![\\\"pi-messenger-swarm\\\"](\\\"https://raw.githubusercontent.com/monotykamary/pi-messenger-swarm/main/banner.png\\\")
\\n\\n \\n
![\\\"image](\\\"docs/images/orchrun-wave2of4-2lanes-withstatus.png\\\")
\\n\\n### STEP 1: Create the tasks\\nTaskplane turns your coding project into an AI-managed task orchestration system. You simply ask your agent to create tasks using the built-in \\\"create-taskplane-tasks\\\" skill. This skill provides an opinionated task definition template designed to drive successful coding outcomes. Tasks define both the prompt.md and the status.md files that together act as the persistent memory store that allows AI coding agents to survive context resets and succeed with very long running tasks that would typically exhaust an agent's context window.\\n\\n### STEP 2: Run batches of tasks\\nTaskplane works out the dependency map for an entire batch of tasks then orchestrates them in waves, lanes, and tasks with appropriate parallelization and serialization. Taskplane can do this for both monorepo and polyrepo projects. For polyrepo projects, Taskplane additionally subdivides tasks into repo-aligned segments and uses a segmentation dependency map (DAG) to manage proper repo/worktree isolation and allow for dynamic segment expansion so worker agents can ask the supervisor agent to add additional segments to the dependency map in real time if required.\\n\\n### Key Features\\n\\n- **Task Orchestrator** — Parallel multi-task execution using git worktrees for full filesystem isolation. Dependency-aware wave scheduling. Automated merges into a dedicated orch branch — your working branch stays stable until you choose to integrate.\\n- **Persistent Worker Context** — Workers handle all steps in a single context, auto-detecting the model's context window. Only iterates on context overflow. Dramatic reduction in spawn count and token cost.\\n- **Worker-Driven Inline Reviews** — Workers invoke a `review_step` tool at step boundaries. Reviewer agents spawn with full telemetry. REVISE feedback is addressed inline without losing context.\\n- **Supervisor Agent** — Conversational supervisor monitors batch progress, handles failures, and can invoke orchestrator commands autonomously (resume, integrate, pause, abort).\\n- **Web Dashboard** — Live browser-based monitoring via `taskplane dashboard`. SSE streaming, lane/task progress, reviewer activity, merge telemetry, batch history.\\n- **Structured Tasks** — PROMPT.md defines the mission, steps, and constraints. STATUS.md tracks progress. Agents follow the plan, not vibes.\\n- **Checkpoint Discipline** — Step boundary commits ensure work is never lost, even if a worker crashes mid-task.\\n- **Cross-Model Review** — Reviewer agent uses a different model than the worker agent (highly recommended, not enforced). Independent quality gate before merge.\\n\\n## Installation\\n\\nTaskplane is a pi package. You need Node.js 22+, pi and Git installed first.\\n\\n### Prerequisites\\n\\n| Dependency | Required | Notes |\\n|-----------|----------|-------|\\n| [Node.js](https://nodejs.org/) ≥ 22 | Yes | Runtime |\\n| [pi](https://github.com/badlogic/pi-mono) | Yes | Agent framework |\\n| [Git](https://git-scm.com/) | Yes | Version control, worktrees |\\n\\nIMPORTANT: If you just installed pi, make sure you've configured at least one model provider and tested before installing Taskplane.\\n\\n### Option A: Global Install (all projects - recommended)\\n\\n```bash\\npi install npm:taskplane\\n```\\n\\n### Option B: Single Project-Local Install\\n\\n```bash\\ncd my-project\\npi install -l npm:taskplane\\n```\\n\\n## Quickstart\\n\\n### 1. Initialize a project (to scaffold settings)\\nThis step assumes you have installed Taskplane using one of the above options. \\n\\n(NOTE: if 'my-project' is a monorepo, be sure to run git init first. Taskplane uses git worktrees to isolate agent coding until you're ready to merge back to your default branch.)