---
id: wiki-2026-0508-ai-connect-llm-tool
title: AI Connect LLM Tool (ConnectAI)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [ConnectAI, Connect-AI-Lab, EZERAI, local AI coding agent, VS Code AI extension]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [vscode-extension, local-llm, ollama, lm-studio, ai-agent, privacy, second-brain, internal-tool]
raw_sources: [Datacollector_Export_Connect-AI-Lab]
last_reinforced: 2026-05-09
github_commit: pending
inferred_by: Claude Opus 4.7 (manual cleanup 2026-05-09)
tech_stack:
  language: TypeScript
  framework: VS Code Extension API / Ollama / LM Studio
applied_in: [Antigravity, ConnectAI]
---

# AI Connect LLM Tool (ConnectAI)

## 📌 한 줄 통찰 (The Karpathy Summary)
> **100% local + offline VS Code AI coding agent**. Ollama / LM Studio 의 hardware 직접 사용 — 외부 server X. File edit + terminal + Second Brain (knowledge base) 통합. 기업 보안 / privacy 친화 의 internal tool.

## 📖 구조화된 지식 (Synthesized Content)

### 핵심 가치
- **100% local**: 매 LLM call 가 사용자 의 machine. Cloud API X.
- **Privacy-first**: code / prompt 가 외부 X. 기업 / 의료 / 법적 case 의 답.
- **Hardware-aware**: 매 사용자 의 GPU / RAM 의 best fit model.
- **VS Code native**: extension API 의 deep 통합.
- **Second Brain**: 매 codebase / wiki / personal note 의 RAG.

### 비교 (with cloud-based)

| | ConnectAI | Cursor / Claude Code |
|---|---|---|
| Privacy | 100% local | Cloud API |
| Cost | Hardware 만 | $20-50 / month |
| Latency | Local GPU 의존 | Network |
| Quality | Local model 의 한계 (Llama 8B-70B) | Frontier (Opus, GPT-4) |
| Offline | Yes | No |
| Setup | Ollama / LM Studio + GPU | Pay + login |
| 매 변경 | Manual update | Server-side (자동) |

→ Privacy / cost / offline 가 critical = ConnectAI.
Quality / 빠른 setup = Cursor / Claude Code.

### Architecture
1. **VS Code Extension** (TS): UI + sidebar + command.
2. **Local LLM Engine**: Ollama 또는 LM Studio.
3. **Tool Registry**: file_read / file_write / shell / search.
4. **Second Brain**: 매 wiki / note 의 vector DB (local).
5. **Agent Loop**: ReAct 식 (think → act → observe).

### Local LLM 옵션
- **Ollama**: 작은 / simple. CLI 친화. Mac M-series 강력.
- **LM Studio**: GUI. 매 model 의 quantize / VRAM 측정.
- **vLLM (advanced)**: production. 큰 model + batching.
- **llama.cpp**: 가장 simple. Mobile / embedded.

### Model 선택 (hardware 따라)
| RAM / VRAM | 추천 model |
|---|---|
| 8 GB | Llama 3.2 3B (Q4) |
| 16 GB | Llama 3.1 8B (Q4) / Mistral 7B |
| 24 GB | Llama 3.1 8B (FP16) / Qwen 14B |
| 32 GB | DeepSeek Coder 33B (Q4) |
| 48 GB | Llama 3 70B (Q4) |
| 96 GB+ | Llama 3 70B (FP16) / DeepSeek V3 |

→ Mac M3 Max 96 GB 가 sweet (Llama 70B 가 fit).

### Sidebar Chat UI
- Streaming response (token 별).
- File reference (@file).
- Multi-turn conversation.
- Code block 의 apply / insert.
- Settings (model, temperature, system prompt).

### Tool 목록
- `read_file(path)`: file content.
- `write_file(path, content)`: write / create.
- `edit_file(path, oldText, newText)`: precise diff.
- `run_command(cmd)`: terminal — 사용자 confirm.
- `search_codebase(query)`: ripgrep / regex.
- `query_brain(question)`: vector DB.

### LM Studio 통합 (lifecycle)
- Model 선택 → load (warm GPU).
- Idle 5 min → unload (VRAM 회수).
- 매 chat 시 → 자동 reload.

→ User 의 다른 work (game) 의 GPU 충돌 방지.

