From f30d0299689d9bce0b90c35c86d6645f126a2d2d Mon Sep 17 00:00:00 2001
From: jenstandstad <jens.tandstad@gmail.com>
Date: Sat, 25 Apr 2026 12:12:40 +0200
Subject: [PATCH] Adding

---
 plugins/festinger/festinger/main.py | 87 ++++++++++++++++++++++++++---
 1 file changed, 78 insertions(+), 9 deletions(-)

diff --git a/plugins/festinger/festinger/main.py b/plugins/festinger/festinger/main.py
index 2373d57..60c4815 100644
--- a/plugins/festinger/festinger/main.py
+++ b/plugins/festinger/festinger/main.py
@@ -477,19 +477,80 @@ def extract_prompt_text(body: dict, path: str) -> str:
     return body.get("prompt", "")
 
 
+def _human_prefix(text: str, max_chars: int = 1000) -> str:
+    """
+    Extract the human-written prefix of a user message, stopping before any
+    agent-framework context injection (EXTRAS, tool results, structured sections).
+
+    Agent Zero (and similar frameworks) append structured context to the user
+    message after a blank line followed by a section marker.  We only want the
+    user's actual words — the first paragraph before any such marker.
+
+    Markers we stop at:
+      \\n\\n[          — Agent Zero [EXTRAS], [context], [solutions], …
+      \\n\\n<          — XML-wrapped context blocks
+      \\n---           — horizontal rule separators
+      \\n# or \\n##    — markdown headings injected by the framework
+    """
+    # Structural section delimiters injected by agent frameworks
+    for delim in ("\n\n[", "\n\n<", "\n---", "\n[EXTRAS]", "\n[context]"):
+        idx = text.find(delim)
+        if 0 < idx < max_chars:
+            return text[:idx].strip()
+    return text[:max_chars].strip()
+
+
+def _is_internal_subcall(user_text: str, sys_text: str = "") -> bool:
+    """
+    Heuristic: returns True if this looks like an agent-internal sub-call
+    (memory extraction, consolidation, keyword search) rather than a main
+    user-facing turn.  We skip recollection injection for these.
+
+    Signals checked in order:
+      1. User message is a JSON/array literal  → memory operation payload
+      2. System message is very short          → utility prompt, not a full agent
+         personality (Agent Zero's main system prompt is typically >5 000 chars;
+         memory sub-call prompts are usually <1 500 chars)
+      3. System message contains memory-task keywords in the first 200 chars
+    """
+    # Signal 1: JSON payload
+    if user_text.strip().startswith(("{", "[")):
+        return True
+
+    if sys_text:
+        # Signal 2: system prompt suspiciously short for a real agent call
+        if len(sys_text) < 1500:
+            return True
+
+        # Signal 3: task-oriented system message (memory ops, keyword extraction…)
+        sys_head = sys_text[:200].lower()
+        task_markers = (
+            "extract keyword", "consolidat", "search for similar",
+            "rate the similarity", "memorize", "memory content",
+        )
+        if any(m in sys_head for m in task_markers):
+            return True
+
+    return False
+
+
 def _last_user_message_text(body: dict, path: str) -> str:
     """
-    Extract only the last user message for the write path.
-    Agent responses, thinking traces, and system prompts are excluded —
-    they are noise for concept discovery.
+    Extract only the human-written prefix of the last user message.
+    Agent Zero and similar frameworks append large context sections (EXTRAS,
+    solutions, tool results) after the user's actual words.  We stop at the
+    first structural delimiter so that domain tokens in the injected context
+    don't spuriously trigger recollection.
     """
     if path in ("/api/chat", "/v1/chat/completions", "/v1/messages"):
         messages = body.get("messages", [])
         last_user = next((m for m in reversed(messages) if m.get("role") == "user"), None)
         if last_user:
-            return " ".join(_extract_text_strings(last_user.get("content", "")))
+            raw = " ".join(_extract_text_strings(last_user.get("content", "")))
+            return _human_prefix(raw)
         return ""
-    return body.get("prompt", "")
+    raw = body.get("prompt", "")
+    return _human_prefix(raw)
 
 
 def _last_assistant_message_text(body: dict, path: str) -> str:
@@ -891,11 +952,20 @@ async def process_prompt(
     if not agent_name:
         agent_name, _ = _extract_agent_name(body, hdrs)  # body already cleaned by route handler
 
-    # Last user message — primary source for recollection reads.
+    # Last user message — human-written prefix only (strips agent framework context).
     user_text = _last_user_message_text(body, path)
     if not user_text.strip():
         return body
 
+    # Skip injection for internal agent sub-calls (memory extraction,
+    # consolidation, keyword search).  These are detected by:
+    #   - user message being a JSON payload, OR
+    #   - system message being very short (utility prompt, not a full agent), OR
+    #   - system message starting with memory-task keywords.
+    raw_sys_text = _system_message_text(body, path)
+    if _is_internal_subcall(user_text, raw_sys_text):
+        return body
+
     # 1. Scan user message for explicit relationship cues (fast, regex-only).
     for cue in scan_cues(user_text):
         await enqueue_cue(cue)
@@ -912,9 +982,8 @@ async def process_prompt(
     #    Concepts grounding the agent's persona or project context rank higher
     #    in the recollection block for the entire session.
     session_boost_ids: set[int] = set()
-    sys_text = _system_message_text(body, path)
-    if sys_text:
-        for tok in tokenize(sys_text):
+    if raw_sys_text:
+        for tok in tokenize(raw_sys_text):
             row = cache.soas_by_token.get(tok)
             if row and row.saliency > 0.0:
                 session_boost_ids.add(row.id)