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@ -1129,184 +1129,148 @@ class Agent:
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self.short_memory.return_history_as_string()
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)
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# If streaming is OFF, use the simple, non-streaming path
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if not self.streaming_on:
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attempt = 0
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success = False
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while attempt < self.retry_attempts and not success:
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try:
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if img is not None:
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response = self.call_llm(
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task=task_prompt,
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img=img,
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current_loop=loop_count,
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streaming_callback=streaming_callback,
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*args,
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**kwargs,
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)
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else:
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response = self.call_llm(
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task=task_prompt,
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current_loop=loop_count,
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streaming_callback=streaming_callback,
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*args,
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**kwargs,
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)
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# Parse the response from the agent with the output type
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if exists(self.tools_list_dictionary):
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if isinstance(response, BaseModel):
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response = response.model_dump()
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response = self.parse_llm_output(response)
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self.short_memory.add(role=self.agent_name, content=response)
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if self.print_on is True:
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if isinstance(response, list):
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self.pretty_print(
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f"Structured Output - Attempting Function Call Execution [{time.strftime('%H:%M:%S')}] \n\n Output: {format_data_structure(response)} ",
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loop_count,
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)
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else:
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self.pretty_print(response, loop_count)
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# Check and execute callable tools
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if exists(self.tools):
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self.tool_execution_retry(response, loop_count)
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# Handle MCP tools
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if (
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exists(self.mcp_url)
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or exists(self.mcp_config)
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or exists(self.mcp_urls)
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):
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if response is not None:
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self.mcp_tool_handling(
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response=response,
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current_loop=loop_count,
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)
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else:
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logger.warning(
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f"LLM returned None response in loop {loop_count}, skipping MCP tool handling"
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)
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success = True # Exit retry loop
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except Exception as e:
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logger.error(f"Attempt {attempt + 1} failed: {e}")
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attempt += 1
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if attempt >= self.retry_attempts:
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logger.error("All retry attempts failed.")
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break # Exit main loop if retries fail
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# If streaming is ON with tools, use streaming tool execution path
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else:
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# Parameters
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attempt = 0
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success = False
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while attempt < self.retry_attempts and not success:
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try:
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# Get streaming response with tools
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if img is not None:
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stream_response = self.call_llm(
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response = self.call_llm(
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task=task_prompt,
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img=img,
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current_loop=loop_count,
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streaming_callback=streaming_callback,
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*args,
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**kwargs,
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)
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else:
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stream_response = self.call_llm(
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response = self.call_llm(
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task=task_prompt,
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current_loop=loop_count,
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streaming_callback=streaming_callback,
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*args,
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**kwargs,
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)
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# Use streaming chunk parser from litellm_wrapper
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if hasattr(self.llm, 'parse_streaming_chunks_with_tools'):
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full_text_response, tool_calls_in_stream = self.llm.parse_streaming_chunks_with_tools(
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stream=stream_response,
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agent_name=self.agent_name,
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print_on=self.print_on,
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verbose=self.verbose,
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)
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else:
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# Fallback to simple text collection if method not available
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full_text_response = ""
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tool_calls_in_stream = []
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if hasattr(stream_response, "__iter__") and not isinstance(stream_response, str):
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for chunk in stream_response:
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# Handle streaming response with tools
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if self.streaming_on and exists(self.tools_list_dictionary) and hasattr(response, "__iter__") and not isinstance(response, str):
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# Parse streaming chunks with tools using wrapper
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if hasattr(self.llm, 'parse_streaming_chunks_with_tools'):
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full_text_response, tool_calls_in_stream = self.llm.parse_streaming_chunks_with_tools(
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stream=response,
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agent_name="Agent",
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print_on=self.print_on,
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verbose=self.verbose,
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)
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# Handle tool calls if any were detected
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if tool_calls_in_stream:
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# Add text response to memory first
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if full_text_response.strip():
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self.short_memory.add(role=self.agent_name, content=full_text_response)
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# Format and execute tool calls
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import json
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formatted_tool_calls = []
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for tc in tool_calls_in_stream:
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if tc and (tc.get("input") or tc.get("arguments_complete")):
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if "input" in tc:
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args_to_use = tc["input"]
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else:
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try:
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args_to_use = json.loads(tc.get("arguments", "{}"))
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except json.JSONDecodeError:
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continue
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formatted_tool_calls.append({
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"type": "function",
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"function": {"name": tc["name"], "arguments": json.dumps(args_to_use)},
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"id": tc["id"]
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})
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if formatted_tool_calls:
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# Execute tools using existing tool structure
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response = {"choices": [{"message": {"tool_calls": formatted_tool_calls}}]}
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else:
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# No tools called, use streamed text as response
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response = full_text_response
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if response.strip():
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self.short_memory.add(role=self.agent_name, content=response)
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else:
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# Fallback: collect text manually
