# Proof of Concept: ChatGPT Integration
## Objective
Validate the feasibility of extending our Claude Memory MCP system to work with ChatGPT by building a minimal working prototype.
## Approach
Start with ChatGPT as it's the most widely used alternative to Claude, making it the highest-value proof of concept.
## Phase 1: Research and Analysis (Week 1)
### **1.1 ChatGPT MCP Support Investigation**
**Research Tasks:**
- [ ] Check OpenAI's official MCP documentation
- [ ] Search OpenAI developer forums for MCP discussions
- [ ] Review OpenAI API documentation for MCP endpoints
- [ ] Test existing MCP servers with ChatGPT (if possible)
**Key Questions:**
- Does ChatGPT natively support MCP protocol?
- Are there any community bridges or proxies?
- What's OpenAI's roadmap for MCP support?
- What alternatives exist if no MCP support?
### **1.2 ChatGPT Export Format Analysis**
**Tasks:**
- [ ] Export sample ChatGPT conversations from web interface
- [ ] Analyze JSON structure and metadata fields
- [ ] Compare with Claude conversation format
- [ ] Identify unique ChatGPT-specific fields
**Sample Export Analysis:**
```json
{
"conversations": [
{
"id": "...",
"title": "...",
"create_time": "...",
"update_time": "...",
"mapping": {
"message_id": {
"id": "...",
"message": {
"author": {"role": "user|assistant"},
"content": {"parts": ["..."]},
"create_time": "...",
"metadata": {}
}
}
}
}
]
}
```
**Analysis Goals:**
- Map ChatGPT fields to our universal schema
- Identify metadata we can extract (model, creation time, etc.)
- Document conversion challenges and data loss
## Phase 2: Basic Importer Prototype (Week 2)
### **2.1 ChatGPT Format Parser**
Create `src/importers/chatgpt_importer.py`:
```python
class ChatGPTImporter:
"""Convert ChatGPT export format to universal conversation format"""
def __init__(self, config=None):
self.config = config or {}
def parse_export(self, export_data: dict) -> List[Dict]:
"""Parse ChatGPT export and return universal format conversations"""
conversations = []
for conv in export_data.get('conversations', []):
conversation = self._convert_conversation(conv)
conversations.append(conversation)
return conversations
def _convert_conversation(self, chatgpt_conv: dict) -> dict:
"""Convert single ChatGPT conversation to universal format"""
# Extract messages from mapping structure
messages = self._extract_messages(chatgpt_conv.get('mapping', {}))
# Convert to our format
return {
'id': chatgpt_conv.get('id'),
'title': chatgpt_conv.get('title', 'Untitled'),
'content': self._format_content(messages),
'platform': 'chatgpt',
'model': self._extract_model(chatgpt_conv),
'created_at': chatgpt_conv.get('create_time'),
'updated_at': chatgpt_conv.get('update_time'),
'metadata': {
'original_id': chatgpt_conv.get('id'),
'message_count': len(messages)
}
}
```
### **2.2 Integration with Memory Server**
Extend `ConversationMemoryServer` to support imports:
```python
def import_chatgpt_conversations(self, export_file_path: str) -> dict:
"""Import ChatGPT conversations from export file"""
importer = ChatGPTImporter()
with open(export_file_path, 'r') as f:
export_data = json.load(f)
conversations = importer.parse_export(export_data)
imported = 0
errors = []
for conv in conversations:
try:
self.add_conversation(
content=conv['content'],
title=conv['title'],
date=conv['created_at'],
metadata=conv.get('metadata', {})
)
imported += 1
except Exception as e:
errors.append(f"Failed to import {conv['title']}: {e}")
return {
'imported': imported,
'total': len(conversations),
'errors': errors
}
```
### **2.3 Testing with Real Data**
Create test script:
```python
# scripts/test_chatgpt_import.py
import json
from src.conversation_memory import ConversationMemoryServer
def test_chatgpt_import():
# Load ChatGPT export
with open('test_data/chatgpt_export.json', 'r') as f:
export_data = json.load(f)
# Initialize memory server
server = ConversationMemoryServer()
# Import conversations
result = server.import_chatgpt_conversations('test_data/chatgpt_export.json')
print(f"Imported {result['imported']}/{result['total']} conversations")
# Test search functionality
search_results = server.search_conversations("python", limit=5)
print(f"Found {len(search_results)} results for 'python'")
# Verify platform filtering
chatgpt_results = [r for r in search_results if r.get('platform') == 'chatgpt']
print(f"ChatGPT-specific results: {len(chatgpt_results)}")
if __name__ == "__main__":
test_chatgpt_import()
```
## Phase 3: MCP Integration Testing (Week 3)
### **3.1 MCP Bridge Investigation**
If ChatGPT doesn't natively support MCP, explore:
**Option A: Direct Integration (if MCP supported)**
- Test our MCP server with ChatGPT client
- Verify tool registration and execution
- Test search functionality from ChatGPT
