feat: move deepseek to new agent loop [LET-4081] (#4266)

* feat: move deepseek to new agent loop

* fixes

Author: carenthomas

letta/llm_api/deepseek_client.py

@@ -1,3 +1,4 @@
+import json
 import os
 from typing import List, Optional
 
@@ -10,6 +11,7 @@
 from letta.otel.tracing import trace_method
 from letta.schemas.llm_config import LLMConfig
 from letta.schemas.message import Message as PydanticMessage
+from letta.schemas.openai.chat_completion_request import ChatMessage, cast_message_to_subtype
 from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
 from letta.settings import model_settings
 
@@ -36,15 +38,19 @@ def build_request_data(
         data = super().build_request_data(messages, llm_config, tools, force_tool_call)
 
         def add_functions_to_system_message(system_message: ChatMessage):
-            system_message.content += f"<available functions> {''.join(json.dumps(f) for f in functions)} </available functions>"
+            system_message.content += f"<available functions> {''.join(json.dumps(f) for f in tools)} </available functions>"
             system_message.content += 'Select best function to call simply respond with a single json block with the fields "name" and "arguments". Use double quotes around the arguments.'
 
+        openai_message_list = [cast_message_to_subtype(m.to_openai_dict(put_inner_thoughts_in_kwargs=False)) for m in messages]
+
         if llm_config.model == "deepseek-reasoner":  # R1 currently doesn't support function calling natively
             add_functions_to_system_message(
-                data["messages"][0]
+                openai_message_list[0]
             )  # Inject additional instructions to the system prompt with the available functions
 
-            data["messages"] = map_messages_to_deepseek_format(data["messages"])
+            openai_message_list = map_messages_to_deepseek_format(openai_message_list)
+
+        data["messages"] = [m.dict() for m in openai_message_list]
 
         return data
 
@@ -94,4 +100,6 @@ def convert_response_to_chat_completion(
         Handles potential extraction of inner thoughts if they were added via kwargs.
         """
         response = ChatCompletionResponse(**response_data)
+        if response.choices[0].message.tool_calls:
+            return super().convert_response_to_chat_completion(response_data, input_messages, llm_config)
         return convert_deepseek_response_to_chatcompletion(response)

letta/server/rest_api/routers/v1/agents.py

@@ -1078,6 +1078,7 @@ async def send_message(
         "azure",
         "xai",
         "groq",
+        "deepseek",
     ]
 
     # Create a new run for execution tracking
@@ -1223,8 +1224,9 @@ async def send_message_streaming(
         "azure",
         "xai",
         "groq",
+        "deepseek",
     ]
-    model_compatible_token_streaming = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "bedrock"]
+    model_compatible_token_streaming = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "bedrock", "deepseek"]
 
     # Create a new job for execution tracking
     if settings.track_agent_run:
@@ -1480,7 +1482,10 @@ async def _process_message_background(
             "google_vertex",
             "bedrock",
             "ollama",
+            "azure",
+            "xai",
             "groq",
+            "deepseek",
         ]
         if agent_eligible and model_compatible:
             if agent.enable_sleeptime and agent.agent_type != AgentType.voice_convo_agent:
@@ -1671,6 +1676,7 @@ async def preview_raw_payload(
         "azure",
         "xai",
         "groq",
+        "deepseek",
     ]
 
     if agent_eligible and model_compatible:
@@ -1742,6 +1748,7 @@ async def summarize_agent_conversation(
         "azure",
         "xai",
         "groq",
+        "deepseek",
     ]
 
     if agent_eligible and model_compatible: