Compression Engine

Context approaching token limit
    ↓
Layer 1: MicroCompact
    Scans all tool outputs in the conversation
    Removes outputs from old turns (Read, Bash, Grep, Glob, WebSearch, Scrape)
    Keeps recent outputs intact
    Replaces cleared content with: [tool output cleared — content was X chars]
    No API call required
    ↓ (if still over limit)
Layer 2: Session Memory Compact
    Uses stored memories as the compression baseline
    Preserves: minimum 10K tokens of context, minimum 5 text messages
    Targets: maximum 40K tokens in active window
    Prevents cutting tool_use / tool_result pairs (API invariant)
    No API call required
    ↓ (if still over limit)
Layer 3: API Digest
    Full LLM summary via fast model
    Strips all images (replaced with [image] placeholders)
    Re-injects 50K token budget of critical content:
        ├── Recent files (max 5 files, max 5K each)
        ├── Active skill instructions
        └── Project instructions
    One API call required

{
  "type": "compact_boundary",
  "compactType": "auto",
  "preCompactTokenCount": 87420,
  "preservedSegment": {
    "summaryMessageUuid": "msg_abc123",
    "preservedMessageUuids": ["msg_xyz789", "msg_def456"]
  }
}