feat: v0.4.1 — GMem memory infra + Opprime synced improvements (CI pass)

CHANGELOG.md

@@ -1,5 +1,27 @@
 # Changelog
 
+## [0.4.1] - 2026-06-09
+
+### Added
+- FTS5 token filter: fixed `detail=column` issue where pure-digit/alpha tokens were parsed as column names (port from Opprime)
+- Knowledge auto-retrieval: record_hit() on every auto-search hit for cache warmth
+- _recorded_tc: track total tool calls per loop execution for SkillOpt gate integration
+- _build_gmem_summary: standalone helper for archive_store compression summary construction
+- tools/remember_info.py: unified memory route — auto-classifies user content into Knowledge/Notes/Experience
+- tools/archive_search.py: Archive Store search tool for cross-session memory recall
+
+### Changed
+- kernel.py: merged Opprime improvements (FTS5 safety filter + Knowledge hit recording + loop tc tracking)
+- session.py: synchronized with Opprime (fixes for L1 compaction type mismatch, image_url filtering)
+- experience.py: synchronized with Opprime (improved JSON error tolerance)
+- storage.py: synchronized with Opprime (aging mechanism for Knowledge entries)
+- archive_store.py: generalized agent-specific DB fallback paths to `~/gbase-home/` (public-ready)
+
+### Fixed
+- knowledge.py: FTS5 MATCH returning 0 matches due to unpurged detail=column token pollution
+- session.py: content null leading to DeepSeek serde enum variant mismatch (400 error)
+- session.py: L1 compression type mismatch (passing str instead of list[dict])
+
 ## [0.4.0] - 2026-06-02
 
 ### Added
@@ -14,6 +36,18 @@
 - L4 permanent notes: auto-serialize when tool calls >= 5 or reply >= 500 chars
 - 20+ new tools: self_edit, feishu_send, note_tool, knowledge, mirror_tool, chain, mail, security_watch, etc.
 
+### Added
+- ArchiveStore: full-text conversation archive with LIKE-based semantic retrieval (replaces LLM compression)
+- Session: removed 3-layer LLM compression, replaced with ArchiveStore append/search
+- Territory safety: cross-agent read/write access control (write blocking, read warning)
+- RSI Dual-Knob: task intent classification → dynamic temperature control
+- Time-decay weighted retrieval: M3 sparse attention inspired (7d full / 7-30d linear / 30d+ exponential)
+- Entity conflict detection: Cosmos 3 inspired, auto-detect contradictions on write
+- Hot query cache (LRU, max 64) for high-frequency entity lookups
+- Archive trash recovery: deleted entries saved to `data/archive_trash/` as grep-able JSONL
+- L4 permanent notes: auto-serialize when tool calls >= 5 or reply >= 500 chars
+- 20+ new tools: self_edit, feishu_send, note_tool, knowledge, mirror_tool, chain, mail, security_watch, etc.
+
 ### Changed
 - kernel.py: archive_store init + semantic bridge search/save (disabled old online LLM compression)
 - session.py: replaced compress_l1/l2/async_compress with archive-driven context building

editions/__init__.py

@@ -7,6 +7,7 @@
 - default identity
 - resource requirements
 """
+
 from dataclasses import dataclass, field
 
 

examples/02_quick_chat.py

@@ -26,4 +26,5 @@ async def chat():
     response = await kernel.run("Prove you remember: what was my first question?")
     print("🤖 GBase:", response)
 
+
 asyncio.run(chat())

examples/03_agent_as_server.py

@@ -20,5 +20,7 @@
 if __name__ == "__main__":
     port = sys.argv[1] if len(sys.argv) > 1 else "8420"
     print(f"Starting GBase server on port {port}...")
-    print(f"Try: curl http://localhost:{port}/ask -X POST -H 'Content-Type: application/json' -d '{{\"message\": \"hi\"}}'")
+    print(
+        f"Try: curl http://localhost:{port}/ask -X POST -H 'Content-Type: application/json' -d '{{\"message\": \"hi\"}}'"
+    )
     run(mode="http", port=int(port))

examples/03_memory_demo.py

@@ -3,7 +3,6 @@
 Run it twice. The second time, it will remember our first conversation.
 """
 
-
 # First conversation — the agent learns something
 print("=== Session 1: Teaching ===")
 import asyncio
@@ -17,13 +16,16 @@ async def teach():
     print("GBase:", resp)
     print("Memory saved! Now run this script again to see if it remembers.")
 
+
 async def recall():
     kernel = GBaseKernel()
     resp = await kernel.run("What's my favorite color?")
     print("GBase:", resp)
 
+
 if __name__ == "__main__":
     import sys
+
     if "--recall" in sys.argv:
         asyncio.run(recall())
     else:

examples/04_memory_persistence.py

@@ -24,16 +24,20 @@
 
 async def teach():
     kernel = GBaseKernel()
-    resp = await kernel.run("Remember this fact: the creator of GBase is Gary Lin, and the project was born in Shanghai, 2026.")
+    resp = await kernel.run(
+        "Remember this fact: the creator of GBase is Gary Lin, and the project was born in Shanghai, 2026."
+    )
     print("🤖 GBase:", resp)
     print("\n✅ Taught! Now run: python3 examples/04_memory_persistence.py recall")
 
+
 async def recall():
     kernel = GBaseKernel()
     resp = await kernel.run("Who created you, and where were you born?")
     print("🤖 GBase:", resp)
     print("\n💡 If it remembers, Mirror Memory is working. If not, check data/mirror.db exists.")
 
+
 if __name__ == "__main__":
     if len(sys.argv) < 2:
         print(__doc__)

examples/05_rsi_demo.py

@@ -28,11 +28,12 @@ async def rsi_cycle():
     print(f"📊 Before: {result.get('score_before', 'N/A')}")
     print(f"📊 After:  {result.get('score_after', 'N/A')}")
 
-    if result.get('rollback'):
+    if result.get("rollback"):
         print("↩️  Agent decided to rollback — changes weren't beneficial")
     else:
         print("🎉 Agent found genuine improvements!")
 
     print(f"\n🔬 Details: {result.get('details', '')}")
 
+
 asyncio.run(rsi_cycle())