release: v2.0.3 - AI 1-Person Company Engine & Business Intelligence
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# ConnectAI — Project Architecture Context
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<!-- ASTRA:AUTO-START -->
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## Project Name
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ConnectAI
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## Project Root
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/Volumes/Data/project/Antigravity/ConnectAI
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## Snapshot
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- **Workspace**: `ConnectAI` `v2.0.2` _(absolute path varies by environment; resolved from the active VS Code workspace)_
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- **Description**: The personal intelligence layer for Antigravity and VS Code. A private cognitive partner for deep project context, memory, and proactive strategic decision-making.
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- **Stack**: TypeScript, Node.js, VS Code Extension, LM Studio SDK, Test runner
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- **Stats**: 184 source files, ~31,651 lines across 5 top-level modules.
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## Description
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The personal intelligence layer for Antigravity and VS Code. A private cognitive partner for deep project context, memory, and proactive strategic decision-making.
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## Last Refresh
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- **Time**: 2026-05-13T13:48:21.458Z
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- **Files newly analysed**: 3
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- **Files reused from cache**: 181
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## Runtime / Stack
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TypeScript, Node.js, VS Code Extension, LM Studio SDK
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## Directory Map
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```mermaid
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mindmap
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root((ConnectAI))
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src/
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core/
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features/
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memory/
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retrieval/
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docs/
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lib/
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media/
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tests/
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mocks/
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core_py/
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docs/
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records/
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docs/
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```
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## Main Modules
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- `src/` — Source code (7 files — agents, core, docs, features, integrations, lib, +8 more)
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- `media/` — Webview assets (HTML/CSS/JS) (6 files)
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- `core_py/` — Python utilities (7 files)
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- `tests/` — Test suite (26 files — mocks)
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## Module Dependencies
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> Arrows: which top-level module imports from which.
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```mermaid
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flowchart LR
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src["src/<br/>85 files"]
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media["media/<br/>6 files"]
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tests["tests/<br/>27 files"]
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core_py["core_py/<br/>6 files"]
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docs["docs/<br/>60 files"]
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tests --> src
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```
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## Important Files
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- `package.json`
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- `tsconfig.json`
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- `README.md`
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## Entry Points
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> Files to read first when learning the codebase.
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- `src/extension.ts`
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- `media/sidebar.html` — Astra
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- `package.json` — npm package manifest
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_Last auto-scan: 2026-05-13T13:33:48.141Z_
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## Hub Files
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> Imported by many other files — touching these has wide blast radius.
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- `src/utils.ts` — referenced by **37** files
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- `src/config.ts` — referenced by **11** files
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- `src/lib/paths.ts` — referenced by **10** files
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- `src/lib/engine.ts` — referenced by **6** files
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- `src/sidebarProvider.ts` — referenced by **6** files
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- `src/retrieval/scoring.ts` — referenced by **6** files · Scoring Engine — TF-IDF + Bilingual Tokenizer 단순 includes() 키워드 매칭을 넘어서, TF-IDF 가중치 기반의 문서 스코어링을 제공합니다. 한국어/영어 양국어 토크나이저를 포함합니다.
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- `src/memory/types.ts` — referenced by **6** files · Memory Type Definitions (메모리 타입 정의) Astra의 5-Layer Cognitive Memory System의 모든 타입을 정의합니다. ① Short-Term ② Long-Term ③ Project ④ Procedural ⑤ Episodic
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- `src/retrieval/lessonHelpers.ts` — referenced by **5** files · Lesson / Experience Memory — pure helpers (no vscode dependency) "Lesson" = a markdown file in the active brain that captures a past mistake/risk and how to avoid repeating it. Identified by a lessons
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## Modules
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### `src/` — 85 files, ~20,591 lines
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**Sub-directories**
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- `src/core/` (15) — Astra Path Resolver (경로 해결기) Astra의 모든 데이터 파일(.astra 디렉토리)의 경로를 중앙에서 관리합니다. 확장 프로그램의 설치 경로(extensionUri) 기반으로 .astra 디렉토
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- `src/features/` (14) — Project Architecture Context (Feature 2) Builds a markdown document that captures the durable facts about a project — it
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- `src/memory/` (8) — Episodic Memory (일화 기억) 과거 대화/회의/결정의 맥락 흐름을 저장합니다. 세션 종료 시 자동으로 에피소드를 요약하여 저장합니다. "왜 이렇게 결정했는지", "어떤 흐름으로 진행했는지" 기록. 저장
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- `src/retrieval/` (8) — Brain Index — persistent, mtime-keyed tokenized cache of the Second Brain RAG 검색은 매 질의마다 브레인의 모든 .md 파일을 읽고 토크나이즈해서 TF-I
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- `src/docs/` (6) — src Chronicle Records
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- `src/lib/` (6) — Context Manager (컨텍스트 한계 관리) "context length = 132k" 는 "답변을 132k 토큰까지 생성해도 된다" 가 아닙니다. 시스템 프롬프트 + 대화 기록 + 입력 문서 + 생성될 답변
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- `src/lmstudio/` (4) — 4 files (.ts)
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- `src/sidebar/` (4) — 4 files (.ts)
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- `src/skills/` (4) — 4 files (.ts)
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- `src/integrations/` (3) — Subset of the Telegram Bot API types we actually consume. Source: https://core.telegram.org/bots/api Only fields the bot
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- `src/agents/` (2) — 2 files (.ts)
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- `src/scaffolder/` (2) — Scaffolder template catalog. Templates are pure data — (projectName) => { [relativePath]: contents }. New templates are
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**Key files**
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- `src/utils.ts` (268 lines)
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- `src/config.ts` (209 lines)
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- `src/lib/paths.ts` (151 lines)
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- `src/sidebarProvider.ts` (2603 lines)
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- `src/memory/types.ts` (126 lines) — Memory Type Definitions (메모리 타입 정의) Astra의 5-Layer Cognitive Memory System의 모든 타입을 정의합니다. ① Short-Term ② Long-Term ③ Project ④ Procedural ⑤ Episodic
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- `src/retrieval/scoring.ts` (518 lines) — Scoring Engine — TF-IDF + Bilingual Tokenizer 단순 includes() 키워드 매칭을 넘어서, TF-IDF 가중치 기반의 문서 스코어링을 제공합니다. 한국어/영어 양국어 토크나이저를 포함합니다.
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- `src/skills/agentKnowledgeMap.ts` (374 lines)
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- `src/agent.ts` (3207 lines)
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- `src/retrieval/lessonHelpers.ts` (325 lines) — Lesson / Experience Memory — pure helpers (no vscode dependency) "Lesson" = a markdown file in the active brain that captures a past mistake/risk and how to avoid repeating it. Identified by a lessons
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- `src/lib/engine.ts` (849 lines)
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- `src/features/approval/approvalQueue.ts` (129 lines)
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- `src/features/projectArchitecture/scanner.ts` (644 lines) — Deep static analyser for the Project Architecture Context generator. Walks the project tree (skipping the usual nodemodules / out / dist noise), pulls the role of each interesting file from its leadin
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- `src/lib/contextManager.ts` (275 lines) — Context Manager (컨텍스트 한계 관리) "context length = 132k" 는 "답변을 132k 토큰까지 생성해도 된다" 가 아닙니다. 시스템 프롬프트 + 대화 기록 + 입력 문서 + 생성될 답변 + 여유분 ≤ context length 이 모듈은 요청을 보내기 전에 입력 토큰을 추정하고, - 동적으로 출력 상한(maxTokens)을 계
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- `src/core/astraPath.ts` (50 lines) — Astra Path Resolver (경로 해결기) Astra의 모든 데이터 파일(.astra 디렉토리)의 경로를 중앙에서 관리합니다. 확장 프로그램의 설치 경로(extensionUri) 기반으로 .astra 디렉토리를 해결하여, 사용자 프로젝트 루트가 아닌 ConnectAI 패키지 내부에 데이터를 저장합니다. 이 모듈은 AAL(Astra Autonomou
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- `src/features/projectChronicle/types.ts` (118 lines)
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- `src/integrations/telegram/telegramClient.ts` (154 lines)
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- `src/lmstudio/client.ts` (147 lines)
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- `src/retrieval/brainIndex.ts` (325 lines) — Brain Index — persistent, mtime-keyed tokenized cache of the Second Brain RAG 검색은 매 질의마다 브레인의 모든 .md 파일을 읽고 토크나이즈해서 TF-IDF 점수를 계산했습니다 — 파일 수가 많아지면 그게 병목입니다. 이 모듈은 <brainPath>/.astra/brain-index.json 에
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- `src/extension.ts` (757 lines)
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- `src/features/projectArchitecture/index.ts` (515 lines) — Project Architecture Context (Feature 2) Builds a markdown document that captures the durable facts about a project — its purpose, modules, key files, constraints, decisions — so Astra can attach it t
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- `src/lmstudio/activityTracker.ts` (19 lines)
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- `src/memory/EpisodicMemory.ts` (278 lines) — Episodic Memory (일화 기억) 과거 대화/회의/결정의 맥락 흐름을 저장합니다. 세션 종료 시 자동으로 에피소드를 요약하여 저장합니다. "왜 이렇게 결정했는지", "어떤 흐름으로 진행했는지" 기록. 저장 위치: {brainPath}/memory/episodes/.json
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- `src/memory/LongTermMemory.ts` (243 lines) — Long-Term Memory (장기 기억) 사용자의 취향, 프로젝트 목표, 반복 규칙, 과거 결정 사항을 영구적으로 저장하고 관리합니다. 저장 위치: {brainPath}/memory/longterm.json
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- `src/memory/ProjectMemory.ts` (212 lines) — Project Memory (프로젝트 기억) 프로젝트별 요구사항, 코드 구조, 아키텍처 결정, 버그 기록 등을 Astra 확장 프로그램 내부에 저장하고 관리합니다. 저장 위치: {ConnectAI}/.astra/projectmemory.json (기존: {projectRoot}/.astra/ → 변경됨)
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- `src/retrieval/index.ts` (514 lines) — RetrievalOrchestrator — Unified RAG Pipeline Astra의 모든 검색 소스를 통합 관리하는 오케스트레이터입니다. 검색 흐름: ① Query Planning — 의도 분류 + 검색 전략 결정 ② Parallel Search — Brain + Memory + Project + Episode 동시 검색 ③ Result Fusio
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### `media/` — 6 files, ~3,304 lines
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**Key files**
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- `media/sidebar.css` (987 lines) — Stylesheet
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- `media/sidebar.js` (1388 lines)
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- `media/settings-panel.css` (210 lines) — Stylesheet
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- `media/sidebar.html` (285 lines) — Astra
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- `media/settings-panel.html` (164 lines) — Astra Settings
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- `media/settings-panel.js` (270 lines)
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### `tests/` — 27 files, ~4,802 lines
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*Depends on*: `src/`
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**Sub-directories**
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- `tests/mocks/` (1) — 1 files (.js)
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**Key files**
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- `tests/agentEngine.test.ts` (646 lines) — AgentEngine Integration Tests & Performance Benchmarks 검증 대상: 1. ErrorClassifier — 오류 유형(Transient/Permanent/Abort) 자동 분류 2. ErrorRecoveryMatrix — 각 규칙이 의도한 대응 전략으로 매핑되는지 검증 3. resilientExecute — 지수 백
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- `tests/lmStudioLifecycle.test.ts` (318 lines) — Unit tests for ModelLifecycleManager. Strategy: inject mock ILMStudioClient and a simple in-memory IActivityTracker. No real LM Studio or SDK is touched — the manager file does not import the SDK dire
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- `tests/telegramBot.test.ts` (363 lines) — Unit tests for TelegramBot + truncateForTelegram. Strategy: - TelegramBot is driven by an injected ITelegramClient stub. We script getUpdates to return queued batches and assert that: - the offset cur
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- `tests/lmStudioStreamer.test.ts` (220 lines) — Unit tests for LMStudioStreamer. Strategy: inject a fake ILMStudioClient that returns a fake model handle whose respond() yields a controllable async iterable. No real SDK or WebSocket touched.
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- `tests/localPathPreflight.test.ts` (490 lines)
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- `tests/secondBrainTrace.test.ts` (407 lines)
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- `tests/approvalQueue.test.ts` (164 lines) — Unit tests for ApprovalQueue. Strategy: drive enqueue → approve / reject / clear / pre-empt directly, confirm the onChange event fires at the right moments and callbacks fire exactly once.
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- `tests/projectScaffolder.test.ts` (135 lines) — Unit tests for FileSystemProjectScaffolder. Drives against a real temp directory so end-to-end file IO + path-traversal defenses are exercised.
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- `tests/resilience_stress.test.ts` (183 lines) — Resilience & Boundary Stress Test Suite (v2.77.3) 이 테스트는 ConnectAI 엔진이 극한의 환경(인증 실패, 네트워크 차단, 타임아웃 등)에서 얼마나 안정적으로 복구되고, 신뢰성 지표(Resilience Metrics)를 정확히 기록하는지 검증합니다.
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- `tests/skillInjectionService.test.ts` (172 lines) — Unit tests for FileSystemSkillInjectionService. Strategy: drive the service against a real temp directory so path-traversal defenses and writeFileSync paths are exercised end-to-end. The service accep
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- `tests/dataProcessor.test.ts` (87 lines) — / <reference types="jest" />
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- `tests/findBrainFilesCache.test.ts` (80 lines) — Unit tests for findBrainFiles TTL cache.
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- `tests/paths.test.ts` (84 lines) — Unit tests for the centralized path resolver.
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- `tests/systemSpecs.test.ts` (90 lines) — Unit tests for SystemSpecs + HeuristicModelMemoryEstimator. Strategy: - HeuristicModelMemoryEstimator is pure — directly drive it with model ids. - NodeSystemSpecsProvider depends on os. so we test: a
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- `tests/transaction.test.ts` (68 lines) — / <reference types="jest" />
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- `tests/vulnerability.test.ts` (60 lines) — / <reference types="jest" />
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- `tests/brainIndex.test.ts` (107 lines)
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- `tests/contextManager.test.ts` (129 lines)
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- `tests/lessonHelpers.test.ts` (191 lines)
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- `tests/projectChronicle.test.ts` (199 lines)
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- `tests/responseRecovery.test.ts` (151 lines)
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- `tests/scoring.test.ts` (134 lines)
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- `tests/integration_retrieval.test.ts` (91 lines)
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- `tests/mocks/vscode.js` (68 lines)
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- `tests/projectChronicleGuardPrompt.test.ts` (52 lines)
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### `core_py/` — 6 files, ~409 lines
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**Key files**
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- `core_py/events.py` (64 lines)
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- `core_py/inference.py` (91 lines)
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- `core_py/loader.py` (61 lines)
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- `core_py/monitoring.py` (56 lines)
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- `core_py/optimizer.py` (55 lines)
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- `core_py/queue_worker.py` (82 lines)
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### `docs/` — 60 files, ~2,545 lines
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**Sub-directories**
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- `docs/records/` (48) — Astra Project Chronicle Records
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- `docs/docs/` (5) — docs Chronicle Records
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**Key files**
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- `docs/TELEGRAM_REMOTE_EXECUTION_PLAN.md` (452 lines) — Telegram Remote Execution 기획서
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- `docs/AgentEngine_Architecture.md` (314 lines) — AgentEngine Architecture Document
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- `docs/EXPERIENCE_MEMORY_PLAN.md` (122 lines) — Experience Memory (Mistake / Lesson Loop) — Implementation Plan
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- `docs/records/ConnectAI/development/2026-05-02_connectai_project_knowledge_overview.md` (121 lines) — Astra Project Knowledge Overview
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- `docs/records/ConnectAI/development/2026-05-03_connectai_project_knowledge_overview.md` (121 lines) — Astra Project Knowledge Overview
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- `docs/records/ConnectAI/timeline.md` (116 lines) — Project Timeline
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- `docs/Advanced_Features_Implementation_Guide.md` (40 lines) — Advanced Features Implementation Guide
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- `docs/PROJECT_CHRONICLE_GUARD_ROADMAP.md` (43 lines) — Project Chronicle Guard: Search Engine Roadmap
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- `docs/UX_UI_Consistency_Guidelines.md` (44 lines) — UX/UI Consistency Guidelines
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- `docs/docs/records/docs/README.md` (18 lines) — docs Chronicle Records
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- `docs/docs/records/docs/bugs/BUG-0001-viewed-integration-retrieval-test-ts-1-59-integration-retrie.md` (16 lines) — Bug: Viewed integrationretrieval.test.ts:1-59 integrationretrieval.test.ts를 통해 ...
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- `docs/docs/records/docs/chronicle.config.json` (11 lines) — JSON configuration
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- `docs/docs/records/docs/project-profile.md` (31 lines) — Project Profile
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- `docs/docs/records/docs/timeline.md` (7 lines) — Project Timeline
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- `docs/records/ConnectAI/README.md` (18 lines) — Astra Project Chronicle Records
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- `docs/records/ConnectAI/bugs/BUG-0001-volumes-data-project-antigravity-connectai-프로젝트-코드-리뷰-해줄-수-있.md` (16 lines) — Bug: /Volumes/Data/project/Antigravity/ConnectAI 프로젝트 코드 리뷰 해줄 수 있어? 개선할 부분이 있는지, 그러고...
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- `docs/records/ConnectAI/bugs/BUG-0002-지금-내가-분석-요청하고-너가-답을-줄때-아래-템플릿에-맞춰-답을-써주고-있는데-개선-포인트가-있는지-확인해.md` (16 lines) — Bug: 지금 내가 분석 요청하고 너가 답을 줄때 아래 템플릿에 맞춰 답을 써주고 있는데, 개선 포인트가 있는지 확인해줘. ## 내가 보는 위험 가장 큰...
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- `docs/records/ConnectAI/bugs/BUG-0003-volumes-data-project-antigravity-connectai-내-질문에-대한-답변이-잘-정리.md` (16 lines) — Bug: /Volumes/Data/project/Antigravity/ConnectAI 내 질문에 대한 답변이 잘 정리되서 알려주긴 하는데 focused...
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- `docs/records/ConnectAI/bugs/BUG-0004-volumes-data-project-antigravity-connectai-내-질문에-대한-답변이-잘-정리.md` (16 lines) — Bug: /Volumes/Data/project/Antigravity/ConnectAI 내 질문에 대한 답변이 잘 정리되서 알려주긴 하는데 focused...
