Migrating from OpenAI Assistants API to the New Responses API – A Developer’s Guide

Developers who built with OpenAI’s Assistants API are now looking at the new Responses API as the future of agent development. This guide explains the key differences and how to transition smoothly, with an emphasis on preserving functionality (like web search, file search, and code execution tools) and leveraging new features (such as vector stores with batch uploads and metadata). We’ll also cover how conversation management has changed and what that means for your implementation.

Transition Experience and Effort

Many developers report that migrating to the Responses API is quicker and easier than expected. The API’s structure has changed significantly from the Assistants API, but it remains simple and straightforward, especially with the strong TypeScript type definitions available in OpenAI’s updated SDK. In practice, this means your code changes will mostly involve adapting to new request/response formats rather than rewriting core logic.

Built-in Tools Continue to Work: Importantly, all the built-in tools you relied on (web browsing, file-based knowledge retrieval, code execution, etc.) are still supported in the Responses API. The new API preserves these capabilities in a more integrated way, so features like OpenAI file search, web search, and even the code interpreter (now called the “computer use” tool) remain available. In fact, the Responses API was designed as a superset of the old system – combining the user-friendly simplicity of chat completions with the powerful tool-use features of Assistants. This continuity means you won’t lose functionality when migrating; instead, you’ll gain a cleaner interface to those same tools.

Minimal Migration Effort: Transitioning from Assistants API to Responses API generally requires updating how you initiate conversations and calls, but not rethinking your entire app. Most of your business logic and integration points remain the same. OpenAI’s official TypeScript/JavaScript client library (v4) was updated for the Responses API and auto-generated from the OpenAPI spec, providing robust typings out of the box. This reduces migration friction for TypeScript users, as your IDE can guide you through new method names and parameters.

Feature Parity and Future Enhancements: OpenAI has made it clear that the Responses API is the path forward. They plan to reach full feature parity with the Assistants API before retiring the old API. This includes supporting all remaining features (like Assistant objects for organizational grouping, and the Code Interpreter tool) in the new system. According to OpenAI, once parity is achieved, the Assistants API will be deprecated (targeting mid-2026). The good news is that until then, you can run both in parallel, and OpenAI will provide migration guides to preserve data. Moreover, the Responses API will continue to get new enhancements beyond parity – the OpenAI team has hinted at additional built-in tools and capabilities coming soon to further simplify agent development. So migrating now not only future-proofs your application but also opens the door to upcoming features.

New API Structure and Features

Under the Responses API, the way you structure conversations and manage state is quite different from the Assistants API’s thread-based model. Here’s a breakdown of the key changes and how to work with them:

