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.

function microCompact(messages: Message[]): Message[] {
  const toolResults: ToolResultBlock[] = [];

  for (const message of messages) {
    if (message.role !== "user" || !Array.isArray(message.content)) continue;
    for (const part of message.content) {
      if (isToolResultBlock(part)) {
        toolResults.push(part);
      }
    }
  }

  if (toolResults.length <= KEEP_RECENT) {
    return messages;
  }

  for (const result of toolResults.slice(0, -KEEP_RECENT)) {
    result.content = `[Previous: used ${toolName}]`;
  }

  return messages;
}

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

async function autoCompact(messages: Message[]): Promise<Message[]> {
  const transcriptPath = resolve(TRANSCRIPT_DIR, `transcript_${Date.now()}.jsonl`);
  for (const message of messages) {
    appendFileSync(transcriptPath, `${JSON.stringify(message)}\n`, "utf8");
  }

  const response = await client.messages.create({
    model: MODEL,
    messages: [{
      role: "user",
      content: "Summarize this conversation for continuity...\n\n" +
        JSON.stringify(messages).slice(0, 80_000),
    }],
    max_tokens: 2000,
  });

  return [
    { role: "user", content: `[Conversation compressed. Transcript: ${transcriptPath}]` },
    { role: "assistant", content: "Understood. I have the context from the summary. Continuing." },
  ];
}

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

2. The loop integrates all three:

export async function agentLoop(messages: Message[]) {
  while (true) {
    microCompact(messages);

    if (estimateTokens(messages) > THRESHOLD) {
      messages.splice(0, messages.length, ...(await autoCompact(messages)));
    }

    const response = await client.messages.create(...);
    // ... tool execution ...

    if (manualCompact) {
      messages.splice(0, messages.length, ...(await autoCompact(messages)));
    }
  }
}

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

cd agents-ts
npm install
npm run s06

1. Read every TypeScript file in the agents-ts 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