\\n\\n```bash\\ncd my-project\\ntaskplane init\\n```\\nYou'll answer a few questions. You can usually just accept the defaults. \\n\\nThis creates: config files in `.pi/`, agent prompts, two example tasks, and adds `.gitignore` entries for runtime artifacts. On first install, init bootstraps global preferences at `~/.pi/agent/taskplane/preferences.json` with thinking defaults set to `high` for worker & reviewer, and off for merger. Init auto-detects whether you're in a monorepo or a polyrepo workspace. See the [install tutorial](docs/tutorials/install.md) for workspace mode and other scenarios.\\n\\nAlready have a task folder (for example `docs/task-management`)? Use:\\n\\n```bash\\ntaskplane init --preset full --tasks-root docs/task-management\\n```\\n\\nWhen `--tasks-root` is provided, example task packets are skipped by default. Add `--include-examples` if you explicitly want examples in that folder.\\n\\n### 2. Check your install with taskplane doctor\\n\\nVerify the installation and scaffolding. You should have all green checkboxes if everything was successful:\\n\\n```bash\\ntaskplane doctor\\n```\\n\\n### 3. Launch the dashboard (recommended)\\n\\nIn a separate terminal:\\n\\n```bash\\ntaskplane dashboard\\n```\\n\\nOpens a live web dashboard at `http://localhost:8099` with real-time batch monitoring.\\n\\n### 4. Run your first orchestration\\n\\n```bash\\npi\\n```\\n\\nInside the pi session:\\n\\n```\\n\\n===== pi-subagents README HEAD =====\\n\\n ![\\\"pi-subagents\\\"](\\\"banner.png\\\")
\\n
\\n \\n
\\n \\n
\\n\\n### STEP 1: Create the tasks\\nTaskplane turns your coding project into an AI-managed task orchestration system. You simply ask your agent to create tasks using the built-in \\\"create-taskplane-tasks\\\" skill. This skill provides an opinionated task definition template designed to drive successful coding outcomes. Tasks define both the prompt.md and the status.md files that together act as the persistent memory store that allows AI coding agents to survive context resets and succeed with very long running tasks that would typically exhaust an agent's context window.\\n\\n### STEP 2: Run batches of tasks\\nTaskplane works out the dependency map for an entire batch of tasks then orchestrates them in waves, lanes, and tasks with appropriate parallelization and serialization. Taskplane can do this for both monorepo and polyrepo projects. For polyrepo projects, Taskplane additionally subdivides tasks into repo-aligned segments and uses a segmentation dependency map (DAG) to manage proper repo/worktree isolation and allow for dynamic segment expansion so worker agents can ask the supervisor agent to add additional segments to the dependency map in real time if required.\\n\\n### Key Features\\n\\n- **Task Orchestrator** — Parallel multi-task execution using git worktrees for full filesystem isolation. Dependency-aware wave scheduling. Automated merges into a dedicated orch branch — your working branch stays stable until you choose to integrate.\\n- **Persistent Worker Context** — Workers handle all steps in a single context, auto-detecting the model's context window. Only iterates on context overflow. Dramatic reduction in spawn count and token cost.\\n- **Worker-Driven Inline Reviews** — Workers invoke a `review_step` tool at step boundaries. Reviewer agents spawn with full telemetry. REVISE feedback is addressed inline without losing context.\\n- **Supervisor Agent** — Conversational supervisor monitors batch progress, handles failures, and can invoke orchestrator commands autonomously (resume, integrate, pause, abort).\\n- **Web Dashboard** — Live browser-based monitoring via `taskplane dashboard`. SSE streaming, lane/task progress, reviewer activity, merge telemetry, batch history.\\n- **Structured Tasks** — PROMPT.md defines the mission, steps, and constraints. STATUS.md tracks progress. Agents follow the plan, not vibes.\\n- **Checkpoint Discipline** — Step boundary commits ensure work is never lost, even if a worker crashes mid-task.\\n- **Cross-Model Review** — Reviewer agent uses a different model than the worker agent (highly recommended, not enforced). Independent quality gate before merge.\\n\\n## Installation\\n\\nTaskplane is a pi package. You need Node.js 22+, pi and Git installed first.\\n\\n### Prerequisites\\n\\n| Dependency | Required | Notes |\\n|-----------|----------|-------|\\n| [Node.js](https://nodejs.org/) ≥ 22 | Yes | Runtime |\\n| [pi](https://github.com/badlogic/pi-mono) | Yes | Agent framework |\\n| [Git](https://git-scm.com/) | Yes | Version control, worktrees |\\n\\nIMPORTANT: If you just installed pi, make sure you've configured at least one model provider and tested before installing Taskplane.\\n\\n### Option A: Global Install (all projects - recommended)\\n\\n```bash\\npi install npm:taskplane\\n```\\n\\n### Option B: Single Project-Local Install\\n\\n```bash\\ncd my-project\\npi install -l npm:taskplane\\n```\\n\\n## Quickstart\\n\\n### 1. Initialize a project (to scaffold settings)\\nThis step assumes you have installed Taskplane using one of the above options. \\n\\n(NOTE: if 'my-project' is a monorepo, be sure to run git init first. Taskplane uses git worktrees to isolate agent coding until you're ready to merge back to your default branch.)\\n\\n```bash\\ncd my-project\\ntaskplane init\\n```\\nYou'll answer a few questions. You can usually just accept the defaults. \\n\\nThis creates: config files in `.pi/`, agent prompts, two example tasks, and adds `.gitignore` entries for runtime artifacts. On first install, init bootstraps global preferences at `~/.pi/agent/taskplane/preferences.json` with thinking defaults set to `high` for worker & reviewer, and off for merger. Init auto-detects whether you're in a monorepo or a polyrepo workspace. See the [install tutorial](docs/tutorials/install.md) for workspace mode and other scenarios.\\n\\nAlready have a task folder (for example `docs/task-management`)? Use:\\n\\n```bash\\ntaskplane init --preset full --tasks-root docs/task-management\\n```\\n\\nWhen `--tasks-root` is provided, example task packets are skipped by default. Add `--include-examples` if you explicitly want examples in that folder.\\n\\n### 2. Check your install with taskplane doctor\\n\\nVerify the installation and scaffolding. You should have all green checkboxes if everything was successful:\\n\\n```bash\\ntaskplane doctor\\n```\\n\\n### 3. Launch the dashboard (recommended)\\n\\nIn a separate terminal:\\n\\n```bash\\ntaskplane dashboard\\n```\\n\\nOpens a live web dashboard at `http://localhost:8099` with real-time batch monitoring.\\n\\n### 4. Run your first orchestration\\n\\n```bash\\npi\\n```\\n\\nInside the pi session:\\n\\n```\\n\\n===== pi-subagents README HEAD =====\\n\\n \\n
\\n \\n
\\n ![](\\\"https://em-content.zobj.net/source/apple/391/rock_1faa8.png\\\")
\\n
\\n why use many token when few do trick\\n
\\n\\n\\n ![\\\"Stars\\\"](\\\"https://img.shields.io/github/stars/JuliusBrussee/caveman?style=flat&color=yellow\\\")
![\\\"Last](\\\"https://img.shields.io/github/last-commit/JuliusBrussee/caveman?style=flat\\\")
![\\\"License\\\"](\\\"https://img.shields.io/github/license/JuliusBrussee/caveman?style=flat\\\")
