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b3f37319e3
This PR fixes a bug where `model_context` was either ignored or explicitly set to `None` during agent deserialization (`_from_config`) in: - `AssistantAgent`: `model_context` was serialized but not restored. - `SocietyOfMindAgent`: `model_context` was neither serialized nor restored. - `CodeExecutorAgent`: `model_context` was serialized but not restored. As a result, restoring an agent from its config silently dropped runtime context settings, potentially affecting agent behavior. This patch: - Adds proper serialization/deserialization of `model_context` using `.dump_component()` and `load_component(...)`. - Ensures round-trip consistency when using declarative agent configs. ## Related issue number Closes #6336 ## Checks - [ ] I've included any doc changes needed for <https://microsoft.github.io/autogen/>. See <https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to build and test documentation locally. - [x] I've added tests (if relevant) corresponding to the changes introduced in this PR. - [x] I've made sure all auto checks have passed. --------- Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
127 lines
5.0 KiB
Python
127 lines
5.0 KiB
Python
import pytest
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from autogen_agentchat.agents import (
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AssistantAgent,
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CodeExecutorAgent,
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SocietyOfMindAgent,
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)
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from autogen_agentchat.teams import RoundRobinGroupChat
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from autogen_core.model_context import (
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BufferedChatCompletionContext,
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ChatCompletionContext,
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HeadAndTailChatCompletionContext,
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TokenLimitedChatCompletionContext,
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UnboundedChatCompletionContext,
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)
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from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor
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from autogen_ext.models.replay import ReplayChatCompletionClient
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@pytest.mark.parametrize(
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"model_context_class",
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[
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UnboundedChatCompletionContext(),
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BufferedChatCompletionContext(buffer_size=5),
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TokenLimitedChatCompletionContext(model_client=ReplayChatCompletionClient([]), token_limit=5),
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HeadAndTailChatCompletionContext(head_size=3, tail_size=2),
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],
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)
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def test_serialize_and_deserialize_model_context_on_assistant_agent(model_context_class: ChatCompletionContext) -> None:
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"""Test the serialization and deserialization of the message context on the AssistantAgent."""
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agent = AssistantAgent(
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name="assistant",
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model_client=ReplayChatCompletionClient([]),
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description="An assistant agent.",
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model_context=model_context_class,
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)
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# Serialize the agent
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serialized_agent = agent.dump_component()
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# Deserialize the agent
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deserialized_agent = AssistantAgent.load_component(serialized_agent)
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# Check that the deserialized agent has the same model context as the original agent
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original_model_context = agent.model_context
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deserialized_model_context = deserialized_agent.model_context
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assert isinstance(original_model_context, type(deserialized_model_context))
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assert isinstance(deserialized_model_context, type(original_model_context))
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assert original_model_context.dump_component() == deserialized_model_context.dump_component()
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@pytest.mark.parametrize(
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"model_context_class",
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[
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UnboundedChatCompletionContext(),
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BufferedChatCompletionContext(buffer_size=5),
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TokenLimitedChatCompletionContext(model_client=ReplayChatCompletionClient([]), token_limit=5),
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HeadAndTailChatCompletionContext(head_size=3, tail_size=2),
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],
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)
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def test_serialize_and_deserialize_model_context_on_society_of_mind_agent(
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model_context_class: ChatCompletionContext,
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) -> None:
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"""Test the serialization and deserialization of the message context on the AssistantAgent."""
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agent1 = AssistantAgent(
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name="assistant1", model_client=ReplayChatCompletionClient([]), description="An assistant agent."
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)
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agent2 = AssistantAgent(
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name="assistant2", model_client=ReplayChatCompletionClient([]), description="An assistant agent."
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)
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team = RoundRobinGroupChat(
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participants=[agent1, agent2],
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)
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agent = SocietyOfMindAgent(
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name="assistant",
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model_client=ReplayChatCompletionClient([]),
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description="An assistant agent.",
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team=team,
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model_context=model_context_class,
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)
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# Serialize the agent
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serialized_agent = agent.dump_component()
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# Deserialize the agent
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deserialized_agent = SocietyOfMindAgent.load_component(serialized_agent)
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# Check that the deserialized agent has the same model context as the original agent
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original_model_context = agent.model_context
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deserialized_model_context = deserialized_agent.model_context
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assert isinstance(original_model_context, type(deserialized_model_context))
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assert isinstance(deserialized_model_context, type(original_model_context))
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assert original_model_context.dump_component() == deserialized_model_context.dump_component()
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@pytest.mark.parametrize(
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"model_context_class",
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[
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UnboundedChatCompletionContext(),
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BufferedChatCompletionContext(buffer_size=5),
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TokenLimitedChatCompletionContext(model_client=ReplayChatCompletionClient([]), token_limit=5),
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HeadAndTailChatCompletionContext(head_size=3, tail_size=2),
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],
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)
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def test_serialize_and_deserialize_model_context_on_code_executor_agent(
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model_context_class: ChatCompletionContext,
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) -> None:
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"""Test the serialization and deserialization of the message context on the AssistantAgent."""
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agent = CodeExecutorAgent(
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name="assistant",
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code_executor=LocalCommandLineCodeExecutor(),
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description="An assistant agent.",
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model_context=model_context_class,
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)
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# Serialize the agent
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serialized_agent = agent.dump_component()
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# Deserialize the agent
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deserialized_agent = CodeExecutorAgent.load_component(serialized_agent)
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# Check that the deserialized agent has the same model context as the original agent
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original_model_context = agent.model_context
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deserialized_model_context = deserialized_agent.model_context
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assert isinstance(original_model_context, type(deserialized_model_context))
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assert isinstance(deserialized_model_context, type(original_model_context))
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assert original_model_context.dump_component() == deserialized_model_context.dump_component()
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