fix: n_ctx retry in createStreamingRequest - compress msgs at API level not handlePrompt

.astra/tests/stress/.astra/cache/259a37934ead3910a8722b82054d46d2ca2057b05c488be1dcf439166ac5a9a1.json

@@ -1,5 +1,5 @@
 {
   "result": "Final report with inconsistencies. This should be long enough to pass validation.",
-  "createdAt": 1778136180630,
+  "createdAt": 1778136474568,
   "modelVersion": "unknown"
 }

.astra/tests/stress/.astra/cache/65775be352df43297b63c7af59c9f4f39d2bc368f77456c37b5eef9a94a66b5c.json

@@ -1,5 +1,5 @@
 {
   "result": "[CONFLICT WARNING] 성능이 200% 증가했습니다. vs 그러나 동시에 50% 감소했습니다. 최적화와 성능 저하가 동시에 발견됨.",
-  "createdAt": 1778136180628,
+  "createdAt": 1778136474566,
   "modelVersion": "unknown"
 }

.astra/tests/stress/.astra/cache/6894d26c5b0a55d25d756a473225c7a44d7661af673b24e3f49551a7a2e50280.json

@@ -1,5 +1,5 @@
 {
   "result": "Detailed Execution Plan: 1. Research 2. Analyze 3. Write report with high quality.",
-  "createdAt": 1778136180626,
+  "createdAt": 1778136474564,
   "modelVersion": "unknown"
 }

.astra/tests/stress/.astra/cache/88cb61499f88ed38165b64bd3e8adc543795e4b427b64540a49c9ab27c7fe213.json

@@ -1,5 +1,5 @@
 {
-  "result": "---\nid: stress_conflict_1778136180610\ndate: 2026-05-07T06:43:00.632Z\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... (14ms)\n- **[RESEARCHER]** 핵심 정보 수집 및 분석 중... (3ms)\n- **[WRITER]** 최종 리포트 작성 및 편집 중... (2ms)\n",
+  "result": "---\nid: stress_conflict_1778136474544\ndate: 2026-05-07T06:47:54.569Z\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... (18ms)\n- **[RESEARCHER]** 핵심 정보 수집 및 분석 중... (3ms)\n- **[WRITER]** 최종 리포트 작성 및 편집 중... (2ms)\n",
-  "createdAt": 1778136180632,
+  "createdAt": 1778136474569,
   "modelVersion": "unknown"
 }

.astra/tests/stress/.astra/missions/stress_conflict_1778136474544.json

@@ -1,8 +1,8 @@
 {
-  "missionId": "stress_conflict_1778136180610",
+  "missionId": "stress_conflict_1778136474544",
   "status": "completed",
-  "startTime": "2026-05-07T06:43:00.610Z",
+  "startTime": "2026-05-07T06:47:54.544Z",
-  "totalElapsedMs": 23,
+  "totalElapsedMs": 26,
   "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": 14,
+      "durationMs": 18,
       "message": "전략 수립 중...",
-      "ts": "2026-05-07T06:43:00.624Z"
+      "ts": "2026-05-07T06:47:54.562Z"
     },
     {
       "from": "planner",
       "to": "researcher",
       "durationMs": 3,
       "message": "핵심 정보 수집 및 분석 중...",
-      "ts": "2026-05-07T06:43:00.627Z"
+      "ts": "2026-05-07T06:47:54.565Z"
     },
     {
       "from": "researcher",
       "to": "writer",
       "durationMs": 2,
       "message": "최종 리포트 작성 및 편집 중...",
-      "ts": "2026-05-07T06:43:00.629Z"
+      "ts": "2026-05-07T06:47:54.567Z"
     },
     {
       "from": "writer",
       "to": "completed",
-      "durationMs": 4,
+      "durationMs": 3,
       "message": "미션 완료",
-      "ts": "2026-05-07T06:43:00.633Z"
+      "ts": "2026-05-07T06:47:54.570Z"
     }
   ],
   "resilienceMetrics": {