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text_chunks = []
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for chunk in response:
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if hasattr(chunk, "choices") and chunk.choices and chunk.choices[0].delta.content:
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content = chunk.choices[0].delta.content
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full_text_response += content
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text_chunks.append(content)
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if self.print_on:
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print(content, end="", flush=True)
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if self.print_on:
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print()
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# Handle tool calls if any were detected
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if tool_calls_in_stream:
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# Add text response to memory
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if full_text_response.strip():
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self.short_memory.add(role=self.agent_name, content=full_text_response)
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# Format and execute tool calls
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formatted_tool_calls = []
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for tc in tool_calls_in_stream:
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if tc and (tc.get("input") or tc.get("arguments_complete")):
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if "input" in tc:
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args_to_use = tc["input"]
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else:
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try:
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args_to_use = json.loads(tc.get("arguments", "{}"))
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except json.JSONDecodeError:
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continue
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formatted_tool_calls.append({
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"type": "function",
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"function": {"name": tc["name"], "arguments": json.dumps(args_to_use)},
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"id": tc["id"]
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})
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if formatted_tool_calls:
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tool_results = self.tool_struct.execute_function_calls_from_api_response(
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{"choices": [{"message": {"tool_calls": formatted_tool_calls}}]}
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)
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self.short_memory.add(role="Tool Executor", content=format_data_structure(tool_results))
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if self.print_on:
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formatter.print_panel(
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f"Tool Executed Successfully [{time.strftime('%H:%M:%S')}]\nResults:\n{format_data_structure(tool_results)}",
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"Tool Execution Result"
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)
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# Tool summary if enabled
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if self.tool_call_summary:
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temp_llm = self.temp_llm_instance_for_tool_summary()
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if self.streaming_on and hasattr(temp_llm, 'run_tool_summary_with_streaming'):
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final_summary_response = temp_llm.run_tool_summary_with_streaming(
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tool_results=format_data_structure(tool_results),
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agent_name=f"{self.agent_name} - Real-time",
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print_on=self.print_on,
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verbose=self.verbose,
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)
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else:
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final_summary_response = temp_llm.run(
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task=f"Please analyze and summarize the following tool execution output:\n\n{format_data_structure(tool_results)}"
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)
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response = self.parse_llm_output(final_summary_response)
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response = "".join(text_chunks)
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if response.strip():
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self.short_memory.add(role=self.agent_name, content=response)
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if self.print_on and not self.streaming_on:
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self.pretty_print(response, loop_count)
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else:
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response = f"Tool execution completed: {format_data_structure(tool_results)}"
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else:
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# No tools called, use streamed text as response
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response = full_text_response
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if response.strip():
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self.short_memory.add(role=self.agent_name, content=response)
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# Parse the response from the agent with the output type
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if exists(self.tools_list_dictionary):
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if isinstance(response, BaseModel):
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response = response.model_dump()
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response = self.parse_llm_output(response)
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self.short_memory.add(role=self.agent_name, content=response)
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# Print
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if self.print_on is True:
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if isinstance(response, list):
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self.pretty_print(
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f"Structured Output - Attempting Function Call Execution [{time.strftime('%H:%M:%S')}] \n\n Output: {format_data_structure(response)} ",
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loop_count,
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)
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elif self.streaming_on:
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pass
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else:
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self.pretty_print(response, loop_count)
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# Check and execute callable tools
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if exists(self.tools):
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self.tool_execution_retry(response, loop_count)
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# Handle MCP tools
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if (
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exists(self.mcp_url)
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or exists(self.mcp_config)
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or exists(self.mcp_urls)
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):
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if response is not None:
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self.mcp_tool_handling(
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response=response,
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current_loop=loop_count,
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)
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else:
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logger.warning(
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f"LLM returned None response in loop {loop_count}, skipping MCP tool handling"
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)
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success = True
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except Exception as e:
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logger.error(f"Error during streaming execution: {e}")
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break
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if self.autosave is True:
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log_agent_data(self.to_dict())
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self.save()
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logger.error(
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f"Attempt {attempt+1}/{self.retry_attempts}: Error generating response in loop {loop_count} for agent '{self.agent_name}': {str(e)} | "
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)
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attempt += 1
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if not success:
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if self.autosave is True:
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log_agent_data(self.to_dict())
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self.save()
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logger.error(
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"Failed to generate a valid response after retry attempts."
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)
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break
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# Check stopping conditions
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if (
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@ -2645,9 +2609,8 @@ class Agent:
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del kwargs["is_last"]
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try:
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# For streaming with tools, return raw stream for custom parsing
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# Special handling for streaming with tools - need raw stream for parsing
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if self.streaming_on and exists(self.tools_list_dictionary):
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# Set stream mode and get raw streaming response
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original_stream = getattr(self.llm, 'stream', False)
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self.llm.stream = True
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@ -2658,7 +2621,6 @@ class Agent:
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stream_response = self.llm.run(task=task, *args, **kwargs)
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return stream_response
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finally:
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# Restore original stream setting
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self.llm.stream = original_stream
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# Use centralized streaming logic from wrapper if streaming is enabled (no tools)
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harshalmore31
2 months ago