feat(openclaw): tune compaction to keepRecentTokens 3k and convert Tier A to deep-compaction
This commit is contained in:
Moritz Graf
@@ -5,8 +5,8 @@
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# Schedule: Every night at 04:45 Europe/Berlin (02:45 UTC) (cron: 45 4 * * *)
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#
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# Architecture:
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# - Tier A: Daily Hard-Reset to 0 for koch (Topic 21), hebamme (Topic 22), shopping (Topic 115)
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# (100% externalized state in DuckDB/JSONL; resets prevent cross-day hallucination)
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# - Tier A: Deep-Compaction for high-frequency topics (koch, hebamme, shopping)
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# (maintains conversational continuity while keeping token count very low)
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# - Tier B: Smart Compaction for long-running project agents (itguy, recherche, regensburg, fitness, doktor)
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# - WAL checkpoints and SQLite optimization without blocking full VACUUM
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# ==============================================================================
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@@ -45,15 +45,15 @@ import subprocess
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import sys
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import time
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THRESHOLD_TOKENS = 20000
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THRESHOLD_TRANSCRIPT_BYTES = 300000
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THRESHOLD_EVENT_COUNT = 40
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THRESHOLD_TOKENS = 15000
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THRESHOLD_TRANSCRIPT_BYTES = 200000
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THRESHOLD_EVENT_COUNT = 30
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CRON_MAX_AGE_HOURS = 48
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RPC_TIMEOUT_MS = 180000
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# Tier A: Event-logging agents whose state is 100% externalized (DuckDB / JSONL / Groceries DB).
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# Nightly reset to 0 prevents cross-day hallucination and eliminates LLM compaction token costs.
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TIER_A_RESET_TARGETS = [
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# Tier A: High-frequency interaction topics (koch, hebamme, shopping).
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# Deep-compaction keeps a compact summary + latest 2 turns without completely wiping conversational memory.
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TIER_A_TARGETS = [
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("koch", "agent:koch:telegram:group:-1004326921510:topic:21"),
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("hebamme", "agent:hebamme:telegram:group:-1004326921510:topic:22"),
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("shopping", "agent:shopping:telegram:group:-1004326921510:topic:115"),
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@@ -145,9 +145,9 @@ def prune_stale_cron_sessions():
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print(f"Cron pruning complete: {total_pruned} sessions pruned, {total_tokens_reclaimed} tokens reclaimed.")
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def reset_tier_a_sessions():
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print("=== Tier A: Daily Hard-Reset to 0 (koch, hebamme, shopping) ===")
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for agent, key in TIER_A_RESET_TARGETS:
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def compact_tier_a_sessions():
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print("=== Tier A: Deep-Compaction (koch, hebamme, shopping) ===")
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for agent, key in TIER_A_TARGETS:
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db_path = f"/home/node/.openclaw/agents/{agent}/agent/openclaw-agent.sqlite"
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if not os.path.exists(db_path):
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continue
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@@ -158,18 +158,32 @@ def reset_tier_a_sessions():
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if not row or not row[0]:
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print(f" [Tier A] {agent} ({key}) already at 0 (no active session).")
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continue
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print(f" [Tier A] Resetting {agent} ({key})...")
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ev_cnt, ev_bytes = get_session_metrics(agent, key, row[0])
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if ev_cnt <= 2 and ev_bytes < 20000:
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print(f" [Tier A] {agent} ({key}) has only {ev_cnt} events ({ev_bytes} bytes), skipping.")
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continue
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print(f" [Tier A] Deep-compacting {agent} ({key}) [{ev_cnt} events, {ev_bytes//1024}KB]...")
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r = subprocess.run(
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["openclaw", "sessions", "delete", key, "--agent", agent, "--yes"],
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capture_output=True, text=True, timeout=60.0
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["openclaw", "sessions", "compact", key, "--agent", agent, "--timeout", str(RPC_TIMEOUT_MS)],
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capture_output=True, text=True
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)
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if r.returncode == 0:
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print(f" ✓ Reset success: {r.stdout.strip()}")
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print(f" ✓ Deep-compacted: {r.stdout.strip()}")
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elif "Already compacted" in r.stderr or "Already compacted" in r.stdout:
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print(f" ✓ Already compacted.")
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else:
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print(f" ✗ Reset error: {r.stderr.strip()}")
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print(f" ✗ Standard compact error: {r.stderr.strip()}; retrying with --max-lines 40...")
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fb = subprocess.run(
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["openclaw", "sessions", "compact", key, "--agent", agent, "--max-lines", "40", "--timeout", str(RPC_TIMEOUT_MS)],
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capture_output=True, text=True
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)
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if fb.returncode == 0:
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print(f" ✓ Truncation fallback success: {fb.stdout.strip()}")
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else:
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print(f" ✗ Truncation fallback failed: {fb.stderr.strip()}")
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except Exception as e:
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print(f" ✗ Exception resetting {agent}: {e}")
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time.sleep(1)
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print(f" ✗ Exception deep-compacting {agent}: {e}")
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time.sleep(2)
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def get_session_metrics(agent, key, sid=None):
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db_path = f"/home/node/.openclaw/agents/{agent}/agent/openclaw-agent.sqlite"
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@@ -194,7 +208,7 @@ def get_session_metrics(agent, key, sid=None):
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return 0, 0
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def compact_active_sessions():
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tier_a_keys = set(t[1] for t in TIER_A_RESET_TARGETS)
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tier_a_keys = set(t[1] for t in TIER_A_TARGETS)
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print(f"=== Tier B: Running Multi-Agent Compaction (threshold: {THRESHOLD_TOKENS} tokens / {THRESHOLD_TRANSCRIPT_BYTES//1024}KB, LLM summarization) ===")
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res = subprocess.run(["openclaw", "sessions", "--all-agents", "--json"], capture_output=True, text=True)
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if res.returncode != 0:
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@@ -294,7 +308,7 @@ def main():
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except Exception:
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pass
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prune_stale_cron_sessions()
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reset_tier_a_sessions()
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compact_tier_a_sessions()
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compact_active_sessions()
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checkpoint_wal_journals()
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vacuum_and_optimize()
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