Bad request error with local LLM

[edo44]

This is the error I get and I don't understand why. The model I'm using supports json schemas.

openai.BadRequestError: Error code: 400 - {'error': "'response_format.type' must be 'json_schema'"}

This is in my example file

"""This example demonstrates MagenticOne performing a task given by the user and returning a final answer."""

import argparse
import asyncio
import json
import logging
import os

from autogen_core import EVENT_LOGGER_NAME, AgentId, AgentProxy, SingleThreadedAgentRuntime
from autogen_core.code_executor import CodeBlock
from autogen_core.models._model_client import ChatCompletionClient
from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
from autogen_magentic_one.agents.coder import Coder, Executor
from autogen_magentic_one.agents.file_surfer import FileSurfer
from autogen_magentic_one.agents.multimodal_web_surfer import MultimodalWebSurfer
from autogen_magentic_one.agents.orchestrator import LedgerOrchestrator
from autogen_magentic_one.agents.user_proxy import UserProxy
from autogen_magentic_one.messages import RequestReplyMessage
from autogen_magentic_one.utils import LogHandler

from autogen_core.models import UserMessage
from autogen_ext.models.openai import OpenAIChatCompletionClient

# NOTE: Don't forget to 'playwright install --with-deps chromium'


async def confirm_code(code: CodeBlock) -> bool:
    response = await asyncio.to_thread(
        input,
        f"Executor is about to execute code (lang: {code.language}):\n{code.code}\n\nDo you want to proceed? (yes/no): ",
    )
    return response.lower() == "yes"


async def main(logs_dir: str, hil_mode: bool, save_screenshots: bool) -> None:
    # Create the runtime.
    runtime = SingleThreadedAgentRuntime()

    # Create an appropriate client
#    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
#    assert client.model_info["family"] == "gpt-4o", "This example requires the gpt-4o model"


    client = OpenAIChatCompletionClient(
    model="qwen2-vl-2b-instruct",
    base_url="http://localhost:1234/v1",
    api_key="lm-studio",
    model_info={
        "vision": True,
        "function_calling": True,
        "json_output": True,
        "family": "unknown",
    },
)

    async with DockerCommandLineCodeExecutor(work_dir=logs_dir) as code_executor:
        # Register agents.
        await Coder.register(runtime, "Coder", lambda: Coder(model_client=client))
        coder = AgentProxy(AgentId("Coder", "default"), runtime)

        await Executor.register(
            runtime,
            "Executor",
            lambda: Executor("A agent for executing code", executor=code_executor, confirm_execution=confirm_code),
        )
        executor = AgentProxy(AgentId("Executor", "default"), runtime)

        # Register agents.
        await MultimodalWebSurfer.register(runtime, "WebSurfer", MultimodalWebSurfer)
        web_surfer = AgentProxy(AgentId("WebSurfer", "default"), runtime)

        await FileSurfer.register(runtime, "file_surfer", lambda: FileSurfer(model_client=client))
        file_surfer = AgentProxy(AgentId("file_surfer", "default"), runtime)

        await UserProxy.register(
            runtime,
            "UserProxy",
            lambda: UserProxy(description="The current user interacting with you."),
        )
        user_proxy = AgentProxy(AgentId("UserProxy", "default"), runtime)

        agent_list = [web_surfer, coder, executor, file_surfer]
        if hil_mode:
            agent_list.append(user_proxy)

        await LedgerOrchestrator.register(
            runtime,
            "Orchestrator",
            lambda: LedgerOrchestrator(
                agents=agent_list,
                model_client=client,
                max_rounds=30,
                max_time=25 * 60,
                return_final_answer=True,
            ),
        )
        # orchestrator = AgentProxy(AgentId("Orchestrator", "default"), runtime)

        runtime.start()

