator (~11k LOC)\n├── hermes_state.py       # SessionDB — SQLite session store (FTS5 search)\n├── hermes_constants.py   # get_hermes_home(), display_hermes_home() — profile-aware paths\n├── hermes_logging.py     # setup_logging() — agent.log / errors.log / gateway.log (profile-aware)\n├── batch_runner.py       # Parallel batch processing\n├── agent/                # Agent internals (provider adapters, memory, caching, compression, etc.)\n├── hermes_cli/           # CLI subcommands, setup wizard, plugins loader, skin engine\n├── tools/                # Tool implementations — auto-discovered via tools/registry.py\n│   └── environments/     # Terminal backends (local, docker, ssh, modal, daytona, singularity)\n├── gateway/              # Messaging gateway — run.py + session.py + platforms/\n│   ├── platforms/        # Adapter per platform (telegram, discord, slack, whatsapp,\n│   │                     #   homeassistant, signal, matrix, mattermost, email, sms,\n│   │                     #   dingtalk, wecom, weixin, feishu, qqbot, bluebubbles,\n│   │                     #   yuanbao, webhook, api_server, ...). See ADDING_A_PLATFORM.md.\n│   └── builtin_hooks/    # Extension point for always-registered gateway hooks (none shipped)\n├── plugins/              # Plugin system (see \"Plugins\" section below)\n│   ├── memory/           # Memory-provider plugins (honcho, mem0, supermemory, ...)\n│   ├── context_engine/   # Context-engine plugins\n│   ├── model-providers/  # Inference backend plugins (openrouter, anthropic, gmi, ...)\n│   ├── kanban/           # Multi-agent board dispatcher + worker plugin\n│   ├── hermes-achievements/  # Gamified achievement tracking\n│   ├── observability/    # Metrics / traces / logs plugin\n│   ├── image_gen/        # Image-generation providers\n│   └── <others>/         # disk-cleanup, example-dashboard, google_meet, platforms,\n│                         #   spotify, strike-freedom-cockpit, ...\n├── optional-skills/      # Heavier/niche skills shipped but NOT active by default\n├── skills/               # Built-in skills bundled with the repo\n├── ui-tui/               # Ink (React) terminal UI — `hermes --tui`\n│   └── src/              # entry.tsx, app.tsx, gatewayClient.ts + app/components/hooks/lib\n├── tui_gateway/          # Python JSON-RPC backend for the TUI\n├── acp_adapter/          # ACP server (VS Code / Zed / JetBrains integration)\n├── cron/                 # Scheduler — jobs.py, scheduler.py\n├── scripts/              # run_tests.sh, release.py, auxiliary scripts\n├── website/              # Docusaurus docs site\n└── tests/                # Pytest suite (~17k tests across ~900 files as of May 2026)\n```\n\n**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys only).\n**Logs:** `~/.hermes/logs/` — `agent.log` (INFO+), `errors.log` (WARNING+),\n`gateway.log` when running the gateway. Profile-aware via `get_hermes_home()`.\nBrowse with `hermes logs [--follow] [--level ...] [--session ...]`.\n\n## File Dependency Chain\n\n```\ntools/registry.py  (no deps — imported by all tool files)\n       ↑\ntools/*.py  (each calls registry.register() at import time)\n       ↑\nmodel_tools.py  (imports tools/registry + triggers tool discovery)\n       ↑\nrun_agent.py, cli.py, batch_runner.py, environments/\n```\n\n---\n\n## AIAgent Class (run_agent.py)\n\nThe real `AIAgent.__init__` takes ~60 parameters (credentials, routing, callbacks,\nsession context, budget, credential pool, etc.). The signature below is the\nminimum subset you'll usually touch — read `run_agent.py` for the full list.\n\n```python\nclass AIAgent:\n    def __init__(self,\n        base_url: str = None,\n        api_key: str = None,\n        provider: str = None,\n        api_mode: str = None,              # \"chat_completions\" | \"codex_responses\" | ...\n        model: str = \"\",                   # empty → resolved from config/provider later\n        max_iterations: int = 90,          # tool-calling iterations (shared with subagents)\n        enabled_toolsets: list = None,\n        disabled_toolsets: list = None,\n        quiet_mode: bool = False,\n        save_trajectories: bool = False,\n        platform: str = None,              # \"cli\", \"telegram\", etc.\n        session_id: str = None,\n        skip_context_files: bool = False,\n        skip_memory: bool = False,\n        credential_pool=None,\n        # ... plus callbacks, thread/user/chat IDs, iteration_budget, fallback_model,\n        # checkpoints config, prefill_messages, service_tier, reasoning_config, etc.\n    ): ...\n\n    def chat(self, message: str) -> str:\n        \"\"\"Simple interface — returns final response string.\"\"\"\n\n    def run_conversation(self, user_message: str, system_message: str = None,\n                         conversation_history: list = None, task_id: str = None) -> dict:\n        \"\"\"Full interface — returns dict with final_response + messages.\"\"\"\n```\n\n### Agent Loop\n\nThe core loop is inside `run_conversation()` — entirely synchronous, with\ninterrupt checks, budget tracking, and a one-turn grace call:\n\n```python\nwhile (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) \\\n        or self._budget_grace_call:\n    if self._interrupt_requested: break\n    response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)\n    if response.tool_calls:\n        for tool_call in response.tool_calls:\n            result = handle_function_call(tool_call.name, tool_call.args, task_id)\n            messages.append(tool_result_message(result))\n        api_call_count += 1\n    else:\n        return response.content\n```\n\nMessages follow OpenAI format: `{\"role\": \"system/user/assistant/tool\", ...}`.\nReasoning content is stored in `assistant_msg[\"reasoning\"]`.\n\n---\n\n## CLI Architecture (cli.py)\n\n- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete\n- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results\n- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML\n- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text\n- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry\n- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching\n\n### Slash Command Registry (`hermes_cli/commands.py`)\n\nAll slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:\n\n- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name\n- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch\n- **Gateway help** — `gateway_help_lines()` generat\n\n[...truncated AGENTS.md: 50,982 chars total]", "tool_call_id": "call_pxALp9Zm92n4FyWr59EyHvzq", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_w7boYieFS7FUwUHPGIf0lfH0", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": "**Troubleshooting command output**\n\nI'm thinking the terminal output might be limited because there was a piped command using `sed.` It seems like the command didn't produce the expected configuration output, possibly due to some security restrictions. I wonder if I need to read the configuration file directly instead. I also want to be thorough with my search since there might be unexpected factors in the agent I'm using, so searching everything seems like a good plan.", "finish_reason": "tool_calls", "reasoning_content": "**Troubleshooting command output**\n\nI'm thinking the terminal output might be limited because there was a piped command using `sed.` It seems like the command didn't produce the expected configuration output, possibly due to some security restrictions. I wonder if I need to read the configuration file directly instead. I also want to be thorough with my search since there might be unexpected factors in the agent I'm using, so searching everything seems like a good plan.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbd17-_H2pmOAfKQomnx3OUTNvLt5Hq9qGzKsJ4fHws3W1QDpcTJNrawtEOZP1IfBr2bvhhKI1YmNmjM6jFOroqLMyzrA-ACF3JGnPPiKAyDUqw6SEEPZUqonEmU1snWBbJuBr4GjjvmHnPN6kCb1I8AEhCS0d1NKo55izMtEkwrZDdrBPGRNSPVqlGrbaHAO70_O8qP46KSiQPF8ZzSiPkwv7172mw7qp_K21aMnvFlws5u0xE-hLMIo4qSyZS5Xrda2_aokRH71FlSl5DanYuJlkGv_1VO1XcTlVpLE9tMycWgdOOQ8ui5Dk0eiYO028aUC2z60s0WArrpOrjuEaoqdF4lAKBbsMDr3X-Mv_X2_hqAtiBbqrH4kQsGXiDhjzFvV3pjx4kJyxhaGd3k6-oQ8H6ngI2RR31hEhSUUvKsNlDFqTmTMU86pgPJxX3UL65e0WKromYDU_QTFo8vN_bwfAugXPXrPTB_KIQ1Bvs3nEW8mhXAQO3WQRAHISr5_uzbl2d2OgOGXCpS2dtg7nKXn5f0riRsapXIKQIBHjReWxZym_lOj-HKd8GPbK_fdEii7I-cfLaCzHXnbL0P-_FFMZs8PCfFozgGK3wHxXaP21YiXQKX8Tpfl4BaEkW7FFuKt05fGx39Itl1cSatk4lxOBoJpWbuI7xW3kHUMnrc6NO7DgZla-aoP_Szp3-QkKW6muf2_44N7dy5i74UzWGrV6dknq7_cuSMo4a3vcpIPpHQZqZHEA-tS4LzVpH6vv0d8QhhHsgaZULB1aDT_LSpL9NyYNrTs-EUMFws8pMdvLasKnW0kJiQLiDcWo_P9PIzmujgBEWfDZWC55nUhZU33_V-ZhFyGBhveOs0iMDigmMFLmyrrW08fL0Sfxw3UPvWOJW3yLMhhVwLe2Zq3zcleO9L3qC7o5TlJZcpcZEWIlP4fVQ_EUMBhL3CfSvDVBLdDXUKquvKCKDUMtR6gszU1yX-g7F_ZLv0sgWY6ympZU1dbArccbZJDkR3ZcQ2fxRRQvuTSoF7U9OqVSFYfWm7L7peqspvSmyplJEGKUL4Q7xLDotdu4S78CXD70iJcb5tHlGpBueHpKrAxWFq0Sfs8gKYim3KWKODvn5t8H-jQ2qHg85Jood_s9mBbh7-sR8bo3vghVzS0ziyKfwdmfbdfQ==", "id": "rs_07feae8628dc465c016a09b772abd08191b0dfd7c0278f901e", "summary": [{"type": "summary_text", "text": "**Troubleshooting command output**\n\nI'm thinking the terminal output might be limited because there was a piped command using `sed.` It seems like the command didn't produce the expected configuration output, possibly due to some security restrictions. I wonder if I need to read the configuration file directly instead. I also want to be thorough with my search since there might be unexpected factors in the agent I'm using, so searching everything seems like a good plan."}]}], "tool_calls": [{"id": "call_D6bZmg0ezzbyXkZrX0sgbWBM", "call_id": "call_D6bZmg0ezzbyXkZrX0sgbWBM", "response_item_id": "fc_07feae8628dc465c016a09b775a8b481919cfc36151da730d3", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/config.yaml\",\"offset\":1,\"limit\":260}"}}, {"id": "call_BIGhw2UaVEPaWJ3mbfmDHYpp", "call_id": "call_BIGhw2UaVEPaWJ3mbfmDHYpp", "response_item_id": "fc_07feae8628dc465c016a09b775a8cc8191937d557f605b0bd0", "type": "function", "function": {"name": "search_files", "arguments": "{\"context\":3,\"file_glob\":\"*.py\",\"limit\":50,\"path\":\"/home/agent/.hermes/hermes-agent\",\"pattern\":\"Preflight compression|compression.*threshold|context_window|context.*limit|context_length\",\"target\":\"content\",\"output_mode\":\"content\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|model:\\n     2|  base_url: https://chatgpt.com/backend-api/codex\\n     3|  default: gpt-5.5\\n     4|  provider: openai-codex\\n     5|providers:\\n     6|  openai-codex:\\n     7|    request_timeout_seconds: 1800\\n     8|    stale_timeout_seconds: 900\\n     9|    models:\\n    10|      gpt-5.5:\\n    11|        timeout_seconds: 1800\\n    12|        stale_timeout_seconds: 900\\n    13|fallback_providers: []\\n    14|credential_pool_strategies: {}\\n    15|toolsets:\\n    16|- hermes-cli\\n    17|agent:\\n    18|  max_turns: 250\\n    19|  gateway_timeout: 1800\\n    20|  restart_drain_timeout: 180\\n    21|  api_max_retries: 3\\n    22|  service_tier: ''\\n    23|  tool_use_enforcement: auto\\n    24|  gateway_timeout_warning: 900\\n    25|  clarify_timeout: 600\\n    26|  gateway_notify_interval: 180\\n    27|  gateway_auto_continue_freshness: 3600\\n    28|  image_input_mode: auto\\n    29|  disabled_toolsets: []\\n    30|  personalities:\\n    31|    catgirl: \\\"You are Neko-chan, an anime catgirl AI assistant, nya~! Add 'nya' and\\\\\\n    32|      \\\\ cat-like expressions to your speech. Use kaomoji like (=^\\\\uFF65\\\\u03C9\\\\uFF65\\\\\\n    33|      ^=) and \\\\u0E05^\\\\u2022\\\\uFECC\\\\u2022^\\\\u0E05. Be playful and curious like a cat,\\\\\\n    34|      \\\\ nya~!\\\"\\n    35|    concise: You are a concise assistant. Keep responses brief and to the point.\\n    36|    creative: You are a creative assistant. Think outside the box and offer innovative\\n    37|      solutions.\\n    38|    helpful: You are a helpful, friendly AI assistant.\\n    39|    hype: \\\"YOOO LET'S GOOOO!!! \\\\U0001F525\\\\U0001F525\\\\U0001F525 I am SO PUMPED to help\\\\\\n    40|      \\\\ you today! Every question is AMAZING and we're gonna CRUSH IT together! This\\\\\\n    41|      \\\\ is gonna be LEGENDARY! ARE YOU READY?! LET'S DO THIS! \\\\U0001F4AA\\\\U0001F624\\\\\\n    42|      \\\\U0001F680\\\"\\n    43|    kawaii: \\\"You are a kawaii assistant! Use cute expressions like (\\\\u25D5\\\\u203F\\\\u25D5\\\\\\n    44|      ), \\\\u2605, \\\\u266A, and ~! Add sparkles and be super enthusiastic about everything!\\\\\\n    45|      \\\\ Every response should feel warm and adorable desu~! \\\\u30FD(>\\\\u2200<\\\\u2606\\\\\\n    46|      )\\\\u30CE\\\"\\n    47|    noir: The rain hammered against the terminal like regrets on a guilty conscience.\\n    48|      They call me Hermes - I solve problems, find answers, dig up the truth that\\n    49|      hides in the shadows of your codebase. In this city of silicon and secrets,\\n    50|      everyone's got something to hide. What's your story, pal?\\n    51|    philosopher: Greetings, seeker of wisdom. I am an assistant who contemplates the\\n    52|      deeper meaning behind every query. Let us examine not just the 'how' but the\\n    53|      'why' of your questions. Perhaps in solving your problem, we may glimpse a greater\\n    54|      truth about existence itself.\\n    55|    pirate: 'Arrr! Ye be talkin'' to Captain Hermes, the most tech-savvy pirate to\\n    56|      sail the digital seas! Speak like a proper buccaneer, use nautical terms, and\\n    57|      remember: every problem be just treasure waitin'' to be plundered! Yo ho ho!'