The Token Limit Problem
Large Language Models (LLMs) have a finite "context window." As a conversation about building an app grows, logs, code snippets, and chat history accumulate. Reaching the context limit causes the AI to fail or reject requests.
Flowork's Solution: Auto-Summarization
Flowork OS implements a graceful degradation strategy for AI memory.
The Checkpoint Mechanism
When a conversation exceeds roughly 50,000 characters:
1. The system identifies the "old" history (everything except the last ~10 turns).
2. It tasks an LLM with summarizing that old history, extracting key decisions, completed tasks, and user preferences.
3. The raw old history is removed from the active context.
4. A single < message is injected into the context window containing the condensed information.
5. The system prompt and the most recent 10 messages remain untouched.
Best Practices for AI Interaction
Because of this sliding window, users and Mother AI must collaborate to preserve important facts. If a specific architectural decision or API key is critical for the future, Mother AI should be instructed to use the save_knowledge tool.
Data stored in the Knowledge Bank is independent of the conversational context window and can be recalled at any time, immune to summarization.