**Option B: Browser Extension Bridge**
- Create browser extension that connects ChatGPT to our MCP server
- Inject search functionality into ChatGPT interface
- Enable memory access from within ChatGPT sessions
**Option C: API Bridge**
- Create proxy server that translates between ChatGPT API and MCP
- Enable programmatic access to memory from ChatGPT workflows
- Support both search and storage operations
### **3.2 Minimal MCP Bridge Prototype**
If needed, create `src/bridges/chatgpt_bridge.py`:
```python
class ChatGPTMCPBridge:
"""Bridge between ChatGPT and our MCP memory server"""
def __init__(self, memory_server_url: str):
self.memory_server_url = memory_server_url
self.mcp_client = MCPClient(memory_server_url)
async def search_memory(self, query: str, limit: int = 5) -> List[Dict]:
"""Search memory and return results for ChatGPT"""
results = await self.mcp_client.call_tool(
"search_conversations",
{"query": query, "limit": limit}
)
return self._format_for_chatgpt(results)
def _format_for_chatgpt(self, results: List[Dict]) -> List[Dict]:
"""Format results for ChatGPT consumption"""
formatted = []
for result in results:
formatted.append({
'title': result.get('title'),
'preview': result.get('preview'),
'relevance': result.get('relevance_score'),
'source': result.get('platform', 'claude'),
'date': result.get('date')
})
return formatted
```
### **3.3 End-to-End Testing**
Test the complete workflow:
1. **Export ChatGPT conversations** → JSON file
2. **Import to memory system** → Searchable storage
3. **Search from memory system** → Find relevant conversations
4. **Access from ChatGPT** → Via bridge/extension
## Phase 4: Feasibility Assessment (Week 4)
### **4.1 Technical Assessment**
Document findings:
**✅ What Works:**
- Import functionality from ChatGPT exports
- Search across ChatGPT + Claude conversations
- Basic metadata extraction and platform identification
**⚠️ What's Challenging:**
- MCP integration (if not natively supported)
- Real-time conversation capture
- Bidirectional synchronization
**❌ What Doesn't Work:**
- Direct MCP protocol support (if unavailable)
- Automatic conversation capture
- Native ChatGPT interface integration
### **4.2 Effort Assessment**
**Low Effort (1-2 weeks):**
- Basic import functionality
- Search across platforms
- Export format conversion
**Medium Effort (1-2 months):**
- Browser extension bridge
- Real-time conversation capture
- Enhanced metadata extraction
**High Effort (3+ months):**
- Native MCP protocol bridge
- Bidirectional synchronization
- Advanced platform integration
### **4.3 Value Assessment**
**High Value:**
- Access to conversation history across platforms
- Universal search functionality
- Platform-agnostic memory system
**Medium Value:**
- Real-time conversation capture
- Advanced metadata filtering
- Cross-platform conversation linking
**Low Value:**
- Perfect format preservation
- Real-time synchronization
- Advanced ChatGPT-specific features
### **4.4 Recommendation Matrix**
| Scenario | Recommendation | Effort | Timeline |
|----------|---------------|---------|----------|
| **ChatGPT has MCP support** | ✅ Full integration | Low | 2-4 weeks |
| **ChatGPT lacks MCP, but exports work** | ⚠️ Import-only system | Medium | 4-6 weeks |
| **No MCP, limited export access** | ❌ Skip ChatGPT integration | N/A | Focus elsewhere |
## Success Metrics
### **Minimum Viable Proof of Concept:**
- [ ] Successfully import ChatGPT conversations
- [ ] Search across ChatGPT + Claude conversations
- [ ] Platform identification working
- [ ] Basic metadata extraction functional
### **Full Success:**
- [ ] Real-time ChatGPT integration (if MCP supported)
- [ ] Bidirectional conversation sync
- [ ] Native ChatGPT interface access to memory
- [ ] Performance acceptable with large datasets
### **Partial Success:**
- [ ] Import-only functionality working
- [ ] Manual conversation capture possible
- [ ] Basic cross-platform search functional
## Risk Mitigation
**Risk: No MCP Support**
- **Mitigation:** Focus on import/export workflows
- **Fallback:** Browser extension bridge
**Risk: Export Format Changes**
- **Mitigation:** Version detection and migration
- **Fallback:** Manual format specification
**Risk: Performance Issues**
- **Mitigation:** Optimize search and storage
- **Fallback:** Platform-specific storage modes
## Deliverables
1. **Technical Report** - Feasibility assessment with recommendations
2. **Working Prototype** - Minimal ChatGPT integration
3. **Performance Analysis** - Benchmarks with real data
4. **Integration Guide** - Setup instructions for end users
5. **Migration Path** - How to upgrade existing Claude-only systems
---
This proof of concept will validate whether the universal memory system is technically feasible and worth the investment before committing to the full 62-task implementation plan.