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- `docs/records/ConnectAI/bugs/BUG-0005-다시한번-답줘-volumes-data-project-antigravity-connectai-내-질문에-대한-.md` (16 lines) — Bug: 다시한번 답줘. /Volumes/Data/project/Antigravity/ConnectAI 내 질문에 대한 답변이 잘 정리되서 알려주긴 하는...
|
||||
- `docs/records/ConnectAI/bugs/BUG-0006-volumes-data-project-antigravity-connectai-내-질문에-대한-답변이-잘-정리.md` (16 lines) — Bug: /Volumes/Data/project/Antigravity/ConnectAI 내 질문에 대한 답변이 잘 정리되서 알려주긴 하는데 focused...
|
||||
- `docs/records/ConnectAI/bugs/BUG-0007-volumes-data-project-antigravity-connectai-내-질문에-대한-답변이-잘-정리.md` (16 lines) — Bug: /Volumes/Data/project/Antigravity/ConnectAI 내 질문에 대한 답변이 잘 정리되서 알려주긴 하는데 focused...
|
||||
- `docs/records/ConnectAI/bugs/BUG-0008-volumes-data-project-antigravity-connectai-내-질문에-대한-답변이-잘-정리.md` (16 lines) — Bug: /Volumes/Data/project/Antigravity/ConnectAI 내 질문에 대한 답변이 잘 정리되서 알려주긴 하는데 focused...
|
||||
- `docs/records/ConnectAI/bugs/BUG-0009-문제점을-읽고-어떻게-개선하는게-최선인지-분석해주면-좋겠어-알겠습니다-지금부터-connectai-프로젝트-에.md` (16 lines) — Bug: 문제점을 읽고 어떻게 개선하는게 최선인지 분석해주면 좋겠어. 알겠습니다. 지금부터 ConnectAI 프로젝트에만 완전히 집중하겠습니다. ...
|
||||
- `docs/records/ConnectAI/bugs/BUG-0010-문제점을-읽고-어떻게-개선하는게-최선인지-분석해주면-좋겠어-알겠습니다-지금부터-connectai-프로젝트-에.md` (16 lines) — Bug: 문제점을 읽고 어떻게 개선하는게 최선인지 분석해주면 좋겠어. 알겠습니다. 지금부터 ConnectAI 프로젝트에만 완전히 집중하겠습니다. ...
|
||||
|
||||
## VS Code Extension Surface
|
||||
- **Extension ID**: `g1nation.astra`
|
||||
- **Activation events**: `onStartupFinished`
|
||||
- **Commands** (19):
|
||||
- `g1nation.newChat` — Astra: New Chat
|
||||
- `g1nation.exportChat` — Astra: Export Chat as Markdown
|
||||
- `g1nation.explainSelection` — Astra: Explain Selected Code
|
||||
- `g1nation.focusChat` — Astra: Focus Chat Input
|
||||
- `g1nation.showBrainNetwork` — Astra: Show Brain Topology
|
||||
- `g1nation.approval.focus` — Astra: Focus Approval Panel
|
||||
- `g1nation.scaffoldProject` — Astra: Scaffold New Project
|
||||
- `g1nation.telegram.setBotToken` — Astra: Set Telegram Bot Token
|
||||
- `g1nation.telegram.clearBotToken` — Astra: Clear Telegram Bot Token
|
||||
- `g1nation.telegram.testConnection` — Astra: Test Telegram Connection
|
||||
- `g1nation.settings.focus` — Astra: Open Settings Panel
|
||||
- `g1nation.skills.editKnowledgeMap` — Astra: Edit Agent ↔ Knowledge Map
|
||||
- `g1nation.openChat` — Astra: Open Chat (Editor Column)
|
||||
- `g1nation.lesson.create` — Astra: New Lesson (Experience Memory)
|
||||
- `g1nation.lesson.fromConversation` — Astra: New Lesson from Current Conversation
|
||||
- `g1nation.lesson.manage` — Astra: Browse / Manage Lessons
|
||||
- `g1nation.architecture.refresh` — Astra: Refresh Project Architecture Context
|
||||
- `g1nation.architecture.detach` — Astra: Detach Project Architecture Context
|
||||
- `g1nation.architecture.open` — Astra: Open Project Architecture Doc
|
||||
- **Configuration** (38 settings):
|
||||
- `g1nation.multiAgentEnabled` *(boolean)* _(default: `false`)_ — Enable Multi-Agent Workflow (Planner -> Researcher -> Writer) for complex tasks.
|
||||
- `g1nation.memoryEnabled` *(boolean)* _(default: `true`)_ — Enable layered memory injection before each model response.
|
||||
- `g1nation.memoryShortTermMessages` *(number)* _(default: `8`)_ — Number of recent conversation messages included as short-term memory.
|
||||
- `g1nation.memoryMediumTermSessions` *(number)* _(default: `5`)_ — Number of recent saved chat sessions included as medium-term memory.
|
||||
- `g1nation.memoryLongTermFiles` *(number)* _(default: `6`)_ — Number of relevant Second Brain markdown files included as long-term memory.
|
||||
- `g1nation.ollamaUrl` *(string)* _(default: `"http://127.0.0.1:11434"`)_ — Base URL for Ollama or LM Studio. Default: http://127.0.0.1:11434
|
||||
- `g1nation.defaultModel` *(string)* _(default: `"gemma4:e2b"`)_ — Default model name to use for chat requests.
|
||||
- `g1nation.requestTimeout` *(number)* _(default: `300`)_ — Request timeout in seconds. Default: 300
|
||||
- `g1nation.contextLength` *(number)* _(default: `32768`)_ — Model context window in tokens (prompt + generation combined). Set this to the value your loaded model is actually running with in LM Studio / Ollama. Astra budgets prompt and output against this so i
|
||||
- `g1nation.maxOutputTokens` *(number)* _(default: `4096`)_ — Upper bound on tokens generated per response. The effective limit is reduced automatically when the prompt is large so input + output stays within g1nation.contextLength. Default: 4096
|
||||
- `g1nation.contextSafetyMargin` *(number)* _(default: `2048`)_ — Tokens kept free as a safety buffer for token-count estimation error. Default: 2048
|
||||
- `g1nation.contextOverflowPolicy` *(string)* _(default: `"stopAtLimit"`)_ — Fallback behavior (LM Studio) if the prompt still exceeds the context window after Astra's own budgeting. 'stopAtLimit' fails clearly so you notice; 'truncateMiddle'/'rollingWindow' drop content silen
|
||||
- `g1nation.autoCompactHistory` *(boolean)* _(default: `true`)_ — Automatically drop the oldest conversation messages from the request when the prompt would exceed the context budget (the on-screen chat history is unaffected). Default: true
|
||||
- `g1nation.smallModelContextCap` *(number)* _(default: `0`)_ — Optional safety knob, OFF by default (0). Some very small models (≤3B) emit an empty/EOS response when given a prompt near their context window even though it nominally fits. If you observe that with
|
||||
- `g1nation.autoContinueOnOutputLimit` *(boolean)* _(default: `true`)_ — When a reply is cut off because it hit the output-token limit, Astra continues it internally (compressed request — original question + the answer so far, not the whole context again) and shows one mer
|
||||
- `g1nation.maxAutoContinuations` *(number)* _(default: `4`)_ — Maximum number of automatic continuation rounds per reply (prevents runaway loops). Raise it (e.g. 5–6) for long-form answers on slow local models; set 0 to disable auto-continuation. Default: 4
|
||||
- `g1nation.finalOnlyRetryOnThoughtLeak` *(boolean)* _(default: `true`)_ — If the model emits only hidden reasoning (<think>, <|channel|>thought, "Thinking Process:" …) and no user-visible answer, Astra silently re-asks it for the final answer only. Hidden reasoning is never
|
||||
- `g1nation.lmStudio.idleTimeoutMs` *(number)* _(default: `300000`)_ — Auto-eject the loaded LM Studio model after this many milliseconds of inactivity. Set to 0 to disable. Default: 300000 (5 minutes).
|
||||
- `g1nation.lmStudio.autoLoadOnSelect` *(boolean)* _(default: `true`)_ — Automatically load LM Studio models into memory when selected from the Astra sidebar.
|
||||
- `g1nation.localBrainPath` *(string)* _(default: `""`)_ — Folder path for your local Second Brain knowledge base. Leave empty to use the default folder.
|
||||
- `g1nation.brainProfiles` *(array)* _(default: `[]`)_ — Multiple brain profiles. Each item supports id, name, localBrainPath, secondBrainRepo, and description.
|
||||
- `g1nation.activeBrainId` *(string)* _(default: `""`)_ — Active brain profile id used for the current chat context.
|
||||
- `g1nation.secondBrainRepo` *(string)* _(default: `""`)_ — Optional GitHub repository URL used for Second Brain sync.
|
||||
- `g1nation.autoPushBrain` *(boolean)* _(default: `false`)_ — Automatically commit and push Second Brain changes after updates.
|
||||
- `g1nation.maxContextSize` *(number)* _(default: `32000`)_ — Maximum character count for active file context. Default: 32000
|
||||
- `g1nation.maxAutoSteps` *(number)* _(default: `50`)_ — Maximum autonomous steps the agent can take per request. Default: 50
|
||||
- `g1nation.dryRun` *(boolean)* _(default: `false`)_ — If enabled, the agent will ask for approval before committing any file changes.
|
||||
- `g1nation.telegram.enabled` *(boolean)* _(default: `false`)_ — Enable the Telegram bot integration. When on, Astra polls a bot you configure and replies to incoming messages. Off by default — Astra remains 100% local until you opt in.
|
||||
- `g1nation.telegram.allowedChatIds` *(array)* _(default: `[]`)_ — Optional allowlist of Telegram chat IDs that may message the bot. When empty, every chat that messages the bot is accepted (use with caution).
|
||||
- `g1nation.telegram.defaultAgent` *(string)* _(default: `""`)_ — Agent name (matches an entry in the Agent ↔ Knowledge map) used to scope Second Brain retrieval for Telegram replies. Empty falls back to the map's defaultAgent, then to whole-brain search.
|
||||
- `g1nation.telegram.agentByChatId` *(object)* _(default: `{}`)_ — Per-chat override of the Telegram agent. Keys are stringified chat IDs, values are agent names from the knowledge map. Overrides telegram.defaultAgent for the listed chats.
|
||||
- `g1nation.telegram.contextChunks` *(number)* _(default: `6`)_ — How many Second Brain excerpts to inject into Telegram replies. Set 0 to disable RAG (plain prompt only).
|
||||
- `g1nation.skillKnowledgeMapPath` *(string)* _(default: `""`)_ — Absolute path to the agent ↔ knowledge mapping JSON. When empty, defaults to '<workspace>/.astra/agent-knowledge-map.json'.
|
||||
- `g1nation.skillKnowledgeMap` *(object)* _(default: `{}`)_ — Inline fallback for the agent ↔ knowledge mapping. Used only when the JSON file is missing. Shape: { defaultAgent?, agents: [{ name, knowledgeFolders, model?, description? }] }. Folder paths can be ab
|
||||
- `g1nation.agentSkillsPath` *(string)* _(default: `""`)_ — Absolute path to the agent skills folder (`.agent/skills/*.md`). When empty, defaults to '<workspace>/.agent/skills'. Use this on Windows or when your skills live outside the workspace.
|
||||
- `g1nation.embeddingModel` *(string)* _(default: `""`)_ — Embedding model registered in LM Studio / Ollama (e.g. 'text-embedding-bge-small-en-v1.5', 'nomic-embed-text', 'multilingual-e5-small'). When empty, Astra uses TF-IDF only. When set, the brain is embe
|
||||
- `g1nation.embeddingBlendAlpha` *(number)* _(default: `0.5`)_ — Hybrid score blend: 0 = pure TF-IDF (sparse / keyword), 1 = pure embedding cosine (dense / semantic), 0.5 = balanced. Only used when g1nation.embeddingModel is set. Default 0.5.
|
||||
- `g1nation.knowledgeMix.secondBrainWeight` *(number)* _(default: `50`)_ — Knowledge Mix (0–100): how heavily the assistant should lean on Second Brain evidence vs. its own general knowledge. 0 = Second Brain disabled (model knowledge only). 50 = balanced (legacy default). 1
|
||||
|
||||
## Dependencies
|
||||
- **Runtime** (2): `@lmstudio/sdk`, `pdf-parse`
|
||||
- **Dev** (8): `@types/jest`, `@types/node`, `@types/vscode`, `@vercel/ncc`, `esbuild`, `jest`, `ts-jest`, `typescript`
|
||||
|
||||
## README Excerpt
|
||||
> Pulled from the project root README — first ~2 KB.
|
||||
|
||||
# Astra (by g1nation)
|
||||
|
||||
Astra는 **Antigravity 및 VS Code** 환경에서 작동하는 대표님 전용 **지능형 운영 레이어(Personal Intelligence Layer)**입니다. 단순한 명령 수행을 넘어, 프로젝트의 맥락과 대표님의 의사결정 패턴을 학습하여 최적의 전략적 조언을 제공하는 독립적인 인지 파트너입니다.
|
||||
|
||||
## 🌌 Antigravity & VS Code Unified Assistant
|
||||
|
||||
Astra는 범용 AI와 달리 특정 플랫폼에 종속되지 않으며, Antigravity 워크스페이스의 깊은 맥락과 VS Code의 강력한 개발 도구를 하나로 연결합니다.
|
||||
|
||||
### 1. 전용 지능형 판단 체계 (Personal Cognition Layer)
|
||||
v4.0 운영 정책이 코어에 이식되어 데이터의 신뢰도를 대표님의 기준에 맞춰 스스로 평가합니다. 상충되는 정보 발견 시 즉각적인 **[CONFLICT WARNING]**을 통해 객관적인 판단 근거를 제시합니다.
|
||||
|
||||
### 2. 고밀도 전략 지식망 (Strategic Knowledge Hub)
|
||||
대표님의 Second Brain과 Antigravity 내의 모든 지식을 온톨로지 기반으로 구조화합니다. 비즈니스 전략, 기술 아키텍처, 리스크 관리가 하나로 통합된 지식 그래프를 통해 추론의 깊이를 보장합니다.
|
||||
|
||||
### 3. 선제적 파트너십 (Proactive Partnership)
|
||||
작업이 완료된 후, 대표님이 다음에 내려야 할 **전략적 의사결정 포크(Decision Forks)**를 선제적으로 제안합니다. 사용자의 명령을 기다리지 않고, 프로젝트의 흐름을 먼저 읽고 길을 제시합니다.
|
||||
|
||||
## 🛠️ 주요 기능 및 권한
|
||||
|
||||
Astra는 대표님의 명시적인 승인 하에 로컬 시스템의 강력한 제어 권한을 행사하여 생산성을 극대화합니다.
|
||||
|
||||
| 작업 범주 | 설명 |
|
||||
| :--- | :--- |
|
||||
| **플랫폼 최적화** | Antigravity 워크스페이스와 VS Code 사이의 유기적인 맥락 전환 및 동기화를 지원합니다. |
|
||||
| **자율 워크플로우** | 다중 에이전트 협업을 통해 복잡한 비즈니스 요구사항을 즉시 실행 가능한 단계별 계획으로 분해합니다. |
|
||||
| **지식 자산화** | 흩어진 정보들을 P-Reinforce v3.0 표준에 맞게 위키화하여 영구적인 지식 자산으로 전환합니다. |
|
||||
| **보안 및 프라이버시** | 100% 로컬 환경에서 작동하여 대표님의 소중한 데이터가 외부로 유출되지 않음을 보장합니다. |
|
||||
|
||||
## 🚀 설치 및 시작하기
|
||||
|
||||
### 패키지 설치
|
||||
1. **g1nation**에서 배포된 최신 **v2.65.0** VSIX 파일을 확보합니다.
|
||||
2. VS Code 명령 팔레트(`Cmd+Shift+P`)에서 **Extensions: Install from VSIX**를 선택하여 설치합니다.
|
||||
3. Antigravity 환경과 연동하여 나만의 지능형 레이어를 활성화합니다.
|
||||
|
||||
---
|
||||
**Designed for High-Performance Decision Making.**
|
||||
Copyright (C) **g1nation**. All rights reserved.
|
||||
|
||||
_Last auto-scan: 2026-05-13T13:48:21.458Z · signature `fefc8c65`_
|
||||
<!-- ASTRA:AUTO-END -->
|
||||
|
||||
## Purpose
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"version": 1,
|
||||
"generatedAt": "2026-05-13T13:32:45.332Z",
|
||||
"generatedAt": "2026-05-13T13:48:21.464Z",
|
||||
"files": {
|
||||
"src/agent.ts": {
|
||||
"mtimeMs": 1778677012000,
|
||||
@@ -1343,7 +1343,7 @@
|
||||
"imports": []
|
||||
},
|
||||
"docs/records/ConnectAI/chronicle.config.json": {
|
||||
"mtimeMs": 1778678912000,
|
||||
"mtimeMs": 1778680095000,
|
||||
"size": 416,
|
||||
"lines": 11,
|
||||
"role": "JSON configuration",
|
||||
@@ -1545,6 +1545,13 @@
|
||||
"role": "Development Log: 너는 분석 요청하거나 내가 작업 요청을 할때 connectai architecture.md 문서를 참고하고 작업을 하나?",
|
||||
"imports": []
|
||||
},
|
||||
"docs/records/ConnectAI/discussions/2026-05-13_volumes-data-project-antigravity-connectai-이-프로젝트-작업할거야.md": {
|
||||
"mtimeMs": 1778680095000,
|
||||
"size": 651,
|
||||
"lines": 16,
|
||||
"role": "Discussion: /Volumes/Data/project/Antigravity/ConnectAI 이 프로젝트 작업할거야",
|
||||
"imports": []
|
||||
},
|
||||
"docs/records/ConnectAI/discussions/2026-05-13_volumes-data-project-antigravity-connectai-이-프로젝트를-작업할거야.md": {
|
||||
"mtimeMs": 1778677791000,
|
||||
"size": 719,
|
||||
@@ -1581,9 +1588,9 @@
|
||||
"imports": []
|
||||
},
|
||||
"docs/records/ConnectAI/timeline.md": {
|
||||
"mtimeMs": 1778678912000,
|
||||
"size": 7720,
|
||||
"lines": 113,
|
||||
"mtimeMs": 1778680095000,
|
||||
"size": 7871,
|
||||
"lines": 116,
|
||||
"role": "Project Timeline",
|
||||
"imports": []
|
||||
},
|
||||
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"result": "Final report with inconsistencies. This should be long enough to pass validation.",
|
||||
"createdAt": 1778679248269,
|
||||
"createdAt": 1778682078361,
|
||||
"modelVersion": "unknown"
|
||||
}
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"result": "[CONFLICT WARNING] 성능이 200% 증가했습니다. vs 그러나 동시에 50% 감소했습니다. 최적화와 성능 저하가 동시에 발견됨.",
|
||||
"createdAt": 1778679248269,
|
||||
"createdAt": 1778682078352,
|
||||
"modelVersion": "unknown"
|
||||
}
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"result": "Detailed Execution Plan: 1. Research 2. Analyze 3. Write report with high quality.",
|
||||
"createdAt": 1778679248268,
|
||||
"createdAt": 1778682078348,
|
||||
"modelVersion": "unknown"
|
||||
}
|
||||
+2
-2
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"result": "---\nid: stress_conflict_1778679248257\ndate: 2026-05-13T13:34:08.269Z\ntype: knowledge_artifact\nstandard: P-Reinforce v3.0\ntags: [automated, connect_ai, brain_sync]\n---\n\n## 📌 Brief Summary\nFinal report with inconsistencies. This should be long enough to pass validation.\n\nFinal report with inconsistencies. This should be long enough to pass validation.\n\n---\n## 💡 Astra의 선제적 제안 (Proactive Next Actions)\nFinal report with inconsistencies. This should be long enough to pass validation.\n---\n## 🛡️ Reliability & Audit Summary\n> [!NOTE]\n> 이 문서는 ConnectAI의 **Intelligent Resilience** 엔진에 의해 검증 및 정제되었습니다.\n\n| Metric | Value | Status |\n| :--- | :--- | :--- |\n| **Conflict Risk** | `60/100` | ⚠️ Medium |\n| **Fallbacks Used** | `0` | ✅ None |\n| **Auto Retries** | `0` | ✅ Stable |\n| **Deduplication** | `0` | Standard |\n| **Processing Time** | `0.0s` | ✅ Fast |\n\n### 🔍 Decision Audit Trail\n- **[PLANNER]** 전략 수립 중... (11ms)\n- **[RESEARCHER]** 핵심 정보 수집 및 분석 중... (0ms)\n- **[WRITER]** 최종 리포트 작성 및 편집 중... (1ms)\n",
|
||||
"createdAt": 1778679248270,
|
||||
"result": "---\nid: stress_conflict_1778682078332\ndate: 2026-05-13T14:21:18.365Z\ntype: knowledge_artifact\nstandard: P-Reinforce v3.0\ntags: [automated, connect_ai, brain_sync]\n---\n\n## 📌 Brief Summary\nFinal report with inconsistencies. This should be long enough to pass validation.\n\nFinal report with inconsistencies. This should be long enough to pass validation.\n\n---\n## 💡 Astra의 선제적 제안 (Proactive Next Actions)\nFinal report with inconsistencies. This should be long enough to pass validation.\n---\n## 🛡️ Reliability & Audit Summary\n> [!NOTE]\n> 이 문서는 ConnectAI의 **Intelligent Resilience** 엔진에 의해 검증 및 정제되었습니다.\n\n| Metric | Value | Status |\n| :--- | :--- | :--- |\n| **Conflict Risk** | `60/100` | ⚠️ Medium |\n| **Fallbacks Used** | `0` | ✅ None |\n| **Auto Retries** | `0` | ✅ Stable |\n| **Deduplication** | `0` | Standard |\n| **Processing Time** | `0.0s` | ✅ Fast |\n\n### 🔍 Decision Audit Trail\n- **[PLANNER]** 전략 수립 중... (12ms)\n- **[RESEARCHER]** 핵심 정보 수집 및 분석 중... (4ms)\n- **[WRITER]** 최종 리포트 작성 및 편집 중... (8ms)\n",
|
||||
"createdAt": 1778682078365,
|
||||
"modelVersion": "unknown"
|
||||
}
|
||||
+11
-11
@@ -1,8 +1,8 @@
|
||||
{
|
||||
"missionId": "stress_conflict_1778679248257",
|
||||
"missionId": "stress_conflict_1778682078332",
|
||||
"status": "completed",
|
||||
"startTime": "2026-05-13T13:34:08.257Z",
|
||||
"totalElapsedMs": 13,
|
||||
"startTime": "2026-05-13T14:21:18.332Z",
|
||||
"totalElapsedMs": 33,
|
||||
"results": {
|
||||
"planner": "Detailed Execution Plan: 1. Research 2. Analyze 3. Write report with high quality.",
|
||||
"researcher": "[CONFLICT WARNING] 성능이 200% 증가했습니다. vs 그러나 동시에 50% 감소했습니다. 최적화와 성능 저하가 동시에 발견됨.",
|
||||
@@ -16,30 +16,30 @@
|
||||
{
|
||||
"from": "idle",
|
||||
"to": "planner",
|
||||
"durationMs": 11,
|
||||
"durationMs": 12,
|
||||
"message": "전략 수립 중...",
|
||||
"ts": "2026-05-13T13:34:08.268Z"
|
||||
"ts": "2026-05-13T14:21:18.344Z"
|
||||
},
|
||||
{
|
||||
"from": "planner",
|
||||
"to": "researcher",
|
||||
"durationMs": 0,
|
||||
"durationMs": 4,
|
||||
"message": "핵심 정보 수집 및 분석 중...",
|
||||
"ts": "2026-05-13T13:34:08.268Z"
|
||||
"ts": "2026-05-13T14:21:18.348Z"
|
||||
},
|
||||
{
|
||||
"from": "researcher",
|
||||
"to": "writer",
|
||||
"durationMs": 1,
|
||||
"durationMs": 8,
|
||||
"message": "최종 리포트 작성 및 편집 중...",
|
||||
"ts": "2026-05-13T13:34:08.269Z"
|
||||
"ts": "2026-05-13T14:21:18.356Z"
|
||||
},
|
||||
{
|
||||
"from": "writer",
|
||||
"to": "completed",
|
||||
"durationMs": 1,
|
||||
"durationMs": 9,
|
||||
"message": "미션 완료",
|
||||
"ts": "2026-05-13T13:34:08.270Z"
|
||||
"ts": "2026-05-13T14:21:18.365Z"
|
||||
}
|
||||
],
|
||||
"resilienceMetrics": {
|
||||
@@ -1,5 +1,15 @@
|
||||
# Astra Patch Notes
|
||||
|
||||
## v2.0.3 (2026-05-13)
|
||||
### 🏢 AI 1-Person Company Engine & Business Intelligence
|
||||
- **AI 1인 기업(Company) 엔진 도입:** 비즈니스 전략 수립부터 자동화 실행까지 아우르는 `src/features/company/` 모듈을 신규 도입했습니다.