        actual_surfer = await runtime.try_get_underlying_agent_instance(web_surfer.id, type=MultimodalWebSurfer)
        await actual_surfer.init(
            model_client=client,
            downloads_folder=logs_dir,
            start_page="https://www.duckduckgo.com",
            browser_channel="chromium",
            headless=True,
            debug_dir=logs_dir,
            to_save_screenshots=save_screenshots,
        )

        await runtime.send_message(RequestReplyMessage(), user_proxy.id)
        await runtime.stop_when_idle()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Run MagenticOne example with log directory.")
    parser.add_argument("--logs_dir", type=str, required=True, help="Directory to store log files and downloads")
    parser.add_argument("--hil_mode", action="store_true", default=False, help="Run in human-in-the-loop mode")
    parser.add_argument(
        "--save_screenshots", action="store_true", default=False, help="Save additional browser screenshots to file"
    )

    args = parser.parse_args()

    # Ensure the log directory exists
    if not os.path.exists(args.logs_dir):
        os.makedirs(args.logs_dir)

    logger = logging.getLogger(EVENT_LOGGER_NAME)
    logger.setLevel(logging.INFO)
    log_handler = LogHandler(filename=os.path.join(args.logs_dir, "log.jsonl"))
    logger.handlers = [log_handler]
    asyncio.run(main(args.logs_dir, args.hil_mode, args.save_screenshots))

This is the output

<>python example.py --logs_dir ./logs
INFO:autogen_core.events:{"payload": "{}", "sender": null, "receiver": "UserProxy/default", "kind": "MessageKind.DIRECT", "delivery_stage": "DeliveryStage.SEND", "type": "Message"}
INFO:autogen_core.events:{"payload": "{}", "sender": null, "receiver": "UserProxy/default", "kind": "MessageKind.DIRECT", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
User input ('exit' to quit):  tiramisu recipe
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "UserProxy/default", "receiver": "default/default", "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.SEND", "type": "Message"}
INFO:autogen_core.events:{"payload": "Message could not be serialized", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.RESPOND", "delivery_stage": "DeliveryStage.SEND", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "Message could not be serialized", "sender": "UserProxy/default", "receiver": null, "kind": "MessageKind.RESPOND", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}

---------------------------------------------------------------------------
[2025-01-29T18:09:27.376378], UserProxy:

tiramisu recipe
INFO:autogen_core.events.default.orchestrator:{"timestamp": "2025-01-29T18:09:27.376378", "source": "UserProxy", "message": "tiramisu recipe", "type": "OrchestrationEvent"}
INFO:autogen_core.events:{"timestamp": "2025-01-29T18:09:31.453103", "prompt_tokens": 315, "completion_tokens": 32, "type": "LLMCallEvent"}
INFO:autogen_core.events:{"timestamp": "2025-01-29T18:30:08.109391", "prompt_tokens": 560, "completion_tokens": 393, "type": "LLMCallEvent"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"\\nWe are working to address the following user request:\\n\\ntiramisu recipe\\n\\n\\nTo answer this request we have assembled the following team:\\n\\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\\nExecutor: A agent for executing code\\nfile_surfer: An agent that can handle local files.\\n\\n\\n\\nHere is an initial fact sheet to consider:\\n\\n1. GIVEN OR VERIFIED FACTS\\n2. FACTS TO LOOK UP\\n3. FACTS TO DERIVE\\n4. EDUCATED GUESSES\\n\\n\\nHere is the plan to follow as best as possible:\\n\\n1. WebSurfer: Search for a recipe for tiramisu.\\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\\n3. Executor: Execute the Python script to retrieve the recipe.\\n4. FileSurfer: Open the recipe file and display it to the user.\\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\\n7. Executor: Execute the Python script to retrieve the recipe.\\n8. FileSurfer: Open the recipe file and display it to the user.\\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\\n11. Executor: Execute the Python script to retrieve the recipe.\\n12. FileSurfer: Open the recipe file and display it to the user.\\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\\n15. Executor: Execute the Python script to retrieve the recipe.\\n16. FileSurfer: Open the recipe file and display it to the user.\\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\\n19. Executor: Execute the Python script to retrieve the recipe.\\n20. FileSurfer: Open the recipe file and display it to the user.\\n\\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\\n\",\"source\":\"Orchestrator\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "Orchestrator/default", "receiver": "default/default", "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.SEND", "type": "Message"}

---------------------------------------------------------------------------
[2025-01-29T18:30:08.110392], Orchestrator (thought):

Initial plan:

We are working to address the following user request:

tiramisu recipe


To answer this request we have assembled the following team:

WebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.
Coder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.
Executor: A agent for executing code
file_surfer: An agent that can handle local files.



