Update 2025: This problem still exists, but I'm building a comprehensive solution

The core issue remains - OpenAI's API is stateless by design. You must send the entire conversation history with each request, which:

• Increases token costs exponentially with conversation length

• Hits context window limits on long conversations

• Requires manual conversation management in your code

Current workarounds:

• Manual history management (what most answers suggest)

• LangChain's ConversationBufferMemory (still sends full history)

• OpenAI's Assistants API (limited, still expensive)

I'm building MindMirror to solve the broader memory problem:

Already working: Long-term memory across sessions

• Remembers your projects, preferences, and goals so you don't re-introduce yourself or the way you tackle challenges/problems

• Works with any AI API through MCP standard (also Claude Code, Windsurf, Cursor etc)

• $7/month unlimited memories (free trial: 25 memories)

Coming soon: Short-term context management

• Persistent conversation threads across AI models

• Intelligent context compression to reduce token costs

• Easy model switching while maintaining conversation state

My vision: Turn AI memory from a "rebuild it every time" problem into managed infrastructure. Handle both the immediate context issue (this thread) and the bigger "AI forgets who I am" problem.

Currently solving the long-term piece: https://usemindmirror.com

Working on the short-term context piece next. The memory problem is bigger than just conversation history - it's about making AI actually remember you and make adapt to your needs, preferences, wants etc.