analysis_claude_code/docs/en/s06-context-compact.md

s06: Context Compact

s01 > s02 > s03 > s04 > s05 > [ s06 ] | s07 > s08 > s09 > s10 > s11 > s12

"Context will fill up; you need a way to make room" -- three-layer compression strategy for infinite sessions.

Harness layer: Compression -- clean memory for infinite sessions.

Problem

The context window is finite. A single read_file on a 1000-line file costs ~4000 tokens. After reading 30 files and running 20 bash commands, you hit 100,000+ tokens. The agent cannot work on large codebases without compression.

Solution

Three layers, increasing in aggressiveness:

Every turn:
+------------------+
| Tool call result |
+------------------+
        |
        v
[Layer 1: micro_compact]        (silent, every turn)
  Replace tool_result > 3 turns old
  with "[Previous: used {tool_name}]"
        |
        v
[Check: tokens > 50000?]
   |               |
   no              yes
   |               |
   v               v
continue    [Layer 2: auto_compact]
              Save transcript to .transcripts/
              LLM summarizes conversation.
              Replace all messages with [summary].
                    |
                    v
            [Layer 3: compact tool]
              Model calls compact explicitly.
              Same summarization as auto_compact.

How It Works

1. Layer 1 -- micro_compact: Before each LLM call, replace old tool results with placeholders.

def micro_compact(messages: list) -> list:
    tool_results = []
    for i, msg in enumerate(messages):
        if msg["role"] == "user" and isinstance(msg.get("content"), list):
            for j, part in enumerate(msg["content"]):
                if isinstance(part, dict) and part.get("type") == "tool_result":
                    tool_results.append((i, j, part))
    if len(tool_results) <= KEEP_RECENT:
        return messages
    for _, _, part in tool_results[:-KEEP_RECENT]:
        if len(part.get("content", "")) > 100:
            part["content"] = f"[Previous: used {tool_name}]"
    return messages

2. Layer 2 -- auto_compact: When tokens exceed threshold, save full transcript to disk, then ask the LLM to summarize.

def auto_compact(messages: list) -> list:
    # Save transcript for recovery
    transcript_path = TRANSCRIPT_DIR / f"transcript_{int(time.time())}.jsonl"
    with open(transcript_path, "w") as f:
        for msg in messages:
            f.write(json.dumps(msg, default=str) + "\n")
    # LLM summarizes
    response = client.messages.create(
        model=MODEL,
        messages=[{"role": "user", "content":
            "Summarize this conversation for continuity..."
            + json.dumps(messages, default=str)[:80000]}],
        max_tokens=2000,
    )
    return [
        {"role": "user", "content": f"[Compressed]\n\n{response.content[0].text}"},
        {"role": "assistant", "content": "Understood. Continuing."},
    ]

3. Layer 3 -- manual compact: The compact tool triggers the same summarization on demand.

4. The loop integrates all three:

def agent_loop(messages: list):
    while True:
        micro_compact(messages)                        # Layer 1
        if estimate_tokens(messages) > THRESHOLD:
            messages[:] = auto_compact(messages)       # Layer 2
        response = client.messages.create(...)
        # ... tool execution ...
        if manual_compact:
            messages[:] = auto_compact(messages)       # Layer 3

Transcripts preserve full history on disk. Nothing is truly lost -- just moved out of active context.

What Changed From s05

Component	Before (s05)	After (s06)
Tools	5	5 (base + compact)
Context mgmt	None	Three-layer compression
Micro-compact	None	Old results -> placeholders
Auto-compact	None	Token threshold trigger
Transcripts	None	Saved to .transcripts/

Try It

cd learn-claude-code
python agents/s06_context_compact.py

1. Read every Python file in the agents/ directory one by one (watch micro-compact replace old results)
2. Keep reading files until compression triggers automatically
3. Use the compact tool to manually compress the conversation