Here is an initial fact sheet to consider:

1. GIVEN OR VERIFIED FACTS
2. FACTS TO LOOK UP
3. FACTS TO DERIVE
4. EDUCATED GUESSES


Here is the plan to follow as best as possible:

1. WebSurfer: Search for a recipe for tiramisu.
2. Coder: Write a Python script that fetches and processes the recipe from the web.
3. Executor: Execute the Python script to retrieve the recipe.
4. FileSurfer: Open the recipe file and display it to the user.
5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.
6. Coder: Write another Python script that fetches and processes the recipe from the web.
7. Executor: Execute the Python script to retrieve the recipe.
8. FileSurfer: Open the recipe file and display it to the user.
9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.
10. Coder: Write another Python script that fetches and processes the recipe from the web.
11. Executor: Execute the Python script to retrieve the recipe.
12. FileSurfer: Open the recipe file and display it to the user.
13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.
14. Coder: Write another Python script that fetches and processes the recipe from the web.
15. Executor: Execute the Python script to retrieve the recipe.
16. FileSurfer: Open the recipe file and display it to the user.
17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.
18. Coder: Write another Python script that fetches and processes the recipe from the web.
19. Executor: Execute the Python script to retrieve the recipe.
20. FileSurfer: Open the recipe file and display it to the user.

Note: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.