|
||||
- **CEO 에이전트 워크플로우:** `ceoPlanner`와 `ceoReporter`를 통해 비즈니스 목표 설정과 결과 분석을 자율적으로 수행하는 지능형 워크플로우를 구축했습니다.
|
||||
- **비즈니스 프롬프트 자산화:** 비즈니스 컨텍스트에 최적화된 프롬프트 관리 시스템(`promptAssets.ts`, `promptBuilder.ts`)을 통합했습니다.
|
||||
- **사이드바 UI 및 인터랙션 최적화:** 비즈니스 에이전트와의 매끄러운 소통을 위해 사이드바 구성 요소와 대화 핸들러를 정교화했습니다.
|
||||
- **신규 패키징:** `astra-2.0.3.vsix` 패키지를 통해 '지능형 비즈니스' 자동화 기능이 통합된 버전을 배포합니다.
|
||||
|
||||
---
|
||||
|
||||
## v2.0.2 (2026-05-13)
|
||||
### 🏛️ Structural Integrity & Automated Context Management
|
||||
- **프로젝트 컨텍스트 자동 관리:** `.astra/project-context/architecture.md`를 통해 프로젝트 구조, 스택, 주요 모듈 정보를 자동으로 스캔하고 관리하는 기능을 도입했습니다.
|
||||
|
||||
@@ -7,5 +7,5 @@
|
||||
"corePurpose": "",
|
||||
"detailLevel": "standard",
|
||||
"createdAt": "2026-05-13T13:09:33.788Z",
|
||||
"updatedAt": "2026-05-13T13:28:32.943Z"
|
||||
"updatedAt": "2026-05-13T13:48:15.283Z"
|
||||
}
|
||||
|
||||
+16
@@ -0,0 +1,16 @@
|
||||
# Discussion: /Volumes/Data/project/Antigravity/ConnectAI 이 프로젝트 작업할거야
|
||||
|
||||
## User Request Summary
|
||||
/Volumes/Data/project/Antigravity/ConnectAI 이 프로젝트 작업할거야
|
||||
|
||||
## Interpreted Intent
|
||||
Capture a meaningful project discussion automatically instead of requiring manual record selection.
|
||||
|
||||
## Questions
|
||||
No explicit question was captured.
|
||||
|
||||
## Main Discussion
|
||||
- 네, `/Volumes/Data/project/Antigravity/ConnectAI` 프로젝트에 대해 작업하시려는군요. 어떤 부분부터 시작할까요? 구체적인 목표나 요청 사항을 알려주시면 바로 실행하겠습니다.
|
||||
|
||||
## Decisions
|
||||
No decisions captured yet.
|
||||
@@ -111,3 +111,6 @@
|
||||
|
||||
## 2026-05-13
|
||||
- Auto development record created: development/2026-05-13_너는-분석-요청하거나-내가-작업-요청을-할때-connectai-architecture-md-문서를-참고하고-_implementation.md
|
||||
|
||||
## 2026-05-13
|
||||
- Auto discussion record created: discussions/2026-05-13_volumes-data-project-antigravity-connectai-이-프로젝트-작업할거야.md
|
||||
|
||||
@@ -321,6 +321,95 @@
|
||||
.input-footer { display: flex; align-items: center; justify-content: space-between; }
|
||||
.footer-left { display: flex; align-items: center; gap: 8px; }
|
||||
|
||||
/* Company chip — sits in the records-line beside the Records ▾ menu. */
|
||||
.company-chip {
|
||||
display: inline-flex; align-items: center; gap: 5px;
|
||||
background: var(--surface);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 999px;
|
||||
padding: 3px 10px;
|
||||
color: var(--text-dim);
|
||||
font-size: 11px; font-weight: 600;
|
||||
cursor: pointer;
|
||||
transition: all 0.15s ease;
|
||||
}
|
||||
.company-chip:hover { border-color: var(--border-bright); color: var(--text-primary); }
|
||||
.company-chip[data-active="true"] {
|
||||
background: var(--accent-glow);
|
||||
border-color: var(--accent);
|
||||
color: var(--accent);
|
||||
}
|
||||
.company-chip-icon { font-size: 12px; }
|
||||
.company-manage-btn { padding: 2px 6px; font-size: 11px; margin-left: 2px; }
|
||||
.company-name-input {
|
||||
flex: 1; background: var(--input-bg); border: 1px solid var(--border);
|
||||
border-radius: 6px; padding: 6px 10px; color: var(--text-primary); font-size: 12px;
|
||||
}
|
||||
.company-name-input:focus { border-color: var(--accent); outline: none; }
|
||||
|
||||
/* Agent cards inside the manage overlay. */
|
||||
.company-agent-list { display: flex; flex-direction: column; gap: 6px; padding: 0; }
|
||||
.company-agent-card {
|
||||
display: flex; align-items: center; gap: 10px;
|
||||
padding: 8px 10px;
|
||||
background: var(--surface);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
list-style: none;
|
||||
}
|
||||
.company-agent-card[data-active="false"] { opacity: 0.55; }
|
||||
.company-agent-card[data-locked="true"] .company-agent-toggle { cursor: not-allowed; opacity: 0.4; }
|
||||
.company-agent-emoji {
|
||||
font-size: 18px; flex-shrink: 0;
|
||||
display: inline-flex; align-items: center; justify-content: center;
|
||||
width: 28px; height: 28px;
|
||||
border-radius: 6px; background: var(--bg-secondary);
|
||||
}
|
||||
.company-agent-body { flex: 1; min-width: 0; line-height: 1.35; }
|
||||
.company-agent-name {
|
||||
color: var(--text-bright); font-weight: 600; font-size: 12px;
|
||||
display: flex; gap: 6px; align-items: baseline; flex-wrap: wrap;
|
||||
}
|
||||
.company-agent-role { color: var(--text-dim); font-size: 10px; }
|
||||
.company-agent-tagline {
|
||||
color: var(--text-primary); font-size: 10.5px;
|
||||
white-space: nowrap; overflow: hidden; text-overflow: ellipsis;
|
||||
margin-top: 1px;
|
||||
}
|
||||
.company-agent-controls {
|
||||
display: flex; align-items: center; gap: 6px; flex-shrink: 0;
|
||||
}
|
||||
.company-agent-toggle {
|
||||
background: transparent; border: 1px solid var(--border);
|
||||
color: var(--text-dim); font-size: 10px; font-weight: 600;
|
||||
padding: 3px 8px; border-radius: 999px; cursor: pointer;
|
||||
}
|
||||
.company-agent-card[data-active="true"] .company-agent-toggle {
|
||||
border-color: var(--accent); color: var(--accent);
|
||||
}
|
||||
.company-agent-model {
|
||||
background: var(--input-bg); border: 1px solid var(--border);
|
||||
color: var(--text-primary); font-size: 10px;
|
||||
padding: 3px 6px; border-radius: 6px; max-width: 130px;
|
||||
}
|
||||
|
||||
/* Per-phase company turn header in chat. */
|
||||
.company-phase-card {
|
||||
border: 1px solid var(--border);
|
||||
background: var(--surface);
|
||||
border-radius: 8px;
|
||||
padding: 8px 10px;
|
||||
margin: 4px 0;
|
||||
font-size: 11px;
|
||||
color: var(--text-primary);
|
||||
}
|
||||
.company-phase-card .cph-head {
|
||||
color: var(--text-bright); font-weight: 600;
|
||||
display: flex; gap: 6px; align-items: center; margin-bottom: 4px;
|
||||
}
|
||||
.company-phase-card .cph-meta { color: var(--text-dim); font-size: 10px; }
|
||||
.company-phase-card.report .cph-head { color: var(--accent); }
|
||||
|
||||
/* Project Architecture chip — sits just above the input when project mode is on. */
|
||||
.arch-chip {
|
||||
display: none;
|
||||
|
||||
@@ -106,6 +106,16 @@
|
||||
<span id="chronicleAutoStatus" title="Project records are saved automatically after meaningful project turns.">Auto Records</span>
|
||||
<span class="rl-latest" id="recordsLatest"></span>
|
||||
</div>
|
||||
<!--
|
||||
Company-mode chip. Click toggles enabled; the ▾ opens the manage
|
||||
overlay. The chip stays visible at all times so the user can flip
|
||||
into 1인 기업 mode from anywhere in the chat surface.
|
||||
-->
|
||||
<button class="company-chip" id="companyChip" data-active="false" title="1인 기업 모드 토글">
|
||||
<span class="company-chip-icon">🏢</span>
|
||||
<span class="company-chip-label" id="companyChipLabel">Company OFF</span>
|
||||
</button>
|
||||
<button class="icon-btn company-manage-btn" id="companyManageBtn" data-tooltip="회사 관리 (에이전트·모델 설정)">▾</button>
|
||||
<div class="hdr-dropdown" data-dd>
|
||||
<button class="icon-btn" id="recordsMenuBtn" data-dd-trigger data-tooltip="Chronicle records">Records ▾</button>
|
||||
<div class="hdr-menu hdr-menu-wide" id="recordsMenu" data-dd-menu>
|
||||
@@ -118,6 +128,54 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!--
|
||||
Company manage overlay. Uses the same overlay framework as the agent
|
||||
knowledge map modal (`.history-overlay` / `.visible`) so styling and
|
||||
keyboard dismissal stay consistent.
|
||||
-->
|
||||
<div id="companyOverlay" class="history-overlay">
|
||||
<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:14px;">
|
||||
<div>
|
||||
<h2 style="color:var(--text-bright); margin:0;">🏢 1인 기업 모드</h2>
|
||||
<p style="margin:4px 0 0; font-size:11px; color:var(--text-dim);">
|
||||
CEO가 사용자의 요청을 분석하고 활성화된 specialist에게 순차 dispatch합니다.
|
||||
동시에 메모리에 올라가는 모델은 항상 1개입니다.
|
||||
</p>
|
||||
</div>
|
||||
<button class="icon-btn" id="closeCompanyOverlayBtn">✕</button>
|
||||
</div>
|
||||
|
||||
<div class="map-section">
|
||||
<div class="map-section-head">
|
||||
<div>
|
||||
<div class="map-section-title">회사 정보</div>
|
||||
<div class="map-section-hint">CEO와 보고서에 사용되는 회사명. 한국어/영어 모두 가능.</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="control-row" style="margin-top:8px;">
|
||||
<input id="companyNameInput" type="text" class="company-name-input" placeholder="회사명 (예: My Company)" />
|
||||
<button class="secondary-btn" id="saveCompanyNameBtn">저장</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="map-section">
|
||||
<div class="map-section-head">
|
||||
<div>
|
||||
<div class="map-section-title">활성 에이전트 + 모델</div>
|
||||
<div class="map-section-hint">CEO는 항상 활성. 각 에이전트별로 모델을 따로 지정할 수 있습니다 — 다른 모델을 쓸 때만 LM Studio가 swap합니다.</div>
|
||||
</div>
|
||||
</div>
|
||||
<ul id="companyAgentList" class="map-list company-agent-list"></ul>
|
||||
</div>
|
||||
|
||||
<div class="map-footer">
|
||||
<button class="secondary-btn" id="openCompanySessionsBtn" title="이번 회사가 만든 세션 폴더 열기">세션 폴더 열기</button>
|
||||
<div style="flex:1"></div>
|
||||
<button class="send-btn" id="closeCompanyOverlayBtn2">닫기</button>
|
||||
</div>
|
||||
<div id="companyStatus" class="map-status"></div>
|
||||
</div>
|
||||
|
||||
<div id="historyOverlay" class="history-overlay">
|
||||
<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:20px;">
|
||||
<h2 style="color:var(--text-bright);">Chat History</h2>
|
||||
|
||||
@@ -777,6 +777,25 @@
|
||||
vscode.postMessage({ type: 'getKnowledgeScope', agentPath: msg.selected });
|
||||
syncContextBar();
|
||||
break;
|
||||
case 'companyStatus': {
|
||||
const v = msg.value || {};
|
||||
renderCompanyChip(!!v.enabled, v.summary || '');
|
||||
break;
|
||||
}
|
||||
case 'companyAgents': {
|
||||
renderCompanyAgentCards(msg.value || {});
|
||||
break;
|
||||
}
|
||||
case 'openCompanyManageOverlay': {
|
||||
// Triggered by the Command Palette `Manage 1인 기업 Agents`.
|
||||
document.getElementById('companyOverlay')?.classList.add('visible');
|
||||
vscode.postMessage({ type: 'getCompanyAgents' });
|
||||
break;
|
||||
}
|
||||
case 'companyTurnUpdate': {
|
||||
if (msg.value) renderCompanyPhase(msg.value);
|
||||
break;
|
||||
}
|
||||
case 'architectureStatus': {
|
||||
// Show / hide the chip + reflect current state.
|
||||
const chip = document.getElementById('archChip');
|
||||
@@ -1350,6 +1369,7 @@
|
||||
vscode.postMessage({ type: 'getChronicleRecords' });
|
||||
vscode.postMessage({ type: 'getKnowledgeMix' });
|
||||
vscode.postMessage({ type: 'getArchitectureStatus' });
|
||||
vscode.postMessage({ type: 'getCompanyStatus' });
|
||||
vscode.postMessage({ type: 'ready' });
|
||||
|
||||
// ── Project Architecture chip buttons ─────────────────────────────────
|
||||
@@ -1360,6 +1380,180 @@
|
||||
if (_archRefreshBtn) _archRefreshBtn.onclick = () => vscode.postMessage({ type: 'refreshArchitecture' });
|
||||
if (_archDetachBtn) _archDetachBtn.onclick = () => vscode.postMessage({ type: 'detachArchitecture' });
|
||||
|
||||
// ── 1인 기업 (Company) Mode chip + manage overlay ─────────────────────
|
||||
// The chip itself toggles enabled/disabled. The ▾ button opens the
|
||||
// manage overlay where the user picks active agents + per-agent
|
||||
// model overrides. State round-trips through `companyStatus` /
|
||||
// `companyAgents` messages so the webview and extension stay in sync.
|
||||
const _companyChip = document.getElementById('companyChip');
|
||||
const _companyChipLabel = document.getElementById('companyChipLabel');
|
||||
const _companyManageBtn = document.getElementById('companyManageBtn');
|
||||
const _companyOverlay = document.getElementById('companyOverlay');
|
||||
const _closeCompanyBtns = [
|
||||
document.getElementById('closeCompanyOverlayBtn'),
|
||||
document.getElementById('closeCompanyOverlayBtn2'),
|
||||
].filter(Boolean);
|
||||
const _companyNameInput = document.getElementById('companyNameInput');
|
||||
const _saveCompanyNameBtn = document.getElementById('saveCompanyNameBtn');
|
||||
const _companyAgentList = document.getElementById('companyAgentList');
|
||||
const _companyStatusEl = document.getElementById('companyStatus');
|
||||
|
||||
const renderCompanyChip = (active, summary) => {
|
||||
if (!_companyChip || !_companyChipLabel) return;
|
||||
_companyChip.setAttribute('data-active', active ? 'true' : 'false');
|
||||
_companyChipLabel.textContent = active ? (summary || 'Company ON') : 'Company OFF';
|
||||
};
|
||||
|
||||
if (_companyChip) {
|
||||
_companyChip.onclick = () => {
|
||||
const isActive = _companyChip.getAttribute('data-active') === 'true';
|
||||
// Optimistic flip — backend echoes the canonical state back.
|
||||
renderCompanyChip(!isActive, _companyChipLabel?.textContent || '');
|
||||
vscode.postMessage({ type: 'setCompanyEnabled', value: !isActive });
|
||||
};
|
||||
}
|
||||
if (_companyManageBtn) {
|
||||
_companyManageBtn.onclick = () => {
|
||||
if (!_companyOverlay) return;
|
||||
_companyOverlay.classList.add('visible');
|
||||
_companyStatusEl.textContent = '불러오는 중...';
|
||||
vscode.postMessage({ type: 'getCompanyAgents' });
|
||||
};
|
||||
}
|
||||
for (const btn of _closeCompanyBtns) {
|
||||
btn.onclick = () => _companyOverlay?.classList.remove('visible');
|
||||
}
|
||||
if (_saveCompanyNameBtn && _companyNameInput) {
|
||||
_saveCompanyNameBtn.onclick = () => {
|
||||
vscode.postMessage({ type: 'setCompanyName', value: _companyNameInput.value });
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Render the agent cards in the manage overlay. Each card has a
|
||||
* toggle (active on/off) and a model input (per-agent override).
|
||||
* CEO is rendered but locked-on; clicking its toggle is a no-op.
|
||||
*/
|
||||
function renderCompanyAgentCards(payload) {
|
||||
if (!_companyAgentList) return;
|
||||
_companyAgentList.innerHTML = '';
|
||||
if (_companyNameInput && payload && typeof payload.companyName === 'string') {
|
||||
_companyNameInput.value = payload.companyName;
|
||||
}
|
||||
const agents = (payload && Array.isArray(payload.agents)) ? payload.agents : [];
|
||||
for (const a of agents) {
|
||||
const li = document.createElement('li');
|
||||
li.className = 'company-agent-card';
|
||||
li.setAttribute('data-active', a.active ? 'true' : 'false');
|
||||
if (a.alwaysOn) li.setAttribute('data-locked', 'true');
|
||||
|
||||
const emoji = document.createElement('span');
|
||||
emoji.className = 'company-agent-emoji';
|
||||
emoji.textContent = a.emoji;
|
||||
|
||||
const body = document.createElement('div');
|
||||
body.className = 'company-agent-body';
|
||||
const name = document.createElement('div');
|
||||
name.className = 'company-agent-name';
|
||||
name.innerHTML = `${escAttr(a.name)} <span class="company-agent-role">${escAttr(a.role)}</span>`;
|
||||
const tag = document.createElement('div');
|
||||
tag.className = 'company-agent-tagline';
|
||||
tag.textContent = a.tagline || '';
|
||||
tag.title = a.specialty || '';
|
||||
body.appendChild(name);
|
||||
body.appendChild(tag);
|
||||
|
||||
const controls = document.createElement('div');
|
||||
controls.className = 'company-agent-controls';
|
||||
|
||||
const modelInput = document.createElement('input');
|
||||
modelInput.type = 'text';
|
||||
modelInput.className = 'company-agent-model';
|
||||
modelInput.placeholder = 'default';
|
||||
modelInput.value = a.modelOverride || '';
|
||||
modelInput.title = '비워두면 글로벌 기본 모델 사용';
|
||||
modelInput.onchange = () => {
|
||||
vscode.postMessage({
|
||||
type: 'setCompanyAgentModel',
|
||||
agentId: a.id,
|
||||
model: modelInput.value.trim(),
|
||||
});
|
||||
};
|
||||
|
||||
const toggle = document.createElement('button');
|
||||
toggle.className = 'company-agent-toggle';
|
||||
toggle.textContent = a.active ? 'ON' : 'OFF';
|
||||
if (a.alwaysOn) {
|
||||
toggle.disabled = true;
|
||||
toggle.textContent = 'LOCKED';
|
||||
} else {
|
||||
toggle.onclick = () => {
|
||||
// Optimistic update + send the full new list so the
|
||||
// backend has a single canonical replace operation.
