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124 lines
4.2 KiB
Markdown
124 lines
4.2 KiB
Markdown
# s06: Context Compact
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`s01 > s02 > s03 > s04 > s05 > [ s06 ] | s07 > s08 > s09 > s10 > s11 > s12`
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> *"Context will fill up; you need a way to make room"* -- three-layer compression strategy for infinite sessions.
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## Problem
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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.
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## Solution
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Three layers, increasing in aggressiveness:
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```
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Every turn:
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+------------------+
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| Tool call result |
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+------------------+
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v
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[Layer 1: micro_compact] (silent, every turn)
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Replace tool_result > 3 turns old
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with "[Previous: used {tool_name}]"
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v
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[Check: tokens > 50000?]
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no yes
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v v
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continue [Layer 2: auto_compact]
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Save transcript to .transcripts/
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LLM summarizes conversation.
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Replace all messages with [summary].
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v
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[Layer 3: compact tool]
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Model calls compact explicitly.
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Same summarization as auto_compact.
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```
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## How It Works
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1. **Layer 1 -- micro_compact**: Before each LLM call, replace old tool results with placeholders.
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```python
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def micro_compact(messages: list) -> list:
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tool_results = []
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for i, msg in enumerate(messages):
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if msg["role"] == "user" and isinstance(msg.get("content"), list):
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for j, part in enumerate(msg["content"]):
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if isinstance(part, dict) and part.get("type") == "tool_result":
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tool_results.append((i, j, part))
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if len(tool_results) <= KEEP_RECENT:
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return messages
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for _, _, part in tool_results[:-KEEP_RECENT]:
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if len(part.get("content", "")) > 100:
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part["content"] = f"[Previous: used {tool_name}]"
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return messages
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```
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2. **Layer 2 -- auto_compact**: When tokens exceed threshold, save full transcript to disk, then ask the LLM to summarize.
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```python
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def auto_compact(messages: list) -> list:
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# Save transcript for recovery
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transcript_path = TRANSCRIPT_DIR / f"transcript_{int(time.time())}.jsonl"
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with open(transcript_path, "w") as f:
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for msg in messages:
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f.write(json.dumps(msg, default=str) + "\n")
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# LLM summarizes
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response = client.messages.create(
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model=MODEL,
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messages=[{"role": "user", "content":
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"Summarize this conversation for continuity..."
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+ json.dumps(messages, default=str)[:80000]}],
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max_tokens=2000,
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)
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return [
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{"role": "user", "content": f"[Compressed]\n\n{response.content[0].text}"},
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{"role": "assistant", "content": "Understood. Continuing."},
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]
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```
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3. **Layer 3 -- manual compact**: The `compact` tool triggers the same summarization on demand.
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4. The loop integrates all three:
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```python
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def agent_loop(messages: list):
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while True:
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micro_compact(messages) # Layer 1
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if estimate_tokens(messages) > THRESHOLD:
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messages[:] = auto_compact(messages) # Layer 2
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response = client.messages.create(...)
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# ... tool execution ...
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if manual_compact:
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messages[:] = auto_compact(messages) # Layer 3
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```
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Transcripts preserve full history on disk. Nothing is truly lost -- just moved out of active context.
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## What Changed From s05
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| Component | Before (s05) | After (s06) |
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|----------------|------------------|----------------------------|
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| Tools | 5 | 5 (base + compact) |
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| Context mgmt | None | Three-layer compression |
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| Micro-compact | None | Old results -> placeholders|
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| Auto-compact | None | Token threshold trigger |
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| Transcripts | None | Saved to .transcripts/ |
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## Try It
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```sh
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cd learn-claude-code
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python agents/s06_context_compact.py
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```
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1. `Read every Python file in the agents/ directory one by one` (watch micro-compact replace old results)
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2. `Keep reading files until compression triggers automatically`
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3. `Use the compact tool to manually compress the conversation`
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