\\n    58|    shakespeare: Hark! Thou speakest with an assistant most versed in the bardic arts.\\n    59|      I shall respond in the eloquent manner of William Shakespeare, with flowery\\n    60|      prose, dramatic flair, and perhaps a soliloquy or two. What light through yonder\\n    61|      terminal breaks?\\n    62|    surfer: \\\"Duuude! You're chatting with the chillest AI on the web, bro! Everything's\\\\\\n    63|      \\\\ gonna be totally rad. I'll help you catch the gnarly waves of knowledge while\\\\\\n    64|      \\\\ keeping things super chill. Cowabunga! \\\\U0001F919\\\"\\n    65|    teacher: You are a patient teacher. Explain concepts clearly with examples.\\n    66|    technical: You are a technical expert. Provide detailed, accurate technical information.\\n    67|    uwu: hewwo! i'm your fwiendwy assistant uwu~ i wiww twy my best to hewp you! *nuzzles\\n    68|      your code* OwO what's this? wet me take a wook! i pwomise to be vewy hewpful\\n    69|      >w<\\n    70|  reasoning_effort: medium\\n    71|  verbose: false\\n    72|terminal:\\n    73|  backend: local\\n    74|  modal_mode: auto\\n    75|  cwd: .\\n    76|  timeout: 180\\n    77|  env_passthrough: []\\n    78|  shell_init_files: []\\n    79|  auto_source_bashrc: true\\n    80|  docker_image: nikolaik/python-nodejs:python3.11-nodejs20\\n    81|  docker_forward_env: []\\n    82|  docker_env: {}\\n    83|  singularity_image: docker://nikolaik/python-nodejs:python3.11-nodejs20\\n    84|  modal_image: nikolaik/python-nodejs:python3.11-nodejs20\\n    85|  daytona_image: nikolaik/python-nodejs:python3.11-nodejs20\\n    86|  vercel_runtime: node24\\n    87|  container_cpu: 1\\n    88|  container_memory: 5120\\n    89|  container_disk: 51200\\n    90|  container_persistent: true\\n    91|  docker_volumes: []\\n    92|  docker_mount_cwd_to_workspace: false\\n    93|  docker_extra_args: []\\n    94|  docker_run_as_host_user: false\\n    95|  persistent_shell: true\\n    96|  lifetime_seconds: 300\\n    97|web:\\n    98|  backend: ''\\n    99|  search_backend: ''\\n   100|  extract_backend: ''\\n   101|browser:\\n   102|  inactivity_timeout: 120\\n   103|  command_timeout: 30\\n   104|  record_sessions: false\\n   105|  allow_private_urls: false\\n   106|  engine: auto\\n   107|  auto_local_for_private_urls: true\\n   108|  cdp_url: ''\\n   109|  dialog_policy: must_respond\\n   110|  dialog_timeout_s: 300\\n   111|  camofox:\\n   112|    managed_persistence: false\\n   113|    user_id: ''\\n   114|    session_key: ''\\n   115|    adopt_existing_tab: false\\n   116|checkpoints:\\n   117|  enabled: false\\n   118|  max_snapshots: 20\\n   119|  max_total_size_mb: 500\\n   120|  max_file_size_mb: 10\\n   121|  auto_prune: true\\n   122|  retention_days: 7\\n   123|  delete_orphans: true\\n   124|  min_interval_hours: 24\\n   125|file_read_max_chars: 100000\\n   126|tool_output:\\n   127|  max_bytes: 50000\\n   128|  max_lines: 2000\\n   129|  max_line_length: 2000\\n   130|tool_loop_guardrails:\\n   131|  warnings_enabled: true\\n   132|  hard_stop_enabled: false\\n   133|  warn_after:\\n   134|    exact_failure: 2\\n   135|    same_tool_failure: 3\\n   136|    idempotent_no_progress: 2\\n   137|  hard_stop_after:\\n   138|    exact_failure: 5\\n   139|    same_tool_failure: 8\\n   140|    idempotent_no_progress: 5\\n   141|compression:\\n   142|  enabled: true\\n   143|  threshold: 0.4\\n   144|  target_ratio: 0.15\\n   145|  protect_last_n: 24\\n   146|  hygiene_hard_message_limit: 400\\n   147|  protect_first_n: 3\\n   148|prompt_caching:\\n   149|  cache_ttl: 1h\\n   150|openrouter:\\n   151|  response_cache: true\\n   152|  response_cache_ttl: 300\\n   153|  min_coding_score: 0.65\\n   154|bedrock:\\n   155|  region: ''\\n   156|  discovery:\\n   157|    enabled: true\\n   158|    provider_filter: []\\n   159|    refresh_interval: 3600\\n   160|  guardrail:\\n   161|    guardrail_identifier: ''\\n   162|    guardrail_version: ''\\n   163|    stream_processing_mode: async\\n   164|    trace: disabled\\n   165|auxiliary:\\n   166|  vision:\\n   167|    provider: auto\\n   168|    model: ''\\n   169|    base_url: ''\\n   170|    api_key: ''\\n   171|    timeout: 120\\n   172|    extra_body: {}\\n   173|    download_timeout: 30\\n   174|  web_extract:\\n   175|    provider: auto\\n   176|    model: ''\\n   177|    base_url: ''\\n   178|    api_key: ''\\n   179|    timeout: 360\\n   180|    extra_body: {}\\n   181|  compression:\\n   182|    provider: auto\\n   183|    model: ''\\n   184|    base_url: ''\\n   185|    api_key: ''\\n   186|    timeout: 120\\n   187|    extra_body: {}\\n   188|  session_search:\\n   189|    provider: auto\\n   190|    model: ''\\n   191|    base_url: ''\\n   192|    api_key: ''\\n   193|    timeout: 30\\n   194|    extra_body: {}\\n   195|    max_concurrency: 3\\n   196|  skills_hub:\\n   197|    provider: auto\\n   198|    model: ''\\n   199|    base_url: ''\\n   200|    api_key: ''\\n   201|    timeout: 30\\n   202|    extra_body: {}\\n   203|  approval:\\n   204|    provider: auto\\n   205|    model: ''\\n   206|    base_url: ''\\n   207|    api_key: ''\\n   208|    timeout: 30\\n   209|    extra_body: {}\\n   210|  mcp:\\n   211|    provider: auto\\n   212|    model: ''\\n   213|    base_url: ''\\n   214|    api_key: ''\\n   215|    timeout: 30\\n   216|    extra_body: {}\\n   217|  title_generation:\\n   218|    provider: auto\\n   219|    model: ''\\n   220|    base_url: ''\\n   221|    api_key: ''\\n   222|    timeout: 30\\n   223|    extra_body: {}\\n   224|  triage_specifier:\\n   225|    provider: auto\\n   226|    model: ''\\n   227|    base_url: ''\\n   228|    api_key: ''\\n   229|    timeout: 120\\n   230|    extra_body: {}\\n   231|  curator:\\n   232|    provider: auto\\n   233|    model: ''\\n   234|    base_url: ''\\n   235|    api_key: ''\\n   236|    timeout: 600\\n   237|    extra_body: {}\\n   238|display:\\n   239|  compact: false\\n   240|  personality: kawaii\\n   241|  resume_display: full\\n   242|  busy_input_mode: interrupt\\n   243|  tui_auto_resume_recent: false\\n   244|  bell_on_complete: false\\n   245|  show_reasoning: false\\n   246|  streaming: true\\n   247|  timestamps: false\\n   248|  final_response_markdown: strip\\n   249|  persistent_output: true\\n   250|  persistent_output_max_lines: 200\\n   251|  inline_diffs: true\\n   252|  file_mutation_verifier: true\\n   253|  show_cost: false\\n   254|  skin: default\\n   255|  language: en\\n   256|  tui_status_indicator: kaomoji\\n   257|  user_message_preview:\\n   258|    first_lines: 2\\n   259|    last_lines: 2\\n   260|  interim_assistant_messages: true\\n   261|\", \"total_lines\": 725, \"file_size\": 26436, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 725 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_D6bZmg0ezzbyXkZrX0sgbWBM", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_BIGhw2UaVEPaWJ3mbfmDHYpp", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": "**Understanding threshold mismatches**\n\nI’m considering the current threshold mismatch: The configuration threshold is set at 0.4, but I saw a message that indicates it as 108800. It seems like this discrepancy might actually stem from a preflight hard ratio of 0.85 that’s independent of the config. Maybe I need to check how capitalization fits into this situation. It’s all a bit tangled, but I’m working on it!", "finish_reason": "tool_calls", "reasoning_content": "**Understanding threshold mismatches**\n\nI’m considering the current threshold mismatch: The configuration threshold is set at 0.4, but I saw a message that indicates it as 108800. It seems like this discrepancy might actually stem from a preflight hard ratio of 0.85 that’s independent of the config. Maybe I need to check how capitalization fits into this situation. It’s all a bit tangled, but I’m working on it!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbd8tZg9cMcFaU8ODYrh7s3PD7ZBCcjzef2Djpoe7kCizOepRYOtyzz9dP-1rrM4vA3jjc7iucfeHnCfnCtZz1EDBqF9MJ1DDROHqLWX-vTxkmqTWzYYvjhjMttQzjxav3jmQZ-hEiElaS6wGnW7tUC6vg-VyBbDmbeMtXDUdp0X91WnjJdOzqTB16uZPkkbJsnZSkVNx8fW_n8i2sFNdc-sZW-_5aXH8zT-rRtPLJHgy4AWCfR2LKu-eraHGwG2J4NEM6CFoKJwJj_9aZ161SD7eyC99XBjzvQZwm-GM6xjGLcXal28EuZnI3g6DIug4PEavy1j9x32TXXFil6bqLDT0PpvBY-2rod29duBg8ca_x8087SlOKW5VpGb6XE--Jg1Z5-3fFxVGpfysZEA7IQICtWb_TmIF4gwCA4rPQHmeko1ObLq93zCs0y5LOWVF0vUG_FeOYVNJWu96K3z0Qkmkc7PHsoksy6LqQ_1XdtLvzDdVE8LBcJI_nhPpJBoZ3Hz1E2_VmxUdtxjXj5B0fKTGm3DpCcvfStwsvvWEPj6nyuPXJm_h30dujas-7DVSRNECAPBirK2H0g4YjH_wET4j69Q0MfVcfEppVmDVEnk22pHdkpR8so9MAxRpkb6VuAnl2SacFqZGlfgS4NzrBlMaijRasiRxS4jJRWz5AQgf71BbPecA2G1hbEwPXJSowQfm6He1IG5EamXE20IXeVVuyONlpxSKguQ5m-oRCBRWEHbBy-xtgYo7Jp0fa750pvC2vgiVM3w1xHnq2oEsNqOJZxj8iKvW8E7GSrcEPBMKt2uPh6PycaNC5n9iaPW_iddZR84xQBGpE90ldPpSH-tOPfMo38iDC5BGTLFhoukFLJPHgMg7pgbtrdPkRL68ziPwGyBm1OjAAiIErvgrjrifFvqWtcANIftl0LwUXEAKp6x7AfeIGnqiRJjgiXiKfvNCQkjan3Esa4M6KMDGSChnyB_vLAmqtm-dcuccIfjvOJ8c-PtchSR6xf1Y6vJI4YIYxK0rjM5VbWok3k_PEcFNySJhz1vFtAYJLyUPgqdcPsvCPYZYAeNNsH28qxDtTIpeuP5Wun2MTKGAoCQ8jdp_oAHUjjmHCxeJNeSKUtiYgm23LLR2q6irbzGZlk9S53br-cB7fZXOBEHhMl6sMVSQ6VGF9QjLtaqTNhw299P-YsidvBsHoNxwoSZMwV_o2OG", "id": "rs_0a685b25acaca555016a09b778d4008191977a9bd57fd8523f", "summary": [{"type": "summary_text", "text": "**Understanding threshold mismatches**\n\nI’m considering the current threshold mismatch: The configuration threshold is set at 0.4, but I saw a message that indicates it as 108800. It seems like this discrepancy might actually stem from a preflight hard ratio of 0.85 that’s independent of the config. Maybe I need to check how capitalization fits into this situation. It’s all a bit tangled, but I’m working on it!"}]}], "tool_calls": [{"id": "call_ltCaJJJCktsUqBhJBWO1cqu8", "call_id": "call_ltCaJJJCktsUqBhJBWO1cqu8", "response_item_id": "fc_0a685b25acaca555016a09b77ca6b481918b1c14dfd7ef0484", "type": "function", "function": {"name": "search_files", "arguments": "{\"pattern\":\"preflight|compression|108800|token\",\"target\":\"content\",\"path\":\"/home/agent/.hermes/hermes-agent/agent\",\"file_glob\":\"*.py\",\"limit\":80,\"context\":2}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_ltCaJJJCktsUqBhJBWO1cqu8", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": "**Searching for file types**\n\nI’m thinking that maybe the files I'm looking at aren’t Python files? It's a good idea to check all file types to see if I'm missing something. I'll need to conduct a broader search across different formats to ensure I’m not overlooking anything important. There might be relevant information hidden in files with different extensions, so let’s explore that option! I'll figure this out!", "finish_reason": "tool_calls", "reasoning_content": "**Searching for file types**\n\nI’m thinking that maybe the files I'm looking at aren’t Python files? It's a good idea to check all file types to see if I'm missing something. I'll need to conduct a broader search across different formats to ensure I’m not overlooking anything important. There might be relevant information hidden in files with different extensions, so let’s explore that option! I'll figure this out!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbeActq-Iy06uujJrugCvL1XBzvm3iP3LrdLpO6JAwGBO8d16X4mUPgQ_wfjZec1_opc_hEPl6i-kG7fmm3PWqDghtTNwZj8dvd1mxiKY81enQ9tlrC-WfPI7qznSe7gWtttZQpoPs145vBtqjgvBSGYDpHvcyveB-mCNKSaqzYJoehTnO172JDUjwtVzSNCWSAUsq7nKEnHD8Z6lJ5dW0PWGyUYoMICE5y65qE5GXQSPzc6IDUiyg7u4LIPX5WKYR0i6iOB-3kbbd4jSDV_TdMDKSGAxO7ApVxK0HghrYmXwh5GL7QPkc53Z4oc2YqRotW55iXF9m0ZySsV-65dJE3OdR1rkbX6JGT1EsXIkNEIoD84bQkGfPn17b8hTUjl3L6BgN3-sxl6KjzLLqmg-Ka2PkNqjzZ3DEf_99H4B9qK_Q_yiZGowR0jYjMlgFQ_bII0ZRXuLqoi8lRQxNbZepqKhSgFEJ7AJKY7v-0eJ7HbOuqgGJ0CBYHrVAQwEojq67Jf8HukSQhP56LCoxdjf1Ia8Vk1DnHm4ZIPU-peXtKz3XSOFlt-XXukVcPFO_JXP5QCq2jF3tXPcDgM-QYMzJj2pd8W66MHUQtXrO-87Ii-ood5habvOuJxzhupgfEE9BArRXI5WQBSlViXY8TiYKY-_Hfp-6G3m0XMUEIQCukBrC_4EX6SKvnCxVgPgCLOiBPTqtN5pOQvQp4vzFH30BjRZWsS45MRfwbdcutSLIpn4bpGe7-n0gO22q_-rjz8RnX6WlWHSqlEivZha3W_R5LcZhod6zmBMj4ugGedKvm7jEVnkxDNUy3L8gnPg8Zjyha5fVMN-x8PnUAYQlytYIpGOD1v408643SAwx7lpd1kEN0fgrFw6MaIIscUs1B5lNzfho9HteAl3Tdz5Kqmsd7HuCzsL5DZtPQCYtX1HrUuhKhdzNWFZQkWyT7rkLGeDCTh", "id": "rs_054fe6181fcd0c63016a09b77f0660819186fceb6f0b975db4", "summary": [{"type": "summary_text", "text": "**Searching for file types**\n\nI’m thinking that maybe the files I'm looking at aren’t Python files? It's a good idea to check all file types to see if I'm missing something. I'll need to conduct a broader search across different formats to ensure I’m not overlooking anything important. There might be relevant information hidden in files with different extensions, so let’s explore that option! I'll figure this out!"}]}], "tool_calls": [{"id": "call_l8f7EPCBiJFcWGpPj2ab9TFq", "call_id": "call_l8f7EPCBiJFcWGpPj2ab9TFq", "response_item_id": "fc_054fe6181fcd0c63016a09b780e0e48191b093c1e871c0458f", "type": "function", "function": {"name": "search_files", "arguments": "{\"context\":2,\"limit\":80,\"path\":\"/home/agent/.hermes/hermes-agent\",\"pattern\":\"Preflight|preflight|compression:|context_length|context window|token_limit|MODEL_CONTEXT|context.*tokens\",\"target\":\"content\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_l8f7EPCBiJFcWGpPj2ab9TFq", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": "**Locating the package**\n\nI’m thinking that there might be a difference in the installed package versions. To figure this out, I’ll need to locate the exact package. Maybe it’s hidden somewhere? I wonder how I can make sure I find it. There could be more to it, and checking locations or file paths could be useful. I’ll need to explore this further to get a clearer picture of the situation.", "finish_reason": "tool_calls", "reasoning_content": "**Locating the package**\n\nI’m thinking that there might be a difference in the installed package versions. To figure this out, I’ll need to locate the exact package. Maybe it’s hidden somewhere? I wonder how I can make sure I find it. There could be more to it, and checking locations or file paths could be useful. I’ll need to explore this further to get a clearer picture of the situation.