cc_compaction

Demonstrates and tests the four context management techniques used internally by Claude Code, reproduced using the plain Anthropic Python API. All tests use real API calls against claude-sonnet-4-6.

Techniques

#	Technique	How
1	Full Compaction	Send history + summarization prompt → replace history with summary
2	Time-Based Micro Compaction	Client-side: if gap since last assistant message > 60 min (cache TTL), replace old tool result bodies with `[Old tool result content cleared]`
3	API Thinking Clearing	`context-management-2025-06-27` beta + `clear_thinking_20251015` directive
4	API Tool Clearing	`context-management-2025-06-27` beta + `clear_tool_uses_20250919` directive

Results

Note: Token savings are measured only for API Tool Clearing and API Thinking Clearing (techniques 3 & 4). These are the only techniques that reduce input_tokens reported by the API and are therefore directly measurable in a before/after comparison.

Full Compaction and Time-Based Micro Compaction are tested for correctness only (right messages replaced, facts preserved) — not token savings. Full compaction resets context entirely so a simple before/after token count is not meaningful; time-based MC is pure client-side string replacement with no API call involved.

API Tool Clearing — 10-turn agent loop (2000-char tool results per turn)

Turn:      1     2     3     4     5     6     7     8     9    10
Baseline: 1141  1712  2283  2854  3425  3996  4567  5138  5709  6280
Cleared:  1141  1712  2283  2357  2431  2505  2579  2653  2727  2801

	Cumulative tokens	Final turn tokens
Baseline	37,105	6,280
Tool clearing	23,189	2,801
Saving	37.5%	55.4%

API Thinking Clearing — 8-turn agent loop (budget 3000 tokens/turn)

Turn:      1    2    3    4     5     6     7     8
Baseline: 640  763  984  1307  1519  1747  1992  2220
Cleared:  640  763  890  1143  1292  1249  1392  1534

	Cumulative tokens	Final turn tokens
Baseline	11,172	2,220
Thinking clearing	8,903	1,534
Saving	20.3%	30.9%

API Tool Clearing + API Thinking Clearing Combined — 8-turn agent loop

Turn:      1     2     3     4     5     6     7     8
Baseline: 640  1763  1985  4346  4511  6753  6930  9166
Tool only: 640  1763  2058  4329  4519  4758  4967  5206
Think only:640  1763  1912  4153  4292  6535  6705  8929
Combined:  640  1763  1851  4092  4184  4428  4557  4795

Strategy	Cumulative	Saving
Baseline	36,094	—
Tool clearing only	28,240	21.8%
Thinking clearing only	34,929	3.2%
Combined	26,310	27.1%

Key Findings

Savings are roughly additive — combined (27.1%) ≈ tool-only (21.8%) + thinking-only (3.2%), with a small positive interaction effect.
Tool clearing dominates when tool results are large. Thinking blocks contribute a smaller share unless budget_tokens is very high.
clear_thinking_20251015 must be first in the edits array when combining both directives — the API returns 400 otherwise.
tool_choice: "any" is incompatible with thinking mode — the API rejects it. Drive tool use via prompt instead.
Time-based MC is purely client-side — no special API features needed. The 60-minute threshold matches the Anthropic prompt cache TTL.

Setup

cp .env.example .env
# add your ANTHROPIC_API_KEY

uv run pytest test_compaction.py -v -s

Requires Python 3.11+. Dependencies installed automatically by uv.

browser-use/cc_compaction

README