feat: introduce advanced input for assistant and collaboration agents

feat: implement automatic memory and context search
maintenance: update version to 1.29.0
refactor: integrate knowledge store and fact extractor into assistant
feat: save user messages as facts to knowledge store

CHANGELOG.md

@@ -24,6 +24,14 @@
 
 
 
+
+
+## Version 1.28.0 - 2025-11-08
+
+This release introduces new features like advanced input for the assistant and collaboration agents, along with improved logging and error handling. Several internal components have been updated for better performance, stability, and maintainability.
+
+**Changes:** 54 files, 638 lines
+**Languages:** Other (10 lines), Python (626 lines), TOML (2 lines)
 
 ## Version 1.27.0 - 2025-11-08
 

README.md

@@ -27,6 +27,7 @@ Version Requirements:
 11. [Git Protocol Integration](#git-protocol-integration)
 12. [Repository Manager Implementation](#repository-manager-implementation)
 13. [Best Practices and Patterns](#best-practices-and-patterns)
+14. [Automatic Memory and Context Search](#automatic-memory-and-context-search)
 
 ---
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "rp"
-version = "1.27.0"
+version = "1.28.0"
 description = "R python edition. The ultimate autonomous AI CLI."
 readme = "README.md"
 requires-python = ">=3.10"

rp/core/assistant.py

@@ -108,6 +108,9 @@ class Assistant:
         self.background_tasks = set()
         self.last_result = None
         self.init_database()
+        from rp.memory import KnowledgeStore, FactExtractor
+        self.knowledge_store = KnowledgeStore(DB_PATH)
+        self.fact_extractor = FactExtractor()
         self.messages.append(init_system_message(args))
         try:
             from rp.core.enhanced_assistant import EnhancedAssistant
@@ -482,6 +485,26 @@ def process_message(assistant, message):
     from rp.core.knowledge_context import inject_knowledge_context
 
     inject_knowledge_context(assistant, message)
+    # Save the user message as a fact
+    import time
+    import uuid
+    from rp.memory import KnowledgeEntry
+
+    categories = assistant.fact_extractor.categorize_content(message)
+    entry_id = str(uuid.uuid4())[:16]
+    entry = KnowledgeEntry(
+        entry_id=entry_id,
+        category=categories[0] if categories else "user_message",
+        content=message,
+        metadata={
+            "type": "user_message",
+            "confidence": 1.0,
+            "source": "user_input",
+        },
+        created_at=time.time(),
+        updated_at=time.time(),
+    )
+    assistant.knowledge_store.add_entry(entry)
     assistant.messages.append({"role": "user", "content": message})
     logger.debug(f"Processing user message: {message[:100]}...")
     logger.debug(f"Current message count: {len(assistant.messages)}")

rp/core/enhanced_assistant.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import time
 import uuid
 from typing import Any, Dict, List, Optional
 
@@ -17,7 +18,7 @@ from rp.config import (
 )
 from rp.core.advanced_context import AdvancedContextManager
 from rp.core.api import call_api
-from rp.memory import ConversationMemory, FactExtractor, KnowledgeStore
+from rp.memory import ConversationMemory, FactExtractor, KnowledgeStore, KnowledgeEntry
 from rp.tools.base import get_tools_definition
 from rp.workflows import WorkflowEngine, WorkflowStorage
 
@@ -132,10 +133,6 @@ class EnhancedAssistant:
         facts = self.fact_extractor.extract_facts(user_message)
         for fact in facts[:5]:
             entry_id = str(uuid.uuid4())[:16]
-            import time
-
-            from rp.memory import KnowledgeEntry
-
             categories = self.fact_extractor.categorize_content(fact["text"])
             entry = KnowledgeEntry(
                 entry_id=entry_id,
@@ -150,6 +147,23 @@ class EnhancedAssistant:
                 updated_at=time.time(),
             )
             self.knowledge_store.add_entry(entry)
+
+        # Save the entire user message as a fact
+        entry_id = str(uuid.uuid4())[:16]
+        categories = self.fact_extractor.categorize_content(user_message)
+        entry = KnowledgeEntry(
+            entry_id=entry_id,
+            category=categories[0] if categories else "user_message",
+            content=user_message,
+            metadata={
+                "type": "user_message",
+                "confidence": 1.0,
+                "source": "user_input",
+            },
+            created_at=time.time(),
+            updated_at=time.time(),
+        )
+        self.knowledge_store.add_entry(entry)
         if self.context_manager and ADVANCED_CONTEXT_ENABLED:
             enhanced_messages, context_info = self.context_manager.create_enhanced_context(
                 self.base.messages, user_message, include_knowledge=True