Merge branch 'main' into lance-db-improvement

cookbook/examples/apps/answer_engine/app.py

@@ -41,8 +41,8 @@ def main() -> None:
     # Model selector
     ####################################################################
     model_options = {
-        "o3-mini": "openai:o3-mini",
         "gpt-4o": "openai:gpt-4o",
+        "o3-mini": "openai:o3-mini",
         "gemini-2.0-flash-exp": "google:gemini-2.0-flash-exp",
         "claude-3-5-sonnet": "anthropic:claude-3-5-sonnet-20241022",
         "llama-3.3-70b": "groq:llama-3.3-70b-versatile",

cookbook/models/ollama_tools/async_tool_use_stream.py

@@ -0,0 +1,14 @@
+"""Run `pip install duckduckgo-search` to install dependencies."""
+
+from agno.agent import Agent
+from agno.models.ollama import OllamaTools
+from agno.tools.duckduckgo import DuckDuckGoTools
+import asyncio
+
+agent = Agent(
+    model=OllamaTools(id="llama3.1:8b"),
+    tools=[DuckDuckGoTools()],
+    show_tool_calls=True,
+    markdown=True,
+)
+asyncio.run(agent.aprint_response("Whats happening in France?", stream=True))

cookbook/playground/multimodal_agents.py

@@ -57,6 +57,32 @@
     ),
 )
 
+ml_music_agent = Agent(
+    name="ModelsLab Music Agent",
+    agent_id="ml_music_agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[ModelsLabTools(wait_for_completion=True, file_type=FileType.MP3)],
+    description="You are an AI agent that can generate music using the ModelsLabs API.",
+    instructions=[
+        "When generating music, use the `generate_media` tool with detailed prompts that specify:",
+        "- The genre and style of music (e.g., classical, jazz, electronic)",
+        "- The instruments and sounds to include",
+        "- The tempo, mood and emotional qualities",
+        "- The structure (intro, verses, chorus, bridge, etc.)",
+        "Create rich, descriptive prompts that capture the desired musical elements.",
+        "Focus on generating high-quality, complete instrumental pieces.",
+        "Keep responses simple and only confirm when music is generated successfully.",
+        "Do not include any file names, URLs or technical details in responses.",
+    ],
+    markdown=True,
+    debug_mode=True,
+    add_history_to_messages=True,
+    add_datetime_to_instructions=True,
+    storage=SqliteAgentStorage(
+        table_name="ml_music_agent", db_file=image_agent_storage_file
+    ),
+)
+
 ml_video_agent = Agent(
     name="ModelsLab Video Agent",
     agent_id="ml_video_agent",
@@ -142,33 +168,16 @@
     ),
 )
 
-image_to_image_agent = Agent(
-    name="Image to Image Agent",
-    agent_id="image_to_image_agent",
-    model=OpenAIChat(id="gpt-4o"),
-    tools=[FalTools()],
-    markdown=True,
-    debug_mode=True,
-    instructions=[
-        "You have to use the `image_to_image` tool to generate the image.",
-        "You are an AI agent that can generate images using the Fal AI API.",
-        "You will be given a prompt and an image URL.",
-        "Don't provide the URL of the image in the response. Only describe what image was generated.",
-    ],
-    storage=SqliteAgentStorage(
-        table_name="image_to_image_agent", db_file=image_agent_storage_file
-    ),
-)
 
 app = Playground(
     agents=[
         image_agent,
         ml_gif_agent,
+        ml_music_agent,
         ml_video_agent,
         fal_agent,
         gif_agent,
         audio_agent,
-        image_to_image_agent,
     ]
 ).get_app(use_async=False)
 