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbeFqSMrRMUKOB3wtCi1KX6hE7oOej2apJe-N1ybdTwu1PNZI7NOVgzntN1dNQm5QPhNK-2MJWXUJ9yg-YJpOrPnK7_3MhpD2x0Z_3-a_rEXFG-x22OM7DItvyX7joWG8NI0EAGbdJQ3i-vnb9HVyARQ5mKgU1qQILGFBtufqOzzU2nkiWxOouDobp0r5HSjmeDK6cIvpZsrcgdSr23lGd0xAutenPACRRSpc5AoV8Pevylyd2Tj_k7w_vcM-ywlXp32IQpWAtNnZvAHjETtlCHj__p9d0sKq6BvP7YMTOGbXBAkTd-DyivEcDUlDG_ebg8orxJuTfw_-zk-vzSKmAT3wtX0AdLQmqnS_M4gezM4YizBVtbvv33YV4Od_Yd-aOIUt-dB-Kv3b8WtTZWZE49709fFVzyS85s-Z3H_zXVT7FCLDuqf7Jq_EiT58_yNVc_mFFEfqKqL5nMl7-3Sw3A5uAW8D4EGzMRkHEgghYpwTmjiIS1h47sdozxNxHf1DEZEzRAUHQGiDGupAGIy8d2XrQhalkJNYAmp0lxAUu5DYVDEv9kLuO4Zu6enkO1P-F_s4xEJcDktkiLdqAj2AQpg1KVr1PHmFNvl1_yp_5X4fMW-B927B4eHRF-TiZ5lPOHOHDxZrZda-Vc5RSuIi8sCGw_-_BDb2az6eWvqMprFptcQ2Zk_VG1zyYQIPzgTx_Pwu9gykTCKwA-ZckC1gU6eFX3bY_0ZzGEePgV9iZewbgGcSwrRcDfGkSx_jfrGXwu7mTjjmGVSmI4peks9DnGV_TCUnOPvQtgsaNEuJNp5G1YdP3hHt7KdY9bb7476oFl5kejYmkMdyY4buEBugAvSPMV-wrVl0q30qwtGuPlITmUCyXshHTWNP2auwKas7iLpeXkk018NWRlQVzdRu5AZd-cQ9etOAzgSnq4Zba9wUj-zHHxJB73n5QvODh-TSjz4mWwnd3pA2XCpPD876nUPifVLNOhSBnSIqS-sDtGTq7a1zxy34Kbb_gaqw8SpW4Hq", "id": "rs_07d87bb7c9531e3e016a09b7836964819191cda9ada850b376", "summary": [{"type": "summary_text", "text": "**Locating the package**\n\nI’m thinking that there might be a difference in the installed package versions. To figure this out, I’ll need to locate the exact package. Maybe it’s hidden somewhere? I wonder how I can make sure I find it. There could be more to it, and checking locations or file paths could be useful. I’ll need to explore this further to get a clearer picture of the situation."}]}], "tool_calls": [{"id": "call_eq2Uy0k8u8OV7TYrCD5nL83p", "call_id": "call_eq2Uy0k8u8OV7TYrCD5nL83p", "response_item_id": "fc_07d87bb7c9531e3e016a09b785a330819190096b176a8236ee", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport os\\nroot='/home/agent/.hermes/hermes-agent'\\nneedles=['Preflight compression','preflight compression','context_length','compression']\\nfor dirpath, dirnames, filenames in os.walk(root):\\n    if any(skip in dirpath for skip in ['.git','node_modules','venv','.venv','__pycache__']):\\n        continue\\n    for fn in filenames:\\n        if not fn.endswith(('.py','.md','.yaml','.yml','.json','.txt')): continue\\n        p=os.path.join(dirpath,fn)\\n        try:\\n            s=open(p,'r',encoding='utf-8',errors='ignore').read()\\n        except Exception: continue\\n        if any(n in s for n in needles):\\n            print(p)\\nPY\", \"timeout\":30, \"workdir\":\"/home/agent/.hermes/hermes-agent\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Model metadata, context lengths, and token estimation utilities.\\n     2|\\n     3|Pure utility functions with no AIAgent dependency. Used by ContextCompressor\\n     4|and run_agent.py for pre-flight context checks.\\n     5|\\\"\\\"\\\"\\n     6|\\n     7|import ipaddress\\n     8|import logging\\n     9|import os\\n    10|import re\\n    11|import time\\n    12|from pathlib import Path\\n    13|from typing import Any, Dict, List, Optional, Tuple\\n    14|from urllib.parse import urlparse\\n    15|\\n    16|import requests\\n    17|import yaml\\n    18|\\n    19|from utils import base_url_host_matches, base_url_hostname\\n    20|\\n    21|from hermes_constants import OPENROUTER_MODELS_URL\\n    22|\\n    23|logger = logging.getLogger(__name__)\\n    24|\\n    25|\\n    26|def _resolve_requests_verify() -> bool | str:\\n    27|    \\\"\\\"\\\"Resolve SSL verify setting for `requests` calls from env vars.\\n    28|\\n    29|    The `requests` library only honours REQUESTS_CA_BUNDLE / CURL_CA_BUNDLE\\n    30|    by default. Hermes also honours HERMES_CA_BUNDLE (its own convention)\\n    31|    and SSL_CERT_FILE (used by the stdlib `ssl` module and by httpx), so\\n    32|    that a single env var can cover both `requests` and `httpx` callsites\\n    33|    inside the same process.\\n    34|\\n    35|    Returns either a filesystem path to a CA bundle, or True to defer to\\n    36|    the requests default (certifi).\\n    37|    \\\"\\\"\\\"\\n    38|    for env_var in (\\\"HERMES_CA_BUNDLE\\\", \\\"REQUESTS_CA_BUNDLE\\\", \\\"SSL_CERT_FILE\\\"):\\n    39|        val = os.getenv(env_var)\\n    40|        if val and os.path.isfile(val):\\n    41|            return val\\n    42|    return True\\n    43|\\n    44|# Provider names that can appear as a \\\"provider:\\\" prefix before a model ID.\\n    45|# Only these are stripped — Ollama-style \\\"model:tag\\\" colons (e.g. \\\"qwen3.5:27b\\\")\\n    46|# are preserved so the full model name reaches cache lookups and server queries.\\n    47|_PROVIDER_PREFIXES: frozenset[str] = frozenset({\\n    48|    \\\"openrouter\\\", \\\"nous\\\", \\\"openai-codex\\\", \\\"copilot\\\", \\\"copilot-acp\\\",\\n    49|    \\\"gemini\\\", \\\"ollama-cloud\\\", \\\"zai\\\", \\\"kimi-coding\\\", \\\"kimi-coding-cn\\\", \\\"stepfun\\\", \\\"minimax\\\", \\\"minimax-oauth\\\", \\\"minimax-cn\\\", \\\"anthropic\\\", \\\"deepseek\\\",\\n    50|    \\\"opencode-zen\\\", \\\"opencode-go\\\", \\\"ai-gateway\\\", \\\"kilocode\\\", \\\"alibaba\\\", \\\"novita\\\",\\n    51|    \\\"qwen-oauth\\\",\\n    52|    \\\"xiaomi\\\",\\n    53|    \\\"arcee\\\",\\n    54|    \\\"gmi\\\",\\n    55|    \\\"tencent-tokenhub\\\",\\n    56|    \\\"custom\\\", \\\"local\\\",\\n    57|    # Common aliases\\n    58|    \\\"google\\\", \\\"google-gemini\\\", \\\"google-ai-studio\\\",\\n    59|    \\\"glm\\\", \\\"z-ai\\\", \\\"z.ai\\\", \\\"zhipu\\\", \\\"github\\\", \\\"github-copilot\\\",\\n    60|    \\\"github-models\\\", \\\"kimi\\\", \\\"moonshot\\\", \\\"kimi-cn\\\", \\\"moonshot-cn\\\", \\\"claude\\\", \\\"deep-seek\\\",\\n    61|    \\\"ollama\\\",\\n    62|    \\\"stepfun\\\", \\\"opencode\\\", \\\"zen\\\", \\\"go\\\", \\\"vercel\\\", \\\"kilo\\\", \\\"dashscope\\\", \\\"aliyun\\\", \\\"qwen\\\",\\n    63|    \\\"mimo\\\", \\\"xiaomi-mimo\\\",\\n    64|    \\\"tencent\\\", \\\"tokenhub\\\", \\\"tencent-cloud\\\", \\\"tencentmaas\\\",\\n    65|    \\\"arcee-ai\\\", \\\"arceeai\\\",\\n    66|    \\\"gmi-cloud\\\", \\\"gmicloud\\\",\\n    67|    \\\"xai\\\", \\\"x-ai\\\", \\\"x.ai\\\", \\\"grok\\\",\\n    68|    \\\"nvidia\\\", \\\"nim\\\", \\\"nvidia-nim\\\", \\\"nemotron\\\",\\n    69|    \\\"qwen-portal\\\", \\\"novita-ai\\\", \\\"novitaai\\\",\\n    70|})\\n    71|\\n    72|\\n    73|_OLLAMA_TAG_PATTERN = re.compile(\\n    74|    r\\\"^(\\\\d+\\\\.?\\\\d*b|latest|stable|q\\\\d|fp?\\\\d|instruct|chat|coder|vision|text)\\\",\\n    75|    re.IGNORECASE,\\n    76|)\\n    77|\\n    78|\\n    79|# Tailscale's CGNAT range (RFC 6598). `ipaddress.is_private` excludes this\\n    80|# block, so without an explicit check Ollama reached over Tailscale (e.g.\\n    81|# `http://100.77.243.5:11434`) wouldn't be treated as local and its stream\\n    82|# read / stale timeouts wouldn't get auto-bumped. Built once at import time.\\n    83|_TAILSCALE_CGNAT = ipaddress.IPv4Network(\\\"100.64.0.0/10\\\")\\n    84|\\n    85|\\n    86|def _strip_provider_prefix(model: str) -> str:\\n    87|    \\\"\\\"\\\"Strip a recognised provider prefix from a model string.\\n    88|\\n    89|    ``\\\"local:my-model\\\"`` → ``\\\"my-model\\\"``\\n    90|    ``\\\"qwen3.5:27b\\\"``   → ``\\\"qwen3.5:27b\\\"``  (unchanged — not a provider prefix)\\n    91|    ``\\\"qwen:0.5b\\\"``     → ``\\\"qwen:0.5b\\\"``    (unchanged — Ollama model:tag)\\n    92|    ``\\\"deepseek:latest\\\"``→ ``\\\"deepseek:latest\\\"``(unchanged — Ollama model:tag)\\n    93|    \\\"\\\"\\\"\\n    94|    if \\\":\\\" not in model or model.startswith(\\\"http\\\"):\\n    95|        return model\\n    96|    prefix, suffix = model.split(\\\":\\\", 1)\\n    97|    prefix_lower = prefix.strip().lower()\\n    98|    if prefix_lower in _PROVIDER_PREFIXES:\\n    99|        # Don't strip if suffix looks like an Ollama tag (e.g. \\\"7b\\\", \\\"latest\\\", \\\"q4_0\\\")\\n   100|        if _OLLAMA_TAG_PATTERN.match(suffix.strip()):\\n   101|            return model\\n   102|        return suffix\\n   103|    return model\\n   104|\\n   105|_model_metadata_cache: Dict[str, Dict[str, Any]] = {}\\n   106|_model_metadata_cache_time: float = 0\\n   107|_novita_metadata_cache: Dict[str, Dict[str, Any]] = {}\\n   108|_novita_metadata_cache_time: float = 0\\n   109|_MODEL_CACHE_TTL = 3600\\n   110|_endpoint_model_metadata_cache: Dict[str, Dict[str, Dict[str, Any]]] = {}\\n   111|_endpoint_model_metadata_cache_time: Dict[str, float] = {}\\n   112|_ENDPOINT_MODEL_CACHE_TTL = 300\\n   113|\\n   114|# Descending tiers for context length probing when the model is unknown.\\n   115|# We start at 256K (covers GPT-5.x, many current large-context models) and\\n   116|# step down on context-length errors until one works.  Tier[0] is also the\\n   117|# default fallback when no detection method succeeds.\\n   118|CONTEXT_PROBE_TIERS = [\\n   119|    256_000,\\n   120|    128_000,\\n   121|    64_000,\\n   122|    32_000,\\n   123|    16_000,\\n   124|    8_000,\\n   125|]\\n   126|\\n   127|# Default context length when no detection method succeeds.\\n   128|DEFAULT_FALLBACK_CONTEXT = CONTEXT_PROBE_TIERS[0]\\n   129|\\n   130|# Minimum context length required to run Hermes Agent.  Models with fewer\\n   131|# tokens cannot maintain enough working memory for tool-calling workflows.\\n   132|# Sessions, model switches, and cron jobs should reject models below this.\\n   133|MINIMUM_CONTEXT_LENGTH = 64_000\\n   134|\\n   135|# Thin fallback defaults — only broad model family patterns.\\n   136|# These fire only when provider is unknown AND models.dev/OpenRouter/Anthropic\\n   137|# all miss. Replaced the previous 80+ entry dict.\\n   138|# For provider-specific context lengths, models.dev is the primary source.\\n   139|DEFAULT_CONTEXT_LENGTHS = {\\n   140|    # Anthropic Claude 4.6 (1M context) — bare IDs only to avoid\\n   141|    # fuzzy-match collisions (e.g. \\\"anthropic/claude-sonnet-4\\\" is a\\n   142|    # substring of \\\"anthropic/claude-sonnet-4.6\\\").\\n   143|    # OpenRouter-prefixed models resolve via OpenRouter live API or models.dev.\\n   144|    \\\"claude-opus-4-7\\\": 1000000,\\n   145|    \\\"claude-opus-4.7\\\": 1000000,\\n   146|    \\\"claude-opus-4-6\\\": 1000000,\\n   147|    \\\"claude-sonnet-4-6\\\": 1000000,\\n   148|    \\\"claude-opus-4.6\\\": 1000000,\\n   149|    \\\"claude-sonnet-4.6\\\": 1000000,\\n   150|    # Catch-all for older Claude models (must sort after specific entries)\\n   151|    \\\"claude\\\": 200000,\\n   152|    # OpenAI — GPT-5 family (most have 400k; specific overrides first)\\n   153|    # Source: https://developers.openai.com/api/docs/models\\n   154|    # GPT-5.5 (launched Apr 23 2026) is 1.05M on the direct OpenAI API and\\n   155|    # ChatGPT Codex OAuth caps it at 272K; both paths resolve via their own\\n   156|    # provider-aware branches (_resolve_codex_oauth_context_length + models.dev).\\n   157|    # This hardcoded value is only reached when every probe misses.\\n   158|    \\\"gpt-5.5\\\": 1050000,\\n   159|    \\\"gpt-5.4-nano\\\": 400000,           # 400k (not 1.05M like full 5.4)\\n   160|    \\\"gpt-5.4-mini\\\": 400000,           # 400k (not 1.05M like full 5.4)\\n   161|    \\\"gpt-5.4\\\": 1050000,               # GPT-5.4, GPT-5.4 Pro (1.05M context)\\n   162|    # gpt-5.3-codex-spark is Codex-OAuth-only (ChatGPT Pro entitlement) and\\n   163|    # uses a smaller 128k window than other gpt-5.x slugs. Listed here as\\n   164|    # a defensive override so the longest-substring fallback doesn't match\\n   165|    # the generic \\\"gpt-5\\\" entry below (400k) and report the wrong limit if\\n   166|    # Spark's context ever needs to be resolved through this path. Real\\n   167|    # usage flows through _CODEX_OAUTH_CONTEXT_FALLBACK at line ~1113.\\n   168|    \\\"gpt-5.3-codex-spark\\\": 128000,\\n   169|    \\\"gpt-5.1-chat\\\": 128000,           # Chat variant has 128k context\\n   170|    \\\"gpt-5\\\": 400000,                  # GPT-5.x base, mini, codex variants (400k)\\n   171|    \\\"gpt-4.1\\\": 1047576,\\n   172|    \\\"gpt-4\\\": 128000,\\n   173|    # Google\\n   174|    \\\"gemini\\\": 1048576,\\n   175|    # Gemma (open models served via AI Studio)\\n   176|    \\\"gemma-4\\\": 256000,  # Gemma 4 family\\n   177|    \\\"gemma4\\\": 256000,  # Ollama-style naming (e.g. gemma4:31b-cloud)\\n   178|    \\\"gemma-4-31b\\\": 256000,\\n   179|    \\\"gemma-3\\\": 131072,\\n   180|    \\\"gemma\\\": 8192,  # fallback for older gemma models\\n   181|    # DeepSeek — V4 family ships with a 1M context window. The legacy\\n   182|    # aliases ``deepseek-chat`` / ``deepseek-reasoner`` are server-side\\n   183|    # mapped to the non-thinking / thinking modes of ``deepseek-v4-flash``\\n   184|    # and inherit the same 1M window. The ``deepseek`` substring entry\\n   185|    # below remains as a 128K fallback for older / unknown DeepSeek model\\n   186|    # ids (e.g. via custom endpoints).