INFO:autogen_core.events.default.orchestrator:{"timestamp": "2025-01-29T18:30:08.110392", "source": "Orchestrator (thought)", "message": "Initial plan:\n\nWe are working to address the following user request:\n\ntiramisu recipe\n\n\nTo answer this request we have assembled the following team:\n\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\nExecutor: A agent for executing code\nfile_surfer: An agent that can handle local files.\n\n\n\nHere is an initial fact sheet to consider:\n\n1. GIVEN OR VERIFIED FACTS\n2. FACTS TO LOOK UP\n3. FACTS TO DERIVE\n4. EDUCATED GUESSES\n\n\nHere is the plan to follow as best as possible:\n\n1. WebSurfer: Search for a recipe for tiramisu.\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\n3. Executor: Execute the Python script to retrieve the recipe.\n4. FileSurfer: Open the recipe file and display it to the user.\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\n7. Executor: Execute the Python script to retrieve the recipe.\n8. FileSurfer: Open the recipe file and display it to the user.\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\n11. Executor: Execute the Python script to retrieve the recipe.\n12. FileSurfer: Open the recipe file and display it to the user.\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\n15. Executor: Execute the Python script to retrieve the recipe.\n16. FileSurfer: Open the recipe file and display it to the user.\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\n19. Executor: Execute the Python script to retrieve the recipe.\n20. FileSurfer: Open the recipe file and display it to the user.\n\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\n", "type": "OrchestrationEvent"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"\\nWe are working to address the following user request:\\n\\ntiramisu recipe\\n\\n\\nTo answer this request we have assembled the following team:\\n\\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\\nExecutor: A agent for executing code\\nfile_surfer: An agent that can handle local files.\\n\\n\\n\\nHere is an initial fact sheet to consider:\\n\\n1. GIVEN OR VERIFIED FACTS\\n2. FACTS TO LOOK UP\\n3. FACTS TO DERIVE\\n4. EDUCATED GUESSES\\n\\n\\nHere is the plan to follow as best as possible:\\n\\n1. WebSurfer: Search for a recipe for tiramisu.\\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\\n3. Executor: Execute the Python script to retrieve the recipe.\\n4. FileSurfer: Open the recipe file and display it to the user.\\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\\n7. Executor: Execute the Python script to retrieve the recipe.\\n8. FileSurfer: Open the recipe file and display it to the user.\\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\\n11. Executor: Execute the Python script to retrieve the recipe.\\n12. FileSurfer: Open the recipe file and display it to the user.\\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\\n15. Executor: Execute the Python script to retrieve the recipe.\\n16. FileSurfer: Open the recipe file and display it to the user.\\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\\n19. Executor: Execute the Python script to retrieve the recipe.\\n20. FileSurfer: Open the recipe file and display it to the user.\\n\\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\\n\",\"source\":\"Orchestrator\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "Orchestrator/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"\\nWe are working to address the following user request:\\n\\ntiramisu recipe\\n\\n\\nTo answer this request we have assembled the following team:\\n\\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\\nExecutor: A agent for executing code\\nfile_surfer: An agent that can handle local files.\\n\\n\\n\\nHere is an initial fact sheet to consider:\\n\\n1. GIVEN OR VERIFIED FACTS\\n2. FACTS TO LOOK UP\\n3. FACTS TO DERIVE\\n4. EDUCATED GUESSES\\n\\n\\nHere is the plan to follow as best as possible:\\n\\n1. WebSurfer: Search for a recipe for tiramisu.\\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\\n3. Executor: Execute the Python script to retrieve the recipe.\\n4. FileSurfer: Open the recipe file and display it to the user.\\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\\n7. Executor: Execute the Python script to retrieve the recipe.\\n8. FileSurfer: Open the recipe file and display it to the user.\\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\\n11. Executor: Execute the Python script to retrieve the recipe.\\n12. FileSurfer: Open the recipe file and display it to the user.\\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\\n15. Executor: Execute the Python script to retrieve the recipe.\\n16. FileSurfer: Open the recipe file and display it to the user.\\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\\n19. Executor: Execute the Python script to retrieve the recipe.\\n20. FileSurfer: Open the recipe file and display it to the user.\\n\\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\\n\",\"source\":\"Orchestrator\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "Orchestrator/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"\\nWe are working to address the following user request:\\n\\ntiramisu recipe\\n\\n\\nTo answer this request we have assembled the following team:\\n\\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\\nExecutor: A agent for executing code\\nfile_surfer: An agent that can handle local files.