On 1M-context models, Claude Code waits until about 967K tokens to auto-compact, so late messages each carry nearly a million tokens of history. I set /autocompact 400k as Lydia Hallie suggested and used it for 11 days: roughly 16–22% less usage, and no noticeable increase in rework or corrections from lost details. This is one person's observation on one kind of work, not a general finding.
To cut the input tokens resent every turn, agent platforms often truncate, clear, or summarize old tool results, and skill instructions, write receipts, and error messages get cut along with them. The Complexity Trap shows observation masking can halve cost, yet Gemini 2.5 Flash with thinking still dropped from 40.4% to 36.4% solve rate. Measure savings against full history and fixes against the current setup, and judge by total cost per successful task, not shorter per-turn input.
CMU 11-768's first assignment starts from an empty ReAct loop: a bash-only CodeAgent fixes a bug in a chess app, context compaction is added to solve a SWE-bench task, and the same loop becomes a ChessAgent that runs a two-ply search with simulate_move, run_python, and a skill. All 100 points are graded by replaying submitted patches and trajectories offline.
omp doesn't have just one compaction: context-full uses LLM summarization with iterative windows and budget halving retries; snapcompact skips the LLM entirely, rendering history as dense PNG bitmaps for vision models to read — solving no-API-key, low-latency, vision-model-cheaper scenarios; branch summary summarizes the abandoned branch during `/tree` navigation so file ops aren't lost; shake mechanically replaces tool-result text and large fenced/XML blocks with placeholders — an emergency hatch when summary is too heavy and prune isn't enough. Four strategies, distinct failure modes, orchestrated by session maintenance or manual triggers.
Mature-agent compaction must handle triggers, complete-turn cut points, and recovery. looplane now has an 85% high-watermark, automatic compaction, a deterministic native-loop fallback summary, persisted checkpoints, and workspace-context reinjection. Cross-runtime fallback, model-quality summaries, and live-provider long-session validation remain open.
Near 85% context pressure, Looplane has two distinct paths: the native loop can apply one bounded deterministic history fallback, while a conversation runtime with native compaction can compact after a completed turn. Both paths re-anchor the next request with workspace context.
By default every DM lands in one main session, and group activity and background work report back into it. Memory is entirely Markdown on disk — the model only remembers what gets saved, with no hidden state. But if more than one person can DM your agent, DM isolation is something you have to turn on.
Claude Code loads the system prompt, MEMORY.md, CLAUDE.md, MCP tool names, and skill descriptions before you type your first word. This post breaks down the startup context, what each of six extension features costs, and how to control auto-compaction with /compact, /autocompact, and autoCompactWindow.