package.json

@@ -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": "2.80.13",
+  "version": "2.80.14",
   "publisher": "g1nation",
   "license": "MIT",
   "icon": "assets/icon.png",

src/agent.ts

@@ -526,38 +526,7 @@ export class AgentExecutor {
                     logInfo('Generation aborted by user.');
                 } else {
                     logError('Stream reading error.', { engine, apiUrl, error: err?.message || String(err) });
-                    // LM Studio llama.cpp n_keep > n_ctx 에러 감지 → 자동 압축 재시도
+                    this.webview?.postMessage({ type: 'error', value: `Connection lost: ${err.message}` });
-                    const errMsg = String(err?.message || err || '');
-                    const nCtxMatch = errMsg.match(/n_keep\s*:\s*(\d+)\s*>=?\s*n_ctx\s*:\s*(\d+)/);
-                    if (nCtxMatch && loopDepth < 1) {
-                        // loopDepth < 1: 재시도는 최초 1회만 (무한루프 방지)
-                        const nCtx = parseInt(nCtxMatch[2], 10);
-                        const nKeep = parseInt(nCtxMatch[1], 10);
-                        logInfo('n_ctx overflow → auto-retrying with compressed system prompt.', { nKeep, nCtx });
-                        // 시스템 프롬프트를 n_ctx - 응답여유(512토큰) 범위로 강제 압축
-                        const maxSysChars = Math.max(800, (nCtx - 512)) * 4;
-                        const compressedSysPrompt = systemPrompt.length > maxSysChars
-                            ? systemPrompt.slice(0, maxSysChars) + `\n[System prompt compressed: n_ctx=${nCtx}]`
-                            : systemPrompt;
-                        this.webview?.postMessage({
-                            type: 'streamChunk',
-                            value: `\n⚠️ *LM Studio n_ctx=${nCtx} 감지. 컨텍스트 압축 후 재시도 중...*\n`
-                        });
-                        await this.handlePrompt(prompt, modelName, {
-                            ...options,
-                            loopDepth: loopDepth + 1, // 재시도 플래그
-                            runId,
-                            systemPrompt: compressedSysPrompt
-                        });
-                    } else if (nCtxMatch) {
-                        // 재시도 이미 했는데도 실패한 경우
-                        this.webview?.postMessage({
-                            type: 'error',
-                            value: `LM Studio n_ctx(${nCtxMatch[2]}) 초과: 압축 재시도도 실패했습니다. LM Studio에서 모델을 재로드하세요.`
-                        });
-                    } else {
-                        this.webview?.postMessage({ type: 'error', value: `Connection lost: ${err.message}` });
-                    }
 
                 }
             }
@@ -2044,6 +2013,7 @@ export class AgentExecutor {
         const primaryEngine = resolveEngine(baseUrl);
         const engines = primaryEngine === 'lmstudio' ? ['lmstudio', 'ollama'] as const : ['ollama', 'lmstudio'] as const;
         let lastError: Error | null = null;
+        let nCtxRetried = false; // n_ctx 재시도 1회 제한
 
         for (const engine of engines) {
             const apiUrl = buildApiUrl(baseUrl, engine, 'chat');
@@ -2052,11 +2022,13 @@ export class AgentExecutor {
 
             for (const candidateModel of modelCandidates) {
                 for (const variant of messageVariants) {
-                    const totalChars = variant.messages.reduce((acc, m) => acc + String(m.content || '').length, 0);
+                    // 실제 전송할 메시지 (n_ctx 재시도 시 수정됨)
+                    let finalMessages = variant.messages;
+                    const totalChars = finalMessages.reduce((acc, m) => acc + String(m.content || '').length, 0);
                     const estimatedTokens = Math.ceil(totalChars / 4);
                     const streamBody = {
                         model: candidateModel,
-                        messages: variant.messages,
+                        messages: finalMessages,
                         stream: true,
                         ...(engine === 'lmstudio'
                             ? { max_tokens: 4096, temperature }
@@ -2065,10 +2037,10 @@ export class AgentExecutor {
                     logInfo('AI streaming request started.', {
                         engine, apiUrl, model: candidateModel,
                         variant: variant.name,
-                        messageCount: variant.messages.length,
+                        messageCount: finalMessages.length,
                         estimatedTokens,
-                        roles: variant.messages.map(message => message.role),
+                        roles: finalMessages.map(message => message.role),
-                        firstUserPreview: summarizeText(String(variant.messages.find(message => message.role === 'user')?.content || ''), 300)
+                        firstUserPreview: summarizeText(String(finalMessages.find(message => message.role === 'user')?.content || ''), 300)
                     });
 
