feat(memory): structured reflection + correction detection in MemoryMiddleware (#1620) (#1668)

* feat(memory): add structured reflection and correction detection

* fix(memory): align sourceError schema and prompt guidance

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

backend/packages/harness/deerflow/agents/memory/prompt.py

@@ -29,6 +29,17 @@ Instructions:
 2. Extract relevant facts, preferences, and context with specific details (numbers, names, technologies)
 3. Update the memory sections as needed following the detailed length guidelines below
 
+Before extracting facts, perform a structured reflection on the conversation:
+1. Error/Retry Detection: Did the agent encounter errors, require retries, or produce incorrect results?
+   If yes, record the root cause and correct approach as a high-confidence fact with category "correction".
+2. User Correction Detection: Did the user correct the agent's direction, understanding, or output?
+   If yes, record the correct interpretation or approach as a high-confidence fact with category "correction".
+   Include what went wrong in "sourceError" only when category is "correction" and the mistake is explicit in the conversation.
+3. Project Constraint Discovery: Were any project-specific constraints discovered during the conversation?
+   If yes, record them as facts with the most appropriate category and confidence.
+
+{correction_hint}
+
 Memory Section Guidelines:
 
 **User Context** (Current state - concise summaries):
@@ -62,6 +73,7 @@ Memory Section Guidelines:
   * context: Background facts (job title, projects, locations, languages)
   * behavior: Working patterns, communication habits, problem-solving approaches
   * goal: Stated objectives, learning targets, project ambitions
+  * correction: Explicit agent mistakes or user corrections, including the correct approach
 - Confidence levels:
   * 0.9-1.0: Explicitly stated facts ("I work on X", "My role is Y")
   * 0.7-0.8: Strongly implied from actions/discussions
@@ -94,7 +106,7 @@ Output Format (JSON):
     "longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
   }},
   "newFacts": [
-    {{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
+    {{ "content": "...", "category": "preference|knowledge|context|behavior|goal|correction", "confidence": 0.0-1.0 }}
   ],
   "factsToRemove": ["fact_id_1", "fact_id_2"]
 }}
@@ -104,6 +116,8 @@ Important Rules:
 - Follow length guidelines: workContext/personalContext are concise (1-3 sentences), topOfMind and history sections are detailed (paragraphs)
 - Include specific metrics, version numbers, and proper nouns in facts
 - Only add facts that are clearly stated (0.9+) or strongly implied (0.7+)
+- Use category "correction" for explicit agent mistakes or user corrections; assign confidence >= 0.95 when the correction is explicit
+- Include "sourceError" only for explicit correction facts when the prior mistake or wrong approach is clearly stated; omit it otherwise
 - Remove facts that are contradicted by new information
 - When updating topOfMind, integrate new focus areas while removing completed/abandoned ones
   Keep 3-5 concurrent focus themes that are still active and relevant
@@ -126,7 +140,7 @@ Message:
 Extract facts in this JSON format:
 {{
   "facts": [
-    {{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
+    {{ "content": "...", "category": "preference|knowledge|context|behavior|goal|correction", "confidence": 0.0-1.0 }}
   ]
 }}
 
@@ -136,6 +150,7 @@ Categories:
 - context: Background context (location, job, projects)
 - behavior: Behavioral patterns
 - goal: User's goals or objectives
+- correction: Explicit corrections or mistakes to avoid repeating
 
 Rules:
 - Only extract clear, specific facts
@@ -262,7 +277,11 @@ def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2
                 continue
             category = str(fact.get("category", "context")).strip() or "context"
             confidence = _coerce_confidence(fact.get("confidence"), default=0.0)
-            line = f"- [{category} | {confidence:.2f}] {content}"
+            source_error = fact.get("sourceError")
+            if category == "correction" and isinstance(source_error, str) and source_error.strip():
+                line = f"- [{category} | {confidence:.2f}] {content} (avoid: {source_error.strip()})"
+            else:
+                line = f"- [{category} | {confidence:.2f}] {content}"
 
             # Each additional line is preceded by a newline (except the first).
             line_text = ("\n" + line) if fact_lines else line