- Handled "incomplete" stop reasons in session recovery and auto-compaction workflows.
- Dropped the prior assistant turn before attempting recovery on incomplete-length stops.
- Expanded auto-compaction reason types and triggers to include "incomplete".
- Updated internal URLs parsing internals, export order, tests, and Obsidian URI prompt docs.
- Converted systemPrompt APIs and state types to ordered `string[]` across agent, AI, and coding-agent surfaces.
- Added `normalizeSystemPrompts` and applied it to context normalization before building provider request payloads.
- Updated AI providers to emit separate normalized prompt blocks/messages instead of a single merged system prompt.
- Removed dedicated `projectPrompt` state and remapped that context into system-context buckets in session, dump, and token accounting.
- Aligned tests and changelogs to pass and assert `systemPrompt` as arrays with ordered prompt semantics.
- Add explicit validation rejecting write:"" with the expected error message in normalizeChunkEditOperations.
- Update spark context-promotion tests (it_1, it_2, concurrent it_5) to expect promotion to gpt-5.5 (the new chain target on openai-codex), since spark variants now promote to gpt-5.5 rather than the base codex model.
- Added `close()` method to SessionManager and AuthStorage for proper resource cleanup and finalization of prepared statements.
- Added `initiatorOverride` option support in OpenAI and Anthropic providers for message attribution control.
- Fixed resource leaks in RpcClient timeout handling by centralizing timeout creation with unref() and adding explicit clearTimeout() calls.
- Fixed AgentSession disposal to call SessionManager's `close()` method for guaranteed resource cleanup instead of fallback flush.
- Updated all test suites to properly dispose AuthStorage instances in cleanup hooks to prevent resource leaks between tests.
- Fixed provider session state not being cleared when branching or navigating tree history, preventing resource leaks with codex provider sessions.
- Added calls to `#closeCodexProviderSessionsForHistoryRewrite()` in branch and navigateTree methods to ensure proper cleanup.
- Added test coverage for provider session cleanup during history branching and tree navigation.
- Changed context promotion to trigger on context overflow errors instead of a configurable threshold percentage.
- Removed the contextPromotion.thresholdPercent configuration setting.
- Updated context promotion to retry immediately on the promoted model without requiring compaction.
- Refactored context promotion logic to attempt promotion before compaction in the overflow handling flow.
- Updated agent session to merge context promotion checks into the compaction method for unified overflow handling.
- Updated tests to reflect overflow-based promotion triggering instead of threshold-based promotion.
- Added contextPromotionTarget model property to specify preferred fallback model when context promotion is triggered.
- Added automatic context promotion target assignment for Spark models to their base model equivalents.
- Updated Qwen model context window and max token limits for improved accuracy.
- Updated o1 model context window from 256000 to 262144 tokens and max tokens from 64000 to 65536 tokens.
- Implemented context promotion logic to use configured contextPromotionTarget when available instead of role-based model resolution.
- Added automatic context promotion feature that switches to larger-context models when approaching context limits.
- Added 'contextPromotion.enabled' setting to control automatic model promotion with default value of enabled.
- Added 'contextPromotion.thresholdPercent' setting to configure context usage threshold for triggering promotion with default value of 90%.
- Implemented context promotion logic in AgentSession that monitors token usage and automatically switches models when threshold is exceeded.
- Added provider session cleanup during model switches to properly handle session state transitions.
- Added comprehensive test coverage for context promotion functionality including threshold-based promotion and non-promotion scenarios.
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