gpt5mcp

# GPT-5 Agent Response Capture Analysis & Solution ## Root Cause Analysis ### Token Limit Discovery - **GPT-5 Context**: 400k total (272k input + 128k reasoning+output combined) - **Critical Issue**: Reasoning tokens are HIDDEN and consume the 128k output budget - **Failure Pattern**: When input >350k + reasoning effort = high, reasoning tokens exhaust the budget ### Test Results Pattern | Input Tokens | Status | Reasoning Load | Outcome | |-------------|--------|---------------|---------| | <50k | ✅ Success | Low context, minimal reasoning | Full capture | | 350k+ + high effort | ❌ Failed | Massive reasoning consumption | No capture | | 350k+ + low effort | ✅ Success | Controlled reasoning usage | Truncated capture | ## Current Limitations from Memory Management Fix ### 1. Output Truncation (50KB limit) - **Impact**: Complex responses get cut off mid-sentence - **Problem**: Arbitrary truncation may lose critical information - **User Experience**: Incomplete answers for detailed research ### 2. Result Formatting Limits (60KB) - **Impact**: Tool execution details, reasoning summaries removed - **Problem**: Loss of debugging information for complex tasks - **User Experience**: Reduced transparency in agent decision-making ### 3. Response Size Validation (10MB limit) - **Impact**: Blocks extremely large JSON responses entirely - **Problem**: Nuclear option - complete failure vs degraded service - **User Experience**: Hard failures on edge cases ## Recommended Solution: External File Storage ### Architecture Overview ``` GPT-5 Agent → Generate Full Response → Save to File → Return File Path + Summary ↓ gpt5_docs/ ├── resp_<thread_id>_<timestamp>_<msg_num>.md ├── resp_<thread_id>_<timestamp>_<msg_num>_reasoning.md └── resp_<thread_id>_<timestamp>_<msg_num>_tools.json ``` ### Implementation Strategy #### 1. File-Based Response Storage - **Location**: `gpt5_docs/<thread_id>/` - **Naming**: `resp_{timestamp}_{msg_num}_{type}.{ext}` - **Types**: - `main.md` - Primary response content - `reasoning.md` - Reasoning summary - `tools.json` - Tool execution details - `raw.json` - Complete API response #### 2. Smart Content Segmentation ```typescript interface AgentFileOutput { summary: string; // 2KB max - always returned in context full_content_path: string; // Path to complete response sections: { main_result: string; // Path to core answer reasoning: string; // Path to reasoning details tool_calls: string; // Path to tool execution log }; metadata: { token_usage: TokenUsage; execution_time: number; truncation_applied: boolean; }; } ``` #### 3. Adaptive Response Management - **Phase 1**: Always save full response to file - **Phase 2**: Generate intelligent summary (key points + file reference) - **Phase 3**: Return summary in context + file paths for details #### 4. Reasoning Token Optimization - **Dynamic Effort Scaling**: Start with `low` effort, escalate only when needed - **Context Windowing**: Summarize intermediate results to reduce input tokens - **Tool-First Architecture**: Push complex processing to tools, minimal reasoning for synthesis ## Implementation Options ### Option A: Minimal Change (Quick Fix) - Add file saving to existing agent - Return file path + 2KB summary - **Pros**: Fast implementation, maintains compatibility - **Cons**: Still has underlying reasoning token issue ### Option B: Adaptive Agent (Recommended) - Implement reasoning effort auto-scaling - File storage with intelligent segmentation - Context management with summarization - **Pros**: Solves root cause, scalable, robust - **Cons**: More complex implementation ### Option C: Hybrid Architecture - Multiple specialized agents for different complexity levels - File storage for complex tasks, in-memory for simple ones - **Pros**: Optimal performance per use case - **Cons**: Most complex, requires task classification ## Technical Implementation Details ### 1. File Storage Structure ``` gpt5_docs/ ├── index.json (metadata registry) ├── {thread_id}/ │ ├── resp_001_main.md │ ├── resp_001_reasoning.md │ ├── resp_001_tools.json │ └── resp_001_raw.json ``` ### 2. Response Processing Pipeline 1. **Execute Agent** → Get full API response 2. **Save Raw Data** → Store complete response as JSON 3. **Extract Sections** → Parse into main/reasoning/tools 4. **Generate Summary** → Create 2KB intelligent summary 5. **Return Reference** → File paths + summary to user ### 3. Context Management - **Before**: 500k+ tokens in context causing failures - **After**: 5k summary + file references = efficient context usage ### 4. User Experience Improvements ```markdown ## 🤖 GPT-5 Agent Task Completed **Response ID**: resp_68ac71125b3881a2... **Full Response**: `gpt5_docs/thread123/resp_001_main.md` ### 📝 Summary [2KB intelligent summary with key points] ### 📄 Detailed Results - **Main Analysis**: [View Details](gpt5_docs/thread123/resp_001_main.md) - **Reasoning Process**: [View Reasoning](gpt5_docs/thread123/resp_001_reasoning.md) - **Tool Executions**: [View Tools](gpt5_docs/thread123/resp_001_tools.json) ### 📊 Token Usage - Input: 489,487 tokens - Output: 3,016 tokens (full content saved to file) - Total: 492,503 tokens ``` ## Advantages of File Storage Solution ### 1. No Size Limitations - **Current**: Arbitrary 50KB truncation - **New**: Complete responses preserved in files - **Benefit**: Full research results always available ### 2. Better User Experience - **Current**: Incomplete responses in chat - **New**: Summary in chat + full details in files - **Benefit**: Quick overview + deep dive available ### 3. Context Efficiency - **Current**: Massive responses consume context - **New**: Small summaries maintain conversation flow - **Benefit**: Longer conversations without context overflow ### 4. Debugging & Transparency - **Current**: Limited tool execution visibility - **New**: Complete execution logs in structured files - **Benefit**: Full transparency for complex agent operations ### 5. Future Extensibility - **Current**: Monolithic response handling - **New**: Modular file-based architecture - **Benefit**: Easy to add features like response caching, search, analysis ## Risk Assessment ### Low Risk - File system I/O performance (modern SSDs handle this easily) - Disk space usage (text files are small, can implement cleanup) ### Medium Risk - File path security (need proper sanitization) - Concurrent access (multiple agents writing simultaneously) ### High Risk - User workflow disruption (need good UX for file references) - Backward compatibility (existing MCP clients expect inline responses) ## Recommended Implementation Plan ### Phase 1: Foundation (1-2 hours) 1. Create `gpt5_docs` folder structure 2. Implement file saving functions 3. Add file path returns to agent response ### Phase 2: Intelligence (2-3 hours) 1. Implement intelligent summarization 2. Add section-based file organization 3. Create file reference UI improvements ### Phase 3: Optimization (2-4 hours) 1. Implement adaptive reasoning effort 2. Add context windowing for large inputs 3. Performance testing and refinement ### Phase 4: Polish (1-2 hours) 1. Error handling and recovery 2. Cleanup utilities 3. Documentation and examples ## Success Metrics - **Before**: ~30% success rate on complex tasks (>350k tokens) - **Target**: >95% success rate with file storage solution - **Context Efficiency**: Reduce in-chat response size by 80% - **User Satisfaction**: Full responses always available, better organized