Fix for asyncstream

cookbook/models/huggingface/async_basic.py

@@ -0,0 +1,12 @@
+import asyncio
+from agno.agent import Agent
+from agno.models.huggingface import HuggingFace
+
+agent = Agent(
+    model=HuggingFace(
+        id="mistralai/Mistral-7B-Instruct-v0.2", max_tokens=4096, temperature=0
+    ),
+)
+asyncio.run(agent.aprint_response(
+    "What is meaning of life and then recommend 5 best books to read about it"
+))

cookbook/models/huggingface/async_basic_stream.py

@@ -0,0 +1,13 @@
+import asyncio
+from agno.agent import Agent
+from agno.models.huggingface import HuggingFace
+
+agent = Agent(
+    model=HuggingFace(
+        id="mistralai/Mistral-7B-Instruct-v0.2", max_tokens=4096, temperature=0
+    ),
+)
+asyncio.run(agent.aprint_response(
+    "What is meaning of life and then recommend 5 best books to read about it",
+    stream=True,
+))

cookbook/models/openai/async_basic.py

@@ -0,0 +1,12 @@
+import asyncio
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIChat
+
+agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
+
+# Get the response in a variable
+# run: RunResponse = agent.run("Share a 2 sentence horror story")
+# print(run.content)
+
+# Print the response in the terminal
+asyncio.run(agent.aprint_response("Share a 2 sentence horror story"))

cookbook/models/openai/async_basic_stream.py

@@ -0,0 +1,14 @@
+import asyncio
+from typing import Iterator  # noqa
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIChat
+
+agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
+
+# Get the response in a variable
+# run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)
+# for chunk in run_response:
+#     print(chunk.content)
+
+# Print the response in the terminal
+asyncio.run(agent.aprint_response("Share a 2 sentence horror story", stream=True))

libs/agno/agno/models/anthropic/claude.py

@@ -1,3 +1,4 @@
+from collections.abc import AsyncIterator
 import json
 from dataclasses import dataclass
 from os import getenv
@@ -393,7 +394,7 @@ async def ainvoke(self, messages: List[Message]) -> AnthropicMessage:
             logger.error(f"Unexpected error calling Claude API: {str(e)}")
             raise ModelProviderError(e, self.name, self.id) from e
 
-    async def ainvoke_stream(self, messages: List[Message]) -> Any:
+    async def ainvoke_stream(self, messages: List[Message]) -> AsyncIterator[Any]:
         """
         Stream an asynchronous response from the Anthropic API.
 
@@ -406,16 +407,13 @@ async def ainvoke_stream(self, messages: List[Message]) -> Any:
         try:
             chat_messages, system_message = _format_messages(messages)
             request_kwargs = self._prepare_request_kwargs(system_message)
-
-            return (
-                await self.get_async_client()
-                .messages.stream(
-                    model=self.id,
-                    messages=chat_messages,  # type: ignore
-                    **request_kwargs,
-                )
-                .__aenter__()
-            )
+            async with self.get_async_client().messages.stream(
+                model=self.id,
+                messages=chat_messages,  # type: ignore
+                **request_kwargs,
+            ) as stream:
+                async for chunk in stream:
+                    yield chunk
         except APIConnectionError as e:
             logger.error(f"Connection error while calling Claude API: {str(e)}")
             raise ModelProviderError(e, self.name, self.id) from e

libs/agno/agno/models/aws/claude.py

@@ -325,7 +325,7 @@ def process_response_stream(
                     )
 
                     # Update metrics
-                    self.add_usage_metrics_to_assistant_message(
+                    self._add_usage_metrics_to_assistant_message(
                         assistant_message=assistant_message, response_usage=response_usage
                     )
 

libs/agno/agno/models/base.py

@@ -349,7 +349,10 @@ async def _aprocess_model_response(
 
         # Update model response with assistant message content and audio
         if assistant_message.content is not None:
-            model_response.content += assistant_message.get_content_string()
+            if model_response.content is None:
+                model_response.content = assistant_message.get_content_string()
+            else:
+                model_response.content += assistant_message.get_content_string()
         if assistant_message.audio_output is not None:
             model_response.audio = assistant_message.audio_output
         if provider_response.extra is not None:
@@ -394,7 +397,7 @@ def _populate_assistant_message(
 
         # Add usage metrics if provided
         if provider_response.response_usage is not None:
-            self.add_usage_metrics_to_assistant_message(
+            self._add_usage_metrics_to_assistant_message(
                 assistant_message=assistant_message, response_usage=provider_response.response_usage
             )
 
@@ -491,7 +494,7 @@ async def aprocess_response_stream(
         """
         Process a streaming response from the model.
         """
-        async for response_delta in await self.ainvoke_stream(messages=messages):
+        async for response_delta in self.ainvoke_stream(messages=messages):
             model_response_delta = self.parse_provider_response_delta(response_delta)
             if model_response_delta:
                 for model_response in self._populate_stream_data_and_assistant_message(
@@ -604,7 +607,7 @@ def _populate_stream_data_and_assistant_message(
             stream_data.extra.update(model_response.extra)
 
         if model_response.response_usage is not None:
-            self.add_usage_metrics_to_assistant_message(
+            self._add_usage_metrics_to_assistant_message(
                 assistant_message=assistant_message, response_usage=model_response.response_usage
             )
 
@@ -956,7 +959,7 @@ def format_function_call_results(
         if len(function_call_results) > 0:
             messages.extend(function_call_results)
 
-    def add_usage_metrics_to_assistant_message(self, assistant_message: Message, response_usage: Any) -> None:
+    def _add_usage_metrics_to_assistant_message(self, assistant_message: Message, response_usage: Any) -> None:
         """
         Add usage metrics from the model provider to the assistant message.
 

libs/agno/agno/models/cohere/chat.py

@@ -292,7 +292,7 @@ def _process_stream_response(
                 and response.delta.usage is not None
                 and response.delta.usage.tokens is not None
             ):
-                self.add_usage_metrics_to_assistant_message(
+                self._add_usage_metrics_to_assistant_message(
                     assistant_message=assistant_message,
                     response_usage=CohereResponseUsage(
                         input_tokens=response.delta.usage.tokens.input_tokens,

libs/agno/agno/models/groq/groq.py

@@ -284,12 +284,14 @@ async def ainvoke_stream(self, messages: List[Message]) -> Any:
             Any: An asynchronous iterator of chat completion chunks.
         """
         try:
-            return await self.get_async_client().chat.completions.create(
+            stream = await self.get_async_client().chat.completions.create(
                 model=self.id,
                 messages=[format_message(m) for m in messages],  # type: ignore
                 stream=True,
                 **self.request_kwargs,
             )
+            async for chunk in stream:
+                yield chunk
         except Exception as e:
             logger.error(f"Unexpected error calling Groq API: {str(e)}")
             raise ModelProviderError(e, self.name, self.id) from e