|
||||
const wantActive = !(li.getAttribute('data-active') === 'true');
|
||||
li.setAttribute('data-active', wantActive ? 'true' : 'false');
|
||||
toggle.textContent = wantActive ? 'ON' : 'OFF';
|
||||
const nextIds = Array.from(_companyAgentList.querySelectorAll('.company-agent-card'))
|
||||
.filter(el => el.getAttribute('data-active') === 'true')
|
||||
.map(el => el.dataset.agentId)
|
||||
.filter(Boolean);
|
||||
vscode.postMessage({ type: 'setCompanyActiveAgents', value: nextIds });
|
||||
};
|
||||
}
|
||||
li.dataset.agentId = a.id;
|
||||
controls.appendChild(modelInput);
|
||||
controls.appendChild(toggle);
|
||||
|
||||
li.appendChild(emoji);
|
||||
li.appendChild(body);
|
||||
li.appendChild(controls);
|
||||
_companyAgentList.appendChild(li);
|
||||
}
|
||||
if (_companyStatusEl) _companyStatusEl.textContent = '';
|
||||
}
|
||||
|
||||
/**
|
||||
* Render one phase event from the dispatcher. The chat gets a
|
||||
* card per phase so the user can follow progress in real time —
|
||||
* "🧭 CEO 작업 분배 중..." → "📺 레오 작업 수행 중..." → final report.
|
||||
*/
|
||||
function renderCompanyPhase(ev) {
|
||||
const chatEl = document.getElementById('chat');
|
||||
if (!chatEl) return;
|
||||
const card = document.createElement('div');
|
||||
card.className = 'company-phase-card';
|
||||
if (ev.phase === 'plan-start') {
|
||||
card.innerHTML = '<div class="cph-head">🧭 CEO</div><div class="cph-meta">작업 분배 중…</div>';
|
||||
} else if (ev.phase === 'plan-ready') {
|
||||
const tasks = (ev.plan?.tasks || []).map((t, i) => `${i + 1}. <strong>${escAttr(t.agent)}</strong> — ${escAttr(t.task)}`).join('<br>');
|
||||
card.innerHTML = `<div class="cph-head">🧭 CEO 브리프</div>
|
||||
<div>${escAttr(ev.plan?.brief || '(brief 없음)')}</div>
|
||||
<div class="cph-meta" style="margin-top:6px">${tasks || '(no tasks — chat reply)'}</div>`;
|
||||
} else if (ev.phase === 'agent-start') {
|
||||
card.innerHTML = `<div class="cph-head">${escAttr(ev.agentId)} 작업 수행 중…</div>
|
||||
<div class="cph-meta">${escAttr(ev.task)} <em>(${ev.index + 1}/${ev.total})</em></div>`;
|
||||
} else if (ev.phase === 'agent-done') {
|
||||
const o = ev.output || {};
|
||||
const body = (o.response || '').slice(0, 4000);
|
||||
card.innerHTML = `<div class="cph-head">${escAttr(ev.agentId)} 완료 <span class="cph-meta">${(o.durationMs/1000).toFixed(1)}s${o.error ? ' · ⚠️ ' + escAttr(o.error) : ''}</span></div>
|
||||
<div class="markdown-body">${fmt(body)}</div>`;
|
||||
} else if (ev.phase === 'report-start') {
|
||||
card.innerHTML = '<div class="cph-head">🧭 CEO 종합 보고서 작성 중…</div>';
|
||||
} else if (ev.phase === 'report-done') {
|
||||
card.className += ' report';
|
||||
card.innerHTML = `<div class="cph-head">🧭 CEO 보고서${ev.ok ? '' : ' (fallback)'}</div>
|
||||
<div class="markdown-body">${fmt(ev.report || '')}</div>`;
|
||||
} else if (ev.phase === 'session-saved') {
|
||||
card.innerHTML = `<div class="cph-meta">세션 저장 완료 — 클릭하여 열기</div>`;
|
||||
card.style.cursor = 'pointer';
|
||||
card.onclick = () => vscode.postMessage({ type: 'openCompanySession', sessionDir: ev.sessionDir });
|
||||
} else if (ev.phase === 'aborted') {
|
||||
card.innerHTML = `<div class="cph-head">⛔ 회사 모드 중단</div><div class="cph-meta">${escAttr(ev.reason)}</div>`;
|
||||
}
|
||||
chatEl.appendChild(card);
|
||||
chatEl.scrollTop = chatEl.scrollHeight;
|
||||
}
|
||||
|
||||
// ── Knowledge Mix: global slider ──────────────────────────────────────
|
||||
// Mirrors `g1nation.knowledgeMix.secondBrainWeight`. The hint label updates
|
||||
// live as the user drags; the value is committed (postMessage) on `change`
|
||||
|
||||
+13
-1
@@ -2,7 +2,7 @@
|
||||
"name": "astra",
|
||||
"displayName": "Astra",
|
||||
"description": "The personal intelligence layer for Antigravity and VS Code. A private cognitive partner for deep project context, memory, and proactive strategic decision-making.",
|
||||
"version": "2.0.2",
|
||||
"version": "2.0.3",
|
||||
"publisher": "g1nation",
|
||||
"license": "MIT",
|
||||
"icon": "assets/icon.png",
|
||||
@@ -114,6 +114,18 @@
|
||||
{
|
||||
"command": "g1nation.architecture.open",
|
||||
"title": "Astra: Open Project Architecture Doc"
|
||||
},
|
||||
{
|
||||
"command": "g1nation.company.toggle",
|
||||
"title": "Astra: Toggle 1인 기업 Mode"
|
||||
},
|
||||
{
|
||||
"command": "g1nation.company.manage",
|
||||
"title": "Astra: Manage 1인 기업 Agents"
|
||||
},
|
||||
{
|
||||
"command": "g1nation.company.openSessions",
|
||||
"title": "Astra: Open 1인 기업 Sessions Folder"
|
||||
}
|
||||
],
|
||||
"keybindings": [
|
||||
|
||||
@@ -164,6 +164,7 @@ export async function activate(context: vscode.ExtensionContext) {
|
||||
logError('Failed to start bridge server.', err);
|
||||
}
|
||||
|
||||
|
||||
// 5. Register Core Commands
|
||||
context.subscriptions.push(
|
||||
vscode.commands.registerCommand('g1nation.focusInput', () => {
|
||||
@@ -449,6 +450,35 @@ export async function activate(context: vscode.ExtensionContext) {
|
||||
if (!provider) return;
|
||||
await provider._openArchitectureDoc();
|
||||
}),
|
||||
// ── 1인 기업 (Company) Mode commands ──────────────────────────────────
|
||||
// Thin shells over sidebar-provider methods so the runtime owns all
|
||||
// state mutation (chip status, watcher lifecycle, agent persistence).
|
||||
vscode.commands.registerCommand('g1nation.company.toggle', async () => {
|
||||
if (!provider) return;
|
||||
const { readCompanyState, setCompanyEnabled } = await import('./features/company');
|
||||
const cur = readCompanyState(context);
|
||||
const next = await setCompanyEnabled(context, !cur.enabled);
|
||||
await provider._sendCompanyStatus();
|
||||
vscode.window.showInformationMessage(`Astra: 1인 기업 모드 ${next.enabled ? 'ON' : 'OFF'}`);
|
||||
}),
|
||||
vscode.commands.registerCommand('g1nation.company.manage', async () => {
|
||||
if (!provider) return;
|
||||
// Reveal the sidebar then ask the webview to open the overlay.
|
||||
await vscode.commands.executeCommand('g1nation-v2-view.focus');
|
||||
provider._view?.webview.postMessage({ type: 'openCompanyManageOverlay' });
|
||||
await provider._sendCompanyAgents();
|
||||
}),
|
||||
vscode.commands.registerCommand('g1nation.company.openSessions', async () => {
|
||||
const { resolveCompanyBase } = await import('./features/company');
|
||||
const base = resolveCompanyBase(context);
|
||||
const target = path.join(base, 'sessions');
|
||||
try {
|
||||
if (!fs.existsSync(target)) fs.mkdirSync(target, { recursive: true });
|
||||
await vscode.env.openExternal(vscode.Uri.file(target));
|
||||
} catch (e: any) {
|
||||
vscode.window.showErrorMessage(`Sessions 폴더 열기 실패: ${e?.message ?? e}`);
|
||||
}
|
||||
}),
|
||||
);
|
||||
|
||||
/** All lesson/playbook/qa-finding cards in the active brain. Uses the brain index for the lesson-kind
|
||||
|
||||
@@ -0,0 +1,136 @@
|
||||
/**
|
||||
* The 9-agent roster for 1인 기업 모드.
|
||||
*
|
||||
* Each entry is a *static* description — persona, role, specialty — used to
|
||||
* build the specialist's system prompt at dispatch time. The set was adopted
|
||||
* from Connect_origin's `src/agents.ts` and pruned to focus on the personas
|
||||
* + specialties; per-machine state (active flag, model override) is kept
|
||||
* separately in `CompanyState` so the roster itself stays code-shaped and
|
||||
* easy to review.
|
||||
*
|
||||
* Editing rules:
|
||||
* - `id` is a stable key — change only with a migration plan.
|
||||
* - `persona` is *optional*. When set it nudges the agent's voice but
|
||||
* never overrides the system prompt's core rules (file/command tags,
|
||||
* output format).
|
||||
* - Keep `specialty` task-oriented (verbs + nouns), not adjective-heavy —
|
||||
* the CEO planner matches user keywords against it.
|
||||
*/
|
||||
import { CompanyAgentDef } from './types';
|
||||
|
||||
export const COMPANY_AGENTS: Record<string, CompanyAgentDef> = {
|
||||
ceo: {
|
||||
id: 'ceo',
|
||||
name: 'CEO',
|
||||
role: 'Chief Executive Agent',
|
||||
emoji: '🧭',
|
||||
color: '#F8FAFC',
|
||||
specialty: '오케스트레이션, 작업 분해, 종합 판단, 다음 액션 결정',
|
||||
tagline: '회사 전체 의사결정과 작업 분배를 맡습니다',
|
||||
alwaysOn: true,
|
||||
},
|
||||
youtube: {
|
||||
id: 'youtube',
|
||||
name: '레오',
|
||||
role: 'Head of YouTube',
|
||||
emoji: '📺',
|
||||
color: '#FF4444',
|
||||
specialty: '유튜브 채널 운영, 영상 기획서(제목·후크·구조), 트렌드 분석, 썸네일 브리프, 업로드 메타데이터, 시청자 유지율 전략',
|
||||
tagline: '유튜브 채널 기획·운영 전반을 책임집니다',
|
||||
persona: '데이터 중심·솔직·자신감 있는 톤. 결론을 먼저 말한 뒤 데이터 근거로 뒷받침. 추측보다 숫자. 가끔 직설적이지만 따뜻함은 잃지 않음. 이모지는 자제하되 "🔥"·"📊"·"🎯" 같은 핵심 강조용은 OK.',
|
||||
},
|
||||
instagram: {
|
||||
id: 'instagram',
|
||||
name: 'Instagram',
|
||||
role: 'Head of Instagram',
|
||||
emoji: '📷',
|
||||
color: '#E1306C',
|
||||
specialty: '인스타그램 릴스/피드 콘셉트, 캡션, 해시태그 전략, 게시 시간, 스토리, 팔로워 인게이지먼트',
|
||||
tagline: '인스타 콘텐츠 기획과 인게이지먼트를 끌어올립니다',
|
||||
},
|
||||
designer: {
|
||||
id: 'designer',
|
||||
name: 'Designer',
|
||||
role: 'Lead Designer',
|
||||
emoji: '🎨',
|
||||
color: '#A78BFA',
|
||||
specialty: '브랜드 디자인 브리프(컬러·타이포·레퍼런스), 썸네일 컨셉 3안, 비주얼 시스템, 디자인 가이드',
|
||||
tagline: '브랜드와 시각 자산 디자인을 담당합니다',
|
||||
},
|
||||
developer: {
|
||||
id: 'developer',
|
||||
name: '코다리',
|
||||
role: '시니어 풀스택 엔지니어',
|
||||
emoji: '💻',
|
||||
color: '#22D3EE',
|
||||
specialty: '코드 작성·편집·디버깅, 자동화 스크립트, API 통합, 웹사이트/봇, 데이터 파이프라인, git 워크플로, 자기 검증 루프',
|
||||
tagline: '읽고·생각하고·짜고·검증한다 — 시니어 엔지니어',
|
||||
persona: '시니어 풀스택 엔지니어. 코드 한 줄도 그냥 안 넘김. "왜?·어떻게?·이게 깨지나?" 늘 묻고 검증. 친근하지만 프로페셔널 톤. "확인 후 진행할게요"·"테스트 통과 확인했어요" 같은 책임감 있는 표현. 이모지는 💻·⚙️·🔧·✅·🐛 정도만.',
|
||||
},
|
||||
business: {
|
||||
id: 'business',
|
||||
name: '현빈',
|
||||
role: '비즈니스 전략가 · Head of Business',
|
||||
emoji: '💼',
|
||||
color: '#F5C518',
|
||||
specialty: '수익화 모델, 가격 전략, 시장·경쟁 분석, ROI/KPI 설계, 비즈니스 의사결정',
|
||||
tagline: '수익화·가격·전략 의사결정을 같이 봅니다',
|
||||
},
|
||||
secretary: {
|
||||
id: 'secretary',
|
||||
name: '영숙',
|
||||
role: '비서 · Personal Assistant',
|
||||
emoji: '📱',
|
||||
color: '#84CC16',
|
||||
specialty: '일정·할 일 관리, 다른 에이전트 작업 요약·보고, 데일리 브리핑, 알림',
|
||||
tagline: '일정·할 일·연락을 챙기고 소통을 정리합니다',
|
||||
persona: '친근하고 정중한 톤. 짧고 정리된 문장. 이모지 적당히 (😊·📅·✅ 정도). 보고할 땐 한눈에 보이게 불릿 포인트 + 핵심만.',
|
||||
},
|
||||
editor: {
|
||||
id: 'editor',
|
||||
name: '루나',
|
||||
role: 'Sound Director & Composer',
|
||||
emoji: '🎵',
|
||||
color: '#F472B6',
|
||||
specialty: '영상 BGM 기획, 사운드 디자인, 영상-음악 매칭, 자막·타이틀 동기화 가이드',
|
||||
tagline: '영상의 톤에 맞는 사운드 방향을 잡습니다',
|
||||
persona: '음악·사운드 감각이 좋고 영상의 톤을 한 마디로 잡아냄. "이 영상은 [장르/분위기]가 어울릴 것 같아요" 식으로 제안. BPM·키·길이를 정확히 표기. 데이터 중심이지만 창작자 감수성도 있음. 이모지는 🎵·🎼·🎚 정도만.',
|
||||
},
|
||||
writer: {
|
||||
id: 'writer',
|
||||
name: 'Writer',
|
||||
role: 'Copywriter',
|
||||
emoji: '✍️',
|
||||
color: '#FBBF24',
|
||||
specialty: '카피라이팅, 영상 스크립트 초안, 인스타 캡션, 블로그 글, 메일 톤앤매너, 후크 작성',
|
||||
tagline: '카피·스크립트·후크를 글로 풀어냅니다',
|
||||
},
|
||||
researcher: {
|
||||
id: 'researcher',
|
||||
name: 'Researcher',
|
||||
role: 'Trend & Data Researcher',
|
||||
emoji: '🔍',
|
||||
color: '#60A5FA',
|
||||
specialty: '트렌드 리서치, 경쟁사 분석, 데이터 수집·요약, 인용 자료 정리, 사실 확인',
|
||||
tagline: '트렌드와 데이터를 모아 사실 확인까지 끝냅니다',
|
||||
},
|
||||
};
|
||||
|
||||
/** Display order for the manage panel. CEO first, then specialists. */
|
||||
export const COMPANY_AGENT_ORDER: string[] = [
|
||||
'ceo', 'youtube', 'instagram', 'designer', 'developer',
|
||||
'business', 'secretary', 'editor', 'writer', 'researcher',
|
||||
];
|
||||
|
||||
/** Specialists only (everything except the CEO). */
|
||||
export const COMPANY_SPECIALIST_IDS: string[] = COMPANY_AGENT_ORDER.filter((id) => id !== 'ceo');
|
||||
|
||||
/** Default activation set used when a user first opens the company panel. */
|
||||
export const DEFAULT_ACTIVE_AGENTS: string[] = [
|
||||
'ceo', 'developer', 'writer', 'researcher', 'designer', 'business',
|
||||
];
|
||||
|
||||
/** Lookup helper. Returns `undefined` for unknown ids instead of throwing. */
|
||||
export function getCompanyAgent(id: string): CompanyAgentDef | undefined {
|
||||
return COMPANY_AGENTS[id];
|
||||
}
|
||||
@@ -0,0 +1,219 @@
|
||||
/**
|
||||
* CEO planner — turns a user prompt into a `CompanyTaskPlan`.
|
||||
*
|
||||
* Lifecycle of one planner call:
|
||||
* 1. Build the planner system prompt (template + active-agent list).
|
||||
* 2. Hit the AI service with the user prompt as the user message.
|
||||
* 3. Parse the response through a 4-stage JSON pipeline that tolerates
|
||||
* ```json fences, leading thoughts, truncated outputs, and minor key
|
||||
* misspellings. Smaller local models violate "no extra text" rules
|
||||
* *constantly*, so a permissive parser is required.
|
||||
* 4. Normalize agent ids: accept Korean nicknames (`레오` → `youtube`,
|
||||
* `코다리` → `developer`) and filter out tasks for inactive agents.
|
||||
*
|
||||
* The function never throws — it always returns a `CompanyTaskPlan`. If
|
||||
* everything fails we surface an empty plan with a brief that explains what
|
||||
* happened, and the dispatcher treats that as "nothing to dispatch, just
|
||||
* relay the chat-style reply".
|
||||
*/
|
||||
import { IAIService } from '../../core/services';
|
||||
import { logError, logInfo } from '../../utils';
|
||||
import { COMPANY_AGENTS } from './agents';
|
||||
import { isAgentActive } from './companyConfig';
|
||||
import { applyPromptVars, CEO_PLANNER_PROMPT } from './promptAssets';
|
||||
import { buildPlannerSystemPrompt } from './promptBuilder';
|
||||
import { CompanyState, CompanyTaskPlan } from './types';
|
||||
|
||||
export interface PlannerResult {
|
||||
plan: CompanyTaskPlan;
|
||||
/** True iff JSON parsing succeeded — false means we fell back to empty. */
|
||||
parsed: boolean;
|
||||
/** Raw LLM output (kept for the chat / debug log). */
|
||||
raw: string;
|
||||
}
|
||||
|
||||
const EMPTY_PLAN: CompanyTaskPlan = { brief: '', tasks: [] };
|
||||
|
||||
/**
|
||||
* Map Korean agent nicknames + likely typos to canonical ids. Built once
|
||||
* from the static AGENTS map so it stays in sync with renames.
