integrate-openai-agents

To build a stateless chat endpoint with FastAPI and the OpenAI Agents SDK, create a POST route that loads conversation history from a database, runs the agent with MCP tools, and saves the updated conversation back to the database after each request.

To execute MCP tool calls in a FastAPI chat endpoint, run the OpenAI agent, parse the tool calls from the agent's response, execute the parsed MCP tools, and incorporate the results back into the agent's final response before returning it to the client.

Yes, you can maintain conversation context without server-side session state by loading conversation history from a database using a user ID and conversation ID, building the message array, and persisting the full dialogue after each interaction.

The best way to persist OpenAI agent conversations in a scalable backend is to save the entire dialogue to a database after each interaction, allowing a stateless FastAPI endpoint to reload the history for subsequent requests.

Your OpenAI Agents SDK endpoint loses conversation history between HTTP requests if it does not load previous messages from a database before running the agent, requiring a stateless architecture that retrieves and saves context per request.

Yes, you need a database to run the OpenAI Agents SDK with FastAPI for customer support assistants to load conversation history, execute MCP tools, and persist the full conversation across stateless requests.