Stateful★

Stateful execution is an application design pattern. While AI models are entirely stateless (they remember nothing between requests), the software wrapping the model (the client harness) is stateful — it stores the conversation history in memory or a database, appends new messages, and compiles the entire log into the prompt context for the next request.

You interface with stateful code when you:

• Open an IDE chat assistant that tracks your active workspace files.
• Log into a web chatbot that keeps a sidebar listing your past conversation sessions.
• Write multi-step agent scripts that track loop counters, file edits, and tool responses.

The Stateful-Stateless Interface

The client harness acts as the "memory bridge" for the model. It translates a user's conversational experience into a sequence of stateless API requests:

[ Stateful Client Harness ] (Tracks history, files, variables)
      │
      ├─► Turn 1: Compiles Context ──► [ Stateless API Request ] ──► Model runs weights
      │                                                                    │
      ├─► Turn 2: Appends Turn 1   ──► [ Stateless API Request ] ◄─────────┘

If the stateful harness fails to track file edits or system logs, the model's awareness breaks, resulting in hallucinations or duplicated work.

Field Applications

1. Fullstack Engineers (Stateful Session Management)

Engineers write class wrappers to maintain session state locally before formatting payloads for the API:

• Code Example (Python Session Wrapper):

  class ChatSession:
      def __init__(self, session_id):
          self.session_id = session_id
          self.messages = [] # Persistent in-memory state
          
      def send_message(self, user_content):
          # 1. Append user message to state
          self.messages.append({"role": "user", "content": user_content})
          
          # 2. Call stateless model
          response = api_client.generate(messages=self.messages)
          
          # 3. Append assistant response to state
          self.messages.append({"role": "assistant", "content": response})
          return response