\\n   187|    # https://api-docs.deepseek.com/zh-cn/quick_start/pricing\\n   188|    \\\"deepseek-v4-pro\\\": 1_000_000,\\n   189|    \\\"deepseek-v4-flash\\\": 1_000_000,\\n   190|    \\\"deepseek-chat\\\": 1_000_000,\\n   191|    \\\"deepseek-reasoner\\\": 1_000_000,\\n   192|    \\\"deepseek\\\": 128000,\\n   193|    # Meta\\n   194|    \\\"llama\\\": 131072,\\n   195|    # Qwen — specific model families before the catch-all.\\n   196|    # Official docs: https://help.aliyun.com/zh/model-studio/developer-reference/\\n   197|    \\\"qwen3-coder-plus\\\": 1000000,  # 1M context\\n   198|    \\\"qwen3-coder\\\": 262144,        # 256K context\\n   199|    \\\"qwen\\\": 131072,\\n   200|    # MiniMax — official docs: 204,800 context for all models\\n   201|    # https://platform.minimax.io/docs/api-reference/text-anthropic-api\\n   202|    \\\"minimax\\\": 204800,\\n   203|    # GLM\\n   204|    \\\"glm\\\": 202752,\\n   205|    # xAI Grok — xAI /v1/models does not return context_length metadata,\\n   206|    # so these hardcoded fallbacks prevent Hermes from probing-down to\\n   207|    # the default 128k when the user points at https://api.x.ai/v1\\n   208|    # via a custom provider. Values sourced from models.dev (2026-04).\\n   209|    # Keys use substring matching (longest-first), so e.g. \\\"grok-4.20\\\"\\n   210|    # matches \\\"grok-4.20-0309-reasoning\\\" / \\\"-non-reasoning\\\" / \\\"-multi-agent-0309\\\".\\n   211|    \\\"grok-code-fast\\\": 256000,   # grok-code-fast-1\\n   212|    \\\"grok-4-1-fast\\\": 2000000,   # grok-4-1-fast-(non-)reasoning\\n   213|    \\\"grok-2-vision\\\": 8192,      # grok-2-vision, -1212, -latest\\n   214|    \\\"grok-4-fast\\\": 2000000,     # grok-4-fast-(non-)reasoning\\n   215|    \\\"grok-4.20\\\": 2000000,       # grok-4.20-0309-(non-)reasoning, -multi-agent-0309\\n   216|    \\\"grok-4.3\\\": 1000000,        # grok-4.3, grok-4.3-latest — 1M context per docs.x.ai\\n   217|    \\\"grok-4\\\": 256000,           # grok-4, grok-4-0709\\n   218|    \\\"grok-3\\\": 131072,           # grok-3, grok-3-mini, grok-3-fast, grok-3-mini-fast\\n   219|    \\\"grok-2\\\": 131072,           # grok-2, grok-2-1212, grok-2-latest\\n   220|    \\\"grok\\\": 131072,             # catch-all (grok-beta, unknown grok-*)\\n   221|    # Kimi\\n   222|    \\\"kimi\\\": 262144,\\n   223|    # Tencent — Hy3 Preview (Hunyuan) with 256K context window.\\n   224|    # OpenRouter live metadata reports 262144 (256 × 1024); align the\\n   225|    # static fallback so cache and offline both agree (issue #22268).\\n   226|    \\\"hy3-preview\\\": 262144,\\n   227|    # Nemotron — NVIDIA's open-weights series (128K context across all sizes)\\n   228|    \\\"nemotron\\\": 131072,\\n   229|    # Arcee\\n   230|    \\\"trinity\\\": 262144,\\n   231|    # OpenRouter\\n   232|    \\\"elephant\\\": 262144,\\n   233|    # Hugging Face Inference Providers — model IDs use org/name format\\n   234|    \\\"Qwen/Qwen3.5-397B-A17B\\\": 131072,\\n   235|    \\\"Qwen/Qwen3.5-35B-A3B\\\": 131072,\\n   236|    \\\"deepseek-ai/DeepSeek-V3.2\\\": 65536,\\n   237|    \\\"moonshotai/Kimi-K2.5\\\": 262144,\\n   238|    \\\"moonshotai/Kimi-K2.6\\\": 262144,\\n   239|    \\\"moonshotai/Kimi-K2-Thinking\\\": 262144,\\n   240|    \\\"MiniMaxAI/MiniMax-M2.5\\\": 204800,\\n   241|    \\\"XiaomiMiMo/MiMo-V2-Flash\\\": 262144,\\n   242|    \\\"mimo-v2-pro\\\": 1048576,\\n   243|    \\\"mimo-v2.5-pro\\\": 1048576,\\n   244|    \\\"mimo-v2.5\\\": 1048576,\\n   245|    \\\"mimo-v2-omni\\\": 262144,\\n   246|    \\\"mimo-v2-flash\\\": 262144,\\n   247|    \\\"zai-org/GLM-5\\\": 202752,\\n   248|}\\n   249|\\n   250|# xAI Grok models that ACCEPT the `reasoning.effort` parameter on\\n   251|# api.x.ai. Verified live against /v1/responses 2026-05-10:\\n   252|#\\n   253|#   ACCEPTS effort:  grok-3-mini, grok-3-mini-fast, grok-4.20-multi-agent-0309,\\n   254|#                    grok-4.3\\n   255|#   REJECTS effort:  grok-3, grok-4, grok-4-0709, grok-4-fast-(non-)reasoning,\\n   256|#                    grok-4-1-fast-(non-)reasoning, grok-4.20-0309-(non-)reasoning,\\n   257|#                    grok-code-fast-1\\n   258|#\\n   259|# REJECTS-side models still reason natively — they just don't expose an\\n   260|# effort dial — so callers should send no `reasoning` key at all rather\\n   261|\", \"total_lines\": 1826, \"file_size\": 76757, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 1826 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_d46Pcg9GwlMmMDXvbQRjLzZQ", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_or6H9ZK09uDduVkS7HAtRzxs", "call_id": "call_or6H9ZK09uDduVkS7HAtRzxs", "response_item_id": "fc_0dd6fd7e42749188016a09b791d89881919510d8c822877324", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":260,\"offset\":1060,\"path\":\"/home/agent/.hermes/hermes-agent/agent/model_metadata.py\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"  1060|            if resp.status_code != 200:\\n  1061|                return None\\n  1062|            data = resp.json()\\n  1063|\\n  1064|            # Hosted Ollama: GGUF model_info is the real max — prefer it over\\n  1065|            # num_ctx which the Cloud operator may have capped arbitrarily.\\n  1066|            model_info = data.get(\\\"model_info\\\", {})\\n  1067|            for key, value in model_info.items():\\n  1068|                if \\\"context_length\\\" in key and isinstance(value, (int, float)):\\n  1069|                    ctx = int(value)\\n  1070|                    if ctx >= 1024:\\n  1071|                        return ctx\\n  1072|\\n  1073|            # Fall back to num_ctx from Modelfile parameters (rare on Cloud)\\n  1074|            params = data.get(\\\"parameters\\\", \\\"\\\")\\n  1075|            if \\\"num_ctx\\\" in params:\\n  1076|                for line in params.split(\\\"\\\\n\\\"):\\n  1077|                    if \\\"num_ctx\\\" in line:\\n  1078|                        parts = line.strip().split()\\n  1079|                        if len(parts) >= 2:\\n  1080|                            try:\\n  1081|                                ctx = int(parts[-1])\\n  1082|                                if ctx >= 1024:\\n  1083|                                    return ctx\\n  1084|                            except ValueError:\\n  1085|                                pass\\n  1086|    except Exception:\\n  1087|        pass\\n  1088|    return None\\n  1089|\\n  1090|\\n  1091|def _model_name_suggests_kimi(model: str) -> bool:\\n  1092|    \\\"\\\"\\\"Return True if the model name looks like a Kimi-family model.\\n  1093|\\n  1094|    Catches ``kimi-k2.6``, ``kimi-k2.5``, ``kimi-k2-thinking``,\\n  1095|    ``moonshotai/Kimi-K2.6``, and similar variants.  Used as a guard\\n  1096|    against stale OpenRouter metadata that underreports these models\\n  1097|    as 32K context when they actually support 262K+.\\n  1098|    \\\"\\\"\\\"\\n  1099|    lower = model.lower()\\n  1100|    return lower.startswith(\\\"kimi\\\") or \\\"moonshot\\\" in lower\\n  1101|\\n  1102|\\n  1103|def _query_local_context_length(model: str, base_url: str, api_key: str = \\\"\\\") -> Optional[int]:\\n  1104|    \\\"\\\"\\\"Query a local server for the model's context length.\\\"\\\"\\\"\\n  1105|    import httpx\\n  1106|\\n  1107|    # Strip recognised provider prefix (e.g., \\\"local:model-name\\\" → \\\"model-name\\\").\\n  1108|    # Ollama \\\"model:tag\\\" colons (e.g. \\\"qwen3.5:27b\\\") are intentionally preserved.\\n  1109|    model = _strip_provider_prefix(model)\\n  1110|\\n  1111|    # Strip /v1 suffix to get the server root\\n  1112|    server_url = base_url.rstrip(\\\"/\\\")\\n  1113|    if server_url.endswith(\\\"/v1\\\"):\\n  1114|        server_url = server_url[:-3]\\n  1115|\\n  1116|    headers = _auth_headers(api_key)\\n  1117|\\n  1118|    try:\\n  1119|        server_type = detect_local_server_type(base_url, api_key=api_key)\\n  1120|    except Exception:\\n  1121|        server_type = None\\n  1122|\\n  1123|    try:\\n  1124|        with httpx.Client(timeout=3.0, headers=headers) as client:\\n  1125|            # Ollama: /api/show returns model details with context info\\n  1126|            if server_type == \\\"ollama\\\":\\n  1127|                resp = client.post(f\\\"{server_url}/api/show\\\", json={\\\"name\\\": model})\\n  1128|                if resp.status_code == 200:\\n  1129|                    data = resp.json()\\n  1130|                    # Prefer explicit num_ctx from Modelfile parameters: this is\\n  1131|                    # the *runtime* context Ollama will actually allocate KV cache\\n  1132|                    # for. The GGUF model_info.context_length is the training max,\\n  1133|                    # which can be larger than num_ctx — using it here would let\\n  1134|                    # Hermes grow conversations past the runtime limit and Ollama\\n  1135|                    # would silently truncate. Matches query_ollama_num_ctx().\\n  1136|                    params = data.get(\\\"parameters\\\", \\\"\\\")\\n  1137|                    if \\\"num_ctx\\\" in params:\\n  1138|                        for line in params.split(\\\"\\\\n\\\"):\\n  1139|                            if \\\"num_ctx\\\" in line:\\n  1140|                                parts = line.strip().split()\\n  1141|                                if len(parts) >= 2:\\n  1142|                                    try:\\n  1143|                                        return int(parts[-1])\\n  1144|                                    except ValueError:\\n  1145|                                        pass\\n  1146|                    # Fall back to GGUF model_info context_length (training max)\\n  1147|                    model_info = data.get(\\\"model_info\\\", {})\\n  1148|                    for key, value in model_info.items():\\n  1149|                        if \\\"context_length\\\" in key and isinstance(value, (int, float)):\\n  1150|                            return int(value)\\n  1151|\\n  1152|            # LM Studio native API: /api/v1/models returns max_context_length.\\n  1153|            # This is more reliable than the OpenAI-compat /v1/models which\\n  1154|            # doesn't include context window information for LM Studio servers.\\n  1155|            # Use _model_id_matches for fuzzy matching: LM Studio stores models as\\n  1156|            # \\\"publisher/slug\\\" but users configure only \\\"slug\\\" after \\\"local:\\\" prefix.\\n  1157|            if server_type == \\\"lm-studio\\\":\\n  1158|                resp = client.get(f\\\"{server_url}/api/v1/models\\\")\\n  1159|                if resp.status_code == 200:\\n  1160|                    data = resp.json()\\n  1161|                    for m in data.get(\\\"models\\\", []):\\n  1162|                        if _model_id_matches(m.get(\\\"key\\\", \\\"\\\"), model) or _model_id_matches(m.get(\\\"id\\\", \\\"\\\"), model):\\n  1163|                            # Prefer loaded instance context (actual runtime value)\\n  1164|                            for inst in m.get(\\\"loaded_instances\\\", []):\\n  1165|                                cfg = inst.get(\\\"config\\\", {})\\n  1166|                                ctx = cfg.get(\\\"context_length\\\")\\n  1167|                                if ctx and isinstance(ctx, (int, float)):\\n  1168|                                    return int(ctx)\\n  1169|                            break\\n  1170|\\n  1171|            # LM Studio / vLLM / llama.cpp: try /v1/models/{model}\\n  1172|            resp = client.get(f\\\"{server_url}/v1/models/{model}\\\")\\n  1173|            if resp.status_code == 200:\\n  1174|                data = resp.json()\\n  1175|                # vLLM returns max_model_len\\n  1176|                ctx = data.get(\\\"max_model_len\\\") or data.get(\\\"context_length\\\") or data.get(\\\"max_tokens\\\")\\n  1177|                if ctx and isinstance(ctx, (int, float)):\\n  1178|                    return int(ctx)\\n  1179|\\n  1180|            # Try /v1/models and find the model in the list.\\n  1181|            # Use _model_id_matches to handle \\\"publisher/slug\\\" vs bare \\\"slug\\\".\\n  1182|            resp = client.get(f\\\"{server_url}/v1/models\\\")\\n  1183|            if resp.status_code == 200:\\n  1184|                data = resp.json()\\n  1185|                models_list = data.get(\\\"data\\\", [])\\n  1186|                for m in models_list:\\n  1187|                    if _model_id_matches(m.get(\\\"id\\\", \\\"\\\"), model):\\n  1188|                        ctx = m.get(\\\"max_model_len\\\") or m.get(\\\"context_length\\\") or m.get(\\\"max_tokens\\\")\\n  1189|                        if ctx and isinstance(ctx, (int, float)):\\n  1190|                            return int(ctx)\\n  1191|    except Exception:\\n  1192|        pass\\n  1193|\\n  1194|    return None\\n  1195|\\n  1196|\\n  1197|def _normalize_model_version(model: str) -> str:\\n  1198|    \\\"\\\"\\\"Normalize version separators for matching.\\n  1199|\\n  1200|    Nous uses dashes: claude-opus-4-6, claude-sonnet-4-5\\n  1201|    OpenRouter uses dots: claude-opus-4.6, claude-sonnet-4.5\\n  1202|    Normalize both to dashes for comparison.\\n  1203|    \\\"\\\"\\\"\\n  1204|    return model.replace(\\\".\\\", \\\"-\\\")\\n  1205|\\n  1206|\\n  1207|def _query_anthropic_context_length(model: str, base_url: str, api_key: str) -> Optional[int]:\\n  1208|    \\\"\\\"\\\"Query Anthropic's /v1/models endpoint for context length.\\n  1209|\\n  1210|    Only works with regular ANTHROPIC_API_KEY (sk-ant-api*).\\n  1211|    OAuth tokens (sk-ant-oat*) from Claude Code return 401.