add multi part conversation support

litellm/llms/a2a/chat/transformation.py

@@ -12,7 +12,11 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMExcepti
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import Choices, Message, ModelResponse
 
-from ..common_utils import A2AError, extract_text_from_a2a_response
+from ..common_utils import (
+    A2AError,
+    convert_messages_to_prompt,
+    extract_text_from_a2a_response,
+)
 from .streaming_iterator import A2AModelResponseIterator
 
 
@@ -140,24 +144,19 @@ class A2AConfig(BaseConfig):
         # Generate request ID
         request_id = str(uuid.uuid4())
         
-        # Convert last message to A2A format (A2A protocol typically sends single message)
-        # For multi-turn conversations, we'd need to handle context differently
         if not messages:
             raise ValueError("At least one message is required for A2A completion")
         
-        last_message = messages[-1]
+        # Convert all messages to maintain conversation history
+        # Use helper to format conversation with role prefixes
+        full_context = convert_messages_to_prompt(messages)
         
-        # Convert to dict if needed
-        msg_dict: Dict[str, Any]
-        if isinstance(last_message, BaseModel):
-            msg_dict = last_message.model_dump()
-        elif isinstance(last_message, dict):
-            msg_dict = cast(Dict[str, Any], last_message)
-        else:
-            msg_dict = dict(last_message)  # type: ignore
-        
-        # Transform to A2A message format
-        a2a_message = self._openai_message_to_a2a_message(msg_dict)
+        # Create single A2A message with full conversation context
+        a2a_message = {
+            "role": "user",
+            "parts": [{"kind": "text", "text": full_context}],
+            "messageId": str(uuid.uuid4()),
+        }
         
         # Build JSON-RPC 2.0 request
         # For A2A protocol, the method is "message/send" for non-streaming

litellm/llms/a2a/common_utils.py

@@ -3,7 +3,13 @@ Common utilities for A2A (Agent-to-Agent) Protocol
 """
 from typing import Any, Dict, List
 
+from pydantic import BaseModel
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    convert_content_list_to_str,
+)
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
 
 
 class A2AError(BaseLLMException):
@@ -22,6 +28,38 @@ class A2AError(BaseLLMException):
         )
 
 
+def convert_messages_to_prompt(messages: List[AllMessageValues]) -> str:
+    """
+    Convert OpenAI messages to a single prompt string for A2A agent.
+    
+    Formats each message as "{role}: {content}" and joins with newlines
+    to preserve conversation history. Handles both string and list content.
+    
+    Args:
+        messages: List of OpenAI-format messages
+    
+    Returns:
+        Formatted prompt string with full conversation context
+    """
+    conversation_parts = []
+    for msg in messages:
+        # Use LiteLLM's helper to extract text from content (handles both str and list)
+        content_text = convert_content_list_to_str(message=msg)
+        
+        # Get role
+        if isinstance(msg, BaseModel):
+            role = msg.model_dump().get("role", "user")
+        elif isinstance(msg, dict):
+            role = msg.get("role", "user")
+        else:
+            role = dict(msg).get("role", "user")  # type: ignore
+        
+        if content_text:
+            conversation_parts.append(f"{role}: {content_text}")
+    
+    return "\n".join(conversation_parts)
+
+
 def extract_text_from_a2a_message(
     message: Dict[str, Any], depth: int = 0, max_depth: int = 10
 ) -> str: