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.

type ToolInput = Record<string, any>;

type ToolSpec = {
  name: string;
  description: string;
  input_schema: Record<string, unknown>;
};

const tool: ToolSpec = {
  name: "compact",
  description: "context compaction",
  input_schema: { type: "object", properties: {} }
};

async function handleS06Step(input: ToolInput) {
  microcompact(history);
  return tool.name;
}

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

type ToolInput = Record<string, any>;

type ToolSpec = {
  name: string;
  description: string;
  input_schema: Record<string, unknown>;
};

const tool: ToolSpec = {
  name: "compact",
  description: "context compaction",
  input_schema: { type: "object", properties: {} }
};

async function handleS06Step(input: ToolInput) {
  microcompact(history);
  return tool.name;
}

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

2. The loop integrates all three:

type ToolInput = Record<string, any>;

type ToolSpec = {
  name: string;
  description: string;
  input_schema: Record<string, unknown>;
};

const tool: ToolSpec = {
  name: "compact",
  description: "context compaction",
  input_schema: { type: "object", properties: {} }
};

async function handleS06Step(input: ToolInput) {
  microcompact(history);
  return tool.name;
}

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
tsx agents/s06_context_compact.ts

1. Read every TypeScript 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