\\n Before/After •\\n Install •\\n Levels •\\n Skills •\\n Benchmarks •\\n Evals\\n
\\n\\n\\n 🪨 Caveman Ecosystem · \\n caveman talk less (you are here) · \\n cavemem remember more · \\n cavekit build better\\n
\\n\\n---\\n\\nA [Claude Code](https://docs.anthropic.com/en/docs/claude-code) skill/plugin and Codex plugin that makes agent talk like caveman — cutting **~75% of output tokens** while keeping full technical accuracy. Now with [文言文 mode](#文言文-wenyan-mode), [terse commits](#caveman-commit), [one-line code reviews](#caveman-review), and a [compression tool](#caveman-compress) that cuts **~46% of input tokens** every session.\\n\\nBased on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.\\n\\n## Before / After\\n\\n| \\n\\n### 🗣️ Normal Claude (69 tokens)\\n\\n> \\\"The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude (19 tokens)\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n
| \\n\\n### 🗣️ Normal Claude\\n\\n> \\\"Sure! I'd be happy to help you with that. The issue you're experiencing is most likely caused by your authentication middleware not properly validating the token expiry. Let me take a look and suggest a fix.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude\\n\\n> \\\"Bug in auth middleware. Token expiry check use `<` not `<=`. Fix:\\\"\\n\\n | \\n
| \\n\\n#### 🪶 Lite\\n\\n> \\\"Your component re-renders because you create a new object reference each render. Inline object props fail shallow comparison every time. Wrap it in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🪨 Full\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🔥 Ultra\\n\\n> \\\"Inline obj prop → new ref → re-render. `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 📜 文言文\\n\\n> \\\"物出新參照，致重繪。useMemo Wrap之。\\\"\\n\\n | \\n
\\n \\n
\\n why use many token when few do trick\\n
\\n\\n\\n
\\n Before/After •\\n Install •\\n Levels •\\n Skills •\\n Benchmarks •\\n Evals\\n
\\n\\n\\n 🪨 Caveman Ecosystem · \\n caveman talk less (you are here) · \\n cavemem remember more · \\n cavekit build better\\n
\\n\\n---\\n\\nA [Claude Code](https://docs.anthropic.com/en/docs/claude-code) skill/plugin and Codex plugin that makes agent talk like caveman — cutting **~75% of output tokens** while keeping full technical accuracy. Now with [文言文 mode](#文言文-wenyan-mode), [terse commits](#caveman-commit), [one-line code reviews](#caveman-review), and a [compression tool](#caveman-compress) that cuts **~46% of input tokens** every session.\\n\\nBased on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.\\n\\n## Before / After\\n\\n| \\n\\n### 🗣️ Normal Claude (69 tokens)\\n\\n> \\\"The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude (19 tokens)\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n
| \\n\\n### 🗣️ Normal Claude\\n\\n> \\\"Sure! I'd be happy to help you with that. The issue you're experiencing is most likely caused by your authentication middleware not properly validating the token expiry. Let me take a look and suggest a fix.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude\\n\\n> \\\"Bug in auth middleware. Token expiry check use `<` not `<=`. Fix:\\\"\\n\\n | \\n
| \\n\\n#### 🪶 Lite\\n\\n> \\\"Your component re-renders because you create a new object reference each render. Inline object props fail shallow comparison every time. Wrap it in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🪨 Full\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🔥 Ultra\\n\\n> \\\"Inline obj prop → new ref → re-render. `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 📜 文言文\\n\\n> \\\"物出新參照，致重繪。useMemo Wrap之。\\\"\\n\\n | \\n
\\n \\n
\\n why use many token when few do trick\\n
\\n\\n\\n
