Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license

evals/compaction/scripts/replay_lineage.py

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+#!/usr/bin/env python3
+"""Replay a ~500K-token prefix of a reconstructed lineage through compaction.
+
+Usage: run_lineage_compaction.py <checkout> <lineage_json> <out_json> [cap_tokens]
+
+Takes the chronological prefix of the lineage at the token cap (default 500K =
+the 50% trigger on a 1M-context model), aligned to a tool-group boundary, and
+runs ContextCompressor.compress() exactly as the live trigger would.
+"""
+import copy
+import json
+import sys
+import time
+from pathlib import Path
+
+CHECKOUT = sys.argv[1]
+LINEAGE = sys.argv[2]
+OUT = sys.argv[3]
+CAP = int(sys.argv[4]) if len(sys.argv) > 4 else 500_000
+
+sys.path.insert(0, CHECKOUT)
+
+data = json.load(open(LINEAGE, encoding="utf-8"))
+msgs = data["messages"]
+
+def tok(m):
+    t = len(m.get("content") or "") // 4
+    tc = m.get("tool_calls")
+    if tc:
+        t += len(json.dumps(tc, default=str)) // 4
+    return t
+
+# chronological prefix up to CAP tokens
+prefix = []
+total = 0
+for m in msgs:
+    t = tok(m)
+    if total + t > CAP and len(prefix) > 10:
+        break
+    prefix.append(m)
+    total += t
+
+# align the end: never end on an assistant msg with tool_calls whose results
+# were cut off; drop trailing orphans
+while prefix and prefix[-1].get("tool_calls"):
+    prefix.pop()
+# also drop trailing tool results with no preceding assistant tool_calls kept
+# (compress()'s _sanitize_tool_pairs would handle it, but keep input clean)
+
+before_tokens = sum(tok(m) for m in prefix)
+print(f"[{Path(CHECKOUT).name}] {Path(LINEAGE).stem}: prefix {len(prefix)} msgs ~{before_tokens:,} tok (cap {CAP:,})")
+
+from agent.context_compressor import ContextCompressor  # noqa: E402
+
+model = "anthropic/claude-fable-5"
+comp = ContextCompressor(model=model, quiet_mode=True)
+before = copy.deepcopy(prefix)
+t0 = time.time()
+compressed = comp.compress(prefix, current_tokens=before_tokens, force=True)
+dt = time.time() - t0
+after_tokens = sum(tok(m) for m in compressed)
+print(f"  -> {len(compressed)} msgs ~{after_tokens:,} tok in {dt:.1f}s  (err={getattr(comp,'_last_summary_error',None)})")
+
+json.dump({
+    "meta": {
+        "checkout": Path(CHECKOUT).name,
+        "session_id": data["root"],
+        "title": f"lineage {data['root']} ({len(data['chain'])} rotations)",
+        "model": model,
+        "elapsed_s": round(dt, 1),
+        "before_msgs": len(before),
+        "after_msgs": len(compressed),
+        "before_tokens_est": before_tokens,
+        "after_tokens_est": after_tokens,
+        "summary_error": getattr(comp, "_last_summary_error", None),
+        "todo_injection_block": None,
+    },
+    "before": before,
+    "after": compressed,
+}, open(OUT, "w", encoding="utf-8"), default=str)
+print(f"  wrote {OUT}")