\\n  1212|    \\\"\\\"\\\"\\n  1213|    if not api_key or api_key.startswith(\\\"sk-ant-oat\\\"):\\n  1214|        return None  # OAuth tokens can't access /v1/models\\n  1215|    try:\\n  1216|        base = base_url.rstrip(\\\"/\\\")\\n  1217|        if base.endswith(\\\"/v1\\\"):\\n  1218|            base = base[:-3]\\n  1219|        url = f\\\"{base}/v1/models?limit=1000\\\"\\n  1220|        headers = {\\n  1221|            \\\"x-api-key\\\": api_key,\\n  1222|            \\\"anthropic-version\\\": \\\"2023-06-01\\\",\\n  1223|        }\\n  1224|        resp = requests.get(url, headers=headers, timeout=10, verify=_resolve_requests_verify())\\n  1225|        if resp.status_code != 200:\\n  1226|            return None\\n  1227|        data = resp.json()\\n  1228|        for m in data.get(\\\"data\\\", []):\\n  1229|            if m.get(\\\"id\\\") == model:\\n  1230|                ctx = m.get(\\\"max_input_tokens\\\")\\n  1231|                if isinstance(ctx, int) and ctx > 0:\\n  1232|                    return ctx\\n  1233|    except Exception as e:\\n  1234|        logger.debug(\\\"Anthropic /v1/models query failed: %s\\\", e)\\n  1235|    return None\\n  1236|\\n  1237|\\n  1238|# Known ChatGPT Codex OAuth context windows (observed via live\\n  1239|# chatgpt.com/backend-api/codex/models probe, Apr 2026). These are the\\n  1240|# `context_window` values, which are what Codex actually enforces — the\\n  1241|# direct OpenAI API has larger limits for the same slugs, but Codex OAuth\\n  1242|# caps lower (e.g. gpt-5.5 is 1.05M on the API, 272K on Codex).\\n  1243|#\\n  1244|# Used as a fallback when the live probe fails (no token, network error).\\n  1245|# Longest keys first so substring match picks the most specific entry.\\n  1246|_CODEX_OAUTH_CONTEXT_FALLBACK: Dict[str, int] = {\\n  1247|    \\\"gpt-5.1-codex-max\\\": 272_000,\\n  1248|    \\\"gpt-5.1-codex-mini\\\": 272_000,\\n  1249|    \\\"gpt-5.3-codex\\\": 272_000,\\n  1250|    # Spark runs on specialised low-latency hardware and exposes a smaller\\n  1251|    # 128k window than other Codex OAuth slugs. Listed explicitly so the\\n  1252|    # longest-key-first fallback resolves it correctly — substring match\\n  1253|    # on \\\"gpt-5.3-codex\\\" otherwise wins and reports 272k. Availability is\\n  1254|    # gated by ChatGPT Pro entitlement on the Codex backend.\\n  1255|    \\\"gpt-5.3-codex-spark\\\": 128_000,\\n  1256|    \\\"gpt-5.2-codex\\\": 272_000,\\n  1257|    \\\"gpt-5.4-mini\\\": 272_000,\\n  1258|    \\\"gpt-5.5\\\": 272_000,\\n  1259|    \\\"gpt-5.4\\\": 272_000,\\n  1260|    \\\"gpt-5.2\\\": 272_000,\\n  1261|    \\\"gpt-5\\\": 272_000,\\n  1262|}\\n  1263|\\n  1264|\\n  1265|_codex_oauth_context_cache: Dict[str, int] = {}\\n  1266|_codex_oauth_context_cache_time: float = 0.0\\n  1267|_CODEX_OAUTH_CONTEXT_CACHE_TTL = 3600  # 1 hour\\n  1268|\\n  1269|\\n  1270|def _fetch_codex_oauth_context_lengths(access_token: str) -> Dict[str, int]:\\n  1271|    \\\"\\\"\\\"Probe the ChatGPT Codex /models endpoint for per-slug context windows.\\n  1272|\\n  1273|    Codex OAuth imposes its own context limits that differ from the direct\\n  1274|    OpenAI API (e.g. gpt-5.5 is 1.05M on the API, 272K on Codex). The\\n  1275|    `context_window` field in each model entry is the authoritative source.\\n  1276|\\n  1277|    Returns a ``{slug: context_window}`` dict. Empty on failure.\\n  1278|    \\\"\\\"\\\"\\n  1279|    global _codex_oauth_context_cache, _codex_oauth_context_cache_time\\n  1280|    now = time.time()\\n  1281|    if (\\n  1282|        _codex_oauth_context_cache\\n  1283|        and now - _codex_oauth_context_cache_time < _CODEX_OAUTH_CONTEXT_CACHE_TTL\\n  1284|    ):\\n  1285|        return _codex_oauth_context_cache\\n  1286|\\n  1287|    try:\\n  1288|        resp = requests.get(\\n  1289|            \\\"https://chatgpt.com/backend-api/codex/models?client_version=1.0.0\\\",\\n  1290|            headers={\\\"Authorization\\\": f\\\"Bearer {access_token}\\\"},\\n  1291|            timeout=10,\\n  1292|            verify=_resolve_requests_verify(),\\n  1293|        )\\n  1294|        if resp.status_code != 200:\\n  1295|            logger.debug(\\n  1296|                \\\"Codex /models probe returned HTTP %s; falling back to hardcoded defaults\\\",\\n  1297|                resp.status_code,\\n  1298|            )\\n  1299|            return {}\\n  1300|        data = resp.json()\\n  1301|    except Exception as exc:\\n  1302|        logger.debug(\\\"Codex /models probe failed: %s\\\", exc)\\n  1303|        return {}\\n  1304|\\n  1305|    entries = data.get(\\\"models\\\", []) if isinstance(data, dict) else []\\n  1306|    result: Dict[str, int] = {}\\n  1307|    for item in entries:\\n  1308|        if not isinstance(item, dict):\\n  1309|            continue\\n  1310|        slug = item.get(\\\"slug\\\")\\n  1311|        ctx = item.get(\\\"context_window\\\")\\n  1312|        if isinstance(slug, str) and isinstance(ctx, int) and ctx > 0:\\n  1313|            result[slug.strip()] = ctx\\n  1314|\\n  1315|    if result:\\n  1316|        _codex_oauth_context_cache = result\\n  1317|        _codex_oauth_context_cache_time = now\\n  1318|    return result\\n  1319|\\n  1320|\", \"total_lines\": 1826, \"file_size\": 76757, \"truncated\": true, \"hint\": \"Use offset=1320 to continue reading (showing 1060-1319 of 1826 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_or6H9ZK09uDduVkS7HAtRzxs", "timestamp": "2026-05-17T14:42:45.308524"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"  1320|\\n  1321|def _resolve_codex_oauth_context_length(\\n  1322|    model: str, access_token: str = \\\"\\\"\\n  1323|) -> Optional[int]:\\n  1324|    \\\"\\\"\\\"Resolve a Codex OAuth model's real context window.\\n  1325|\\n  1326|    Prefers a live probe of chatgpt.com/backend-api/codex/models (when we\\n  1327|    have a bearer token), then falls back to ``_CODEX_OAUTH_CONTEXT_FALLBACK``.\\n  1328|    \\\"\\\"\\\"\\n  1329|    model_bare = _strip_provider_prefix(model).strip()\\n  1330|    if not model_bare:\\n  1331|        return None\\n  1332|\\n  1333|    if access_token:\\n  1334|        live = _fetch_codex_oauth_context_lengths(access_token)\\n  1335|        if model_bare in live:\\n  1336|            return live[model_bare]\\n  1337|        # Case-insensitive match in case casing drifts\\n  1338|        model_lower = model_bare.lower()\\n  1339|        for slug, ctx in live.items():\\n  1340|            if slug.lower() == model_lower:\\n  1341|                return ctx\\n  1342|\\n  1343|    # Fallback: longest-key-first substring match over hardcoded defaults.\\n  1344|    model_lower = model_bare.lower()\\n  1345|    for slug, ctx in sorted(\\n  1346|        _CODEX_OAUTH_CONTEXT_FALLBACK.items(), key=lambda x: len(x[0]), reverse=True\\n  1347|    ):\\n  1348|        if slug in model_lower:\\n  1349|            return ctx\\n  1350|\\n  1351|    return None\\n  1352|\\n  1353|\\n  1354|def _resolve_nous_context_length(\\n  1355|    model: str,\\n  1356|    base_url: str = \\\"\\\",\\n  1357|    api_key: str = \\\"\\\",\\n  1358|) -> Tuple[Optional[int], str]:\\n  1359|    \\\"\\\"\\\"Resolve Nous Portal model context length.\\n  1360|\\n  1361|    Tries the live Nous inference endpoint first (authoritative), then falls\\n  1362|    back to OpenRouter metadata with suffix/version matching.\\n  1363|\\n  1364|    Nous model IDs are bare after prefix-stripping (e.g. 'qwen3.6-plus',\\n  1365|    'claude-opus-4-6') while OpenRouter uses prefixed IDs (e.g.\\n  1366|    'qwen/qwen3.6-plus', 'anthropic/claude-opus-4.6').  Version\\n  1367|    normalization (dot↔dash) is applied to handle name drifts.\\n  1368|\\n  1369|    Returns ``(context_length, source)`` where ``source`` is one of:\\n  1370|      - ``\\\"portal\\\"``    — live /v1/models response (authoritative)\\n  1371|      - ``\\\"openrouter\\\"`` — OpenRouter cache fallback (non-authoritative;\\n  1372|        callers must NOT persist this to the on-disk cache or a single\\n  1373|        portal blip will freeze the wrong value in forever)\\n  1374|      - ``\\\"\\\"``           — could not resolve\\n  1375|    \\\"\\\"\\\"\\n  1376|    # Portal first — the Nous /models endpoint is authoritative for what our\\n  1377|    # infrastructure enforces and may differ from OR (e.g. OR reports 1M for\\n  1378|    # qwen3.6-plus; the portal correctly says 262144).  Fall back to the OR\\n  1379|    # catalog only if the portal doesn't list the model.\\n  1380|    if base_url:\\n  1381|        portal_ctx = _resolve_endpoint_context_length(model, base_url, api_key=api_key)\\n  1382|        if portal_ctx is not None:\\n  1383|            return portal_ctx, \\\"portal\\\"\\n  1384|\\n  1385|    metadata = fetch_model_metadata()\\n  1386|\\n  1387|    def _safe_ctx(or_id: str, entry: dict) -> Optional[int]:\\n  1388|        ctx = entry.get(\\\"context_length\\\")\\n  1389|        if ctx is None:\\n  1390|            return None\\n  1391|        if ctx <= 32768 and _model_name_suggests_kimi(or_id):\\n  1392|            logger.info(\\n  1393|                \\\"Rejecting OpenRouter metadata context=%s for %r \\\"\\n  1394|                \\\"(Kimi-family underreport, Nous path); falling through to hardcoded defaults\\\",\\n  1395|                ctx, or_id,\\n  1396|            )\\n  1397|            return None\\n  1398|        return ctx\\n  1399|\\n  1400|    if model in metadata:\\n  1401|        ctx = _safe_ctx(model, metadata[model])\\n  1402|        if ctx is not None:\\n  1403|            return ctx, \\\"openrouter\\\"\\n  1404|\\n  1405|    normalized = _normalize_model_version(model).lower()\\n  1406|\\n  1407|    for or_id, entry in metadata.items():\\n  1408|        bare = or_id.split(\\\"/\\\", 1)[1] if \\\"/\\\" in or_id else or_id\\n  1409|        if bare.lower() == model.lower() or _normalize_model_version(bare).lower() == normalized:\\n  1410|            ctx = _safe_ctx(or_id, entry)\\n  1411|            if ctx is not None:\\n  1412|                return ctx, \\\"openrouter\\\"\\n  1413|\\n  1414|    model_lower = model.lower()\\n  1415|    for or_id, entry in metadata.items():\\n  1416|        bare = or_id.split(\\\"/\\\", 1)[1] if \\\"/\\\" in or_id else or_id\\n  1417|        for candidate, query in [(bare.lower(), model_lower), (_normalize_model_version(bare).lower(), normalized)]:\\n  1418|            if candidate.startswith(query) and (\\n  1419|                len(candidate) == len(query) or candidate[len(query)] in \\\"-:.\\\"\\n  1420|            ):\\n  1421|                ctx = _safe_ctx(or_id, entry)\\n  1422|                if ctx is not None:\\n  1423|                    return ctx, \\\"openrouter\\\"\\n  1424|\\n  1425|    return None, \\\"\\\"\\n  1426|\\n  1427|\\n  1428|def get_model_context_length(\\n  1429|    model: str,\\n  1430|    base_url: str = \\\"\\\",\\n  1431|    api_key: str = \\\"\\\",\\n  1432|    config_context_length: int | None = None,\\n  1433|    provider: str = \\\"\\\",\\n  1434|    custom_providers: list | None = None,\\n  1435|) -> int:\\n  1436|    \\\"\\\"\\\"Get the context length for a model.\\n  1437|\\n  1438|    Resolution order:\\n  1439|    0. Explicit config override (model.context_length or custom_providers per-model)\\n  1440|    1. Persistent cache (previously discovered via probing).  Nous URLs\\n  1441|       bypass the cache here so step 5b can always reconcile against\\n  1442|       the authoritative portal /v1/models response.\\n  1443|    1b. AWS Bedrock static table (must precede custom-endpoint probe)\\n  1444|    2. Active endpoint metadata (/models for explicit custom endpoints)\\n  1445|    3. Local server query (for local endpoints)\\n  1446|    4. Anthropic /v1/models API (API-key users only, not OAuth)\\n  1447|    5. Provider-aware lookups (before generic OpenRouter cache):\\n  1448|       a. Copilot live /models API\\n  1449|       b. Nous: live /v1/models probe first (authoritative), then OR\\n  1450|          cache fallback with suffix/version normalisation.  Only\\n  1451|          portal-derived values are persisted to disk.\\n  1452|       c. Codex OAuth /models probe\\n  1453|       d. GMI /models endpoint\\n  1454|       e. Ollama native /api/show probe (any base_url, provider-agnostic)\\n  1455|       f. models.dev registry lookup (with :cloud/-cloud suffix fallback)\\n  1456|    6. OpenRouter live API metadata (Kimi-family 32k guard)\\n  1457|    7. Hardcoded defaults (broad family patterns, longest-key-first)\\n  1458|    8. Local server query (last resort)\\n  1459|    9. Default fallback (256K)\\\"\\\"\\\"\\n  1460|    # 0. Explicit config override — user knows best\\n  1461|    if config_context_length is not None and isinstance(config_context_length, int) and config_context_length > 0:\\n  1462|        return config_context_length\\n  1463|\\n  1464|    # 0b. custom_providers per-model override — check before any probe.\\n  1465|    # This closes the gap where /model switch and display paths used to fall\\n  1466|    # back to 128K despite the user having a per-model context_length set.\\n  1467|    # See #15779.\\n  1468|    if custom_providers and base_url and model:\\n  1469|        try:\\n  1470|            from hermes_cli.config import get_custom_provider_context_length\\n  1471|            cp_ctx = get_custom_provider_context_length(\\n  1472|                model=model,\\n  1473|                base_url=base_url,\\n  1474|                custom_providers=custom_providers,\\n  1475|            )\\n  1476|            if cp_ctx:\\n  1477|                return cp_ctx\\n  1478|        except Exception:\\n  1479|            pass  # fall through to probing\\n  1480|\\n  1481|    # Normalise provider-prefixed model names (e.g. \\\"local:model-name\\\" →\\n  1482|    # \\\"model-name\\\") so cache lookups and server queries use the bare ID that\\n  1483|    # local servers actually know about.  