\\n Before/After •\\n Install •\\n Levels •\\n Skills •\\n Benchmarks •\\n Evals\\n
\\n\\n\\n 🪨 Caveman Ecosystem · \\n caveman talk less (you are here) · \\n cavemem remember more · \\n cavekit build better\\n
\\n\\n---\\n\\nA [Claude Code](https://docs.anthropic.com/en/docs/claude-code) skill/plugin and Codex plugin that makes agent talk like caveman — cutting **~75% of output tokens** while keeping full technical accuracy. Now with [文言文 mode](#文言文-wenyan-mode), [terse commits](#caveman-commit), [one-line code reviews](#caveman-review), and a [compression tool](#caveman-compress) that cuts **~46% of input tokens** every session.\\n\\nBased on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.\\n\\n## Before / After\\n\\n| \\n\\n### 🗣️ Normal Claude (69 tokens)\\n\\n> \\\"The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude (19 tokens)\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n
| \\n\\n### 🗣️ Normal Claude\\n\\n> \\\"Sure! I'd be happy to help you with that. The issue you're experiencing is most likely caused by your authentication middleware not properly validating the token expiry. Let me take a look and suggest a fix.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude\\n\\n> \\\"Bug in auth middleware. Token expiry check use `<` not `<=`. Fix:\\\"\\n\\n | \\n
| \\n\\n#### 🪶 Lite\\n\\n> \\\"Your component re-renders because you create a new object reference each render. Inline object props fail shallow comparison every time. Wrap it in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🪨 Full\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🔥 Ultra\\n\\n> \\\"Inline obj prop → new ref → re-render. `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 📜 文言文\\n\\n> \\\"物出新參照，致重繪。useMemo Wrap之。\\\"\\n\\n | \\n
\\n \\n
\\n why use many token when few do trick\\n
\\n\\n\\n
\\n Before/After •\\n Install •\\n Levels •\\n Skills •\\n Benchmarks •\\n Evals\\n
\\n\\n\\n 🪨 Caveman Ecosystem · \\n caveman talk less (you are here) · \\n cavemem remember more · \\n cavekit build better\\n
\\n\\n---\\n\\nA [Claude Code](https://docs.anthropic.com/en/docs/claude-code) skill/plugin and Codex plugin that makes agent talk like caveman — cutting **~75% of output tokens** while keeping full technical accuracy. Now with [文言文 mode](#文言文-wenyan-mode), [terse commits](#caveman-commit), [one-line code reviews](#caveman-review), and a [compression tool](#caveman-compress) that cuts **~46% of input tokens** every session.\\n\\nBased on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.\\n\\n## Before / After\\n\\n| \\n\\n### 🗣️ Normal Claude (69 tokens)\\n\\n> \\\"The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude (19 tokens)\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n
| \\n\\n### 🗣️ Normal Claude\\n\\n> \\\"Sure! I'd be happy to help you with that. The issue you're experiencing is most likely caused by your authentication middleware not properly validating the token expiry. Let me take a look and suggest a fix.\\\"\\n\\n | \\n\\n\\n### 🪨 Caveman Claude\\n\\n> \\\"Bug in auth middleware. Token expiry check use `<` not `<=`. Fix:\\\"\\n\\n | \\n
| \\n\\n#### 🪶 Lite\\n\\n> \\\"Your component re-renders because you create a new object reference each render. Inline object props fail shallow comparison every time. Wrap it in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🪨 Full\\n\\n> \\\"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 🔥 Ultra\\n\\n> \\\"Inline obj prop → new ref → re-render. `useMemo`.\\\"\\n\\n | \\n\\n\\n#### 📜 文言文\\n\\n> \\\"物出新參照，致重繪。useMemo Wrap之。\\\"\\n\\n | \\n
Error code: 400
\nMessage: Missing field: 'type'.
\nError code explanation: 400 - Bad request syntax or unsupported method.