libs/agno/agno/agent/agent.py

@@ -881,7 +881,6 @@ def run(
                     import time
 
                     time.sleep(delay)
-
         if last_exception is not None:
             raise Exception(
                 f"Failed after {num_attempts} attempts. Last error using {last_exception.model_name}({last_exception.model_id}): {str(last_exception)}"
@@ -1942,40 +1941,41 @@ def get_system_message(self) -> Optional[Message]:
                 f"<transfer_instructions>\n{self.get_transfer_instructions().strip()}\n</transfer_instructions>\n\n"
             )
         # 3.3.11 Then add memories to the system prompt
-        if self.memory.create_user_memories:
-            if self.memory.memories and len(self.memory.memories) > 0:
-                system_message_content += (
-                    "You have access to memories from previous interactions with the user that you can use:\n\n"
-                )
-                system_message_content += "<memories_from_previous_interactions>"
-                for _memory in self.memory.memories:
-                    system_message_content += f"\n- {_memory.memory}"
-                system_message_content += "\n</memories_from_previous_interactions>\n\n"
-                system_message_content += (
-                    "Note: this information is from previous interactions and may be updated in this conversation. "
-                    "You should always prefer information from this conversation over the past memories.\n\n"
-                )
-            else:
-                system_message_content += (
-                    "You have the capability to retain memories from previous interactions with the user, "
-                    "but have not had any interactions with the user yet.\n"
-                    "If the user asks about previous memories, you can let them know that you dont have any memory about the user because you haven't had any interactions yet.\n\n"
-                )
-            system_message_content += (
-                "You can add new memories using the `update_memory` tool.\n"
-                "If you use the `update_memory` tool, remember to pass on the response to the user.\n\n"
-            )
-        # 3.3.12 Then add a summary of the interaction to the system prompt
-        if self.memory.create_session_summary:
-            if self.memory.summary is not None:
-                system_message_content += "Here is a brief summary of your previous interactions if it helps:\n\n"
-                system_message_content += "<summary_of_previous_interactions>\n"
-                system_message_content += str(self.memory.summary)
-                system_message_content += "\n</summary_of_previous_interactions>\n\n"
+        if self.memory:
+            if self.memory.create_user_memories:
+                if self.memory.memories and len(self.memory.memories) > 0:
+                    system_message_content += (
+                        "You have access to memories from previous interactions with the user that you can use:\n\n"
+                    )
+                    system_message_content += "<memories_from_previous_interactions>"
+                    for _memory in self.memory.memories:
+                        system_message_content += f"\n- {_memory.memory}"
+                    system_message_content += "\n</memories_from_previous_interactions>\n\n"
+                    system_message_content += (
+                        "Note: this information is from previous interactions and may be updated in this conversation. "
+                        "You should always prefer information from this conversation over the past memories.\n\n"
+                    )
+                else:
+                    system_message_content += (
+                        "You have the capability to retain memories from previous interactions with the user, "
+                        "but have not had any interactions with the user yet.\n"
+                        "If the user asks about previous memories, you can let them know that you dont have any memory about the user because you haven't had any interactions yet.\n\n"
+                    )
                 system_message_content += (
-                    "Note: this information is from previous interactions and may be outdated. "
-                    "You should ALWAYS prefer information from this conversation over the past summary.\n\n"
+                    "You can add new memories using the `update_memory` tool.\n"
+                    "If you use the `update_memory` tool, remember to pass on the response to the user.\n\n"
                 )
+            # 3.3.12 Then add a summary of the interaction to the system prompt
+            if self.memory.create_session_summary:
+                if self.memory.summary is not None:
+                    system_message_content += "Here is a brief summary of your previous interactions if it helps:\n\n"
+                    system_message_content += "<summary_of_previous_interactions>\n"
+                    system_message_content += str(self.memory.summary)
+                    system_message_content += "\n</summary_of_previous_interactions>\n\n"
+                    system_message_content += (
+                        "Note: this information is from previous interactions and may be outdated. "
+                        "You should ALWAYS prefer information from this conversation over the past summary.\n\n"
+                    )
 
         # Add the JSON output prompt if response_model is provided and structured_outputs is False
         if self.response_model is not None and not self.structured_outputs:

libs/agno/agno/models/base.py

@@ -768,7 +768,7 @@ def run_function_calls(
             )
             yield ModelResponse(
                 content=f"{fc.get_call_str()} completed in {function_call_timer.elapsed:.4f}s.",
-                tool_calls=[function_call_result.to_fc_result()],
+                tool_calls=[function_call_result.to_function_call_dict()],
                 event=ModelResponseEvent.tool_call_completed.value,
             )
 
@@ -871,7 +871,7 @@ async def arun_function_calls(self, function_calls: List[FunctionCall], function
             )
             yield ModelResponse(
                 content=f"{fc.get_call_str()} completed in {function_call_timer.elapsed:.4f}s.",
-                tool_calls=[function_call_result.to_fc_result()],
+                tool_calls=[function_call_result.to_function_call_dict()],
                 event=ModelResponseEvent.tool_call_completed.value,
             )
 

libs/agno/agno/models/message.py

@@ -206,7 +206,23 @@ def serialize_for_model(self) -> Dict[str, Any]:
 
     def to_dict(self) -> Dict[str, Any]:
         """Returns the message as a dictionary."""
-        message_dict = self.model_dump(exclude_none=True)
+        message_dict = {
+            "content": self.content,
+            "reasoning_content": self.reasoning_content,
+            "from_history": self.from_history,
+            "stop_after_tool_call": self.stop_after_tool_call,
+            "role": self.role,
+            "name": self.name,
+            "tool_call_id": self.tool_call_id,
+            "tool_name": self.tool_name,
+            "tool_args": self.tool_args,
+            "tool_call_error": self.tool_call_error,
+            "tool_calls": self.tool_calls,
+        }
+        # Filter out None and empty collections
+        message_dict = {
+            k: v for k, v in message_dict.items() if v is not None and not (isinstance(v, (list, dict)) and len(v) == 0)
+        }
 
         # Convert media objects to dictionaries
         if self.images:
@@ -228,7 +244,7 @@ def to_dict(self) -> Dict[str, Any]:
         message_dict["created_at"] = self.created_at
         return message_dict
 
-    def to_fc_result(self) -> Dict[str, Any]:
+    def to_function_call_dict(self) -> Dict[str, Any]:
         return {
             "content": self.content,
             "tool_call_id": self.tool_call_id,

libs/agno/agno/models/response.py

@@ -38,3 +38,4 @@ class ModelResponse:
 class FileType(str, Enum):
     MP4 = "mp4"
     GIF = "gif"
+    MP3 = "mp3"