context-engineering

To manage LLM context for long conversations, apply context trimming to drop older turns while using summarization to compress histories. This preserves critical decisions and constraints, maximizing task performance while minimizing token usage.

Context summarization compresses long conversation histories into structured, actionable summaries to ensure token efficiency. Use it during long-horizon reasoning or multi-step tasks to curate high-signal information and prevent degraded performance from noisy context.

Optimize context window information for multi-step reasoning by coordinating trimming, summarization, and just-in-time context loading. This fetches information on demand, ensuring robust fault tolerance and deterministic context behavior across complex tool orchestration.

You can maintain cross-session memory and continuity for long-running LLM tasks by implementing external memory patterns. This approach maintains notes outside the active context window, providing durable continuity without exceeding token limits.

The best way to handle context trimming while preserving system prompts is to use hybrid approaches that combine trimming and summarization. This selectively drops older conversation turns while retaining system prompts and recent history for durable task performance.