Conversation State via previous_response_id: The Assistants API required you to manage conversation threads histories manually (by referencing thread IDs). In the Responses API - in each response you get has a unique id, and to continue the conversation you include that as previous_response_id in the next request. This tells OpenAI to retrieve the entire conversation history associated with that response ID automatically – you don’t need to resend old messages or maintain thread IDs in your client. The API stitches the context together behind the scenes, making multi-turn conversations easier. For example, if you make an initial call and get response.id = "xyz123", your follow-up request would include previous_response_id: "xyz123" and just the new user input. The model will then know it’s in the same conversation. This is a big ergonomic win, the Responses API maintains context seamlessly, allowing for more straightforward, stateful interactions. Do note that including previous_response_id will still incur token costs for the model to consider prior conversation – all earlier messages in the chain count as input tokens for billing. You should also consider using the truncation: "auto" setting if conversations get very long, so the API can discard oldest messages when hitting context limits.
No More Thread IDs – Simplified Context: Because of the above mechanism, you no longer create or reference thread objects. Each conversation can be continued just by passing the last response ID. This eliminates a lot of boilerplate. In the Assistants API, you had to create an Assistant instance, start a thread, and keep track of message IDs. Now, the flow is: make a openai.responses.create() call with your prompt and tools, get a response, then call again with previous_response_id to continue. The Responses API “streamlines this by handling state management more efficiently,” removing the need to manually track threads and messages in your code. For developers, this means less state bookkeeping and fewer chances for error.
Response Object Persistence and Logs: Every response generated is stored by OpenAI for a limited time as part of an “Application State.” By default, response objects (with their conversation history) are retained for 30 days and accessible via your developer dashboard or API logs. This is handy for debugging or analysis – you can review conversations after the fact. If you have privacy concerns or don’t want OpenAI to store the dialogue, you can opt out by setting store: false when creating the response. This will avoid saving that interaction server-side. The 30-day log retention is simply a convenience feature. In summary: state is persisted on OpenAI’s side between calls (unless you disable it), so you get continuity without managing a database of chats yourself.
Stateless instructions: Similar to the Assistants API, in the Responses API, there is an instructions parameter on each request. You include a single instructions string (think of it as the system prompt) alongside the user input. Importantly, the instructions do not carry over automatically from one response to the next – they aren’t stored as part of the conversation state. Every call is treated independently with regard to instructions. This means if your assistant needs persistent persona or guidelines, you must pass the same instructions each time you call the API. The official guidance is that “in the new Responses API, you use the key-value instructions, and this parameter only applies to the current response generation request. Essentially, instructions is an explicit, per-request system prompt. This design gives you flexibility to adjust the assistant’s behavior on the fly, but it does put the onus on you to remember to include those instructions every time (since, unlike with Assistants API, there’s no persistent assistant profile stored on the server). Here’s a quick example of a call using instructions in the Node.js SDK:
```
const response = await openai.responses.create({
  model: 'gpt-4o',
  instructions: 'You are a coding assistant that talks like a pirate.',
  input: 'Are semicolons optional in JavaScript?',
})
console.log(response.output_text)
```
In each request, we include the instructions. If we make another request with a previous_response_id to continue the conversation, we should repeat the instructions if we want the assistant to remain a pirate coder.
Vector Store Support for Knowledge Bases: One of the powerful features of both the Assistants API and Responses API is knowledge retrieval from your own data (files, documents, etc.). The new API continues the Vector Store concept to manage these knowledge bases more flexibly. In practice, this means you can create a vector store (a collection of embeddings) and upload multiple files to it, allowing the model to search that store when answering queries. The built-in file_search tool is how you expose this knowledge base to the AI. In the Responses API, file search is essentially an embedded vector database lookup. As the OpenAI docs describe: “File search is a tool available in the Responses API. It enables models to retrieve information in a knowledge base of previously uploaded files through semantic and keyword search. By creating vector stores and uploading files to them, you can augment the models’ inherent knowledge by giving them access to these knowledge bases”.
Setting up File Search: To enable file search in your agent, you’ll first create a vector store via the API (this is analogous to a knowledge base). Then upload your documents to that store – the API will chunk and vectorize the files behind the scenes. Each file can have metadata (attributes) attached, like titles, authors, dates, or tags, which the model can later use for filtered searching. For example, when uploading files you might provide an attributes object per file with custom fields (e.g. { "title": "Q3 Report", "department": "Sales" }). In the new API, you can attach such metadata so that queries can be constrained or results annotated with those attributes. The Responses API’s vector store is more advanced than the Assistants API’s file upload mechanism – it supports batch uploading and management of multiple files at once. Instead of adding files one by one, you can use a batch endpoint to upload and register many files in a single call. For instance, the OpenAI SDK provides a upload_and_poll method to combine uploading multiple files and indexing them in the vector store in one go. This is a new convenience that speeds up initialization of your knowledge base. After your files are uploaded and indexed into a vector store, you configure your Responses API requests to include the file_search tool referencing that vector store. The model can then use semantic search to pull relevant snippets from your documents when answering the user. (Under the hood, it’s performing an embeddings search, but as a developer you just see the tool’s result in the response).
Note: The Responses API also allows web search via a web_search tool and code execution via a computer_use tool, analogous to what Assistants API offered. Using them is as simple as listing them in the tools array of your request. For example, tools: [{ type: "web_search_preview" }] would allow the model to do an internet search during that response. All these built-in tools can be combined – e.g., a single query might involve both a file search and a web search step, if your tools array includes both.

Example – Multi-turn Q&A with File Search: To tie it all together, let’s consider a typical usage pattern in code. Below is a Python pseudocode snippet showing a conversation loop that uses previous_response_id for context continuity and includes a file search tool (assuming we have a knowledge base vector store already set up and linked):

import openai

openai.api_key = "YOUR_API_KEY"
vector_store_id = "your-vector-store-id"  # ID of an existing vector store with files

last_response_id = None
instructions = "You are a helpful financial assistant. Use the file_search tool to answer with facts from our uploaded reports."

while True:
    user_input = input("User: ")
    if user_input.lower() in ["exit", "quit"]:
        break

    response = openai.Response.create(
        model="gpt-4o",
        input=user_input,
        instructions=instructions,
        tools=[{"type": "file_search", "vector_store_id": vector_store_id}],
        previous_response_id=last_response_id   # None for first turn
    )
    print("Assistant:", response.output_text)
    last_response_id = response.id

In this loop, the assistant carries on a conversation with the user. We always include the same instructions to define the assistant’s role. We include the file_search tool (pointing to our vector store of company reports) so the model can fetch relevant info from our files. The previous_response_id links each turn to the last, maintaining context. The result is a multi-turn dialogue where the assistant can answer using both its own trained knowledge and our custom data.

Finally

Migrating to the OpenAI Responses API from the Assistants API is a worthwhile upgrade for any developer building AI-driven applications. The transition effort is relatively small – often just a few hours of refactoring – thanks to a cleaner API design and excellent SDK support. Once on the Responses API, you’ll benefit from simpler conversation state management, rich built-in tool integration, and new capabilities like vector stores for knowledge retrieval.

In summary, the differences between the Responses API and the Assistants API boil down to a more streamlined developer experience with no loss of power. You no longer juggle multiple object types (assistants, threads, messages) or manually handle context; the Responses API takes care of that via the previous_response_id mechanism and server-side state. You still have all the OpenAI agent tools at your disposal (from web browsing to code execution), invoked with simple parameters instead of complex thread management. And with OpenAI’s commitment to feature parity and beyond, the Responses API will soon encompass everything its predecessor did – plus future enhancements as the platform evolves.

By understanding these key changes (instructions handling, context management, and the new vector store paradigm), developers can migrate their apps to the Responses API with confidence. The result is cleaner code, potentially faster responses, and a more scalable foundation for building advanced AI agents. If you haven’t started already, now is the perfect time to explore the Responses API – it’s the future of OpenAI’s platform for AI assistants, designed with developer feedback in mind and ready to support the next generation of AI-powered applications.