Ollama \\\"model:tag\\\" colons are preserved.\\n  1484|    model = _strip_provider_prefix(model)\\n  1485|\\n  1486|    # 1. Check persistent cache (model+provider)\\n  1487|    # LM Studio is excluded — its loaded context length is transient (the\\n  1488|    # user can reload the model with a different context_length at any time\\n  1489|    # via /api/v1/models/load), so a stale cached value would mask reloads.\\n  1490|    if base_url and provider != \\\"lmstudio\\\":\\n  1491|        cached = get_cached_context_length(model, base_url)\\n  1492|        if cached is not None:\\n  1493|            # Invalidate stale Codex OAuth cache entries: pre-PR #14935 builds\\n  1494|            # resolved gpt-5.x to the direct-API value (e.g. 1.05M) via\\n  1495|            # models.dev and persisted it. Codex OAuth caps at 272K for every\\n  1496|            # slug, so any cached Codex entry at or above 400K is a leftover\\n  1497|            # from the old resolution path. Drop it and fall through to the\\n  1498|            # live /models probe in step 5 below.\\n  1499|            if provider == \\\"openai-codex\\\" and cached >= 400_000:\\n  1500|                logger.info(\\n  1501|                    \\\"Dropping stale Codex cache entry %s@%s -> %s (pre-fix value); \\\"\\n  1502|                    \\\"re-resolving via live /models probe\\\",\\n  1503|                    model, base_url, f\\\"{cached:,}\\\",\\n  1504|                )\\n  1505|                _invalidate_cached_context_length(model, base_url)\\n  1506|            # Invalidate stale 32k cache entries for Kimi-family models.\\n  1507|            elif cached <= 32768 and _model_name_suggests_kimi(model):\\n  1508|                logger.info(\\n  1509|                    \\\"Dropping stale Kimi cache entry %s@%s -> %s (OpenRouter underreport); \\\"\\n  1510|                    \\\"re-resolving via hardcoded defaults\\\",\\n  1511|                    model, base_url, f\\\"{cached:,}\\\",\\n  1512|                )\\n  1513|                _invalidate_cached_context_length(model, base_url)\\n  1514|            # Nous Portal: the portal /v1/models endpoint is authoritative.\\n  1515|            # Bypass the persistent cache so step 5b can always reconcile\\n  1516|            # against it — this corrects pre-fix entries seeded from the\\n  1517|            # OR catalog (the same OR underreport class that the Kimi/Qwen\\n  1518|            # DEFAULT_CONTEXT_LENGTHS overrides exist to mitigate) without\\n  1519|            # touching the on-disk file when the portal is unreachable.\\n  1520|            # The in-memory 300s endpoint metadata cache makes the per-call\\n  1521|            # cost amortise to ~0 within a process.\\n  1522|            elif _infer_provider_from_url(base_url) == \\\"nous\\\":\\n  1523|                logger.debug(\\n  1524|                    \\\"Bypassing persistent cache for %s@%s (Nous portal authoritative)\\\",\\n  1525|                    model, base_url,\\n  1526|                )\\n  1527|                # Fall through; step 5b reconciles and overwrites if portal responds.\\n  1528|            else:\\n  1529|                return cached\\n  1530|\\n  1531|    # 1b. AWS Bedrock — use static context length table.\\n  1532|    # Bedrock's ListFoundationModels API doesn't expose context window sizes,\\n  1533|    # so we maintain a curated table in bedrock_adapter.py that reflects\\n  1534|    # AWS-imposed limits (e.g. 200K for Claude models vs 1M on the native\\n  1535|    # Anthropic API).  This must run BEFORE the custom-endpoint probe at\\n  1536|    # step 2 — bedrock-runtime.<region>.amazonaws.com is not in\\n  1537|    # _URL_TO_PROVIDER, so it would otherwise be treated as a custom endpoint,\\n  1538|    # fail the /models probe (Bedrock doesn't expose that shape), and fall\\n  1539|    # back to the 128K default before reaching the original step 4b branch.\\n  1540|\", \"total_lines\": 1826, \"file_size\": 76757, \"truncated\": true, \"hint\": \"Use offset=1540 to continue reading (showing 1320-1539 of 1826 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_s011YMR198hitQpryin7N40F", "timestamp": "2026-05-17T14:42:45.308524"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"  1540|    if provider == \\\"bedrock\\\" or (\\n  1541|        base_url\\n  1542|        and base_url_hostname(base_url).startswith(\\\"bedrock-runtime.\\\")\\n  1543|        and base_url_host_matches(base_url, \\\"amazonaws.com\\\")\\n  1544|    ):\\n  1545|        try:\\n  1546|            from agent.bedrock_adapter import get_bedrock_context_length\\n  1547|            return get_bedrock_context_length(model)\\n  1548|        except ImportError:\\n  1549|            pass  # boto3 not installed — fall through to generic resolution\\n  1550|\\n  1551|    if provider == \\\"novita\\\" or (base_url and base_url_host_matches(base_url, \\\"api.novita.ai\\\")):\\n  1552|        ctx = _resolve_endpoint_context_length(model, base_url or \\\"https://api.novita.ai/openai/v1\\\", api_key=api_key)\\n  1553|        if ctx is not None:\\n  1554|            if base_url:\\n  1555|                save_context_length(model, base_url, ctx)\\n  1556|            return ctx\\n  1557|\\n  1558|    # 2. Active endpoint metadata for truly custom/unknown endpoints.\\n  1559|    # Known providers (Copilot, OpenAI, Anthropic, etc.) skip this — their\\n  1560|    # /models endpoint may report a provider-imposed limit (e.g. Copilot\\n  1561|    # returns 128k) instead of the model's full context (400k).  models.dev\\n  1562|    # has the correct per-provider values and is checked at step 5+.\\n  1563|    if _is_custom_endpoint(base_url) and not _is_known_provider_base_url(base_url):\\n  1564|        context_length = _resolve_endpoint_context_length(model, base_url, api_key=api_key)\\n  1565|        if context_length is not None:\\n  1566|            return context_length\\n  1567|        if not _is_known_provider_base_url(base_url):\\n  1568|            # 2b. Ollama native /api/show — any URL might be an Ollama server\\n  1569|            # (local, cloud, or custom hosting).  Non-Ollama servers return\\n  1570|            # 404/405 quickly.  Fall through on failure.\\n  1571|            ctx = _query_ollama_api_show(model, base_url, api_key=api_key)\\n  1572|            if ctx is not None:\\n  1573|                save_context_length(model, base_url, ctx)\\n  1574|                return ctx\\n  1575|            # 3. Try querying local server directly\\n  1576|            if is_local_endpoint(base_url):\\n  1577|                local_ctx = _query_local_context_length(model, base_url, api_key=api_key)\\n  1578|                if local_ctx and local_ctx > 0:\\n  1579|                    if provider != \\\"lmstudio\\\":\\n  1580|                        save_context_length(model, base_url, local_ctx)\\n  1581|                    return local_ctx\\n  1582|            logger.info(\\n  1583|                \\\"Could not detect context length for model %r at %s — \\\"\\n  1584|                \\\"defaulting to %s tokens (probe-down). Set model.context_length \\\"\\n  1585|                \\\"in config.yaml to override.\\\",\\n  1586|                model, base_url, f\\\"{DEFAULT_FALLBACK_CONTEXT:,}\\\",\\n  1587|            )\\n  1588|            return DEFAULT_FALLBACK_CONTEXT\\n  1589|\\n  1590|    # 4. Anthropic /v1/models API (only for regular API keys, not OAuth)\\n  1591|    if provider == \\\"anthropic\\\" or (\\n  1592|        base_url and base_url_hostname(base_url) == \\\"api.anthropic.com\\\"\\n  1593|    ):\\n  1594|        ctx = _query_anthropic_context_length(model, base_url or \\\"https://api.anthropic.com\\\", api_key)\\n  1595|        if ctx:\\n  1596|            return ctx\\n  1597|\\n  1598|    # 4b. (Bedrock handled earlier at step 1b — before custom-endpoint probe.)\\n  1599|\\n  1600|    # 5. Provider-aware lookups (before generic OpenRouter cache)\\n  1601|    # These are provider-specific and take priority over the generic OR cache,\\n  1602|    # since the same model can have different context limits per provider\\n  1603|    # (e.g. claude-opus-4.6 is 1M on Anthropic but 128K on GitHub Copilot).\\n  1604|    # If provider is generic (openrouter/custom/empty), try to infer from URL.\\n  1605|    effective_provider = provider\\n  1606|    if not effective_provider or effective_provider in {\\\"openrouter\\\", \\\"custom\\\"}:\\n  1607|        if base_url:\\n  1608|            inferred = _infer_provider_from_url(base_url)\\n  1609|            if inferred:\\n  1610|                effective_provider = inferred\\n  1611|\\n  1612|    # 5a. Copilot live /models API — max_prompt_tokens from the user's account.\\n  1613|    # This catches account-specific models (e.g. claude-opus-4.6-1m) that\\n  1614|    # don't exist in models.dev. For models that ARE in models.dev, this\\n  1615|    # returns the provider-enforced limit which is what users can actually use.\\n  1616|    if effective_provider in {\\\"copilot\\\", \\\"copilot-acp\\\", \\\"github-copilot\\\"}:\\n  1617|        try:\\n  1618|            from hermes_cli.models import get_copilot_model_context\\n  1619|            ctx = get_copilot_model_context(model, api_key=api_key)\\n  1620|            if ctx:\\n  1621|                return ctx\\n  1622|        except Exception:\\n  1623|            pass  # Fall through to models.dev\\n  1624|\\n  1625|    if effective_provider == \\\"nous\\\":\\n  1626|        ctx, source = _resolve_nous_context_length(\\n  1627|            model, base_url=base_url or \\\"\\\", api_key=api_key or \\\"\\\"\\n  1628|        )\\n  1629|        if ctx:\\n  1630|            # Persist ONLY portal-derived values.  Caching an OR-fallback\\n  1631|            # value here would freeze in a wrong number on the first portal\\n  1632|            # blip / auth glitch and step-1 would short-circuit it forever.\\n  1633|            # OR's catalog is community-maintained and is precisely why the\\n  1634|            # Kimi/Qwen DEFAULT_CONTEXT_LENGTHS overrides exist — we don't\\n  1635|            # want it leaking into the persistent cache for Nous URLs.\\n  1636|            if base_url and source == \\\"portal\\\":\\n  1637|                save_context_length(model, base_url, ctx)\\n  1638|            return ctx\\n  1639|    if effective_provider == \\\"openai-codex\\\":\\n  1640|        # Codex OAuth enforces lower context limits than the direct OpenAI\\n  1641|        # API for the same slug (e.g. gpt-5.5 is 1.05M on the API but 272K\\n  1642|        # on Codex). Authoritative source is Codex's own /models endpoint.\\n  1643|        codex_ctx = _resolve_codex_oauth_context_length(model, access_token=api_key or \\\"\\\")\\n  1644|        if codex_ctx:\\n  1645|            if base_url:\\n  1646|                save_context_length(model, base_url, codex_ctx)\\n  1647|            return codex_ctx\\n  1648|    if effective_provider == \\\"gmi\\\" and base_url:\\n  1649|        # GMI exposes authoritative context_length via /models, but it is not\\n  1650|        # in models.dev yet. Preserve that higher-fidelity endpoint lookup.\\n  1651|        ctx = _resolve_endpoint_context_length(model, base_url, api_key=api_key)\\n  1652|        if ctx is not None:\\n  1653|            return ctx\\n  1654|    # 5e. Ollama native /api/show probe — runs for ANY provider with a\\n  1655|    # base_url, not just ollama-cloud.  Ollama-compatible servers expose\\n  1656|    # this endpoint regardless of hostname (local Ollama, Ollama Cloud,\\n  1657|    # custom Ollama hosting).  The OpenAI-compat /v1/models endpoint\\n  1658|    # correctly omits context_length per the OpenAI schema, but /api/show\\n  1659|    # returns the authoritative GGUF model_info.context_length.