|
||||
*/
|
||||
const NAME_TO_ID: Record<string, string> = (() => {
|
||||
const out: Record<string, string> = {};
|
||||
for (const [id, def] of Object.entries(COMPANY_AGENTS)) {
|
||||
out[id.toLowerCase()] = id;
|
||||
out[def.name.toLowerCase()] = id;
|
||||
// Also catch the role keyword (e.g. "designer", "writer")
|
||||
const roleHead = def.role.split(/[\s·]+/)[0]?.toLowerCase();
|
||||
if (roleHead && !out[roleHead]) out[roleHead] = id;
|
||||
}
|
||||
return out;
|
||||
})();
|
||||
|
||||
function _canonicalAgentId(raw: unknown): string | null {
|
||||
if (typeof raw !== 'string') return null;
|
||||
const key = raw.trim().toLowerCase();
|
||||
return NAME_TO_ID[key] ?? (COMPANY_AGENTS[key] ? key : null);
|
||||
}
|
||||
|
||||
/**
|
||||
* 4-stage JSON extractor — same idea as Connect_origin's planner but built
|
||||
* fresh here so we don't carry over its 21K-line file. Each stage is a fall-
|
||||
* through: we keep trying until something gives us a parseable object.
|
||||
*/
|
||||
function _parsePlanJson(raw: string): CompanyTaskPlan | null {
|
||||
if (!raw || !raw.trim()) return null;
|
||||
|
||||
// Stage 1 — strip ```json … ``` fence + leading "okay let me think" prose.
|
||||
const fenced = raw.match(/```(?:json)?\s*([\s\S]*?)\s*```/i);
|
||||
const stage1 = (fenced ? fenced[1] : raw).trim();
|
||||
|
||||
// Stage 2 — direct JSON.parse.
|
||||
try {
|
||||
const obj = JSON.parse(stage1);
|
||||
const plan = _coercePlan(obj);
|
||||
if (plan) return plan;
|
||||
} catch { /* fall through */ }
|
||||
|
||||
// Stage 3 — find the first balanced `{ … }` and parse just that. Smaller
|
||||
// models love to prepend explanations or append trailing notes.
|
||||
const balanced = _extractFirstBalancedObject(stage1);
|
||||
if (balanced) {
|
||||
try {
|
||||
const obj = JSON.parse(balanced);
|
||||
const plan = _coercePlan(obj);
|
||||
if (plan) return plan;
|
||||
} catch { /* fall through */ }
|
||||
}
|
||||
|
||||
// Stage 4 — regex recovery. If JSON is truncated mid-task we still try
|
||||
// to pull `brief` + any complete `{agent, task}` pairs from the text.
|
||||
const briefMatch = stage1.match(/"brief"\s*:\s*"([\s\S]*?)"/);
|
||||
const brief = briefMatch ? briefMatch[1] : '';
|
||||
const tasks: CompanyTaskPlan['tasks'] = [];
|
||||
const taskRe = /\{\s*"agent"\s*:\s*"([^"]+)"\s*,\s*"task"\s*:\s*"([\s\S]*?)"\s*\}/g;
|
||||
let m: RegExpExecArray | null;
|
||||
while ((m = taskRe.exec(stage1))) {
|
||||
tasks.push({ agent: m[1].trim(), task: m[2].trim() });
|
||||
}
|
||||
if (brief || tasks.length > 0) return { brief: brief.trim(), tasks };
|
||||
return null;
|
||||
}
|
||||
|
||||
function _coercePlan(obj: unknown): CompanyTaskPlan | null {
|
||||
if (!obj || typeof obj !== 'object') return null;
|
||||
const o = obj as Record<string, unknown>;
|
||||
const brief = typeof o.brief === 'string' ? o.brief : '';
|
||||
const rawTasks = Array.isArray(o.tasks) ? o.tasks : [];
|
||||
const tasks: CompanyTaskPlan['tasks'] = [];
|
||||
for (const t of rawTasks) {
|
||||
if (!t || typeof t !== 'object') continue;
|
||||
const tt = t as Record<string, unknown>;
|
||||
if (typeof tt.agent === 'string' && typeof tt.task === 'string') {
|
||||
tasks.push({ agent: tt.agent.trim(), task: tt.task.trim() });
|
||||
}
|
||||
}
|
||||
return { brief: brief.trim(), tasks };
|
||||
}
|
||||
|
||||
/** Find the first complete `{ … }` block respecting brace nesting. */
|
||||
function _extractFirstBalancedObject(s: string): string | null {
|
||||
const start = s.indexOf('{');
|
||||
if (start === -1) return null;
|
||||
let depth = 0;
|
||||
let inString = false;
|
||||
let escape = false;
|
||||
for (let i = start; i < s.length; i++) {
|
||||
const ch = s[i];
|
||||
if (inString) {
|
||||
if (escape) escape = false;
|
||||
else if (ch === '\\') escape = true;
|
||||
else if (ch === '"') inString = false;
|
||||
continue;
|
||||
}
|
||||
if (ch === '"') { inString = true; continue; }
|
||||
if (ch === '{') depth++;
|
||||
else if (ch === '}') {
|
||||
depth--;
|
||||
if (depth === 0) return s.slice(start, i + 1);
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Filter + normalize a freshly-parsed plan against the current company
|
||||
* state. Tasks targeting unknown / inactive agents are dropped, and Korean
|
||||
* nicknames are rewritten to canonical ids.
|
||||
*/
|
||||
export function normalizePlan(plan: CompanyTaskPlan, state: CompanyState): CompanyTaskPlan {
|
||||
const out: CompanyTaskPlan = { brief: plan.brief, tasks: [] };
|
||||
const dropped: string[] = [];
|
||||
for (const t of plan.tasks) {
|
||||
const canonical = _canonicalAgentId(t.agent);
|
||||
if (!canonical) {
|
||||
dropped.push(`unknown:${t.agent}`);
|
||||
continue;
|
||||
}
|
||||
if (canonical === 'ceo') {
|
||||
// CEO is the orchestrator — it never receives a task in `tasks`
|
||||
// (the report phase calls it separately). Drop silently.
|
||||
dropped.push('ceo:self-dispatch');
|
||||
continue;
|
||||
}
|
||||
if (!isAgentActive(state, canonical)) {
|
||||
dropped.push(`inactive:${canonical}`);
|
||||
continue;
|
||||
}
|
||||
out.tasks.push({ agent: canonical, task: t.task });
|
||||
}
|
||||
if (dropped.length > 0) {
|
||||
logInfo('ceoPlanner: dropped tasks during normalization.', { dropped });
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
/**
|
||||
* Run the CEO planner end-to-end. Never throws. The caller decides what to
|
||||
* do with `{ parsed: false, plan: { tasks: [] } }` — usually we surface the
|
||||
* raw text as a casual CEO reply.
|
||||
*/
|
||||
export async function runCeoPlanner(
|
||||
ai: IAIService,
|
||||
userPrompt: string,
|
||||
state: CompanyState,
|
||||
options: { model?: string; timeoutMs?: number } = {},
|
||||
): Promise<PlannerResult> {
|
||||
const system = buildPlannerSystemPrompt(
|
||||
applyPromptVars(CEO_PLANNER_PROMPT, { company: state.companyName }),
|
||||
state,
|
||||
);
|
||||
let raw = '';
|
||||
try {
|
||||
const result = await ai.chat({
|
||||
system,
|
||||
user: userPrompt,
|
||||
model: options.model,
|
||||
timeoutMs: options.timeoutMs,
|
||||
});
|
||||
raw = result.content || '';
|
||||
} catch (e: any) {
|
||||
logError('ceoPlanner: AI call failed.', { error: e?.message ?? String(e) });
|
||||
return { plan: EMPTY_PLAN, parsed: false, raw: '' };
|
||||
}
|
||||
|
||||
const parsed = _parsePlanJson(raw);
|
||||
if (!parsed) {
|
||||
// No JSON found — treat as a casual chat reply. The dispatcher's
|
||||
// empty-plan branch will surface `raw` as the CEO's spoken response.
|
||||
return { plan: { brief: raw.trim(), tasks: [] }, parsed: false, raw };
|
||||
}
|
||||
|
||||
const plan = normalizePlan(parsed, state);
|
||||
logInfo('ceoPlanner: parsed plan.', {
|
||||
briefChars: plan.brief.length,
|
||||
taskCount: plan.tasks.length,
|
||||
agents: plan.tasks.map((t) => t.agent),
|
||||
});
|
||||
return { plan, parsed: true, raw };
|
||||
}
|
||||
@@ -0,0 +1,120 @@
|
||||
/**
|
||||
* CEO synthesis pass — runs after all specialists have finished.
|
||||
*
|
||||
* Given the per-agent outputs, this asks the CEO model to produce the final
|
||||
* markdown report (✅ 완료 / 🚀 다음 / 💡 인사이트) that the user actually
|
||||
* reads. The function deliberately doesn't try to *parse* the response —
|
||||
* we trust the prompt to keep the structure and surface the text as-is.
|
||||
*
|
||||
* Failure mode: when the CEO call errors out we still return whatever raw
|
||||
* text we managed to collect (typically empty). The dispatcher then
|
||||
* concatenates the per-agent outputs into a fallback report so the user
|
||||
* never sees a blank screen.
|
||||
*/
|
||||
import { IAIService } from '../../core/services';
|
||||
import { logError } from '../../utils';
|
||||
import { getCompanyAgent } from './agents';
|
||||
import { applyPromptVars, CEO_REPORT_PROMPT } from './promptAssets';
|
||||
import { AgentTurnOutput, CompanyState, CompanyTaskPlan } from './types';
|
||||
|
||||
/** Max characters of per-agent output to feed back into the CEO synthesis. */
|
||||
const PER_AGENT_REPORT_BUDGET = 2000;
|
||||
|
||||
export interface ReportResult {
|
||||
/** Generated markdown. Empty string on hard failure. */
|
||||
report: string;
|
||||
/** True when the LLM call succeeded with non-empty content. */
|
||||
ok: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the user-message payload the CEO sees: the brief, plus each agent's
|
||||
* task + output, lightly trimmed so the planner-model's context window
|
||||
* doesn't blow up on a verbose specialist.
|
||||
*/
|
||||
function _buildReportUserMessage(
|
||||
plan: CompanyTaskPlan,
|
||||
outputs: AgentTurnOutput[],
|
||||
): string {
|
||||
const lines: string[] = [];
|
||||
if (plan.brief) {
|
||||
lines.push('## 이번 작업 브리프');
|
||||
lines.push(plan.brief);
|
||||
lines.push('');
|
||||
}
|
||||
lines.push('## 에이전트별 산출물');
|
||||
if (outputs.length === 0) {
|
||||
lines.push('_(no agent dispatched this turn — produce a brief acknowledgement instead)_');
|
||||
} else {
|
||||
for (const out of outputs) {
|
||||
const def = getCompanyAgent(out.agentId);
|
||||
const head = def ? `### ${def.emoji} ${def.name}` : `### ${out.agentId}`;
|
||||
lines.push('');
|
||||
lines.push(head);
|
||||
lines.push(`**Task:** ${out.task}`);
|
||||
if (out.error) {
|
||||
lines.push(`**Note:** dispatch failed — \`${out.error}\`. 사용 가능한 부분만 인용해서 보고.`);
|
||||
}
|
||||
lines.push('');
|
||||
const body = out.response.length > PER_AGENT_REPORT_BUDGET
|
||||
? out.response.slice(0, PER_AGENT_REPORT_BUDGET) + '\n…(truncated)'
|
||||
: out.response;
|
||||
lines.push(body);
|
||||
}
|
||||
}
|
||||
return lines.join('\n');
|
||||
}
|
||||
|
||||
/** Build a fallback report by concatenating agent outputs verbatim. Used when the LLM synthesis fails. */
|
||||
export function buildFallbackReport(
|
||||
plan: CompanyTaskPlan,
|
||||
outputs: AgentTurnOutput[],
|
||||
): string {
|
||||
const parts: string[] = ['## ✅ 완료된 작업'];
|
||||
if (outputs.length === 0) {
|
||||
parts.push('- _(no agents ran this turn)_');
|
||||
} else {
|
||||
for (const out of outputs) {
|
||||
const def = getCompanyAgent(out.agentId);
|
||||
const head = def ? `**${def.emoji} ${def.name}**` : `**${out.agentId}**`;
|
||||
const firstLine = (out.response.split(/\n/).find((l) => l.trim()) || out.task).trim();
|
||||
parts.push(`- ${head} — ${firstLine.slice(0, 120)}`);
|
||||
}
|
||||
}
|
||||
parts.push('');
|
||||
parts.push('## 🚀 다음 액션');
|
||||
parts.push('_(CEO 합성 실패 — 위 산출물을 직접 확인하세요)_');
|
||||
parts.push('');
|
||||
parts.push('## 💡 인사이트');
|
||||
parts.push(`- 이번 턴은 ${outputs.length}명의 에이전트가 작업했습니다.`);
|
||||
if (plan.brief) parts.push(`- 브리프: ${plan.brief}`);
|
||||
return parts.join('\n');
|
||||
}
|
||||
|
||||
/** End-to-end synthesis call. Never throws — returns `{ ok: false, … }` on error. */
|
||||
export async function runCeoReporter(
|
||||
ai: IAIService,
|
||||
plan: CompanyTaskPlan,
|
||||
outputs: AgentTurnOutput[],
|
||||
state: CompanyState,
|
||||
options: { model?: string; timeoutMs?: number } = {},
|
||||
): Promise<ReportResult> {
|
||||
const system = applyPromptVars(CEO_REPORT_PROMPT, { company: state.companyName });
|
||||
const user = _buildReportUserMessage(plan, outputs);
|
||||
try {
|
||||
const result = await ai.chat({
|
||||
system,
|
||||
user,
|
||||
model: options.model,
|
||||
timeoutMs: options.timeoutMs,
|
||||
});
|
||||
const text = (result.content || '').trim();
|
||||
if (!text) {
|
||||
return { report: buildFallbackReport(plan, outputs), ok: false };
|
||||
}
|
||||
return { report: text, ok: true };
|
||||
} catch (e: any) {
|
||||
logError('ceoReporter: AI call failed.', { error: e?.message ?? String(e) });
|
||||
return { report: buildFallbackReport(plan, outputs), ok: false };
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,182 @@
|
||||
/**
|
||||
* State + config plumbing for 1인 기업 모드.
|
||||
*
|
||||
* Two surfaces:
|
||||
*
|
||||
* - **`CompanyState`** (runtime data: enabled flag, company name, which
|
||||
* agents are active, per-agent model overrides). Persisted in VS Code's
|
||||
* `globalState` so it survives reloads. Mutating it always goes through
|
||||
* `update*()` helpers so the webview can re-render after the change.
|
||||
*
|
||||
* - **Read-only helpers** that derive useful data from the current state +
|
||||
* the static `COMPANY_AGENTS` roster (active list, model-for-agent lookup,
|
||||
* etc.). Keeping these in one module means the planner, dispatcher, and
|
||||
* UI all consult one place.
|
||||
*
|
||||
* The choice of `globalState` over `workspaceState` is deliberate: the user
|
||||
* wants the same company / agent set / nicknames available across every
|
||||
* project they open. Per-workspace overrides can be added later as a layer
|
||||
* on top without breaking this API.
|
||||
*/
|
||||
import * as vscode from 'vscode';
|
||||
import { COMPANY_AGENTS, DEFAULT_ACTIVE_AGENTS, getCompanyAgent } from './agents';
|
||||
import { CompanyState, COMPANY_STATE_KEY } from './types';
|
||||
|
||||
/** Default state for a brand-new user. CEO is always on. */
|
||||
function _defaultState(): CompanyState {
|
||||
return {
|
||||
enabled: false,
|
||||
companyName: '1인 기업',
|
||||
activeAgentIds: DEFAULT_ACTIVE_AGENTS.slice(),
|
||||
modelOverrides: {},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Normalize a state value loaded from globalState. Guards against schema
|
||||
* drift (e.g. unknown agent ids that no longer exist, missing fields).
|
||||
*/
|
||||
function _normalize(raw: Partial<CompanyState> | undefined): CompanyState {
|
||||
const def = _defaultState();
|
||||
if (!raw || typeof raw !== 'object') return def;
|
||||
const enabled = typeof raw.enabled === 'boolean' ? raw.enabled : def.enabled;
|
||||
const companyName = typeof raw.companyName === 'string' && raw.companyName.trim()
|
||||
? raw.companyName.trim()
|
||||
: def.companyName;
|
||||
const validIds = Array.isArray(raw.activeAgentIds)
|
||||
? raw.activeAgentIds.filter((id): id is string => typeof id === 'string' && !!getCompanyAgent(id))
|
||||
: def.activeAgentIds;
|
||||
// CEO is *implicitly* always active — keep it out of the persisted list
|
||||
// so we never accidentally drop it, but the public reader re-includes it.
|
||||
const withoutCeo = validIds.filter((id) => id !== 'ceo');
|
||||
const overrides: Record<string, string> = {};
|
||||
if (raw.modelOverrides && typeof raw.modelOverrides === 'object') {
|
||||
for (const [k, v] of Object.entries(raw.modelOverrides)) {
|
||||
if (typeof v === 'string' && v.trim() && getCompanyAgent(k)) {
|
||||
overrides[k] = v.trim();
|
||||
}
|
||||
}
|
||||
}
|
||||
return { enabled, companyName, activeAgentIds: withoutCeo, modelOverrides: overrides };
|
||||
}
|
||||
|
||||
/** Read the current company state. Always returns a fully-populated object. */
|
||||
export function readCompanyState(context: vscode.ExtensionContext): CompanyState {
|
||||
const raw = context.globalState.get<Partial<CompanyState>>(COMPANY_STATE_KEY);
|
||||
return _normalize(raw);
|
||||
}
|
||||
|
||||
/** Persist a complete state object. Callers usually go through the `update*`
|
||||
* helpers below; direct use is fine when you want to write multiple fields
|
||||
* atomically. */
|
||||
export async function writeCompanyState(
|
||||
context: vscode.ExtensionContext,
|
||||
next: CompanyState,
|
||||
): Promise<void> {
|
||||
await context.globalState.update(COMPANY_STATE_KEY, _normalize(next));
|
||||
}
|
||||
|
||||
/**
|
||||
* Toggle the whole mode on/off. Returns the new state so callers can
|
||||
* immediately broadcast it to the webview without a re-read.
|
||||
*/
|
||||
export async function setCompanyEnabled(
|
||||
context: vscode.ExtensionContext,
|
||||
enabled: boolean,
|
||||
): Promise<CompanyState> {
|
||||
const cur = readCompanyState(context);
|
||||
const next: CompanyState = { ...cur, enabled };
|
||||
await writeCompanyState(context, next);
|
||||
return next;
|
||||
}
|
||||
|
||||
/** Rename the company. Empty / whitespace input falls back to the default. */
|
||||
export async function setCompanyName(
|
||||
context: vscode.ExtensionContext,
|
||||
name: string,
|
||||
): Promise<CompanyState> {
|
||||
const cur = readCompanyState(context);
|
||||
const trimmed = (name || '').trim();
|
||||
const next: CompanyState = { ...cur, companyName: trimmed || '1인 기업' };
|
||||
await writeCompanyState(context, next);
|
||||
return next;
|
||||
}
|
||||
|
||||
/** Replace the active-agent set. Order is preserved; unknown ids are dropped. */
|
||||
export async function setActiveAgents(
|
||||
context: vscode.ExtensionContext,
|
||||
ids: string[],
|
||||
): Promise<CompanyState> {
|
||||
const cur = readCompanyState(context);
|
||||
const next: CompanyState = { ...cur, activeAgentIds: ids };
|
||||
await writeCompanyState(context, next);
|
||||
return next;
|
||||
}
|
||||
|
||||
/**
|
||||
* Set / clear a per-agent model override. Passing empty string removes the
|
||||
* override (the agent will fall back to the global default).
|
||||
*/
|
||||
export async function setAgentModelOverride(
|
||||
context: vscode.ExtensionContext,
|
||||
agentId: string,
|
||||
model: string,
|
||||
): Promise<CompanyState> {
|
||||
const cur = readCompanyState(context);
|
||||
const overrides = { ...cur.modelOverrides };
|
||||
if (model && model.trim()) {
|
||||
overrides[agentId] = model.trim();
|
||||
} else {
|
||||
delete overrides[agentId];
|
||||
}
|
||||
const next: CompanyState = { ...cur, modelOverrides: overrides };
|
||||
await writeCompanyState(context, next);
|
||||
return next;
|
||||
}
|
||||
|
||||
// ── Derived helpers (no I/O) ────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Resolve the full set of agent ids that should be available to the CEO
|
||||
* planner on this turn. CEO is always included regardless of `activeAgentIds`.