### Second Brain (RAG)
- Wiki / note 의 vector embed (local model).
- 매 query 의 top-K retrieval.
- LLM context 에 inject.
- Privacy: 모든 거 local.

## 💻 코드 패턴 (Code Patterns)

### Extension activation
```ts
// src/extension.ts
import * as vscode from 'vscode';

export function activate(context: vscode.ExtensionContext) {
  const provider = new SidebarChatProvider(context);
  
  context.subscriptions.push(
    vscode.window.registerWebviewViewProvider('connectai.sidebar', provider),
    vscode.commands.registerCommand('connectai.chat', () => provider.show()),
  );
}
```

### LLM call (Ollama)
```ts
async function chat(prompt: string, model: string) {
  const r = await fetch('http://localhost:11434/api/chat', {
    method: 'POST',
    body: JSON.stringify({
      model,
      messages: [{ role: 'user', content: prompt }],
      stream: true,
    }),
  });
  
  const reader = r.body!.getReader();
  let buffer = '';
  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
    buffer += new TextDecoder().decode(value);
    
    let idx;
    while ((idx = buffer.indexOf('\n')) >= 0) {
      const line = buffer.slice(0, idx);
      buffer = buffer.slice(idx + 1);
      if (!line.trim()) continue;
      
      const chunk = JSON.parse(line);
      if (chunk.message?.content) yield chunk.message.content;
    }
  }
}
```

### LM Studio 통합 (lifecycle manager)
```ts
import { LMStudioClient } from '@lmstudio/sdk';

class ModelLifecycleManager {
  private client = new LMStudioClient({ baseUrl: 'http://localhost:1234' });
  private currentModel?: string;
  private idleTimer?: NodeJS.Timeout;
  
  async onModelSelected(modelKey: string) {
    if (this.idleTimer) clearTimeout(this.idleTimer);
    if (this.currentModel === modelKey) return;
    
    if (this.currentModel) await this.client.llm.unload(this.currentModel);
    await this.client.llm.load(modelKey);
    this.currentModel = modelKey;
    
    this.scheduleIdleUnload();
  }
  
  onActivity() {
    if (this.idleTimer) {
      clearTimeout(this.idleTimer);
      this.scheduleIdleUnload();
    }
  }
  
  private scheduleIdleUnload() {
    const timeout = vscode.workspace.getConfiguration('connectai').get<number>('idleTimeoutMs', 300_000);
    if (timeout <= 0) return;
    
    this.idleTimer = setTimeout(async () => {
      if (this.currentModel) {
        await this.client.llm.unload(this.currentModel);
        this.currentModel = undefined;
      }
    }, timeout);
  }
}
```

### Tool execution (file edit)
```ts
async function editFile(path: string, oldText: string, newText: string) {
  const uri = vscode.Uri.file(path);
  const doc = await vscode.workspace.openTextDocument(uri);
  
  const text = doc.getText();
  const idx = text.indexOf(oldText);
  if (idx === -1) throw new Error('oldText not found');
  
  const edit = new vscode.WorkspaceEdit();
  const start = doc.positionAt(idx);
  const end = doc.positionAt(idx + oldText.length);
  edit.replace(uri, new vscode.Range(start, end), newText);
  
  await vscode.workspace.applyEdit(edit);
}
```

### Run command (with user confirmation)
```ts
async function runCommand(cmd: string): Promise<string> {
  // Always ask user first
  const ok = await vscode.window.showWarningMessage(
    `Run command: ${cmd}?`,
    { modal: true },
    'Yes', 'No'
  );
  
  if (ok !== 'Yes') return 'cancelled';
  
  const term = vscode.window.createTerminal('ConnectAI');
  term.show();
  term.sendText(cmd);
  // Wait + capture output (separate logic).
  return await waitForOutput(term);
}
```

### Second Brain (RAG)
```ts
import { ChromaClient } from 'chromadb';
const chroma = new ChromaClient({ path: 'http://localhost:8000' });

async function queryBrain(question: string): Promise<string[]> {
  const collection = await chroma.getCollection({ name: 'wiki' });
  const emb = await embedLocal(question);   // Ollama embedding model
  
  const results = await collection.query({
    queryEmbeddings: [emb],
    nResults: 5,
  });
  
  return results.documents[0];
}
```

### Configuration
```json
// .vscode/settings.json
{
  "connectai.engine": "lmstudio",  // "ollama" | "lmstudio"
  "connectai.ollamaUrl": "http://localhost:11434",
  "connectai.lmStudioUrl": "http://localhost:1234",
  "connectai.defaultModel": "llama-3.1-8b-instruct",
  "connectai.lmStudio.idleTimeoutMs": 300000,
  "connectai.lmStudio.autoLoadOnSelect": true
}
```

## 🤔 의사결정 기준 (Decision Criteria)

| 상황 | ConnectAI | Cursor / Claude Code |
|---|---|---|
| Sensitive code (의료, 금융, 정부) | ✅ ConnectAI | ❌ |
| Quality 우선 (frontier model) | ❌ | ✅ |
| Offline 작업 | ✅ | ❌ |
| 매월 cost ↓ | ✅ | ❌ ($20+/month) |
| 빠른 setup | ❌ (model download) | ✅ |
| Multi-file refactor | 작은 model 의 한계 | ✅ |
| Air-gapped | ✅ | ❌ |

**기본값**: Privacy / offline 가 hard requirement → ConnectAI. Productivity / quality 우선 → Cursor / Claude Code.

## ⚠️ 모순 및 업데이트 (Contradictions & Updates)
- **Quality gap**: Local 70B 가 Cloud Opus 보다 약. 매 task 의 reality check.
- **Hardware cost**: M3 Max + 96 GB = $4000+. ROI 가 매월 cloud subscription 와 비교 (1-2 year breakeven).
- **Architecture**: 현재 monolithic (extension.ts heavy) → modular 권장. lmstudio module 의 분리 가 best practice.
- **`run_command` security**: 매 user 의 confirmation 가 critical. 자동 실행 = system 위험.
- **Model lifecycle**: 옛 = 매 chat 의 load (slow). 모던 = persistent + idle eject (LM Studio 통합).

## 🔗 지식 연결 (Graph)
- 관련 tool: [[Ollama]] · [[LM-Studio]] · [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[llama.cpp]]
- Cloud alternative: [[Claude-Code]]
- 적용: [[Connect-AI-Lab]]

## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

**언제 이 지식을 쓰는가:**
- 기업 의 internal AI tool 의 design.
- Privacy-sensitive code 의 AI assist.
- ConnectAI 의 새 기능 / refactor.
- Local LLM 의 hardware sizing.
- LM Studio / Ollama 통합.
- Second Brain / RAG architecture.

**언제 쓰면 안 되는가:**
- 매 dev 가 cloud OK + cost 가 OK = Cursor 가 더 좋음.
- 매우 큰 codebase 의 mass refactor = 큰 model (Opus / GPT-4) 가 quality.
- Quick prototype — setup overhead.
- 사용자 의 hardware 가 부족 — 매 model 가 slow.

## ❌ 안티패턴 (Anti-Patterns)
- **`run_command` 자동 실행**: 매 LLM 의 hallucination = `rm -rf` 위험.
- **Monolithic extension.ts**: 매 feature 의 추가 시 maintainability ↓. Module 화.
- **No idle eject**: VRAM 영구 점유 → 다른 work 의 GPU contention.
- **Cloud model 의 fallback 가 default**: privacy 의 가치 X.
- **Embedding 가 cloud (OpenAI)**: privacy violation.
- **No tool whitelisting**: shell + file 의 unrestricted = 사고.
- **Quality 의 cloud-comparison expectation**: 매 user 의 gap 인지.

## 🧪 검증 상태 (Validation)
- **정보 상태:** verified (applied — Antigravity 프로젝트 의 active dev).
- **출처 신뢰도:** A (project's primary tool).
- **검토 이유:** Manual cleanup. 매 architecture 의 implementation detail 가 ConnectAI repo 와 sync.

## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[Local-LLM-Inference]] (concept), [[VS-Code-Extension-Patterns]] (technical), [[Code Agent — Devin / Cursor / Claude Code]] (general).
- **처리 방식:** KEEP (specific tool 의 documentation).
- **처리 이유:** ConnectAI 의 own design / architecture 가 distinct.

## 🕓 변경 이력 (Changelog)
| 날짜 | 변경 내용 | 처리 방식 | 신뢰도 |
|------|-----------|-----------|--------|
| 2026-05-08 | P-Reinforce Phase 1 정규화 | UPDATE | A |
| 2026-05-09 | Manual cleanup — code pattern + lifecycle 통합 + 의사결정 기준 + 안티패턴 추가 | UPDATE | A |