\\n\\n\\n\\nHere is an initial fact sheet to consider:\\n\\n1. GIVEN OR VERIFIED FACTS\\n2. FACTS TO LOOK UP\\n3. FACTS TO DERIVE\\n4. EDUCATED GUESSES\\n\\n\\nHere is the plan to follow as best as possible:\\n\\n1. WebSurfer: Search for a recipe for tiramisu.\\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\\n3. Executor: Execute the Python script to retrieve the recipe.\\n4. FileSurfer: Open the recipe file and display it to the user.\\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\\n7. Executor: Execute the Python script to retrieve the recipe.\\n8. FileSurfer: Open the recipe file and display it to the user.\\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\\n11. Executor: Execute the Python script to retrieve the recipe.\\n12. FileSurfer: Open the recipe file and display it to the user.\\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\\n15. Executor: Execute the Python script to retrieve the recipe.\\n16. FileSurfer: Open the recipe file and display it to the user.\\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\\n19. Executor: Execute the Python script to retrieve the recipe.\\n20. FileSurfer: Open the recipe file and display it to the user.\\n\\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\\n\",\"source\":\"Orchestrator\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "Orchestrator/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"\\nWe are working to address the following user request:\\n\\ntiramisu recipe\\n\\n\\nTo answer this request we have assembled the following team:\\n\\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\\nExecutor: A agent for executing code\\nfile_surfer: An agent that can handle local files.\\n\\n\\n\\nHere is an initial fact sheet to consider:\\n\\n1. GIVEN OR VERIFIED FACTS\\n2. FACTS TO LOOK UP\\n3. FACTS TO DERIVE\\n4. EDUCATED GUESSES\\n\\n\\nHere is the plan to follow as best as possible:\\n\\n1. WebSurfer: Search for a recipe for tiramisu.\\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\\n3. Executor: Execute the Python script to retrieve the recipe.\\n4. FileSurfer: Open the recipe file and display it to the user.\\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\\n7. Executor: Execute the Python script to retrieve the recipe.\\n8. FileSurfer: Open the recipe file and display it to the user.\\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\\n11. Executor: Execute the Python script to retrieve the recipe.\\n12. FileSurfer: Open the recipe file and display it to the user.\\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\\n15. Executor: Execute the Python script to retrieve the recipe.\\n16. FileSurfer: Open the recipe file and display it to the user.\\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\\n19. Executor: Execute the Python script to retrieve the recipe.\\n20. FileSurfer: Open the recipe file and display it to the user.\\n\\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\\n\",\"source\":\"Orchestrator\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "Orchestrator/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"\\nWe are working to address the following user request:\\n\\ntiramisu recipe\\n\\n\\nTo answer this request we have assembled the following team:\\n\\nWebSurfer: A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.) It can also summarize the entire page, or answer questions based on the content of the page. It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\\nExecutor: A agent for executing code\\nfile_surfer: An agent that can handle local files.\\n\\n\\n\\nHere is an initial fact sheet to consider:\\n\\n1. GIVEN OR VERIFIED FACTS\\n2. FACTS TO LOOK UP\\n3. FACTS TO DERIVE\\n4. EDUCATED GUESSES\\n\\n\\nHere is the plan to follow as best as possible:\\n\\n1. WebSurfer: Search for a recipe for tiramisu.\\n2. Coder: Write a Python script that fetches and processes the recipe from the web.\\n3. Executor: Execute the Python script to retrieve the recipe.\\n4. FileSurfer: Open the recipe file and display it to the user.\\n5. WebSurfer: Search for a recipe for tiramisu again, this time using the Python script.\\n6. Coder: Write another Python script that fetches and processes the recipe from the web.\\n7. Executor: Execute the Python script to retrieve the recipe.\\n8. FileSurfer: Open the recipe file and display it to the user.\\n9. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n10. Coder: Write another Python script that fetches and processes the recipe from the web.\\n11. Executor: Execute the Python script to retrieve the recipe.\\n12. FileSurfer: Open the recipe file and display it to the user.\\n13. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n14. Coder: Write another Python script that fetches and processes the recipe from the web.\\n15. Executor: Execute the Python script to retrieve the recipe.\\n16. FileSurfer: Open the recipe file and display it to the user.\\n17. WebSurfer: Search for a recipe for tiramisu again, this time using the Python scripts.\\n18. Coder: Write another Python script that fetches and processes the recipe from the web.\\n19. Executor: Execute the Python script to retrieve the recipe.\\n20. FileSurfer: Open the recipe file and display it to the user.\\n\\nNote: The above plan is a rough outline, and may need to be adjusted based on the specific requirements of the request.\\n\",\"source\":\"Orchestrator\",\"type\":\"UserMessage\"},\"request_halt\":false}", "sender": "Orchestrator/default", "receiver": null, "kind": "MessageKind.PUBLISH", "delivery_stage": "DeliveryStage.DELIVER", "type": "Message"}