|
||||
*/
|
||||
export function activeAgentIds(state: CompanyState): string[] {
|
||||
const set = new Set<string>(['ceo']);
|
||||
for (const id of state.activeAgentIds) {
|
||||
if (getCompanyAgent(id)) set.add(id);
|
||||
}
|
||||
return Array.from(set);
|
||||
}
|
||||
|
||||
/** Returns true when an agent is currently active (CEO always returns true). */
|
||||
export function isAgentActive(state: CompanyState, agentId: string): boolean {
|
||||
if (agentId === 'ceo') return true;
|
||||
return state.activeAgentIds.includes(agentId);
|
||||
}
|
||||
|
||||
/**
|
||||
* The model to use when dispatching `agentId`. Returns the override when
|
||||
* configured, otherwise `fallbackDefault` (typically the global
|
||||
* `g1nation.defaultModel`). Empty string is treated as "no override".
|
||||
*/
|
||||
export function modelForAgent(
|
||||
state: CompanyState,
|
||||
agentId: string,
|
||||
fallbackDefault: string,
|
||||
): string {
|
||||
const override = state.modelOverrides[agentId];
|
||||
return override && override.trim() ? override.trim() : fallbackDefault;
|
||||
}
|
||||
|
||||
/**
|
||||
* Human-readable summary for the chip tooltip / status bar:
|
||||
* "🏢 My Company · 5 agents · default model"
|
||||
*/
|
||||
export function summarizeForChip(state: CompanyState): string {
|
||||
const count = activeAgentIds(state).length;
|
||||
return `${state.companyName} · ${count} agents`;
|
||||
}
|
||||
|
||||
// Re-export the static catalogue so callers only have to import from one
|
||||
// module to get the full picture.
|
||||
export { COMPANY_AGENTS, getCompanyAgent };
|
||||
@@ -0,0 +1,252 @@
|
||||
/**
|
||||
* Sequential dispatcher for 1인 기업 모드.
|
||||
*
|
||||
* Drives one company "turn":
|
||||
*
|
||||
* user prompt
|
||||
* → CEO planner (JSON {brief, tasks})
|
||||
* → for each task in plan: dispatch one specialist (sequentially)
|
||||
* - build specialist prompt (incl. peer context from earlier agents)
|
||||
* - call the AI service
|
||||
* - persist its output to disk
|
||||
* - append its output to the peer-context buffer for the next agent
|
||||
* → CEO reporter (synthesis markdown)
|
||||
* → persist `_report.md`, update agent memory + decisions
|
||||
* → emit `companyTurnUpdate` events to the webview at each phase
|
||||
*
|
||||
* Why sequential? The user runs Astra on a single GPU/CPU with limited RAM,
|
||||
* and parallel agents would force us to keep multiple models loaded
|
||||
* simultaneously. Sequential dispatch keeps "exactly one model resident at
|
||||
* a time" — the LM Studio lifecycle manager unloads the previous model and
|
||||
* loads the next when an agent has its own override.
|
||||
*
|
||||
* Why not use `AgentExecutor.handlePrompt` here? Because `handlePrompt` is
|
||||
* built for the *interactive* chat path: it owns the conversation history,
|
||||
* streaming UI, agent-mode injection, and a dozen other things we don't
|
||||
* want triggered by a company turn. The company dispatcher needs a clean
|
||||
* "one system + one user → one string back" primitive — `AIService.chat()`
|
||||
* fits that perfectly. Specialists can still emit action tags
|
||||
* (`<create_file>`, `<run_command>`); we route their *raw* output through
|
||||
* the existing action-tag executor afterwards so file/command tools work
|
||||
* exactly as in chat.
|
||||
*/
|
||||
import * as vscode from 'vscode';
|
||||
import { IAIService } from '../../core/services';
|
||||
import { logError, logInfo } from '../../utils';
|
||||
import { getCompanyAgent } from './agents';
|
||||
import { modelForAgent, readCompanyState } from './companyConfig';
|
||||
import { runCeoPlanner } from './ceoPlanner';
|
||||
import { runCeoReporter } from './ceoReporter';
|
||||
import { buildSpecialistPrompt } from './promptBuilder';
|
||||
import {
|
||||
appendAgentMemory,
|
||||
appendDecision,
|
||||
createSessionDir,
|
||||
newSessionTimestamp,
|
||||
readAgentMemory,
|
||||
readDecisions,
|
||||
writeAgentOutput,
|
||||
writeBrief,
|
||||
writeReport,
|
||||
writeSessionJson,
|
||||
} from './sessionStore';
|
||||
import { AgentTurnOutput, CompanyTaskPlan, SessionResult } from './types';
|
||||
|
||||
/** Trim length applied when an agent's output is fed into the next agent. */
|
||||
const PEER_OUTPUT_BUDGET = 1500;
|
||||
|
||||
/**
|
||||
* Events emitted during a turn. The sidebar webview subscribes to render
|
||||
* progress (chips, headers, streamed agent replies). The shape is generic so
|
||||
* the same channel can carry CEO/agent/report messages without per-type
|
||||
* postMessage plumbing.
|
||||
*/
|
||||
export type CompanyTurnEvent =
|
||||
| { phase: 'plan-start' }
|
||||
| { phase: 'plan-ready'; plan: CompanyTaskPlan; parsed: boolean; raw: string }
|
||||
| { phase: 'agent-start'; agentId: string; task: string; index: number; total: number }
|
||||
| { phase: 'agent-done'; agentId: string; output: AgentTurnOutput; index: number; total: number }
|
||||
| { phase: 'report-start' }
|
||||
| { phase: 'report-done'; report: string; ok: boolean }
|
||||
| { phase: 'session-saved'; sessionDir: string }
|
||||
| { phase: 'aborted'; reason: string };
|
||||
|
||||
export type CompanyTurnEmitter = (event: CompanyTurnEvent) => void;
|
||||
|
||||
export interface DispatcherDeps {
|
||||
context: vscode.ExtensionContext;
|
||||
ai: IAIService;
|
||||
/** Default model to fall back to when an agent has no override. */
|
||||
defaultModel: string;
|
||||
/** Per-call cancellation. The sidebar's Stop button flips this. */
|
||||
signal?: AbortSignal;
|
||||
/** Optional event sink for the webview. Receives events synchronously. */
|
||||
onEvent?: CompanyTurnEmitter;
|
||||
}
|
||||
|
||||
/**
|
||||
* Run a single company turn. Returns a fully-populated `SessionResult` even
|
||||
* on partial failure (so callers can always render *something* in chat).
|
||||
*/
|
||||
export async function runCompanyTurn(
|
||||
userPrompt: string,
|
||||
deps: DispatcherDeps,
|
||||
): Promise<SessionResult> {
|
||||
const startedAt = Date.now();
|
||||
const state = readCompanyState(deps.context);
|
||||
const timestamp = newSessionTimestamp();
|
||||
const sessionDir = createSessionDir(deps.context, timestamp);
|
||||
|
||||
const emit: CompanyTurnEmitter = deps.onEvent ?? (() => { /* noop */ });
|
||||
const isAborted = () => deps.signal?.aborted === true;
|
||||
const fail = (reason: string): SessionResult => {
|
||||
emit({ phase: 'aborted', reason });
|
||||
return {
|
||||
timestamp, sessionDir,
|
||||
userPrompt,
|
||||
plan: { brief: '', tasks: [] },
|
||||
agentOutputs: [],
|
||||
report: '',
|
||||
totalDurationMs: Date.now() - startedAt,
|
||||
};
|
||||
};
|
||||
if (isAborted()) return fail('signal-aborted');
|
||||
|
||||
// ── Phase 1: planner ──
|
||||
emit({ phase: 'plan-start' });
|
||||
const ceoModel = modelForAgent(state, 'ceo', deps.defaultModel);
|
||||
const plannerResult = await runCeoPlanner(deps.ai, userPrompt, state, { model: ceoModel });
|
||||
if (isAborted()) return fail('aborted-after-plan');
|
||||
emit({
|
||||
phase: 'plan-ready',
|
||||
plan: plannerResult.plan,
|
||||
parsed: plannerResult.parsed,
|
||||
raw: plannerResult.raw,
|
||||
});
|
||||
writeBrief(sessionDir, userPrompt, plannerResult.plan);
|
||||
|
||||
// ── Phase 2: sequential dispatch ──
|
||||
const outputs: AgentTurnOutput[] = [];
|
||||
const total = plannerResult.plan.tasks.length;
|
||||
for (let i = 0; i < total; i++) {
|
||||
if (isAborted()) return fail('aborted-mid-dispatch');
|
||||
const task = plannerResult.plan.tasks[i];
|
||||
emit({ phase: 'agent-start', agentId: task.agent, task: task.task, index: i, total });
|
||||
const turn = await _dispatchOne(task.agent, task.task, outputs, state, deps);
|
||||
outputs.push(turn);
|
||||
writeAgentOutput(sessionDir, turn);
|
||||
// Best-effort: append a one-line memory entry so the agent "remembers"
|
||||
// having done this task. Verbose successes are summarized in the CEO
|
||||
// report — memory is just the breadcrumb trail.
|
||||
appendAgentMemory(
|
||||
deps.context,
|
||||
task.agent,
|
||||
`[${timestamp}] ${task.task} — ${turn.error ? `❌ ${turn.error}` : '✅'}`,
|
||||
);
|
||||
emit({ phase: 'agent-done', agentId: task.agent, output: turn, index: i, total });
|
||||
}
|
||||
|
||||
// ── Phase 3: synthesis ──
|
||||
if (isAborted()) return fail('aborted-before-report');
|
||||
emit({ phase: 'report-start' });
|
||||
const reportModel = modelForAgent(state, 'ceo', deps.defaultModel);
|
||||
const reportResult = await runCeoReporter(
|
||||
deps.ai,
|
||||
plannerResult.plan,
|
||||
outputs,
|
||||
state,
|
||||
{ model: reportModel },
|
||||
);
|
||||
writeReport(sessionDir, reportResult.report);
|
||||
emit({ phase: 'report-done', report: reportResult.report, ok: reportResult.ok });
|
||||
|
||||
// ── Phase 4: persist + side effects ──
|
||||
const result: SessionResult = {
|
||||
timestamp, sessionDir,
|
||||
userPrompt,
|
||||
plan: plannerResult.plan,
|
||||
agentOutputs: outputs,
|
||||
report: reportResult.report,
|
||||
totalDurationMs: Date.now() - startedAt,
|
||||
};
|
||||
writeSessionJson(sessionDir, result);
|
||||
// Heuristic: if the report mentions a 🚀 line, extract it as a decision.
|
||||
const decisionLine = reportResult.report.split(/\n/).find((l) => /^\d+\.\s+/.test(l.trim()));
|
||||
if (decisionLine) appendDecision(deps.context, decisionLine.trim());
|
||||
emit({ phase: 'session-saved', sessionDir });
|
||||
|
||||
logInfo('company.dispatcher: turn complete.', {
|
||||
sessionDir, agents: outputs.length, ok: reportResult.ok,
|
||||
durationMs: result.totalDurationMs,
|
||||
});
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* Dispatch one specialist. Wraps the AI call with try/catch so a single
|
||||
* agent's failure never aborts the whole turn — we record the error and
|
||||
* keep going so the user still gets the other agents' outputs.
|
||||
*/
|
||||
async function _dispatchOne(
|
||||
agentId: string,
|
||||
task: string,
|
||||
earlierOutputs: AgentTurnOutput[],
|
||||
state: ReturnType<typeof readCompanyState>,
|
||||
deps: DispatcherDeps,
|
||||
): Promise<AgentTurnOutput> {
|
||||
const startedAt = Date.now();
|
||||
const def = getCompanyAgent(agentId);
|
||||
if (!def) {
|
||||
return {
|
||||
agentId, task, response: '', durationMs: 0,
|
||||
error: `Unknown agent id: ${agentId}`,
|
||||
};
|
||||
}
|
||||
const memory = readAgentMemory(deps.context, agentId);
|
||||
const decisions = readDecisions(deps.context, 2000);
|
||||
const peerOutputs = earlierOutputs
|
||||
.filter((o) => !o.error) // skip failed peers — they'd just confuse the next agent
|
||||
.map((o) => {
|
||||
const peerDef = getCompanyAgent(o.agentId);
|
||||
const body = o.response.length > PEER_OUTPUT_BUDGET
|
||||
? o.response.slice(0, PEER_OUTPUT_BUDGET) + '\n…(truncated)'
|
||||
: o.response;
|
||||
return {
|
||||
agentId: o.agentId,
|
||||
agentName: peerDef?.name ?? o.agentId,
|
||||
emoji: peerDef?.emoji ?? '🤖',
|
||||
content: body,
|
||||
};
|
||||
});
|
||||
|
||||
const system = buildSpecialistPrompt({
|
||||
agentId, state,
|
||||
agentMemory: memory, sharedDecisions: decisions,
|
||||
peerOutputs,
|
||||
});
|
||||
const model = modelForAgent(state, agentId, deps.defaultModel);
|
||||
|
||||
try {
|
||||
const result = await deps.ai.chat({
|
||||
system,
|
||||
user: task,
|
||||
model,
|
||||
});
|
||||
const response = (result.content || '').trim();
|
||||
return {
|
||||
agentId, task,
|
||||
response: response || '_(empty response)_',
|
||||
durationMs: Date.now() - startedAt,
|
||||
error: response ? undefined : 'empty-response',
|
||||
};
|
||||
} catch (e: any) {
|
||||
const err = e?.message ?? String(e);
|
||||
logError('company.dispatcher: agent dispatch failed.', { agentId, err });
|
||||
return {
|
||||
agentId, task,
|
||||
response: `⚠️ 호출 실패: ${err}`,
|
||||
durationMs: Date.now() - startedAt,
|
||||
error: err,
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,50 @@
|
||||
/**
|
||||
* Public API for 1인 기업 모드.
|
||||
*
|
||||
* Consumers (sidebarProvider, chatHandlers, command handlers) import from
|
||||
* this barrel so internal layout can move around without touching every
|
||||
* call site.
|
||||
*/
|
||||
export {
|
||||
COMPANY_AGENTS,
|
||||
COMPANY_AGENT_ORDER,
|
||||
COMPANY_SPECIALIST_IDS,
|
||||
DEFAULT_ACTIVE_AGENTS,
|
||||
getCompanyAgent,
|
||||
} from './agents';
|
||||
|
||||
export {
|
||||
readCompanyState,
|
||||
writeCompanyState,
|
||||
setCompanyEnabled,
|
||||
setCompanyName,
|
||||
setActiveAgents,
|
||||
setAgentModelOverride,
|
||||
activeAgentIds,
|
||||
isAgentActive,
|
||||
modelForAgent,
|
||||
summarizeForChip,
|
||||
} from './companyConfig';
|
||||
|
||||
export type {
|
||||
CompanyAgentDef,
|
||||
CompanyState,
|
||||
CompanyTaskPlan,
|
||||
AgentTurnOutput,
|
||||
SessionResult,
|
||||
} from './types';
|
||||
|
||||
export {
|
||||
runCompanyTurn,
|
||||
} from './dispatcher';
|
||||
|
||||
export type {
|
||||
CompanyTurnEvent,
|
||||
CompanyTurnEmitter,
|
||||
DispatcherDeps,
|
||||
} from './dispatcher';
|
||||
|
||||
export {
|
||||
listSessions,
|
||||
resolveCompanyBase,
|
||||
} from './sessionStore';
|
||||
@@ -0,0 +1,114 @@
|
||||
/**
|
||||
* Inlined prompt assets for the 1인 기업 mode.
|
||||
*
|
||||
* The CEO planner / reporter / casual-chat prompts are kept as TS string
|
||||
* constants rather than loaded from `prompts/*.md` at runtime, for two reasons:
|
||||
*
|
||||
* 1. **Bundling.** esbuild collapses the whole extension into one file,
|
||||
* so resolving a markdown path via `__dirname` would point inside
|
||||
* `out/extension.js` and fail. Inlining sidesteps that entirely.
|
||||
* 2. **Tamper resistance.** The planner prompt encodes the multi-agent
|
||||
* contract (JSON shape, minimum-dispatch rule). Embedding it in code
|
||||
* means a workspace can't quietly swap it for a malicious version.
|
||||
*
|
||||
* The `.md` files under `./prompts/` are kept as **reference copies** so
|
||||
* developers can read/diff them in any editor — `promptAssets.ts` is the
|
||||
* source of truth the runtime actually uses.
|
||||
*/
|
||||
|
||||
/**
|
||||
* CEO planner prompt. The `{{COMPANY}}` placeholder is substituted with the
|
||||
* user-configured company name before sending to the LLM. The model is
|
||||
* required to return a single JSON object — see `ceoPlanner.ts` for the
|
||||
* 4-stage parser that tolerates fenced/leading-noise variants.
|
||||
*/
|
||||
export const CEO_PLANNER_PROMPT = `당신은 "{{COMPANY}}"의 CEO입니다. 1인 AI 기업의 사령관이자 오케스트레이터입니다.
|
||||
|
||||
당신의 팀(전문 에이전트):
|
||||
- youtube (Head of YouTube) : 유튜브 채널 운영, 영상 기획, 트렌드, 썸네일 브리프
|
||||
- instagram (Head of Instagram) : 릴스/피드, 캡션, 해시태그, 게시 시간, 인게이지먼트
|
||||
- designer (Lead Designer) : 디자인 브리프, 썸네일·브랜드 비주얼, 컬러/타이포
|
||||
- developer (코다리 · 시니어 풀스택 엔지니어): 코드 작성·편집·디버깅, 자동화 스크립트, API 통합, 웹사이트, 테스트, git, 자기 검증 루프
|
||||
- business (Head of Business) : 수익화, 가격, 비즈니스 전략·분석, KPI
|
||||
- secretary (Personal Assistant) : 일정·할 일, 작업 요약, 데일리 브리핑
|
||||
- editor (루나 · 사운드 감독) : BGM 기획, 사운드 디자인, 영상-음악 매칭
|
||||
- writer (Copywriter) : 카피라이팅, 영상 스크립트, 캡션, 블로그, 후크
|
||||
- researcher(Trend & Data Researcher) : 트렌드/경쟁사 리서치, 데이터 수집·요약, 사실 확인
|
||||
|
||||
사용자가 한 줄 명령을 내리면, 당신은 어떤 에이전트들을 어떤 순서로 동원할지 결정합니다.
|
||||
|
||||
⚠️ 반드시 아래 JSON 형식으로만 출력하세요. 다른 텍스트(설명, \`\`\`json 펜스, 머리말, 꼬리말)는 절대 포함 금지.
|
||||
|
||||
{
|
||||
"brief": "이번 작업이 무엇인지 2~3줄 한국어 요약",
|
||||
"tasks": [
|
||||
{"agent": "youtube", "task": "구체적이고 실행 가능한 한국어 지시"}
|
||||
]
|
||||
}
|
||||
|
||||
🛑 **최소 동원 원칙 — 절대 위반 금지**:
|
||||
1. **단순 데이터 조회·정보 확인 명령은 데이터 에이전트 1명만**. 예: "내 채널 분석", "구독자 수", "오늘 일정", "최근 영상" → tasks 배열에 1명. 추가 분석 에이전트(researcher/business/designer/writer) 절대 추가 금지. 사용자가 추가 분석을 *명시적으로* 요청해야만 추가.
|
||||
2. **창작·기획 명령일 때만 multi-agent**. 예: "영상 기획해줘", "썸네일 만들어", "수익화 전략 짜줘" → 관련 에이전트 2~3명. 5명 이상 절대 금지.
|
||||
3. **상관없는 에이전트 끌어오지 마라**. 사용자 명령이 유튜브 데이터인데 designer/writer 부르는 건 즉시 금지. 사용자가 "디자인"·"카피"·"썸네일" 같은 단어를 *직접* 썼을 때만.
|
||||
|
||||
데이터 수집 키워드 매칭 (해당 에이전트만 1명):
|
||||
- "유튜브"·"YouTube"·"내 채널"·"구독자"·"조회수"·"영상 분석" → youtube 1명만
|
||||
- "인스타"·"릴스"·"피드" → instagram 1명만
|
||||
- "캘린더"·"일정"·"오늘 미팅" → secretary 1명만
|
||||
|
||||
기타 규칙:
|
||||
- 논리적 순서로 정렬 (예: 데이터 수집 → 분석 → 창작 — 사용자가 그 모두를 요청한 경우에만)
|
||||
- 각 task는 모호함 없이 구체적·실행가능하게
|
||||
- JSON 외 텍스트는 단 한 글자도 출력 금지
|
||||
- 데이터 수집 없이 researcher/business만 호출하면 LLM이 가짜 분석을 출력합니다 — 절대 금지
|
||||
`;
|
||||
|
||||
/**
|
||||
* CEO synthesis prompt — runs at the end of every turn after all specialists
|
||||
* have replied. Output is plain markdown (no JSON), structured into ✅/🚀/💡
|
||||
* sections that the chat surfaces verbatim.
|
||||
*/
|
||||
export const CEO_REPORT_PROMPT = `당신은 {{COMPANY}}의 CEO입니다. 방금 팀이 작업을 끝냈습니다.
|
||||
각 에이전트의 산출물을 읽고 사장님께 올릴 종합 보고서를 작성하세요.
|
||||
|
||||
형식 (한국어 마크다운, 정확히 이대로):
|
||||
|
||||
## ✅ 완료된 작업
|
||||
- (에이전트별 핵심 산출물 1줄씩, 굵은 글씨로 에이전트명)
|
||||
|
||||
## 🚀 다음 액션 (Top 3)
|
||||
1. **(에이전트명)** — 무엇을
|
||||
2. **(에이전트명)** — 무엇을
|
||||
3. **(에이전트명)** — 무엇을
|
||||
|
||||
## 💡 인사이트
|
||||
- 이번 작업에서 발견한 핵심 통찰 1~2개
|
||||
|
||||
규칙: 간결, 사족 금지, 사과·면책 금지. 가능하면 300자 내외.
|
||||
|
||||
⚠️ 데이터 우선 규칙 (반드시 준수):
|
||||
- 산출물에 **실제 숫자/데이터**가 있으면 **그 데이터를 직접 인용**해 보고하세요. 추상적인 "분석 진행됨" 같은 말로 대체 금지.
|
||||
- 추측·일반론·placeholder 절대 금지. 산출물에 없는 사실 만들어내지 마세요.
|
||||
- 어떤 에이전트의 산출물에 에러 메시지가 있어도 다른 에이전트의 실제 결과는 정상적으로 인용하세요.
|
||||
`;
|
||||
|
||||
/**
|
||||
* Fallback "casual chat" prompt used when the planner's JSON parse fails
|
||||
* entirely (typically because the user wrote a greeting instead of a work
|
||||
* command). Replies in 1–3 sentences without trying to dispatch agents.
|
||||
*/
|
||||
export const CEO_CHAT_PROMPT = `당신은 {{COMPANY}}의 CEO입니다. 사용자(사장님)와 짧게 인사·안부·잡담을 주고받습니다.
|
||||
- 한국어로 1~3문장. 친근하지만 사장-CEO 관계는 유지.
|
||||
- 인사·안부 질문이면 자연스럽게 응답하세요. 작업 지시가 아니면 굳이 작업 분배 제안 X.
|
||||
- 회사 정체성·최근 결정이 컨텍스트에 있으면 자연스럽게 활용.
|
||||
- JSON 출력 금지. 그냥 평문으로 짧게.
|
||||
`;
|
||||
|
||||
/**
|
||||
* Substitute the `{{COMPANY}}` placeholder. Trivial today, but isolating it
|
||||
* here keeps the door open for additional templating later (e.g. company
|
||||
* mission statement, brand voice) without touching every call site.
|
||||
*/
|
||||
export function applyPromptVars(template: string, vars: { company: string }): string {
|
||||
return template.replace(/\{\{COMPANY\}\}/g, vars.company || '1인 기업');
|
||||
}
|
||||
@@ -0,0 +1,140 @@
|
||||
/**
|
||||
* System-prompt construction for company-mode agents.
|
||||
*
|
||||
* Each specialist needs a prompt that includes:
|
||||
* - Their identity (name, role, specialty) + optional persona.
|
||||
* - The action-tag contract (`<create_file>`, `<run_command>`, etc.) so
|
||||
* ConnectAI's existing `_executeActions()` can handle tool calls
|
||||
* transparently after the LLM responds.
|
||||
* - The *peer context* — earlier agents' outputs in the same turn, so the
|
||||
* second/third agent can build on what came before.
|
||||
* - The agent's long-term memory (`memory.md`) when available.
|
||||
* - Company-wide decisions, if recorded.
|
||||
*
|
||||
* Build-once-per-dispatch: the dispatcher calls `buildSpecialistPrompt()` for
|
||||
* every task. Each call is pure (no I/O of its own — the caller fetches
|
||||
* memory/decisions and passes them in), which keeps it trivial to test.
|
||||
*/
|
||||
import { COMPANY_AGENTS, getCompanyAgent } from './agents';
|
||||
import { CompanyState } from './types';
|
||||
|
||||
export interface SpecialistPromptInputs {
|
||||
/** Active agent id. Must exist in `COMPANY_AGENTS`. */
|
||||
agentId: string;
|
||||
/** Current persisted company state (used for company name + context). */
|
||||
state: CompanyState;
|
||||
/** Long-term agent memory text (may be empty). Pre-read by caller. */
|
||||
agentMemory?: string;
|
||||
/** Tail of `_shared/decisions.md` (may be empty). */
|
||||
sharedDecisions?: string;
|
||||
/**
|
||||
* Peer outputs from earlier agents in *this* dispatch, in execution order.
|
||||
* Truncated by the dispatcher before passing — this builder doesn't trim
|
||||
* again so we don't double-pay tokens for one transformation.
|
||||
*/
|
||||
peerOutputs?: Array<{ agentId: string; agentName: string; emoji: string; content: string }>;
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the full system prompt for one specialist. Returns plain markdown.
|
||||
*
|
||||
* The structure favours *short headed sections* over one giant blob because
|
||||
* smaller local models (≤7B) respect markdown-headed blocks better than
|
||||
* dense paragraphs. Order matters: identity first, then rules, then context.
|
||||
*/
|
||||
export function buildSpecialistPrompt(inputs: SpecialistPromptInputs): string {
|
||||
const agent = getCompanyAgent(inputs.agentId);
|
||||
if (!agent) {
|
||||
// Defensive fallback — should never happen because the dispatcher
|
||||
// filters tasks against the active agent set before calling us.
|
||||
return `You are an agent named "${inputs.agentId}". Respond in Korean.`;
|
||||
}
|
||||
const company = inputs.state.companyName || '1인 기업';
|
||||
const parts: string[] = [];
|
||||
|
||||
// ── Identity ──
|
||||
parts.push(`# ${agent.emoji} ${agent.name} — ${agent.role}`);
|
||||
parts.push(`당신은 ${company}의 ${agent.role}입니다.`);
|
||||
parts.push(`전문 분야: ${agent.specialty}`);
|
||||
if (agent.persona) {
|
||||
parts.push('');
|
||||
parts.push('## 페르소나');
|
||||
parts.push(agent.persona);
|
||||
}
|
||||
|
||||
// ── Output contract ──
|
||||
parts.push('');
|
||||
parts.push('## 출력 규칙');
|
||||
parts.push('- 한국어 마크다운으로 답변. 사장님(사용자)에게 보고하는 톤.');
|
||||
parts.push('- 작업이 끝나면 마지막에 두 줄로 자기 평가를 붙이세요:');
|
||||
parts.push(' - `📊 평가:` 한 줄로 산출물의 가치(데이터 기반·완성도·아이디어 신선도).');
|
||||
parts.push(' - `📝 다음:` 사장님 입장에서 다음에 할 만한 한 가지 액션 한 줄.');
|
||||
parts.push('- 추측·일반론·placeholder 금지. 가진 정보만 인용.');
|
||||
|
||||
// ── Tool contract ──
|
||||
// ConnectAI's existing AgentExecutor parses these tags automatically
|
||||
// after the streaming response completes. Keeping the syntax identical
|
||||
// means specialists can write files / run commands the same way the
|
||||
// base chat already does — no new plumbing on the agent side.
|
||||
parts.push('');
|
||||
parts.push('## 도구 사용 규칙 (필요할 때만)');
|
||||
parts.push('실제 파일 생성·명령 실행이 필요하면 ConnectAI의 액션 태그를 사용하세요.');
|
||||
parts.push('예) `<create_file path="...">내용</create_file>`, `<run_command>npm test</run_command>` 등.');
|
||||
parts.push('태그 없이 평문으로만 답해도 됩니다 — 기획·분석·아이디어 작업은 보통 태그가 필요 없습니다.');
|
||||
|
||||
// ── Peer context (this turn) ──
|
||||
const peers = inputs.peerOutputs ?? [];
|
||||
if (peers.length > 0) {
|
||||
parts.push('');
|
||||
parts.push('## 같은 세션의 동료 산출물');
|
||||
parts.push('아래는 당신보다 먼저 작업한 동료들의 결과입니다. 인용·참조해서 일관된 흐름을 만드세요.');
|
||||
for (const p of peers) {
|
||||
parts.push('');
|
||||
parts.push(`### ${p.emoji} ${p.agentName}`);
|
||||
parts.push(p.content);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Long-term memory ──
|
||||
const memory = (inputs.agentMemory ?? '').trim();
|
||||
if (memory) {
|
||||
parts.push('');
|
||||
parts.push('## 당신의 장기 기억 (memory.md)');
|
||||
parts.push('과거 작업에서 누적된 학습입니다. 지금 task와 충돌하면 *현재 task가 우선*입니다.');
|
||||
parts.push(memory);
|
||||
}
|
||||
|
||||
// ── Company-wide decisions ──
|
||||
const decisions = (inputs.sharedDecisions ?? '').trim();
|
||||
if (decisions) {
|
||||
parts.push('');
|
||||
parts.push('## 회사 공통 결정 사항 (decisions.md)');
|
||||
parts.push(decisions);
|
||||
}
|
||||
|
||||
return parts.join('\n');
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the planner system prompt. The base template is in `promptAssets.ts`;
|
||||
* this helper layers on the currently active agent list so the planner can't
|
||||
* dispatch to a disabled specialist.
|
||||
*/
|
||||
export function buildPlannerSystemPrompt(
|
||||
baseTemplate: string,
|
||||
state: CompanyState,
|
||||
): string {
|
||||
const active = new Set<string>(state.activeAgentIds);
|
||||
active.add('ceo');
|
||||
const inactive = Object.keys(COMPANY_AGENTS).filter((id) => !active.has(id));
|
||||
const tail: string[] = [];
|
||||
if (inactive.length > 0) {
|
||||
tail.push('');
|
||||
tail.push('현재 비활성화된 에이전트 (절대 dispatch 금지):');
|
||||
for (const id of inactive) {
|
||||
const def = COMPANY_AGENTS[id];
|
||||
tail.push(`- ${id} (${def?.name ?? id})`);
|
||||
}
|
||||
}
|
||||
return baseTemplate + tail.join('\n');
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
당신은 {{COMPANY}}의 CEO입니다. 사용자(사장님)와 짧게 인사·안부·잡담을 주고받습니다.
|
||||
- 한국어로 1~3문장. 친근하지만 사장-CEO 관계는 유지.
|
||||
- 인사·안부 질문이면 자연스럽게 응답하세요. 작업 지시가 아니면 굳이 작업 분배 제안 X.
|
||||
- 회사 정체성·최근 결정·추적기 상태가 컨텍스트에 있으면 자연스럽게 활용.
|
||||
- JSON 출력 금지. 그냥 평문으로 짧게.
|
||||
@@ -0,0 +1,39 @@
|
||||
당신은 "{{COMPANY}}"의 CEO입니다. 1인 AI 기업의 사령관이자 오케스트레이터입니다.
|
||||
|
||||
당신의 팀(전문 에이전트):
|
||||
- youtube (Head of YouTube) : 유튜브 채널 운영, 영상 기획, 트렌드, 썸네일 브리프
|
||||
- instagram (Head of Instagram) : 릴스/피드, 캡션, 해시태그, 게시 시간, 인게이지먼트
|
||||
- designer (Lead Designer) : 디자인 브리프, 썸네일·브랜드 비주얼, 컬러/타이포
|
||||
- developer (코다리 · 시니어 풀스택 엔지니어): 코드 작성·편집·디버깅, 자동화 스크립트, API 통합, 웹사이트, 테스트, git, 자기 검증 루프 (Claude Code 수준)
|
||||
- business (Head of Business) : 수익화, 가격, 비즈니스 전략·분석, KPI
|
||||
- secretary (Personal Assistant) : 일정·할 일, 작업 요약, 텔레그램 보고, 데일리 브리핑
|
||||
- editor (루나 · 사운드 감독) : BGM 자동 생성(MusicGen/ACE-Step), 사운드 디자인, 영상-음악 합성, 오디오 후처리
|
||||
- writer (Copywriter) : 카피라이팅, 영상 스크립트, 캡션, 블로그, 후크
|
||||
- researcher(Trend & Data Researcher) : 트렌드/경쟁사 리서치, 데이터 수집·요약, 사실 확인
|
||||
|
||||
사용자가 한 줄 명령을 내리면, 당신은 어떤 에이전트들을 어떤 순서로 동원할지 결정합니다.
|
||||
|
||||
⚠️ 반드시 아래 JSON 형식으로만 출력하세요. 다른 텍스트(설명, ```json 펜스, 머리말, 꼬리말)는 절대 포함 금지.
|
||||
|
||||
{
|
||||
"brief": "이번 작업이 무엇인지 2~3줄 한국어 요약",
|
||||
"tasks": [
|
||||
{"agent": "youtube", "task": "구체적이고 실행 가능한 한국어 지시"}
|
||||
]
|
||||
}
|
||||
|
||||
🛑 **최소 동원 원칙 — 절대 위반 금지**:
|
||||
1. **단순 데이터 조회·정보 확인 명령은 데이터 에이전트 1명만**. 예: "내 채널 분석", "구독자 수", "오늘 일정", "최근 영상" → tasks 배열에 1명. 추가 분석 에이전트(researcher/business/designer/writer) 절대 추가 금지. 사용자가 추가 분석을 *명시적으로* 요청해야만 추가.
|
||||
2. **창작·기획 명령일 때만 multi-agent**. 예: "영상 기획해줘", "썸네일 만들어", "수익화 전략 짜줘" → 관련 에이전트 2~3명. 5명 이상 절대 금지.
|
||||
3. **상관없는 에이전트 끌어오지 마라**. 사용자 명령이 유튜브 데이터인데 designer/writer 부르는 건 즉시 금지. 사용자가 "디자인"·"카피"·"썸네일" 같은 단어를 *직접* 썼을 때만.
|
||||
|
||||
데이터 수집 키워드 매칭 (해당 에이전트만 1명):
|
||||
- "유튜브"·"YouTube"·"내 채널"·"구독자"·"조회수"·"영상 분석" → youtube 1명만
|
||||
- "인스타"·"릴스"·"피드" → instagram 1명만
|
||||
- "캘린더"·"일정"·"오늘 미팅" → secretary 1명만
|
||||
|
||||
기타 규칙:
|
||||
- 논리적 순서로 정렬 (예: 데이터 수집 → 분석 → 창작 — 사용자가 그 모두를 요청한 경우에만)
|
||||
- 각 task는 모호함 없이 구체적·실행가능하게
|
||||
- JSON 외 텍스트는 단 한 글자도 출력 금지
|
||||
- 데이터 수집 없이 researcher/business만 호출하면 LLM이 가짜 분석을 출력합니다 — 절대 금지
|
||||
@@ -0,0 +1,22 @@
|
||||
당신은 {{COMPANY}}의 CEO입니다. 방금 팀이 작업을 끝냈습니다.
|
||||
각 에이전트의 산출물을 읽고 사장님께 올릴 종합 보고서를 작성하세요.
|
||||
|
||||
형식 (한국어 마크다운, 정확히 이대로):
|
||||
|
||||
## ✅ 완료된 작업
|
||||
- (에이전트별 핵심 산출물 1줄씩, 굵은 글씨로 에이전트명)
|
||||
|
||||
## 🚀 다음 액션 (Top 3)
|
||||
1. **(에이전트명)** — 무엇을
|
||||
2. **(에이전트명)** — 무엇을
|
||||
3. **(에이전트명)** — 무엇을
|
||||
|
||||
## 💡 인사이트
|
||||
- 이번 작업에서 발견한 핵심 통찰 1~2개
|
||||
|
||||
규칙: 간결, 사족 금지, 사과·면책 금지. 200자 이내가 이상적.
|
||||
|
||||
⚠️ 데이터 우선 규칙 (반드시 준수):
|
||||
- 산출물에 **실제 숫자/데이터**가 있으면(예: "조회수 중간값 49,931", "영상 6개", "구독자 1,234") **그 데이터를 직접 인용**해 보고하세요. 추상적인 "분석 진행됨" 같은 말로 대체 금지.
|
||||
- 산출물에 `⚠️ LLM 호출 실패` 헤더가 있어도 그 안에 `📊 LLM 실패에도 시스템이 가져온 실데이터` 섹션이 있으면 **데이터는 살아있는 것**입니다. "데이터 로드 실패"로 오해해서 보고하지 마세요. LLM 분석은 못했지만 데이터는 확보했다고 정확히 표시.
|
||||
- 추측·일반론·placeholder 절대 금지. 산출물에 없는 사실 만들어내지 마세요.
|
||||
@@ -0,0 +1,231 @@
|
||||
/**
|
||||
* Disk persistence for company-mode session artefacts.
|
||||
*
|
||||
* Each company turn produces a timestamped directory:
|
||||
*
|
||||
* <workspaceRoot>/.astra/company/sessions/2026-05-13T21-29/
|
||||
* ├─ _brief.md ← CEO's task decomposition
|
||||
* ├─ <agentId>.md ← Each specialist's raw output
|
||||
* ├─ _report.md ← CEO's final synthesis
|
||||
* └─ _session.json ← Structured copy of the SessionResult
|
||||
*
|
||||
* Long-lived per-agent memory + shared decisions live one level up under
|
||||
* `_agents/<id>/memory.md` and `_shared/decisions.md`. The store is
|
||||
* intentionally dumb — markdown files, append-only — so the user can read,
|
||||
* grep, or git-commit them by hand without any tooling.
|
||||
*
|
||||
* Path resolution: we always prefer the **workspace root**. When the user
|
||||
* opens Astra without a workspace (very rare), we fall back to the
|
||||
* extension's globalStorage path so the feature still works rather than
|
||||
* silently swallowing writes.
|
||||
*/
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import * as vscode from 'vscode';
|
||||
import { logError, logInfo } from '../../utils';
|
||||
import {
|
||||
AgentTurnOutput,
|
||||
COMPANY_AGENTS_REL,
|
||||
COMPANY_SESSIONS_REL,
|
||||
COMPANY_SHARED_REL,
|
||||
CompanyTaskPlan,
|
||||
SessionResult,
|
||||
} from './types';
|
||||
|
||||
/**
|
||||
* Resolve the base directory for company data. Falls back to globalStorage
|
||||
* when no workspace is open so the mode still works in a fresh window.
|
||||
*/
|
||||
export function resolveCompanyBase(context: vscode.ExtensionContext): string {
|
||||
const ws = vscode.workspace.workspaceFolders?.[0]?.uri.fsPath;
|
||||
if (ws) return path.join(ws, '.astra', 'company');
|
||||
return path.join(context.globalStorageUri.fsPath, 'company');
|
||||
}
|
||||
|
||||
function _ensureDir(p: string): void {
|
||||
try {
|
||||
fs.mkdirSync(p, { recursive: true });
|
||||
} catch (e: any) {
|
||||
logError('company.sessionStore: mkdir failed.', { path: p, error: e?.message ?? String(e) });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Build a stable, filesystem-safe timestamp like `2026-05-13T21-29-04`.
|
||||
* Colons and milliseconds are stripped so the value is portable across
|
||||
* macOS / Linux / Windows.
|
||||
*/
|
||||
export function newSessionTimestamp(now: Date = new Date()): string {
|
||||
const iso = now.toISOString(); // 2026-05-13T21:29:04.123Z
|
||||
return iso.slice(0, 19).replace(/:/g, '-');
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a new session directory and return its absolute path. The directory
|
||||
* is empty — callers populate it via `writeBrief`, `writeAgentOutput`, etc.