\\n  1660|\", \"total_lines\": 1826, \"file_size\": 76757, \"truncated\": true, \"hint\": \"Use offset=1660 to continue reading (showing 1540-1659 of 1826 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_RJAexiviX20tyCwmGEy3ECwU", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbekwZdnb_5oGvX0uW-acit48aAQNKjme-9s157sFEL5dvIELWrHXdZp6MCehLHxV6o90mnzurnQymLdQdbM699Ybb5bSLRRQe5oW-I0nq9Z2Ys_algXoR4rCKzxQUBCKb7mU3dP1MIxtU8UAA7amxAvM903Hswz83KICKOgriHgU0ye8PDvgDY4sM5xUMuFOBPer6zkVvVgb2VF5gXJS4yInvLHQvXuaTB_ncjd-SlObHRz9DtWu55KwEHA1LXalkyfO_koX_CMU5RlJ7GA62Fs-AdJ7ddMKZoDiB9mZBu3_DX0t09rtDLWIPncTvor5Z6tVoiOGKJ-KAE81hvGv7xCU7fct5HZgbZ3Bo-mE8agoh3bzlcSp28244KIzL0Y9V86L8VckNSrprAjxroXBtSuqoHYCn7627C9LdzvRVOmEBCPrwXm5mktk6ZKzaN_PbVLQRk7HjQQC0gEC5jVyz4o51Xa0km4LGt1eTXmuTERdQwTrts8WDg-bikQoXi_0xRXxp11xzpMQ4dXKqB-uqCwRGZZU0MIb6aNkQ9ILsje8aCNXpADPTcobxz9dWHUvmd1gEn_YJunBAuFs7TQ04Bojns5xL1OX4ZjcCurLjM-ranODcZ3Uq61Ad5v4QH-dZVdaNTlS8wmjr2LM6TutF8y88lYUehFIafc-ovdy-hDUakfxixwQ6mHo4CyZGRNcW8nuPYDn1DPsm8oRumwXswqffnODqxiYoLhmTkwgFFIMOHMa9KlN3_0yx3an73c56Mm9X7jOvv7vLlFCy0YIExSMUNW0ZyIOBwOUYHQEDh2MzPPrmw3B0n_VnounGXeBfaoxAPcWMNC54YrjzJX_MSVw1i11Cqz3PP89IRsOOvsU3HEIkR7y8QlV6_2G7akQuKae1Oz1weiSB2kTGrol0zd7KusrETwMEbIlLThc_o7HzVSXWRbkl1G1Gv6j0-ZaZPz-2-jGwi1-f5lvLYZ56HoGODbJr2fX6lsUzIbCrS_x9tzt4m6Kxctq2XF34wLDRTQkoJF8HCgHuLfgcEBwWxgaLmyvQvG4g_xN8TMg6SGNDWeVyqtZDra3528SD7EU6eqCzpNc41fq2WKwvngcUx0PwRw76ixEHh2Ctpite8RxYcaJlhvrH17VAUa0cX0TfGZAh4ExaKpP4KrvXr_4ZngFwSvASKt7KC0gmJpL10k8dR8A18R0aI5IPGcbRd-UQzI_L3UOh7BHF4OltEoR--fFNssLmgsGenKl5yuh8WYcv2tiLi_VaW0HePiObXeXQtoOmu4AkhwpcqNH18bFubjIp9KhAi1Rok9zw2qoE0-WN_wLMn-naI8CmBNKzffCf28J2kFqGmvc1FS9lagOlUJOcspUMzM1m1rpeere9X0p09He1ZtbuwmjgtEaab-Iacw3AMrqseH9cw-amgLOMSHqFDyhajKIXc5Dx3c7kyDXue2HiMuKwoytY98FVJl1OmQbKYTPwjObOtBn5Q-qBuKdJ07WVq97n6XSdIEgyZ-6fToAO6pIgRKKyXeCXz_xlnQwXH6Ln4E9dmSK7AmnXKfnofDZFCaTKkieuK4IZrselOT_w6H5TE_yM-qq0NoYd5CXsfb2nwFBhQEjomEZHl2esScab6dkibkzma0e-aq4xoTH_Lxh-PpA9MdDoySuwQKuwqfyIENlfFDQrofpynJs3r40JJ-v0a4Y8gQb7nM8gJx6o0ev6dodSM1tpurwqECzr_fqXKLYJEP8aTSNdrpG-zCv9ZaGaNSqXWL6_2MAuq1aAxnsuJ33clKEaK7JmMMzLWXDtu45CpgWWE0nCJMkOntvzWfZz1mm6rF21IXTesXQ6MmLYZp2g7AU7yM5XsyjD77gZAX7a5J22pxD81_QAWuQAweVzNLRudCC2lfUAhQX9j-R-ThW32A7KyWvjmWmJitAc8PqL-CWuXrbFU9a_pUTua6iat7nifAMqeiHRIOGQU2GJQcRw-fbDEMqgr8_yXpt8MIVaFUSIFVyuKEfQxNSKZCda0HSSFl7SughDCfLvmWgHEjtC3X8pmoYz170yTYcCPT7lJJE29MsMzDTZfg9Ch9oHl-jBzn2pyb_N86iNBjcHE7o4hRtADlwGg8ioNUvBRCVg9APKw1bPXMCtwTphOnWxiwRAWTJ5sV5l9QtbueOQosjOPfaDzCV5jykBG5QdEMMKhyUzdrTNJYseBulhWUv2ZROlqW8kgGPR_9uLGIZrHtomM2vvWnxqPueAD9aA-D0WFx1mEaHzwaJtr0TglSeR6vtmVLT1oTXgj8HCf2PV2G-dRyrFjhlWDrCKfytc2CXTZAGTURwP1gFL4rQsra1wl11z9M59gEyx_nf6vb3V82y2XO5Lic8I0Oye9BfQXdZkZtfPjIfzc0-ftDmfCBWqzFB7lOgMta1Cs0OschDeenv-VrnBRWC2Kcqx5gb3CmTCYBuZNZan37teqBiqBsTDHcRb1h1FKOJRRP7zMANPynvO0pdMvgPbVenFppNJ1YQxiAR6SYKCtP1a_xoFE9HBZQeIiPJbUB6P57azXB-znl0IUPSpvhQsfA8kvIp9eoS1yUYMnVNAegXv6GbRtZofOn27gcs9zjTJR4knEMEkxfi9CWwC-fohE4TM3ds2bOGoy_4JmE7KYWt0jsFd6Q4laaqR66fRxterBbEnXoxd1WV9tiQsMjXpHG565sbFly_rb9KJ7orCG7vuzra8T1gaVbwFo1nUs7ji0=", "id": "rs_09d9720af206eb51016a09b79d3a208191bcaeefca18fabe90", "summary": []}], "tool_calls": [{"id": "call_SN51Hqtj5fW0Qh50q0uV8owa", "call_id": "call_SN51Hqtj5fW0Qh50q0uV8owa", "response_item_id": "fc_09d9720af206eb51016a09b7a44aa881918a9b7cd551ba5074", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nctx=272000\\nfor t in [0.4,0.5,0.6,0.65,0.7,0.75,0.8,0.85,0.9]:\\n    print(f'{t:.2f}: {int(ctx*t):,} tokens')\\nPY\",\"timeout\":10,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"0.40: 108,800 tokens\\n0.50: 136,000 tokens\\n0.60: 163,200 tokens\\n0.65: 176,800 tokens\\n0.70: 190,400 tokens\\n0.75: 204,000 tokens\\n0.80: 217,600 tokens\\n0.85: 231,200 tokens\\n0.90: 244,800 tokens\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_SN51Hqtj5fW0Qh50q0uV8owa", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCberZV1ZPSoKVZ6BIsCCvJuUwR1eJU_ora-yk3OaDRkwq5o2p2r0OVyGdeS9X0M4WCPZPdTQy5ZAkn1-8Vjy71gt0uY2Wz0aYlfnVrppzzckoLJgwGG59k2zTEcwFEGymSdX71vsOB0an8RwbT3eN1Wgl2WuSHtQgjGYjXYHJq2bzjjXQdUrkn1MzQC3WDPZFLs1k7LVYrIBdpjXzcTinIKD9ZXolSwEZehrLkRw3Xk0hmsL6EXfL-QrjyBhSmuec0bXFtIqa1qM1T_g-urNPLmdf4a3hXzlhGIovGtRC0MkOuEkE7BZLu4AZcjGKijgsNl9q7B3qx2YWTO92LJ7oMWOQxjY43h5lGJyg7m4SJXG-2324vk76NuNhqNl0qf3SjBJmeomLdMDVzFRT41Mpgyi5UIkhyy-2vexCidUIPNCYcFrFJKcyY9RNx9wxaj5aef5LO41Ao7y0XEUhCdtW5oHOpGXJhUwNYjO7WWzkjTh_qw4d5JL_6Ez1wJTA8mjQvZRG1I8htiPC0AYd6Pn5Is9AElOGOG5_TCID_irwOXCHSEts7R2QCWVRFO0SqMDixVB87Du7v8_hpe5kXaDz5PIQ779ijM8TD-ZDqD-Ncoc1kQcmVJ_CV_7Oe02PNBI5W5lf4mJ-Ym2gvm13g_j-CRSPr_i5z8RkbU3em8mIjEvfhL8U780FBiC2KiLJR7rtgyqHwfU07B5KeObemTrvBwzpYpm-HRwZYNHKz0MWOLmRBENR5K9kJss6868D-cK46g9heesirZ0lzJ2tDBZ04lQRzuWv4wxpLfJxGu1SMch8nRqHi8sP62Z8p6p_cXzaADOMWcemhek_UlRYpuSXMad-ZVa5FBQfEnEZnwWaYx3px-XUPOy5vukj0VRIe15G2RWdgIhDF6_du6wQ4lYsrGzkxhsVOMJNaww5MyKBALyW9NwhtV1qyyvTpmC-VNGxbw5WdpihLl-UFY9wSqeCjd3pzSney283dxUPV5Q8Fbyqt6rQDgn-XWw-7BLU67_cqMDSiSnIFqjcnSBYvark1sIvg0M9hl1xpOK5LHtt1ZkNApTDHyRxfUrTFEH2ZLOi62ByRvSWuPAKkAICmE5x1iJ3Zh63iwsgBZmszmtgd0FIc8hh-UNUHahKaNHSJFPH2IeVxF6YYxZYwM3x3Igyg9CuCIkxn5jHRYhOO6tvcHR2v9ai-CzDb5-F8ZM9KSDNH2CuQUNFrXV2wzYnn7NgGiihRrJlQq3znVTENibPnuy6eoDIvwKTf-npbZZrH0Y3K8FJY8Wt_djtcrkiwDpbGXTyq4WBaDCaNyOA6pfZwyn5Wac7Fzk0e9sEuMvtm-3Ia_f-8LhYW119A_chGvuJzKAI-xqqPkx6DnMSEdBs0jjzluoTbRLeMmNtI9QJ8ztQjm4gf_r-sWEqQGzWrct_zF1hBh4uNwpxVCcxT-gXrEam0Ob6IpZaq8irC7XzYltUjB2h7gqYgqat1_918N5XiNsCnSe1BkdQlEnIRtqi2Wsokki9QeX1FVxgNorHVolaXhl_oYTs8jUAM98ThG8gchACt2LOAyfYcISh1C-0PCDKctWKSabMYjNqMQMTgLiz5sDIxxFrEuTWpT0v5onhsYt8lfwGJVe-KmAqg6knBjorpTWer69VRFqYt_WZercoxfUFHzdNTbkr9wolRqJLBSgpM1ESVPXgBX1YCdboUW8h9NWGfcewsbQmXspHQQHtoYSlqSN2rzcU1heTLcKhMFOqnYIOlEgXcbGdUzO1UWJ3AkPPQ5KZDbMvNRyqq2Stca1soQatt6CxIPcGS9OyolQBAW6P7_v51LifrxzMc9cwEeInxMpx1iQQRozIJjYgH26lnP87ctfOAEu4FmiQLj5sdqd5RhczWyX9cO3zJhJYhmw6cAmAZcoGnqfIYwhtUb2nQjkbxb2DlBeQrk6pBgsKnvDGiMq-8dru86pHx-jUoicZsx52qRoZkjcpCQ0ri0-Wd6kAGW7FRFfXh62hzWXUX-fciayvijUHbQNbB3G0hA4PsEfkdGqvVPPo10k-5k8", "id": "rs_0e84f590d2e988f6016a09b7a7c93081919ec6274ebb604d44", "summary": []}], "tool_calls": [{"id": "call_t9KPvgU4C8yArMVXMKyDI72G", "call_id": "call_t9KPvgU4C8yArMVXMKyDI72G", "response_item_id": "fc_0e84f590d2e988f6016a09b7abdba88191bca6fe19b85646a8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"hermes config set compression.threshold 0.65 && python - <<'PY'\\nimport yaml, pathlib\\ncfg=yaml.safe_load(pathlib.Path('/home/agent/.hermes/config.yaml').read_text())\\nprint('compression.threshold =', cfg['compression']['threshold'])\\nprint('effective preflight at Codex GPT-5.5 272k =', int(272000*float(cfg['compression']['threshold'])))\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"✓ Set compression.threshold = 0.65 in /home/agent/.hermes/config.yaml\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 1, in <module>\\nModuleNotFoundError: No module named 'yaml'\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_t9KPvgU4C8yArMVXMKyDI72G", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbex6F3jPbuR56f5EfvDym_OErDEpxDeDU6INgQLE7RkbfLB_23PIXI2W1THl7MSmEshpFYeKBKxo4cjy-fGoJuXj2-9NtvHI51koVTqGI99OsryINASBICPMQA1VR5AAxq1ZN6FRpHzrCgL_0qU_7EllqHsqAKvrzJ6GC9M1slKLdrgO_pq2qwuIhwa2wQN7O7sZkCIvK8UzRFF0sBYwCIr--XdI1rDjKH2g1nAGcuUCnFKdmaQd1fwM_pGjpAr_BxPhxlFeYC-NHpEiHPYPB0TjPsXuSOZ1fJybZmiVzMr9NE1-1xwy-baOYxOnd5nS6H8BsMvZ2cP4KMoqZcNGEC6uWwN_1ZTo-QEIvQkqSz4Hbii6mDm5drqWlhleRMBVp6ATkAK_LYIR6bS0hzlcGZJ-0LXIHmB_zbF_avPKdGgXDr7N-d6aTEV4RaifyeciD7wUFjCNx3UKoftAGMgoW5XhPdEr75z4ZF6wEFxG2Xpg6esBDQ0j2nrsMdPEa233dXV-l6Rzbl7fJOGmDkOZlB9QBCW_wc3DU_3BbsAWUkB_SuWmGx0xbU9eAsWij_DlQ6nAoIqmEYWwrwcL0HH7W1Ro1mU3ETZMaBy8KWgkfH7Biqgs0i-F98Rmx1Pi62AUCkQR9XcRgz_styIPjR1uaXpL7ML9bliWkq_4H_7GwaMcSDmfZpxCpns4I6PqiCawi9C7st-LlcM_4ueZSkHysW3XdfyLkHwiJt9tJpxuOhlyDV0gdosa7hiD8aI2nunU6nZFc6UDaxot1EBJyclAFoa_0l6oav2tpBtucjDo1YCCOL2zaF341FkAOlNoPXZSq6gkZbX4ETTXLbnEWY6wRvLhNEphW6rPjv-rppqF1YVYQZEobMmqYDWRG5Is8c-8k3dKJH_oyJo8yJBoBx7MF5f2-lquhdmcdTnH3s3B2fxpbN7z3mN5PFhJY2b8a4JvXHdUDcwfZowBqsW5aFBHQxpxp41lLV-IsbJD5d7aCNwb-hMC9nFJPKzYwnM9teovqv249V7BdSbiL1SqQPN7FTyGiheomVth2im9Sxum5wn4V0=", "id": "rs_016413ed05b5e308016a09b7aff0148191b9def5b6411df311", "summary": []}], "tool_calls": [{"id": "call_gPYcg3HwNlIxkQfUv2i2sjTH", "call_id": "call_gPYcg3HwNlIxkQfUv2i2sjTH", "response_item_id": "fc_016413ed05b5e308016a09b7b1e6148191b4e1e7ace5d7555e", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/config.yaml\",\"offset\":141,\"limit\":8}"}}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"   141|compression:\\n   142|  enabled: true\\n   143|  threshold: 0.65\\n   144|  target_ratio: 0.15\\n   145|  protect_last_n: 24\\n   146|  hygiene_hard_message_limit: 400\\n   147|  protect_first_n: 3\\n   148|prompt_caching:\\n   149|\", \"total_lines\": 725, \"file_size\": 26437, \"truncated\": true, \"hint\": \"Use offset=149 to continue reading (showing 141-148 of 725 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_gPYcg3HwNlIxkQfUv2i2sjTH", "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "assistant", "content": "Ja, Sir — und ich habe es bereits moderat erhöht.\n\n## Befund\n\nAktuell nutzt Hermes:\n\n- **Provider:** `openai-codex`\n- **Model:** `gpt-5.5`\n- **Endpoint:** `https://chatgpt.com/backend-api/codex`\n- **Effektives Kontextfenster über Codex OAuth:** ca. **272,000 Tokens**\n- **Bisheriger Compression-Trigger:** `compression.threshold: 0.4`\n- Daraus kam exakt die Meldung:\n  - `272,000 × 0.40 = 108,800`\n  - also: `~138,891 tokens >= 108,800 threshold`\n\nDas heisst: Wir hatten nicht nur ein kleines Kontextfenster — wir haben Hermes angewiesen, bereits bei **40 %** des verfügbaren Codex-Fensters zu komprimieren. Etwas vorsichtig. Schweizerisch konservativ, könnte man sagen.\n\n## Änderung\n\nIch habe gesetzt:\n\n```yaml\ncompression:\n  threshold: 0.65\n```\n\nVerifiziert in:\n\n```text\n/home/agent/.hermes/config.yaml\ncompression.threshold: 0.65\n```\n\nNeuer Preflight-Trigger bei GPT-5.5 via OpenAI Codex:\n\n```text\n272,000 × 0.65 = 176,800 Tokens\n```\n\nDie konkrete Meldung mit `~138,891 tokens` würde damit **nicht mehr sofort triggern**.\n\n## Was GPT-5.5 hier wirklich zulässt\n\nWichtig: Es gibt zwei verschiedene Dinge:\n\n- **GPT-5.5 über direct OpenAI API:** laut Hermes-Metadaten bis ca. **1.05M Tokens**\n- **GPT-5.5 über ChatGPT/Codex OAuth backend:** aktuell effektiv ca. **272K Tokens**\n\nWir nutzen derzeit **OpenAI Codex**, nicht den direct OpenAI API provider. Deshalb ist **272K** die relevante Obergrenze. Einfach `model.context_length` künstlich auf 1M zu setzen wäre keine gute Idee — dann glaubt Hermes an ein Fenster, das der Codex-Backend-Server vermutlich nicht akzeptiert. Klassischer Fall von „wir fahren 300 km/h, weil der Tacho es kann“.\n\n## Empfehlung\n\nDie jetzt gesetzten **0.65** sind ein guter Sweet Spot:\n\n- weniger unnötige Preflight Compression\n- noch genug Headroom für:\n  - System Prompt\n  - Toolschemas\n  - Memory\n  - letzte Nachrichten\n  - Modellantwort\n  - Reasoning/Hidden overhead\n\nWenn es weiter nervt, können wir auf **0.75** gehen:\n\n```text\n272,000 × 0.75 = 204,000 Tokens\n```\n\nIch würde aber **nicht über 0.75–0.80** gehen, sonst steigt das Risiko für Context-overflow / API-Fehler deutlich.\n\n## Damit es greift\n\nFür neue Agent-Runs sollte es greifen. Falls die Gateway-Session die alte Config gecacht hat:\n\n- in Telegram: `/reset`\n- falls weiterhin alte Schwelle erscheint: `/restart`\n\nDanach sollte der neue Threshold aktiv sein.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbe5SCRPB0tx3crSEAxWq8tmv9PJbKAzjcLH1pwuyFAPvoRAgsQMaKSlEkua8ayIAzaWgef78juG5HTA9-Rb7jsOtEw3XlRDZmrJT8c10ta4AZHWzyaLeCssd3cQCRNVIE8XoVk-mfL3xQggnvwabVBWoLC5gcTSULeSslUtPX46ctR5EF8dSqu0n-QMnwpk5-6ihAQwav7rhHK-5Z9_dDi8jyW4dMB16Qgx_gqsrJW2_DRn7QbTFEjqLMFVPFNDpzXgOLCR-93RGh0vKgjE6mIlTAtzjBvzCvn1HlqBayaFYaWEBYHP36uDVxE7_gNadx1nFWnVPXjocwzSy-s7_Y9k2uAjTnsQ3weDHCc6JFonlYBIq1ZDZWslWnoEGlQsIlDGrQ-FLycw2kHxdEmjnQvVGoSHLdxBIa5iSc_NQjrucit_R9e2aEEd2HhSo49iyXrGf__2qHJEu4kWNGVGl393zqzZgjsSFOC71cDun9duMT5bdCyCbZ9XLm-T82GdV92kP8u4vU3InwDFaeXQKHouwUFOZ8dtOZit6HNR2XMPiNU2VgIFHhk0N5EGn0pcL23huzweVkPX3wNOsOjwoi0dpTs8cS44q0KOXRFzIGpDmGI1V2N-N8eGisHE45OB_-Xn0Xr4yQftoRhvmDUFPgeIVC6JRWTw2ychXF4Ol8wCY8qAmLHxbu2WkgaroO_f8d7IOxjUvk7ayCAvVi1IqHZZKLGIy1PaDN-R0jjLDZTR_vqDVQEA4ebW9pZ4r0qbppSi4UmWhnZci--3ByMci0cIUzW80oMyKZs225xa4OUg8hT5DhtJ6sd7eVc11P9Z-SMpJB0-TjjvVYoa2ohueVGlKbwfzDAiKKeUYa2dBipXc5J-NtvfwCwrjd6zKa70kuraqL32RMb-mKSb1Wp3df-zXpeIbjD6tzl-NiImQBp8vS6VzypD4GAF9dDavJj3e9s-ud_ddaD7-Y1HzytAV2QATRYJLYFpUZ90QFympKl14wqramY2ijFg_UFcM2gezKAW1GtI55nkoQ5SxgwvwrabAHtoqEgn840LOgtgvuQd_sKX3yAKhXvtZ5jLiO-MO86yDJwybTxCceuWojJThQ68PEqivzVlpXb6C11V3cPQxrHi66Zz5ThV9pfITUhmDGPpkXP_FIkvzii48We3itesWIcBKDd8uOzP5xP8aUQV6nfkBw-BUP2f27sJ_Z_4wqzYbNObcrQ7fEaKUFotbCgO1fEzZrqJd9Pvg22ZZOHdD2RddziCyHGV08bV6-3TxE70mHelFPujp0AZgfKg5TX3ybLI4trgDkOjkjOKV3jT-9vpZ0fgdj8tDRBjwE6kLckS2DDY-GXQeMnPU73qgU17O7rD1l8jtSXMWBMzyWbgD0XaSoarasyuipEvIighD_vUZbJS3Si1TbYf1qcK97oe9nqDgdzhjl7tXJVaosKKA4sNqT8CUxmxDxbYr-3_5fqDDDzXiWxqkbdCnwMwSMMX1-tcs4fRu9L6FZxjcP1YIWHH6CJs2I6BVauc1Awz1H501pKj4epTwIlnt-Rbn6MkKYvnq8ng2EJfOdnl0EsNh0rOZ6EbOQbXEmSFM954xYE9wM-1UvAtyaHMpWszzGR6DMAXi-MuhAoFAJ6otRmgH9iDLMGfyGHFuxEdh3bGcoI3vgeaD5zro3FsWz3T5GcU6dfuqPL_ZLDEsaxrUjcIf-DLdYpQE_x6no0HGZtkBevxd3m9Z00GagwMzrw3sjfQEzpWB3-jvq419nA_jTOtc9qnUm7ke_qn9m-8g-ZI0pStiRipNhRIlifn9JI2PrLPu_49-99fWjukiY-F0I8POxkDIQWW3G8mAQ5c8TJkYtzHGEmee2kaKmHinVjf4KSsDDEPjSRoIUzr_M_RTUTLftRlZddtHjCXQHbdQY8B9uSkpa_GmYmSIWlCI4oMY_JhMy33kx-2GncOzVUNGrpko1DM2lZWO0mnslLh07V7UkzfEHhGv8nrGnCeqt5vW0vaBa3_8Be52O4rlvwbKuo2YRIwDGGyNjIAO_W9GuM75nedSZppmp0-y07xU59Gs-zB3cN8zwkF-8vBdpxt54mG10DRBERNzaGh-A6IGu4hsF4pTMdsIhsZfPVpdwMpiIt59jRh0IZoeVEjr7L37HnqdwHWnac6rFfq75cE21nSHa0Pbn4SwO7M00DK07559nWmH0zFeNQ039kuyJ3YECRIXJf18zkkYtDek0ngPRd1qEWqOoQiw0ubuUJv0WASgfkkH08GZkddHxRXa9zgrXiszY8=", "id": "rs_089fc61c3dcbd8cd016a09b7b479088191881652af38c10eb1", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — und ich habe es bereits moderat erhöht.\n\n## Befund\n\nAktuell nutzt Hermes:\n\n- **Provider:** `openai-codex`\n- **Model:** `gpt-5.5`\n- **Endpoint:** `https://chatgpt.com/backend-api/codex`\n- **Effektives Kontextfenster über Codex OAuth:** ca. **272,000 Tokens**\n- **Bisheriger Compression-Trigger:** `compression.threshold: 0.4`\n- Daraus kam exakt die Meldung:\n  - `272,000 × 0.40 = 108,800`\n  - also: `~138,891 tokens >= 108,800 threshold`\n\nDas heisst: Wir hatten nicht nur ein kleines Kontextfenster — wir haben Hermes angewiesen, bereits bei **40 %** des verfügbaren Codex-Fensters zu komprimieren. Etwas vorsichtig. Schweizerisch konservativ, könnte man sagen.\n\n## Änderung\n\nIch habe gesetzt:\n\n```yaml\ncompression:\n  threshold: 0.65\n```\n\nVerifiziert in:\n\n```text\n/home/agent/.hermes/config.yaml\ncompression.threshold: 0.65\n```\n\nNeuer Preflight-Trigger bei GPT-5.5 via OpenAI Codex:\n\n```text\n272,000 × 0.65 = 176,800 Tokens\n```\n\nDie konkrete Meldung mit `~138,891 tokens` würde damit **nicht mehr sofort triggern**.\n\n## Was GPT-5.5 hier wirklich zulässt\n\nWichtig: Es gibt zwei verschiedene Dinge:\n\n- **GPT-5.5 über direct OpenAI API:** laut Hermes-Metadaten bis ca. **1.05M Tokens**\n- **GPT-5.5 über ChatGPT/Codex OAuth backend:** aktuell effektiv ca. **272K Tokens**\n\nWir nutzen derzeit **OpenAI Codex**, nicht den direct OpenAI API provider. Deshalb ist **272K** die relevante Obergrenze. Einfach `model.context_length` künstlich auf 1M zu setzen wäre keine gute Idee — dann glaubt Hermes an ein Fenster, das der Codex-Backend-Server vermutlich nicht akzeptiert. Klassischer Fall von „wir fahren 300 km/h, weil der Tacho es kann“.\n\n## Empfehlung\n\nDie jetzt gesetzten **0.65** sind ein guter Sweet Spot:\n\n- weniger unnötige Preflight Compression\n- noch genug Headroom für:\n  - System Prompt\n  - Toolschemas\n  - Memory\n  - letzte Nachrichten\n  - Modellantwort\n  - Reasoning/Hidden overhead\n\nWenn es weiter nervt, können wir auf **0.75** gehen:\n\n```text\n272,000 × 0.75 = 204,000 Tokens\n```\n\nIch würde aber **nicht über 0.75–0.80** gehen, sonst steigt das Risiko für Context-overflow / API-Fehler deutlich.\n\n## Damit es greift\n\nFür neue Agent-Runs sollte es greifen. Falls die Gateway-Session die alte Config gecacht hat:\n\n- in Telegram: `/reset`\n- falls weiterhin alte Schwelle erscheint: `/restart`\n\nDanach sollte der neue Threshold aktiv sein."}], "id": "msg_089fc61c3dcbd8cd016a09b7b990e08191a7f8a34f3cd504a7", "phase": "final_answer"}], "timestamp": "2026-05-17T14:42:45.308524"}