ERROR:autogen_core:Error processing publish message for Orchestrator/default
Traceback (most recent call last):
  File "C:\Users\Username\autogen\python\packages\autogen-core\src\autogen_core\_single_threaded_agent_runtime.py", line 505, in _on_message
    return await agent.on_message(
  File "C:\Users\Username\autogen\python\packages\autogen-core\src\autogen_core\_base_agent.py", line 113, in on_message
    return await self.on_message_impl(message, ctx)
  File "C:\Users\Username\autogen\python\packages\autogen-core\src\autogen_core\_routed_agent.py", line 485, in on_message_impl
    return await h(self, message, ctx)
  File "C:\Users\Username\autogen\python\packages\autogen-core\src\autogen_core\_routed_agent.py", line 149, in wrapper
    return_value = await func(self, message, ctx)
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\base_agent.py", line 81, in handle_incoming_message
    await future
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\base_agent.py", line 47, in _process
    await self._handle_broadcast(message, ctx)
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\orchestrator.py", line 278, in _handle_broadcast
    await super()._handle_broadcast(message, ctx)
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\base_orchestrator.py", line 75, in _handle_broadcast
    next_agent = await self._select_next_agent(message.content)
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\orchestrator.py", line 319, in _select_next_agent
    return await self._select_next_agent(synthesized_message, cancellation_token)
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\orchestrator.py", line 322, in _select_next_agent
    ledger_dict = await self.update_ledger(cancellation_token)
  File "C:\Users\Username\autogen\python\packages\autogen-magentic-one\src\autogen_magentic_one\agents\orchestrator.py", line 216, in update_ledger
    ledger_response = await self._model_client.create(
  File "C:\Users\Username\autogen\python\packages\autogen-ext\src\autogen_ext\models\openai\_openai_client.py", line 517, in create
    result: Union[ParsedChatCompletion[BaseModel], ChatCompletion] = await future
  File "C:\Users\Username\autogen\python\.venv\lib\site-packages\openai\resources\chat\completions.py", line 1702, in create
    return await self._post(
  File "C:\Users\Username\autogen\python\.venv\lib\site-packages\openai\_base_client.py", line 1849, in post
    return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
  File "C:\Users\Username\autogen\python\.venv\lib\site-packages\openai\_base_client.py", line 1543, in request
    return await self._request(
  File "C:\Users\Username\autogen\python\.venv\lib\site-packages\openai\_base_client.py", line 1644, in _request
    raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'error': "'response_format.type' must be 'json_schema'"}
INFO:autogen_core.events:{"payload": "{\"content\":{\"content\":\"tiramisu recipe\",\"source\":\"UserProxy\",\"type\":\"UserMessage\"},\"request_halt\":false}", "handling_agent": "Orchestrator/default", "exception": "Error code: 400 - {'error': \"'response_format.type' must be 'json_schema'\"}", "type": "MessageHandlerException"}
Exception ignored in: <function _ProactorBasePipeTransport.__del__ at 0x000002A362A332E0>
Traceback (most recent call last):
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 115, in __del__
    _warn(f"unclosed transport {self!r}", ResourceWarning, source=self)
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 79, in __repr__
    info.append(f'fd={self._sock.fileno()}')
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\windows_utils.py", line 102, in fileno
    raise ValueError("I/O operation on closed pipe")
ValueError: I/O operation on closed pipe
Exception ignored in: <function BaseSubprocessTransport.__del__ at 0x000002A362A31870>
Traceback (most recent call last):
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\base_subprocess.py", line 125, in __del__
    _warn(f"unclosed transport {self!r}", ResourceWarning, source=self)
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\base_subprocess.py", line 70, in __repr__
    info.append(f'stdin={stdin.pipe}')
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 79, in __repr__
    info.append(f'fd={self._sock.fileno()}')
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\windows_utils.py", line 102, in fileno
    raise ValueError("I/O operation on closed pipe")
ValueError: I/O operation on closed pipe
Exception ignored in: <function _ProactorBasePipeTransport.__del__ at 0x000002A362A332E0>
Traceback (most recent call last):
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 115, in __del__
    _warn(f"unclosed transport {self!r}", ResourceWarning, source=self)
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 79, in __repr__
    info.append(f'fd={self._sock.fileno()}')
  File "C:\Users\Username\AppData\Local\Programs\Python\Python310\lib\asyncio\windows_utils.py", line 102, in fileno
    raise ValueError("I/O operation on closed pipe")
ValueError: I/O operation on closed pipe