To move your ChatGPT Team workspace data to Claude Team, you’ll need to export conversation history via the OpenAI platform, back up custom GPTs and workspace settings, then manually recreate projects and workflows in Claude. Since no direct migration tool exists, the process involves strategic data extraction and careful reconstruction of your team’s knowledge base.

Understanding ChatGPT Team Workspace Data

ChatGPT Team workspaces store several types of data that you’ll want to preserve during migration. Conversation histories represent the bulk of your workspace content—thousands of interactions containing code snippets, architectural decisions, and problem-solving sessions. Custom GPTs are another critical asset if your team built specialized assistants with custom instructions. Workspace settings include team member access controls, usage preferences, and any integrated tools or APIs.

Claude Team organizes work around Projects, which serve as containers for conversation history, knowledge files, and custom instructions. Understanding this structural difference helps you plan an effective migration strategy.

Exporting ChatGPT Team Data

OpenAI provides several export pathways for team administrators. The primary method involves accessing the admin panel at platform.openai.com and navigating to the workspace settings. From there, you can request a full data export that includes conversation history, custom GPT definitions, and workspace configuration.

For programmatic export, you can use the OpenAI API to fetch conversation threads. Here’s a Python example that retrieves conversation history:

import openai
from datetime import datetime, timedelta

# Initialize with your team API key
client = openai.OpenAI(api_key="your-team-api-key")

def export_conversations(days_back=90):
    """Export conversations from the last N days"""
    conversations = []

    # List all conversations (adjust pagination as needed)
    response = client.chat.completions.list(
        limit=100,
        order="desc"
    )

    for thread in response.data:
        conversations.append({
            "id": thread.id,
            "title": thread.title,
            "created_at": thread.created_at,
            "messages": [
                {
                    "role": msg.role,
                    "content": msg.content[0].text.value if msg.content else ""
                }
                for msg in thread.messages
            ]
        })

    return conversations

# Export and save to JSON
conversations = export_conversations()
with open("chatgpt-export.json", "w") as f:
    import json
    json.dump(conversations, f, indent=2)

This script exports conversation data to a JSON file that you can later reference when recreating workflows in Claude.

Preserving Custom GPT Configurations

If your team uses custom GPTs, you’ll need to manually recreate them in Claude. While there’s no automatic migration, you can export your GPT configurations by accessing each custom GPT’s settings page and copying the instructions, knowledge files, and conversation starters.

For GPTs with API integrations, document the specific endpoints and authentication methods. Claude achieves similar functionality through its Model Context Protocol (MCP), so you’ll need to set up equivalent connections in your Claude Team workspace.

Migrating to Claude Team Projects

Claude Team uses a Projects-based structure that differs from ChatGPT’s workspace model. Each project can contain multiple conversations, uploaded files for context, and custom instructions. Here’s how to organize your migrated data:

Step 1: Create Projects Based on Use Cases

Organize your exported conversations into logical groups—perhaps by project code, client, or topic area. Create corresponding projects in Claude Team for each group.

Step 2: Import Key Conversations

Copy important conversation threads into your new Claude Projects. While you cannot import the full JSON directly, you can paste relevant exchanges as reference material:

Previous ChatGPT Context:
---
User: Help me implement user authentication
Assistant: Here's a JWT-based authentication flow...
---

Step 3: Set Custom Instructions

Recreate your custom GPT instructions as project-level system prompts in Claude. Navigate to each project’s settings and add instructions that mirror your original GPT behavior.

Handling Shared Links and Collaborations

ChatGPT Team workspaces often contain shared links to specific conversations. These links do not transfer to Claude. Create new shared links in Claude for conversations you want to collaborate on with team members.

For real-time collaboration, Claude Team supports multi-user workspaces where team members can participate in the same conversation thread, similar to ChatGPT’s collaborative features.

API Integration Considerations

If your ChatGPT Team workspace uses the API for automated workflows, you’ll need to update your integration code to use Claude’s API. The SDK differences are minimal:

# ChatGPT API (OpenAI)
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a function"}]
)

# Claude API (Anthropic)
import anthropic
client = anthropic.Anthropic(api_key="your-key")
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a function"}]
)

Update your environment variables, adjust API endpoint calls, and test your integrations with Claude’s models before fully transitioning.

Best Practices for a Smooth Transition

Test the migration with a small team subset before rolling out to everyone. This lets you identify gaps in your documentation and refine the process. Maintain a transition period where both platforms are active, allowing team members to reference ChatGPT conversations while building up the Claude knowledge base.

Document your migration process internally so future team members understand how the workspace was set up and what decisions shaped the migration.

Complete Migration Checklist

Here’s a practical checklist for teams migrating between platforms:

## ChatGPT Team to Claude Team Migration Checklist

### Week 1: Inventory and Planning
- [ ] Export all conversations (OpenAI API)
- [ ] List all custom GPTs with descriptions
- [ ] Document API integrations and endpoints
- [ ] Identify critical conversations to preserve
- [ ] Create team communication plan

### Week 2: Data Export
- [ ] Run export scripts for all conversation history
- [ ] Backup custom GPT configurations
- [ ] Export workspace settings and member permissions
- [ ] Test export file integrity
- [ ] Create migration documentation

### Week 3: Claude Team Setup
- [ ] Create Claude Team workspace
- [ ] Set up team members and permissions
- [ ] Create corresponding projects
- [ ] Configure custom instructions
- [ ] Test user access and permissions

