Stop giving your coding agent a million-token context window

Long coding-agent sessions in pi can suffer from context loss, incomplete implementations, and overflow retries, which are influenced by compaction settings such as reserveTokens and keepRecentTokens and by each route’s configured contextWindow. A quality-focused starting profile for verified 320K-token routes is a 64K reserve, 40K retained recent context, and a 320K per-model window, causing compaction around 256K tokens while preserving roughly 60K of generation capacity near that threshold; however, settings must reflect the actual provider route limits, since overstating a window can cause failed or truncated requests. The discussion argues that using a model’s maximum advertised context is not necessarily optimal, as long transcripts and irrelevant but related material can degrade model performance, while large tool outputs and stale logs often make up much of a coding session’s context. Reserve tokens also determine summarization budgets, while keepRecentTokens controls how much exact recent conversation survives compaction, creating a tradeoff between preserving immediate execution details and retaining log noise. Users should manually compact at meaningful workflow transitions, store critical state in files or commits because tool outputs are truncated in summaries, verify provider-specific model metadata, and collect diagnostics from representative sessions before changing multiple settings.

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