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fix(s08): support recursive glob and conditional micro compact

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Jared
2026-08-20 16:39:45 +08:00
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s08_context_compact/README.md
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@@ -117,7 +117,7 @@ This step controls the number of messages. Tool results inside the retained mess
## Step 3: micro_compact
`micro_compact` preserves every `tool_result` added after the most recent assistant response, so the model sees each new result in full once. Among results the model has already consumed, it keeps the latest 3 and shortens older results longer than 120 characters. Persisted results keep their file path; the rest become placeholders:
After the first two steps, `prepare` estimates the remaining context size and runs `micro_compact` only when it is above `CONTEXT_CHAR_LIMIT`. `micro_compact` preserves every `tool_result` added after the most recent assistant response, so the model sees each new result in full once. Among results the model has already consumed, it keeps the latest 3 and shortens older results longer than 120 characters. Persisted results keep their file path; the rest become placeholders:
![Replacing old results](images/micro-compact.en.svg)
@@ -142,12 +142,12 @@ for _, _, block in consumed[:-self.KEEP_RECENT_RESULTS]:
An old result that was not persisted keeps only a placeholder. Results saved in Step 1 retain the path to their complete output.
The first three steps are deterministic text and structure operations. They do not add API calls.
The first two steps run every round. Step 3 runs only when the context is above the limit. All three are deterministic text and structure operations; they do not add API calls.
## Step 4: compact_history
After the first three steps, the code counts the characters in the current messages with `estimate_chars(messages)`:
After `micro_compact`, the code estimates the context again with `estimate_chars(messages)`:
```python
CONTEXT_CHAR_LIMIT = 50000
@@ -156,7 +156,7 @@ def estimate_chars(messages):
return len(json.dumps(messages, default=str, ensure_ascii=False))
```
When the count exceeds `CONTEXT_CHAR_LIMIT`, `compact_history` does four things:
When the count still exceeds `CONTEXT_CHAR_LIMIT`, `compact_history` does four things:
1. Writes the complete message history to `.transcripts/`.
2. Asks the model for a factual state summary.
@@ -181,18 +181,20 @@ This lesson uses character count as its trigger, and all related thresholds use
## Why the Order Is Fixed
The pipeline always runs in this order:
The pipeline uses this order and only enters the lossy steps when necessary:
```text
tool_result_budget
→ snip_compact
→ micro_compact
→ compact_history (only above the limit)
```python
messages = self.tool_result_budget(messages)
messages = self.snip_compact(messages)
if self.estimate_chars(messages) > self.CONTEXT_CHAR_LIMIT:
messages = self.micro_compact(messages)
if self.estimate_chars(messages) > self.CONTEXT_CHAR_LIMIT:
messages = self.compact_history(messages, active_request)
```
This order satisfies two constraints:
1. The first three steps do not call the model. Only Step 4 adds an API request.
1. Steps 1 and 2 run every round. Step 3 runs only above the limit, and only Step 4 adds an API request.
2. `tool_result_budget` must run before `micro_compact`. Large results need to reach disk before older results can become placeholders.
Each round therefore starts with the lowest-cost operation whose information is easiest to recover.
@@ -243,7 +245,7 @@ def agent_loop(messages, active_request):
raise
```
Every model call enters through the same pipeline. After appending `query`, the CLI calls `agent_loop(history, query)`, so repeated compaction cannot lose the current request. The code asks for a summary only when the first three steps leave the context above the limit or when the API rejects it.
Every model call enters through the same pipeline. After appending `query`, the CLI calls `agent_loop(history, query)`, so repeated compaction cannot lose the current request. The code asks for a summary only when `micro_compact` still leaves the context above the limit or when the API rejects it.
## The compact Tool