                     try {
@@ -2086,6 +2058,68 @@ export class AgentExecutor {
 
                         if (!response.ok) {
                             const errText = await response.text();
+
+                            // ── LM Studio n_keep >= n_ctx 에러 감지 및 자동 재시도 ──
+                            const nCtxMatch = errText.match(/n_keep\s*:\s*(\d+)\s*>=?\s*n_ctx\s*:\s*(\d+)/);
+                            if (nCtxMatch && engine === 'lmstudio' && !nCtxRetried) {
+                                nCtxRetried = true;
+                                const nCtx = parseInt(nCtxMatch[2], 10);
+                                logInfo(`n_ctx overflow detected (n_ctx=${nCtx}). Compressing messages and retrying...`);
+
+                                // system 메시지를 n_ctx 크기에 맞게 강제 압축
+                                const maxResponseTokens = 512;
+                                const maxSysTokens = Math.max(500, nCtx - maxResponseTokens);
+                                const maxSysChars = maxSysTokens * 4;
+
+                                // 히스토리는 마지막 user 메시지만 유지
+                                const sysMsg = finalMessages.find(m => m.role === 'system');
+                                const lastUserMsg = [...finalMessages].reverse().find(m => m.role === 'user');
+
+                                const compressedMessages: ChatMessage[] = [];
+                                if (sysMsg) {
+                                    const sysContent = String(sysMsg.content || '');
+                                    compressedMessages.push({
+                                        role: 'system',
+                                        content: sysContent.length > maxSysChars
+                                            ? sysContent.slice(0, maxSysChars) + `\n[Compressed for n_ctx=${nCtx}]`
+                                            : sysContent,
+                                        internal: true
+                                    });
+                                }
+                                if (lastUserMsg) {
+                                    compressedMessages.push(lastUserMsg);
+                                }
+
+                                // 압축된 메시지로 즉시 재요청
+                                const retryBody = {
+                                    model: candidateModel,
+                                    messages: compressedMessages.map(m => ({ role: m.role, content: m.content })),
+                                    stream: true,
+                                    max_tokens: Math.min(1024, maxResponseTokens),
+                                    temperature,
+                                };
+
+                                logInfo('Retrying with compressed context.', {
+                                    originalTokens: estimatedTokens,
+                                    compressedTokens: Math.ceil(compressedMessages.reduce((a, m) => a + String(m.content || '').length, 0) / 4),
+                                    nCtx,
+                                    messageCount: compressedMessages.length
+                                });
+
+                                const retryResponse = await fetch(apiUrl, {
+                                    method: 'POST',
+                                    headers: { 'Content-Type': 'application/json', 'Accept': 'text/event-stream', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive' },
+                                    body: JSON.stringify(retryBody),
+                                    signal: this.abortController?.signal
+                                });
+
+                                if (retryResponse.ok) {
+                                    logInfo('n_ctx retry succeeded.', { apiUrl });
+                                    return { response: retryResponse, engine, apiUrl };
+                                }
+                                logError('n_ctx retry also failed.', { status: retryResponse.status });
+                            }
+
                             lastError = new Error(`AI Engine error (${engine}/${variant.name}): ${response.status} - ${summarizeText(errText, 300)}`);
                             logError('AI streaming request returned non-OK status.', { engine, variant: variant.name, apiUrl, status: response.status, body: summarizeText(errText, 500) });
                             continue;