fix: external model name now recorded properly in responses

test: http cassettes added for improved integration tests
refactor: generic name for the CLI agent

utils/conversation_memory.py

@@ -3,8 +3,8 @@ Conversation Memory for AI-to-AI Multi-turn Discussions
 
 This module provides conversation persistence and context reconstruction for
 stateless MCP (Model Context Protocol) environments. It enables multi-turn
-conversations between Claude and Gemini by storing conversation state in memory
-across independent request cycles.
+conversations between the agent and downstream models by storing conversation
+state in memory across independent request cycles.
 
 CRITICAL ARCHITECTURAL REQUIREMENT:
 This conversation memory system is designed for PERSISTENT MCP SERVER PROCESSES.
@@ -97,7 +97,7 @@ Collection Phase (Newest-First Priority):
 - Excludes: Turn 2, Turn 1 (oldest, dropped due to token limits)
 
 Presentation Phase (Chronological Order):
-- LLM sees: "--- Turn 3 (Claude) ---", "--- Turn 4 (Gemini) ---", "--- Turn 5 (Claude) ---"
+- LLM sees: "--- Turn 3 (Agent) ---", "--- Turn 4 (Model) ---", "--- Turn 5 (Agent) ---"
 - Natural conversation flow maintained despite prioritizing recent context
 
 This enables true AI-to-AI collaboration across the entire tool ecosystem with optimal
@@ -152,7 +152,7 @@ class ConversationTurn(BaseModel):
     the content and metadata needed for cross-tool continuation.
 
     Attributes:
-        role: "user" (Claude) or "assistant" (Gemini/O3/etc)
+        role: "user" (Agent request) or "assistant" (model response)
         content: The actual message content/response
         timestamp: ISO timestamp when this turn was created
         files: List of file paths referenced in this specific turn
@@ -321,7 +321,7 @@ def add_turn(
 
     Args:
         thread_id: UUID of the conversation thread
-        role: "user" (Claude) or "assistant" (Gemini/O3/etc)
+        role: "user" (Agent request) or "assistant" (model response)
         content: The actual message/response content
         files: Optional list of files referenced in this turn
         images: Optional list of images referenced in this turn
@@ -707,7 +707,7 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
 
         <turn_content>
 
-        --- Turn 2 (Gemini using analyze via google/gemini-2.5-flash) ---
+        --- Turn 2 (gemini-2.5-flash using analyze via google) ---
         Files used in this turn: file3.py
 
         <turn_content>
@@ -921,7 +921,11 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
     for idx in range(len(all_turns) - 1, -1, -1):
         turn = all_turns[idx]
         turn_num = idx + 1
-        role_label = "Claude" if turn.role == "user" else "Gemini"
+
+        if turn.role == "user":
+            role_label = "Agent"
+        else:
+            role_label = turn.model_name or "Assistant"
 
         # Build the complete turn content
         turn_parts = []
@@ -932,8 +936,13 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
             turn_header += f" using {turn.tool_name}"
 
         # Add model info if available
-        if turn.model_provider and turn.model_name:
-            turn_header += f" via {turn.model_provider}/{turn.model_name}"
+        if turn.model_provider:
+            provider_descriptor = turn.model_provider
+            if turn.model_name and turn.model_name != role_label:
+                provider_descriptor += f"/{turn.model_name}"
+            turn_header += f" via {provider_descriptor}"
+        elif turn.model_name and turn.model_name != role_label:
+            turn_header += f" via {turn.model_name}"
 
         turn_header += ") ---"
         turn_parts.append(turn_header)
@@ -970,7 +979,7 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
     turn_entries.reverse()
 
     # Add the turns in chronological order for natural LLM comprehension
-    # The LLM will see: "--- Turn 1 (Claude) ---" followed by "--- Turn 2 (Gemini) ---" etc.
+    # The LLM will see: "--- Turn 1 (Agent) ---" followed by "--- Turn 2 (Model) ---" etc.
     for _, turn_content in turn_entries:
         history_parts.append(turn_content)