How to Migrate from Assistants API to Responses API

Migrating from the Assistants API to the Responses API is no longer optional if you plan to stay on OpenAI’s current platform direction. OpenAI has deprecated Assistants and set a shutdown date of August 26, 2026, so the goal is to move your application onto Responses, and where needed, Conversations, without breaking the user experience.

The migration is usually straightforward in concept but not always trivial in execution. The main work is translating older assistant objects, thread handling, and run logic into the newer Responses mental model. The good news is that OpenAI’s migration path is explicit, and feature parity is the reason the old API was deprecated in the first place.

What you are moving from and to

In Assistants, many apps were organized around assistants, threads, messages, and runs. In the current model, OpenAI recommends:

• Responses API for request and tool execution
• Conversations API for durable multi-turn state when needed
• Prompts created from your existing assistant definitions where useful

What you need before you start

• an inventory of your current assistants, tools, and thread patterns
• the prompts or instructions your assistants rely on
• a list of production use cases, not just test assistants
• a decision on whether each flow should be stateless or conversation-based
• a staging environment to compare old and new behavior

Step 1: Identify the assistants that matter

Do not migrate everything blindly. Start with the assistants that drive real user sessions, revenue, support load, or internal operations. In many products, a small number of assistants account for most actual usage.

Step 2: Create prompts from existing assistants

OpenAI explicitly recommends creating prompts from your existing assistant objects. This is the fastest way to preserve the core behavior, tone, and instruction set of each assistant before you redesign the runtime flow.

At this stage, validate that the prompt still reflects what users actually need. Some teams discover that old assistant settings included legacy instructions they no longer want to keep.

Step 3: Move new chats to Conversations and Responses

OpenAI recommends moving new user chats to the newer stack first. This is usually safer than trying to bulk-convert every existing thread before shipping anything. New traffic can start on Conversations and Responses while older sessions are backfilled only where necessary.

If your product depends on persistent chat history, you should map Assistants threads to Conversations objects and make sure the stored items you recreate still preserve the business context your UI depends on.

Step 4: Replace run handling with the Responses flow

One of the biggest implementation changes is that you stop centering your app around runs. In Responses, you create responses directly, pass input items, and manage continuation through Conversations or previous response references depending on your design.

This usually simplifies the control flow. The tradeoff is that you may need to refactor parts of your backend that assumed polling run status as the central loop.

Step 5: Re-map tool usage

Check every place your Assistants integration relied on tool use, retrieval, or custom functions. Responses supports built-in tools and custom functions, but you still need to verify the schemas, the tool-calling loop, and the way your application handles outputs.

This is where many migrations fail. The endpoint change is easy; broken tool assumptions are harder. Test every tool independently before testing the full chat experience.

Step 6: Decide whether each flow should be stateless or stateful

Not every assistant app needs durable state. A support triage endpoint may only need one request at a time. A long-running customer conversation probably needs a Conversations object. Separating those cases early prevents overengineering.

Step 7: Backfill older threads only where needed

OpenAI does not provide an automatic migration tool for moving old threads into Conversations. That means you should backfill only when the historical context matters. If old threads are mostly disposable, it is often better to migrate new traffic first and leave history behind.

Common migration mistakes

• trying to migrate every legacy assistant before validating one production path
• copying prompts without checking whether old instructions are still correct
• forgetting to redesign thread persistence around Conversations
• moving endpoints first and leaving tool behavior untested
• assuming the new API should reproduce every old object one-for-one

Troubleshooting checklist

• Outputs differ from the old assistant: compare prompt content and tool availability first
• Conversation history feels broken: confirm you are persisting state with Conversations where needed
• Tool calls stop appearing: verify tool schemas and response handling in your backend
• Old threads are missing: decide whether to backfill them or intentionally start fresh
• User experience changes unexpectedly: test the UI assumptions, not just the raw API response

How to validate the migration

Run both versions in parallel on a narrow test set before a full cutover. A good validation pass checks:

• whether a new user chat can complete end to end
• whether conversation state persists where expected
• whether all required tool calls still work
• whether structured outputs still match your application contract
• whether one sample production workflow succeeds before you move more traffic

Where templates help

A template helps if your assistant is tied to a common workflow pattern such as knowledge retrieval, support summarization, or structured action-taking. It can speed up prompt design and orchestration. It does not remove migration work around persistent state, tool schemas, or application-specific business logic.

Conclusion

The safest migration strategy is phased, not heroic. Convert your important assistants into prompts, move new traffic to Responses and Conversations, test tool behavior carefully, and only then decide which historical threads need backfilling. That approach reduces risk and avoids spending time preserving parts of the old Assistants model that your product no longer needs.