### Week 4: Content Migration
- [ ] Import key conversations into projects
- [ ] Recreate custom GPTs as Claude configurations
- [ ] Migrate shared files and knowledge bases
- [ ] Update internal documentation links
- [ ] Run parallel testing

### Week 5: Cutover and Validation
- [ ] Communicate cutover date to team
- [ ] Monitor ChatGPT usage (should decline)
- [ ] Verify Claude Team usage increases
- [ ] Collect team feedback
- [ ] Plan deactivation of ChatGPT Team

Detailed Export Script for Conversations

import openai
import json
from datetime import datetime
import os

class ChatGPTExporter:
    def __init__(self, api_key):
        self.client = openai.OpenAI(api_key=api_key)
        self.conversations = []

    def export_conversations(self, output_dir="chatgpt_export"):
        """Export all conversations from ChatGPT Team workspace."""
        os.makedirs(output_dir, exist_ok=True)

        # Get all threads (paginated)
        offset = 0
        limit = 100
        total_exported = 0

        try:
            while True:
                threads = self.client.beta.threads.list(limit=limit)

                if not threads.data:
                    break

                for thread in threads.data:
                    thread_data = self._extract_thread(thread)
                    if thread_data:
                        self.conversations.append(thread_data)
                        total_exported += 1

                        # Save individual thread as JSON
                        with open(
                            f"{output_dir}/thread_{thread.id}.json", "w"
                        ) as f:
                            json.dump(thread_data, f, indent=2)

                offset += limit

        except Exception as e:
            print(f"Error exporting conversations: {e}")

        # Save comprehensive export
        with open(f"{output_dir}/all_conversations.json", "w") as f:
            json.dump(
                {
                    "exported_at": datetime.utcnow().isoformat(),
                    "total_conversations": total_exported,
                    "conversations": self.conversations,
                },
                f,
                indent=2,
            )

        return total_exported

    def _extract_thread(self, thread):
        """Extract full conversation from a thread."""
        try:
            messages = self.client.beta.threads.messages.list(
                thread_id=thread.id, limit=100
            )

            return {
                "thread_id": thread.id,
                "created_at": str(thread.created_at),
                "title": getattr(thread, "title", "Untitled"),
                "messages": [
                    {
                        "role": msg.role,
                        "content": msg.content[0].text.value
                        if msg.content
                        else "",
                        "created_at": str(msg.created_at),
                    }
                    for msg in messages.data
                ],
            }
        except Exception as e:
            print(f"Error extracting thread {thread.id}: {e}")
            return None

    def export_custom_gpts(self, output_dir="chatgpt_export"):
        """Export custom GPT definitions."""
        try:
            # List all custom GPTs
            response = self.client.beta.assistants.list()

            gpts = []
            for assistant in response.data:
                gpt_data = {
                    "id": assistant.id,
                    "name": assistant.name,
                    "description": assistant.description,
                    "instructions": assistant.instructions,
                    "tools": [tool.type for tool in assistant.tools],
                    "model": assistant.model,
                    "file_ids": assistant.file_ids,
                }
                gpts.append(gpt_data)

            with open(f"{output_dir}/custom_gpts.json", "w") as f:
                json.dump(gpts, f, indent=2)

            return len(gpts)
        except Exception as e:
            print(f"Error exporting custom GPTs: {e}")
            return 0


# Usage
exporter = ChatGPTExporter(api_key="your-openai-api-key")
num_conversations = exporter.export_conversations()
num_gpts = exporter.export_custom_gpts()

print(f"Exported {num_conversations} conversations and {num_gpts} custom GPTs")

Cost Comparison: ChatGPT Team vs Claude Team

For a 5-person team:

• ChatGPT Team: $150/month = $1,800/year
• Claude Team: $125/month = $1,500/year

Claude Team saves $300/year for this size, plus better admin controls.

Migration Impact on Team Workflows

Plan for these workflow changes:

Conversation structure: ChatGPT: Conversations exist in Team workspace Claude: Conversations organized by Projects

Migration requires:

• Think about logical project groupings
• Brief team on new organizational structure
• Update internal documentation links

API usage: ChatGPT: Single team API key Claude: Can create separate API keys per project

Migration opportunity:

• Separate billing by project
• Better cost tracking
• Improved access control

Validation After Migration

Verify the migration was successful:

#!/bin/bash
# verify-migration.sh

echo "Checking ChatGPT Team..."
CHATGPT_USAGE=$(curl -s https://api.openai.com/dashboard/usage \
  -H "Authorization: Bearer $OPENAI_KEY" \
  | jq '.total_usage')

echo "Checking Claude Team..."
CLAUDE_USAGE=$(curl -s https://api.anthropic.com/v1/usage \
  -H "x-api-key: $CLAUDE_KEY" \
  | jq '.usage.total_messages')

echo "ChatGPT Team usage: $CHATGPT_USAGE"
echo "Claude Team usage: $CLAUDE_USAGE"

# Compare conversation counts
echo ""
echo "Validating conversation preservation..."
EXPORTED_COUNT=$(jq '.total_conversations' chatgpt_export/all_conversations.json)
CLAUDE_COUNT=$(curl -s https://api.anthropic.com/v1/projects \
  -H "x-api-key: $CLAUDE_KEY" \
  | jq '.projects | length')

echo "Conversations exported from ChatGPT: $EXPORTED_COUNT"
echo "Projects created in Claude: $CLAUDE_COUNT"

if [ "$CHATGPT_USAGE" -gt 0 ] && [ "$CLAUDE_USAGE" -gt 0 ]; then
    echo ""
    echo "WARNING: Both systems still in use. Ensure deprecation plan is on track."
fi

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