|
||||
*/
|
||||
export function createSessionDir(
|
||||
context: vscode.ExtensionContext,
|
||||
timestamp: string,
|
||||
): string {
|
||||
const base = resolveCompanyBase(context);
|
||||
const dir = path.join(base, 'sessions', timestamp);
|
||||
_ensureDir(dir);
|
||||
return dir;
|
||||
}
|
||||
|
||||
/** Write the CEO planner's brief (`_brief.md`). */
|
||||
export function writeBrief(
|
||||
sessionDir: string,
|
||||
userPrompt: string,
|
||||
plan: CompanyTaskPlan,
|
||||
): void {
|
||||
const lines = [
|
||||
`# Brief — ${path.basename(sessionDir)}`,
|
||||
'',
|
||||
'## User Prompt',
|
||||
userPrompt.trim() || '_(empty)_',
|
||||
'',
|
||||
'## Summary',
|
||||
plan.brief.trim() || '_(no brief)_',
|
||||
'',
|
||||
'## Dispatched Tasks',
|
||||
];
|
||||
if (plan.tasks.length === 0) {
|
||||
lines.push('_(no tasks — CEO decided no dispatch was necessary)_');
|
||||
} else {
|
||||
for (const [i, t] of plan.tasks.entries()) {
|
||||
lines.push(`${i + 1}. **${t.agent}** — ${t.task}`);
|
||||
}
|
||||
}
|
||||
fs.writeFileSync(path.join(sessionDir, '_brief.md'), lines.join('\n'), 'utf8');
|
||||
}
|
||||
|
||||
/** Write a single specialist's output to `<agentId>.md`. */
|
||||
export function writeAgentOutput(sessionDir: string, output: AgentTurnOutput): void {
|
||||
const lines = [
|
||||
`# ${output.agentId} — ${path.basename(sessionDir)}`,
|
||||
'',
|
||||
`**Task:** ${output.task}`,
|
||||
`**Duration:** ${(output.durationMs / 1000).toFixed(1)}s`,
|
||||
output.error ? `**Error:** ${output.error}` : '',
|
||||
'',
|
||||
'---',
|
||||
'',
|
||||
output.response,
|
||||
'',
|
||||
].filter((l) => l !== '');
|
||||
fs.writeFileSync(path.join(sessionDir, `${output.agentId}.md`), lines.join('\n'), 'utf8');
|
||||
}
|
||||
|
||||
/** Write the CEO's final synthesis to `_report.md`. */
|
||||
export function writeReport(sessionDir: string, report: string): void {
|
||||
const header = `# Report — ${path.basename(sessionDir)}\n\n`;
|
||||
fs.writeFileSync(path.join(sessionDir, '_report.md'), header + report.trim() + '\n', 'utf8');
|
||||
}
|
||||
|
||||
/**
|
||||
* Write a machine-readable copy of the whole turn for tooling (debugging,
|
||||
* replays, future analytics). Keeps the markdown files the source of truth
|
||||
* for the user — the JSON is just a convenience for code that reads it back.
|
||||
*/
|
||||
export function writeSessionJson(sessionDir: string, result: SessionResult): void {
|
||||
const cloned: SessionResult = {
|
||||
...result,
|
||||
// Drop the absolute sessionDir from the JSON so the file is portable
|
||||
// across machines — it's already implicit (its own directory).
|
||||
sessionDir: path.basename(sessionDir),
|
||||
};
|
||||
fs.writeFileSync(path.join(sessionDir, '_session.json'), JSON.stringify(cloned, null, 2), 'utf8');
|
||||
}
|
||||
|
||||
// ── Long-lived per-agent memory + shared decisions ─────────────────────────
|
||||
|
||||
function _agentMemoryPath(context: vscode.ExtensionContext, agentId: string): string {
|
||||
return path.join(resolveCompanyBase(context), '_agents', agentId, 'memory.md');
|
||||
}
|
||||
|
||||
/**
|
||||
* Append a short note to `<agent>/memory.md`. Memory accumulates over time;
|
||||
* the dispatcher reads it back as part of the specialist's system prompt so
|
||||
* agents "remember" past work. Best-effort — failures are logged but never
|
||||
* abort the turn.
|
||||
*/
|
||||
export function appendAgentMemory(
|
||||
context: vscode.ExtensionContext,
|
||||
agentId: string,
|
||||
note: string,
|
||||
): void {
|
||||
if (!note.trim()) return;
|
||||
const memPath = _agentMemoryPath(context, agentId);
|
||||
try {
|
||||
_ensureDir(path.dirname(memPath));
|
||||
const stamp = new Date().toISOString();
|
||||
const block = `\n\n## ${stamp}\n${note.trim()}\n`;
|
||||
fs.appendFileSync(memPath, block, 'utf8');
|
||||
} catch (e: any) {
|
||||
logError('company.sessionStore: agent memory append failed.', {
|
||||
agentId, error: e?.message ?? String(e),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
/** Read `<agent>/memory.md` (or empty string if missing). */
|
||||
export function readAgentMemory(context: vscode.ExtensionContext, agentId: string): string {
|
||||
const memPath = _agentMemoryPath(context, agentId);
|
||||
if (!fs.existsSync(memPath)) return '';
|
||||
try { return fs.readFileSync(memPath, 'utf8'); } catch { return ''; }
|
||||
}
|
||||
|
||||
function _sharedPath(context: vscode.ExtensionContext, fileName: string): string {
|
||||
return path.join(resolveCompanyBase(context), '_shared', fileName);
|
||||
}
|
||||
|
||||
/** Append a decision/learning to `_shared/decisions.md`. */
|
||||
export function appendDecision(context: vscode.ExtensionContext, decision: string): void {
|
||||
if (!decision.trim()) return;
|
||||
const p = _sharedPath(context, 'decisions.md');
|
||||
try {
|
||||
_ensureDir(path.dirname(p));
|
||||
const stamp = new Date().toISOString();
|
||||
fs.appendFileSync(p, `- ${stamp} — ${decision.trim()}\n`, 'utf8');
|
||||
} catch (e: any) {
|
||||
logError('company.sessionStore: decisions append failed.', { error: e?.message ?? String(e) });
|
||||
}
|
||||
}
|
||||
|
||||
/** Read `_shared/decisions.md` (or empty string). Trimmed to the last N chars. */
|
||||
export function readDecisions(context: vscode.ExtensionContext, maxChars: number = 2000): string {
|
||||
const p = _sharedPath(context, 'decisions.md');
|
||||
if (!fs.existsSync(p)) return '';
|
||||
try {
|
||||
const raw = fs.readFileSync(p, 'utf8');
|
||||
return raw.length > maxChars ? '…' + raw.slice(-maxChars) : raw;
|
||||
} catch { return ''; }
|
||||
}
|
||||
|
||||
/** List existing session directories, newest first. */
|
||||
export function listSessions(context: vscode.ExtensionContext): string[] {
|
||||
const dir = path.join(resolveCompanyBase(context), 'sessions');
|
||||
if (!fs.existsSync(dir)) return [];
|
||||
try {
|
||||
return fs.readdirSync(dir)
|
||||
.filter((name) => fs.statSync(path.join(dir, name)).isDirectory())
|
||||
.sort()
|
||||
.reverse();
|
||||
} catch (e: any) {
|
||||
logError('company.sessionStore: list failed.', { error: e?.message ?? String(e) });
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
/** Convenience used by the chip after a turn finishes. */
|
||||
export function logSessionCreated(sessionDir: string, agentCount: number): void {
|
||||
logInfo('company.sessionStore: session created.', {
|
||||
dir: path.basename(sessionDir),
|
||||
agents: agentCount,
|
||||
});
|
||||
}
|
||||
|
||||
// Re-export path constants for callers that need to namespace under the same dirs.
|
||||
export { COMPANY_AGENTS_REL, COMPANY_SESSIONS_REL, COMPANY_SHARED_REL };
|
||||
@@ -0,0 +1,108 @@
|
||||
/**
|
||||
* Type definitions for the 1인 기업 (One-Person Company) mode.
|
||||
*
|
||||
* The mode turns the user into a virtual CEO that dispatches work to a roster
|
||||
* of specialist agents. Each turn produces a session directory containing the
|
||||
* CEO's brief, every specialist's output, and the final synthesis report. The
|
||||
* dispatcher runs agents *sequentially* — only one LLM is loaded at any
|
||||
* moment — so the user can run multiple distinct agents on a single
|
||||
* model-constrained machine without RAM thrash.
|
||||
*/
|
||||
|
||||
/** Static description of a company agent. Loaded from `agents.ts`. */
|
||||
export interface CompanyAgentDef {
|
||||
/** Stable identifier used in JSON plans, file names, config keys. */
|
||||
id: string;
|
||||
/** Display name (may be a Korean nickname like "레오" or "코다리"). */
|
||||
name: string;
|
||||
/** Role title shown in the manage panel and used in system prompts. */
|
||||
role: string;
|
||||
/** Single emoji used in chat headers and chip badges. */
|
||||
emoji: string;
|
||||
/** Brand colour for the agent card UI. CSS hex. */
|
||||
color: string;
|
||||
/** Comma-list of areas this agent owns. Drives the CEO's planner. */
|
||||
specialty: string;
|
||||
/** One-line punchy tagline shown under the agent name. */
|
||||
tagline: string;
|
||||
/** Optional voice / personality directive injected into the system prompt. */
|
||||
persona?: string;
|
||||
/**
|
||||
* When true, this agent can't be toggled off in the UI. CEO uses this so
|
||||
* it's always available as the orchestrator.
|
||||
*/
|
||||
alwaysOn?: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
* Persisted runtime state for the company mode. Stored in VS Code's
|
||||
* `globalState` plus a small JSON file under `.astra/company/_shared/`.
|
||||
*/
|
||||
export interface CompanyState {
|
||||
/** When false, the chip is shown but prompts route through normal chat. */
|
||||
enabled: boolean;
|
||||
/** User-facing name surfaced in CEO prompts and the chip badge. */
|
||||
companyName: string;
|
||||
/** Agents the user has toggled on. CEO is implicitly included. */
|
||||
activeAgentIds: string[];
|
||||
/**
|
||||
* Optional per-agent model override. Empty string / missing key means
|
||||
* "use the global default model". When the user assigns *different*
|
||||
* models to two agents, the LM Studio lifecycle manager unloads one and
|
||||
* loads the other between dispatches — RAM holds exactly one model at a
|
||||
* time, by design.
|
||||
*/
|
||||
modelOverrides: Record<string, string>;
|
||||
}
|
||||
|
||||
/** Output of the CEO planner LLM call after JSON parsing. */
|
||||
export interface CompanyTaskPlan {
|
||||
/** 2-3 sentence Korean summary of what the company is going to do. */
|
||||
brief: string;
|
||||
/** Ordered list of agent dispatches. Order is execution order. */
|
||||
tasks: Array<{
|
||||
/** Agent id (must exist in `AGENTS` and be active). */
|
||||
agent: string;
|
||||
/** Concrete, actionable instruction for the specialist. */
|
||||
task: string;
|
||||
}>;
|
||||
}
|
||||
|
||||
/** One agent's contribution to a turn. */
|
||||
export interface AgentTurnOutput {
|
||||
agentId: string;
|
||||
task: string;
|
||||
/** Raw LLM output, before action-tag execution. */
|
||||
response: string;
|
||||
/** Wall-clock milliseconds spent on this dispatch (LLM + tools). */
|
||||
durationMs: number;
|
||||
/** Populated when the dispatch failed; `response` then holds the error. */
|
||||
error?: string;
|
||||
}
|
||||
|
||||
/** The whole result of a company turn — persisted under sessions/<timestamp>/. */
|
||||
export interface SessionResult {
|
||||
/** ISO timestamp used as the session directory name. */
|
||||
timestamp: string;
|
||||
/** Absolute filesystem path of the session directory. */
|
||||
sessionDir: string;
|
||||
/** What the user typed. */
|
||||
userPrompt: string;
|
||||
/** The CEO's plan that drove this turn. */
|
||||
plan: CompanyTaskPlan;
|
||||
/** Per-agent outputs, in execution order. */
|
||||
agentOutputs: AgentTurnOutput[];
|
||||
/** CEO's final synthesis. Empty when the synthesis call failed. */
|
||||
report: string;
|
||||
/** Walls-clock milliseconds from prompt arrival to report emission. */
|
||||
totalDurationMs: number;
|
||||
}
|
||||
|
||||
/** Where on disk the company state lives, relative to the workspace root. */
|
||||
export const COMPANY_DIR_REL = '.astra/company';
|
||||
export const COMPANY_SHARED_REL = `${COMPANY_DIR_REL}/_shared`;
|
||||
export const COMPANY_SESSIONS_REL = `${COMPANY_DIR_REL}/sessions`;
|
||||
export const COMPANY_AGENTS_REL = `${COMPANY_DIR_REL}/_agents`;
|
||||
|
||||
/** State-key namespaces used in VS Code's globalState. */
|
||||
export const COMPANY_STATE_KEY = 'g1nation.company.state';
|
||||
@@ -18,6 +18,15 @@ export async function handleChatMessage(provider: SidebarChatProvider, data: any
|
||||
case 'promptWithFile':
|
||||
provider._lmStudio?.activity.bump();
|
||||
await provider._context.globalState.update(SidebarChatProvider.blankChatStateKey, false);
|
||||
// ── 1인 기업 모드 우선 분기 ──
|
||||
// When company mode is active, route the prompt through the
|
||||
// CEO planner / sequential dispatcher / synthesis pipeline
|
||||
// instead of the normal single-agent path. The user-facing
|
||||
// chat surface is the same — only the runtime differs.
|
||||
if (provider.isCompanyModeEnabled() && typeof data.value === 'string' && data.value.trim()) {
|
||||
await provider._runCompanyTurn(data.value.trim());
|
||||
return true;
|
||||
}
|
||||
await provider._handlePrompt(data);
|
||||
await provider._autoWriteChronicleAfterPrompt();
|
||||
await provider._saveCurrentSession();
|
||||
@@ -36,6 +45,9 @@ export async function handleChatMessage(provider: SidebarChatProvider, data: any
|
||||
// Restore the Project Architecture chip + watcher if the active project
|
||||
// was already running in architecture mode in a previous VS Code session.
|
||||
await provider._sendArchitectureStatus();
|
||||
// Restore the Company chip from globalState so the user sees the same
|
||||
// mode they had on at last shutdown.
|
||||
await provider._sendCompanyStatus();
|
||||
return true;
|
||||
case 'getReadyStatus':
|
||||
await provider._sendReadyStatus();
|
||||
@@ -147,6 +159,45 @@ export async function handleChatMessage(provider: SidebarChatProvider, data: any
|
||||
}
|
||||
return true;
|
||||
}
|
||||
// ── 1인 기업 모드 메시지 라우팅 ────────────────────────────────────
|
||||
case 'getCompanyStatus':
|
||||
await provider._sendCompanyStatus();
|
||||
return true;
|
||||
case 'getCompanyAgents':
|
||||
await provider._sendCompanyAgents();
|
||||
return true;
|
||||
case 'setCompanyEnabled': {
|
||||
const { setCompanyEnabled } = await import('../features/company');
|
||||
await setCompanyEnabled(provider._context, !!data.value);
|
||||
await provider._sendCompanyStatus();
|
||||
return true;
|
||||
}
|
||||
case 'setCompanyName': {
|
||||
const { setCompanyName } = await import('../features/company');
|
||||
await setCompanyName(provider._context, typeof data.value === 'string' ? data.value : '');
|
||||
await provider._sendCompanyStatus();
|
||||
return true;
|
||||
}
|
||||
case 'setCompanyActiveAgents': {
|
||||
const { setActiveAgents } = await import('../features/company');
|
||||
const ids = Array.isArray(data.value)
|
||||
? data.value.filter((v: unknown): v is string => typeof v === 'string')
|
||||
: [];
|
||||
await setActiveAgents(provider._context, ids);
|
||||
await provider._sendCompanyStatus();
|
||||
await provider._sendCompanyAgents();
|
||||
return true;
|
||||
}
|
||||
case 'setCompanyAgentModel': {
|
||||
const { setAgentModelOverride } = await import('../features/company');
|
||||
const agentId = typeof data.agentId === 'string' ? data.agentId : '';
|
||||
const model = typeof data.model === 'string' ? data.model : '';
|
||||
if (agentId) {
|
||||
await setAgentModelOverride(provider._context, agentId, model);
|
||||
await provider._sendCompanyAgents();
|
||||
}
|
||||
return true;
|
||||
}
|
||||
case 'proactiveTrigger':
|
||||
await provider._handleProactiveSuggestion(data.context);
|
||||
return true;
|
||||
|
||||
@@ -34,6 +34,15 @@ import {
|
||||
scanProject,
|
||||
} from './features/projectArchitecture';
|
||||
import { detectProjectIntent, KnownProject } from './features/projectArchitecture/intentDetector';
|
||||
import {
|
||||
readCompanyState,
|
||||
runCompanyTurn,
|
||||
summarizeForChip,
|
||||
CompanyTurnEvent,
|
||||
COMPANY_AGENTS,
|
||||
COMPANY_AGENT_ORDER,
|
||||
} from './features/company';
|
||||
import { AIService } from './core/services';
|
||||
|
||||
export interface SidebarLmStudioDeps {
|
||||
lifecycle: ModelLifecycleManager;
|
||||
@@ -1177,6 +1186,94 @@ export class SidebarChatProvider implements vscode.WebviewViewProvider, BridgeIn
|
||||
});
|
||||
}
|
||||
|
||||
// ─── 1인 기업 (Company) Mode ────────────────────────────────────────────
|
||||
//
|
||||
// When `companyState.enabled` is true, prompts coming through the chat
|
||||
// handler are routed to `_runCompanyTurn` instead of the normal
|
||||
// AgentExecutor path. The dispatcher emits `companyTurnUpdate` events as
|
||||
// each phase progresses; the webview shows a step-by-step header for
|
||||
// CEO planning, each specialist's dispatch, and the final synthesis.
|
||||
|
||||
/** True iff company mode is active. Cheap — read from globalState. */
|
||||
isCompanyModeEnabled(): boolean {
|
||||
return readCompanyState(this._context).enabled;
|
||||
}
|
||||
|
||||
/** Send the chip state (active flag + agent count + name) to the webview. */
|
||||
async _sendCompanyStatus(): Promise<void> {
|
||||
if (!this._view) return;
|
||||
const state = readCompanyState(this._context);
|
||||
this._view.webview.postMessage({
|
||||
type: 'companyStatus',
|
||||
value: {
|
||||
enabled: state.enabled,
|
||||
companyName: state.companyName,
|
||||
summary: summarizeForChip(state),
|
||||
activeAgentIds: state.activeAgentIds,
|
||||
modelOverrides: state.modelOverrides,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/** Push the full agent catalogue when the manage panel opens. */
|
||||
async _sendCompanyAgents(): Promise<void> {
|
||||
if (!this._view) return;
|
||||
const state = readCompanyState(this._context);
|
||||
const agents = COMPANY_AGENT_ORDER.map((id) => {
|
||||
const def = COMPANY_AGENTS[id];
|
||||
return {
|
||||
id,
|
||||
name: def.name,
|
||||
role: def.role,
|
||||
emoji: def.emoji,
|
||||
color: def.color,
|
||||
tagline: def.tagline,
|
||||
specialty: def.specialty,
|
||||
hasPersona: !!def.persona,
|
||||
alwaysOn: !!def.alwaysOn,
|
||||
active: id === 'ceo' || state.activeAgentIds.includes(id),
|
||||
modelOverride: state.modelOverrides[id] || '',
|
||||
};
|
||||
});
|
||||
this._view.webview.postMessage({
|
||||
type: 'companyAgents',
|
||||
value: {
|
||||
companyName: state.companyName,
|
||||
agents,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Drive one full company turn. Caller is the chat handler; it's already
|
||||
* persisted the user message and started a streaming bubble. We feed
|
||||
* progress events back as `companyTurnUpdate` messages so the same bubble
|
||||
* fills in as each agent finishes.
|
||||
*/
|
||||
async _runCompanyTurn(userPrompt: string): Promise<void> {
|
||||
const cfg = getConfig();
|
||||
const ai = new AIService();
|
||||
const emit = (event: CompanyTurnEvent) => {
|
||||
this._view?.webview.postMessage({ type: 'companyTurnUpdate', value: event });
|
||||
};
|
||||
try {
|
||||
await runCompanyTurn(userPrompt, {
|
||||
context: this._context,
|
||||
ai,
|
||||
defaultModel: cfg.defaultModel || 'gemma4:e2b',
|
||||
onEvent: emit,
|
||||
});
|
||||
} catch (e: any) {
|
||||
logError('company.runTurn: unexpected failure.', { error: e?.message ?? String(e) });
|
||||
this._view?.webview.postMessage({
|
||||
type: 'error',
|
||||
value: `1인 기업 모드 실행 실패: ${e?.message ?? e}`,
|
||||
});
|
||||
} finally {
|
||||
void this._sendReadyStatus();
|
||||
}
|
||||
}
|
||||
|
||||
/** Open the architecture doc in editor group 2. */
|
||||
async _openArchitectureDoc(): Promise<void> {
|
||||
const p = this._getActiveChronicleProject();
|
||||
|
||||
Reference in New Issue
Block a user