ContextForge recycles LLM context to slash tokens in long conversations

Large language models (LLMs) excel in short-context reasoning but degrade over long conversational horizons due to context window limits and inefficient token usage. Derek Thomas's ContextForge tackles this by recycling prior computation instead of replaying full context. The system uses three components: structured query generation to pinpoint what's relevant, external memory retrieval to store and fetch past information, and controlled synthesis to blend new inputs with recycled context. This approach cuts token overhead significantly while maintaining answer fidelity, enabling extended multi-turn interactions without architectural changes.

ContextForge was tested on a 15-turn benchmark covering multi-turn reasoning, back-references, and domain shifts in structured healthcare queries. Compared to a baseline using the same underlying model, ContextForge demonstrated improved consistency and lower token consumption, with comparable or better response accuracy. These results show that context recycling is a practical alternative to expanding context windows—reducing cost and latency for long-horizon tasks like customer support, medical interviews, and legal document analysis. The code and evaluation artifacts are publicly available.