\n \n\n\n"}],"isError":false,"timestamp":1777651718002}} {"type":"message","id":"4917dc19","parentId":"7f2ab316","timestamp":"2026-05-01T16:08:43.742Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_01d85e1c553e7c520169f4d008a77481938a0723d9e8a97cee\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp9NAJvDOqSDjhQipf0M9FnPy2A0dCMcDaZDJMUYsfej8jmVYlBSOVzwtbl_mpvgDPyTGOX-bl5dyG3jDyXKuED5vWbe9U65cMfxrY8oYXDmYZbo3qLuNVwto3P7fbOyg_Q2-JT0zdPnjIBOvAx6C5LTts2vDhmtkHVU7yv4OUaDHxWvIk2wgtRHqEn9uOqZnAnVCYFvz6GNbuCJOaEwolwCINO3LYPiME-uhnXwc9AKJ0HucfAYGsYTdJAOOh6szLs9fYMHS6xrc-uUExSo4HwsPV3-AvmQPjW1voUE9HE8d1tWavShs1Uzdnjz-Lf9iXM9jMKuqBCN2U1dRTfOAAvunrhBs9OapIwFky06cVwV178b5STNs0xE2uHFCSp7OVV09KzdwxD1pDDxVZzLTixnwhWLohtt5ueWv5AE71BAe9ay1Dl764XZRzzXl1TgkKdHSjsD8RWlHX1AkQC2RYcSVPuCxzy1pavys1F5yBQmYfiOuYz3fc-WnYq3Q6Joc4fMrzzY8Ho1B3nez0clu0aKUgtlqmSDvZz6O3tJCbUMRB63WSMQJt0MgKskHEa0IONAinecftBHXjsZvmzejiu_vrpzCv3u4rMD66z_8Syw3tHvkq5S3SkuvWHqUlj8cBnD6rMenEAWqNTrXvCENlMyKuP_qVuX_Ba9LOiQEBdw9JvBYBjlS1LxpR_lKxZiEZzNcKfvCvz_FXJzal_aEZ4bxjWb8SH0N5XrGx4PjTkyw5nRdkHHXTS19N_vCjoOTYJxV72r3LPAnZhP7hpmV6OgW9fdcTsAUm4SFpFT-r_PhPyPiDm2ZJrVCfX7lkqIQz5U0pWKBQ4HMPrGp7xbqGdWtlms5EMvWg2DYfVizBrHmVCb2mr5IvwpK-aesqIpoOCQhEAkoXhOX8aF0ZIFxBhI4_1iGlkqArLm7IiNKev4fLk4HCNZD3ZzSgz77Zo5eJOuaKudfcfdF-fkgGyZb6gWjdOElnvmsuDWRfxgLr5BDsE5o=\",\"summary\":[]}"},{"type":"toolCall","id":"call_RRMJzMKedN8djjqOfknGinCR|fc_01d85e1c553e7c520169f4d0092c908193a04b86f2dff605a0","name":"read","arguments":{"path":"/home/sebas/.agents/skills/memory-write/SKILL.md"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1032,"output":48,"cacheRead":53760,"cacheWrite":0,"totalTokens":54840,"cost":{"input":0.0025800000000000003,"output":0.00072,"cacheRead":0.013439999999999999,"cacheWrite":0,"total":0.016739999999999998}},"stopReason":"toolUse","timestamp":1777651718009,"responseId":"resp_01d85e1c553e7c520169f4d006500081938b0e4d4fd898b177"}} {"type":"message","id":"45077c8a","parentId":"4917dc19","timestamp":"2026-05-01T16:08:43.782Z","message":{"role":"toolResult","toolCallId":"call_RRMJzMKedN8djjqOfknGinCR|fc_01d85e1c553e7c520169f4d0092c908193a04b86f2dff605a0","toolName":"read","content":[{"type":"text","text":"---\nname: memory-write\ndescription: Save a durable high-signal memory into agents-database\n---\n\n# memory-write\n\n## Use When\n\n- new information is likely useful beyond current chat or task\n- saving a decision, preference, project fact, reusable source/artifact, or handoff summary\n- user asks to remember something durable\n\n## Do Not Use When\n\n- content is trivial, redundant, ephemeral, or likely useless later\n- content is secret unless user explicitly asked to store it\n\n## Required Workflow\n\n1. If useful context may already exist, optionally check first:\n - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=