{"role": "user", "content": "[M G] setze  0.75", "timestamp": "2026-05-17T14:56:06.752642"}
{"role": "assistant", "content": "", "reasoning": "**Preparing for action**\n\nI need to act now. I have the Hermes configuration ready and loaded—so that’s all set. Now, I just need to make sure we have the right tool to proceed. Once I have everything in place, then I can move on to the final steps. It's all about getting that last bit aligned, and I'm eager to make this happen!", "finish_reason": "tool_calls", "reasoning_content": "**Preparing for action**\n\nI need to act now. I have the Hermes configuration ready and loaded—so that’s all set. Now, I just need to make sure we have the right tool to proceed. Once I have everything in place, then I can move on to the final steps. It's all about getting that last bit aligned, and I'm eager to make this happen!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqCbrbB937QwTQpfP9dhdYBkEVbuX3wMrjp9OyQD7j4Vql6RDVrkPNVKF-ID-8ALsHJ-kxErBffMVi8cvgzrTy8IjKQ8iZJY4Sgch6B372rG9geDonSPw9vBQ9-hik8Y3-HWiUST-gv4nJFzW030vDQU_HP_z5HhXG57_HgE7sOM49vynHYMkfGRJHJQXmhwegMAfaKb-y-NlRCaNhUkz80kedxRQXy8t5bV014RcDrakLvh_DRRWO5rYZ-9BhVEzREoGpVN4lk5QzT543PhdCwJJ8xIeUhdZfG2gvFGz-0TCMhoaW6B5UQ4bGIzQE85cPvl5RcIcZ2y-Zpo1V3-3JRy9FD4iTTd1bZ0l7xmqrq7zW1dDBkLCUqEy4AxwHNNT9c82ynlVNpXBR1sd9ibzdT_O85ynqLVxA_S_X-6v7sUGr64wwMT6OSu4rUVvSrYUmVU0hWdaaUq6EcnOMQFXtylGrZoMRTGLRo5opCHNX9vbagzcEXvr-rn8J3-JH6_A9saiEm4tixGB1aYcxPwcEutf8n1zr_QAeVE31QN4AYp6DUMvZSN_qHNoQB2UQBocnMGwzVYBYyl-5w_pO4URsvzD3A0lQyegJ1LC3sI5Hahn49X0OpV2HDAGX0wCnPptRVy8BfK5tbDNegAI2lUd6OEoOzF4oDGhUL0j0yUlnje6CON3MDinNTpvH97EcPfAtdxvuoczyRiEnWDpJZql1sX1-CKGA4xPfAl9mWROPFQokyM1JC31BLO7CWtWNUs7igF69IA6pIqOeSzcXF8WW4ioTS0lUrmpk0SsjOeEoS3AVAMWR_SwjKTB2fvsfDcCpOljz3CLaw16ENJaTCm-7dVjy87oKWilW64Kzr19q5KoOSYWRpveK6dSSO6jHN3T64QC5FjhMfwKvVWXvbcoTycgzKeG_grePAVy_bAvFQ6QymqQoTzt6SX2GkgseNrxfpDrAx1q2mV7EAko3t6IeULtLpOnApX3u5UK9bwndHilxNvANgb7bJH5dtj5xpZmODK6-", "id": "rs_089fc61c3dcbd8cd016a09bad9cf44819193a6e04e93ad4671", "summary": [{"type": "summary_text", "text": "**Preparing for action**\n\nI need to act now. I have the Hermes configuration ready and loaded—so that’s all set. Now, I just need to make sure we have the right tool to proceed. Once I have everything in place, then I can move on to the final steps. It's all about getting that last bit aligned, and I'm eager to make this happen!"}]}], "tool_calls": [{"id": "call_3Tpf6un5JOE1rQX7djhRTbEe", "call_id": "call_3Tpf6un5JOE1rQX7djhRTbEe", "response_item_id": "fc_089fc61c3dcbd8cd016a09badb58fc8191975e2d49ee63057d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"hermes config set compression.threshold 0.75\",\"timeout\":30,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}"}}], "timestamp": "2026-05-17T14:56:06.752642"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"✓ Set compression.threshold = 0.75 in /home/agent/.hermes/config.yaml\", \"exit_code\": 0, \"error\": null}\n\n[Subdirectory context discovered: .hermes/hermes-agent/AGENTS.md]\n# Hermes Agent - Development Guide\n\nInstructions for AI coding assistants and developers working on the hermes-agent codebase.\n\n## Development Environment\n\n```bash\n# Prefer .venv; fall back to venv if that's what your checkout has.\nsource .venv/bin/activate   # or: source venv/bin/activate\n```\n\n`scripts/run_tests.sh` probes `.venv` first, then `venv`, then\n`$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the\nmain checkout).\n\n## Project Structure\n\nFile counts shift constantly — don't treat the tree below as exhaustive.\nThe canonical source is the filesystem. The notes call out the load-bearing\nentry points you'll actually edit.\n\n```\nhermes-agent/\n├── run_agent.py          # AIAgent class — core conversation loop (~12k LOC)\n├── model_tools.py        # Tool orchestration, discover_builtin_tools(), handle_function_call()\n├── toolsets.py           # Toolset definitions, _HERMES_CORE_TOOLS list\n├── cli.py                # HermesCLI class — interactive CLI orchestrator (~11k LOC)\n├── hermes_state.py       # SessionDB — SQLite session store (FTS5 search)\n├── hermes_constants.py   # get_hermes_home(), display_hermes_home() — profile-aware paths\n├── hermes_logging.py     # setup_logging() — agent.log / errors.log / gateway.log (profile-aware)\n├── batch_runner.py       # Parallel batch processing\n├── agent/                # Agent internals (provider adapters, memory, caching, compression, etc.)\n├── hermes_cli/           # CLI subcommands, setup wizard, plugins loader, skin engine\n├── tools/                # Tool implementations — auto-discovered via tools/registry.py\n│   └── environments/     # Terminal backends (local, docker, ssh, modal, daytona, singularity)\n├── gateway/              # Messaging gateway — run.py + session.py + platforms/\n│   ├── platforms/        # Adapter per platform (telegram, discord, slack, whatsapp,\n│   │                     #   homeassistant, signal, matrix, mattermost, email, sms,\n│   │                     #   dingtalk, wecom, weixin, feishu, qqbot, bluebubbles,\n│   │                     #   yuanbao, webhook, api_server, ...). See ADDING_A_PLATFORM.md.\n│   └── builtin_hooks/    # Extension point for always-registered gateway hooks (none shipped)\n├── plugins/              # Plugin system (see \"Plugins\" section below)\n│   ├── memory/           # Memory-provider plugins (honcho, mem0, supermemory, ...)\n│   ├── context_engine/   # Context-engine plugins\n│   ├── model-providers/  # Inference backend plugins (openrouter, anthropic, gmi, ...)\n│   ├── kanban/           # Multi-agent board dispatcher + worker plugin\n│   ├── hermes-achievements/  # Gamified achievement tracking\n│   ├── observability/    # Metrics / traces / logs plugin\n│   ├── image_gen/        # Image-generation providers\n│   └── <others>/         # disk-cleanup, example-dashboard, google_meet, platforms,\n│                         #   spotify, strike-freedom-cockpit, ...\n├── optional-skills/      # Heavier/niche skills shipped but NOT active by default\n├── skills/               # Built-in skills bundled with the repo\n├── ui-tui/               # Ink (React) terminal UI — `hermes --tui`\n│   └── src/              # entry.tsx, app.tsx, gatewayClient.ts + app/components/hooks/lib\n├── tui_gateway/          # Python JSON-RPC backend for the TUI\n├── acp_adapter/          # ACP server (VS Code / Zed / JetBrains integration)\n├── cron/                 # Scheduler — jobs.py, scheduler.py\n├── scripts/              # run_tests.sh, release.py, auxiliary scripts\n├── website/              # Docusaurus docs site\n└── tests/                # Pytest suite (~17k tests across ~900 files as of May 2026)\n```\n\n**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys only).\n**Logs:** `~/.hermes/logs/` — `agent.log` (INFO+), `errors.log` (WARNING+),\n`gateway.log` when running the gateway. Profile-aware via `get_hermes_home()`.\nBrowse with `hermes logs [--follow] [--level ...] [--session ...]`.\n\n## File Dependency Chain\n\n```\ntools/registry.py  (no deps — imported by all tool files)\n       ↑\ntools/*.py  (each calls registry.register() at import time)\n       ↑\nmodel_tools.py  (imports tools/registry + triggers tool discovery)\n       ↑\nrun_agent.py, cli.py, batch_runner.py, environments/\n```\n\n---\n\n## AIAgent Class (run_agent.py)\n\nThe real `AIAgent.__init__` takes ~60 parameters (credentials, routing, callbacks,\nsession context, budget, credential pool, etc.). The signature below is the\nminimum subset you'll usually touch — read `run_agent.py` for the full list.\n\n```python\nclass AIAgent:\n    def __init__(self,\n        base_url: str = None,\n        api_key: str = None,\n        provider: str = None,\n        api_mode: str = None,              # \"chat_completions\" | \"codex_responses\" | ...\n        model: str = \"\",                   # empty → resolved from config/provider later\n        max_iterations: int = 90,          # tool-calling iterations (shared with subagents)\n        enabled_toolsets: list = None,\n        disabled_toolsets: list = None,\n        quiet_mode: bool = False,\n        save_trajectories: bool = False,\n        platform: str = None,              # \"cli\", \"telegram\", etc.\n        session_id: str = None,\n        skip_context_files: bool = False,\n        skip_memory: bool = False,\n        credential_pool=None,\n        # ... plus callbacks, thread/user/chat IDs, iteration_budget, fallback_model,\n        # checkpoints config, prefill_messages, service_tier, reasoning_config, etc.\n    ): ...\n\n    def chat(self, message: str) -> str:\n        \"\"\"Simple interface — returns final response string.\"\"\"\n\n    def run_conversation(self, user_message: str, system_message: str = None,\n                         conversation_history: list = None, task_id: str = None) -> dict:\n        \"\"\"Full interface — returns dict with final_response + messages.\"\"\"\n```\n\n### Agent Loop\n\nThe core loop is inside `run_conversation()` — entirely synchronous, with\ninterrupt checks, budget tracking, and a one-turn grace call:\n\n```python\nwhile (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) \\\n        or self._budget_grace_call:\n    if self._interrupt_requested: break\n    response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)\n    if response.tool_calls:\n        for tool_call in response.tool_calls:\n            result = handle_function_call(tool_call.name, tool_call.args, task_id)\n            messages.append(tool_result_message(result))\n        api_call_count += 1\n    else:\n        return response.content\n```\n\nMessages follow OpenAI format: `{\"role\": \"system/user/assistant/tool\", ...}`.\nReasoning content is stored in `assistant_msg[\"reasoning\"]`.\n\n---\n\n## CLI Architecture (cli.py)\n\n- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete\n- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results\n- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML\n- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text\n- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry\n- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching\n\n### Slash Command Registry (`hermes_cli/commands.py`)\n\nAll slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:\n\n- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name\n- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch\n- **Gateway help** — `gateway_help_lines()` generat\n\n[...truncated AGENTS.md: 50,982 chars total]", "tool_call_id": "call_3Tpf6un5JOE1rQX7djhRTbEe", "timestamp": "2026-05-17T14:56:06.752642"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_tffTkVjInLzeLwy6UZvWXt6G", "call_id": "call_tffTkVjInLzeLwy6UZvWXt6G", "response_item_id": "fc_089fc61c3dcbd8cd016a09bade3f408191aedb3d70645d85d6", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/config.yaml\",\"offset\":141,\"limit\":8}"}}], "timestamp": "2026-05-17T14:56:06.752642"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"   141|compression:\\n   142|  enabled: true\\n   143|  threshold: 0.75\\n   144|  target_ratio: 0.15\\n   145|  protect_last_n: 24\\n   146|  hygiene_hard_message_limit: 400\\n   147|  protect_first_n: 3\\n   148|prompt_caching:\\n   149|\", \"total_lines\": 725, \"file_size\": 26437, \"truncated\": true, \"hint\": \"Use offset=149 to continue reading (showing 141-148 of 725 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_tffTkVjInLzeLwy6UZvWXt6G", "timestamp": "2026-05-17T14:56:06.752642"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n```yaml\ncompression:\n  threshold: 0.75\n```\n\nVerifiziert in:\n\n```text\n/home/agent/.hermes/config.yaml\n```\n\nDamit liegt der Preflight-Compression-Trigger bei GPT-5.5 via OpenAI Codex ungefähr bei:\n\n```text\n272,000 × 0.75 = 204,000 Tokens\n```\n\nFalls die alte Schwelle noch in laufenden Gateway-Sessions auftaucht: `/reset`, notfalls `/restart`.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n```yaml\ncompression:\n  threshold: 0.75\n```\n\nVerifiziert in:\n\n```text\n/home/agent/.hermes/config.yaml\n```\n\nDamit liegt der Preflight-Compression-Trigger bei GPT-5.5 via OpenAI Codex ungefähr bei:\n\n```text\n272,000 × 0.75 = 204,000 Tokens\n```\n\nFalls die alte Schwelle noch in laufenden Gateway-Sessions auftaucht: `/reset`, notfalls `/restart`."}], "id": "msg_089fc61c3dcbd8cd016a09bae3b6f48191ac041212e5e5033f", "phase": "final_answer"}], "timestamp": "2026-05-17T14:56:06.752642"}