Merge branch 'main' into examples/app/podcast-generator

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))

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/tests/integration/model/openai/test_sync.py

@@ -3,11 +3,12 @@
 from agno.agent import Agent, AgentMemory, RunResponse  # noqa
 from agno.media import Image
 from agno.models.openai import OpenAIChat
+from agno.storage.agent.postgres import PostgresAgentStorage
 from agno.tools.duckduckgo import DuckDuckGoTools
 
 
 def test_basic():
-    agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
+    agent = Agent(model=OpenAIChat(id="gpt-4o-mini"), markdown=True)
 
     # Print the response in the terminal
     response: RunResponse = agent.run("Share a 2 sentence horror story")
@@ -30,7 +31,7 @@ def test_basic():
 
 
 def test_basic_stream():
-    agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
+    agent = Agent(model=OpenAIChat(id="gpt-4o-mini"), markdown=True)
 
     response_stream = agent.run("Share a 2 sentence horror story", stream=True)
 
@@ -45,7 +46,7 @@ def test_basic_stream():
 
 def test_tool_use():
     agent = Agent(
-        model=OpenAIChat(id="gpt-4o"),
+        model=OpenAIChat(id="gpt-4o-mini"),
         tools=[DuckDuckGoTools()],
         show_tool_calls=True,
         markdown=True,
@@ -61,7 +62,7 @@ def test_tool_use():
 
 def test_with_memory():
     agent = Agent(
-        model=OpenAIChat(id="gpt-4o"),
+        model=OpenAIChat(id="gpt-4o-mini"),
         add_history_to_messages=True,
         num_history_responses=5,
         markdown=True,
@@ -100,7 +101,7 @@ class MovieScript(BaseModel):
         plot: str = Field(..., description="Brief plot summary")
 
     agent = Agent(
-        model=OpenAIChat(id="gpt-4o"),
+        model=OpenAIChat(id="gpt-4o-mini"),
         response_model=MovieScript,
     )
 
@@ -115,7 +116,7 @@ class MovieScript(BaseModel):
 
 def test_image_input():
     agent = Agent(
-        model=OpenAIChat(id="gpt-4o"),
+        model=OpenAIChat(id="gpt-4o-mini"),
         tools=[DuckDuckGoTools()],
         markdown=True,
     )
@@ -126,3 +127,19 @@ def test_image_input():
     )
 
     assert "golden" in response.content.lower()
+
+def test_history_grows_exponentially():
+    db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+    agent = Agent(
+        model=OpenAIChat(id="gpt-4o-mini"),
+        storage=PostgresAgentStorage(table_name="agent_sessions", db_url=db_url),
+        add_history_to_messages=True,
+    )
+    agent.run("Hello")
+    assert len(agent.run_response.messages) == 2
+    agent.run("Hello 2")
+    assert len(agent.run_response.messages) == 4
+    agent.run("Hello 3")
+    assert len(agent.run_response.messages) == 6
+    agent.run("Hello 4")
+    assert len(agent.run_response.messages) == 8