Use Models Directly

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from athena import GeneralAgentConfig, GeneralAgentRequest
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from athena.client import Athena
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# Initialize client
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athena = Athena(api_key="<YOUR_API_KEY>")

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# Create a simple request with no tools
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config = GeneralAgentConfig(enabled_tools=[])
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# Simple question
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response = athena.agents.general.invoke(
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    request=GeneralAgentRequest(
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        config=config,
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        messages=[{"type": "human", "content": "What is the capital of France?"}]
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    )
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)
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# Get the response text
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print(response.messages[-1]["kwargs"]["content"])

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# Specify a model
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config = GeneralAgentConfig(
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    enabled_tools=[],
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    model="claude_3_7_sonnet"
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)
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response = athena.agents.general.invoke(
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    request=GeneralAgentRequest(
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        config=config,
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        messages=[{"type": "human", "content": "Who are you?"}]
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    )
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)
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print(response.messages[-1]["kwargs"]["content"])
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# Use another model
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config_gpt4 = GeneralAgentConfig(
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    enabled_tools=[],
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    model="openai_gpt_4o"
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)
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response = athena.agents.general.invoke(
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    request=GeneralAgentRequest(
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        config=config_gpt4,
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        messages=[{"type": "human", "content": "Explain quantum computing briefly"}]
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    )
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)
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print(response.messages[-1]["kwargs"]["content"])

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prompts = [
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    "Explain the theory of relativity",
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    "What is machine learning?",
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    "How does photosynthesis work?",
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    "Describe the water cycle"
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]
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config = GeneralAgentConfig(enabled_tools=[])
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for i, prompt in enumerate(prompts):
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    response = athena.agents.general.invoke(
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        request=GeneralAgentRequest(
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            config=config,
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            messages=[{"type": "human", "content": prompt}]
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        )
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    )
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    print(f"Response {i+1}:")
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    print(response.messages[-1]["kwargs"]["content"])
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    print("-" * 40)

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from langchain_core.messages import HumanMessage
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from langchain_core.load import load
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config = GeneralAgentConfig(enabled_tools=[])
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# First message
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response = athena.agents.general.invoke(
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    request=GeneralAgentRequest(
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        config=config,
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        messages=[{"type": "human", "content": "What is Python?"}]
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    )
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)
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# Continue the conversation with context
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messages = load(response.messages) + [HumanMessage(content="What are its main use cases?")]
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continued_response = athena.agents.general.invoke(
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    request=GeneralAgentRequest(
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        config=config,
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        messages=messages
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    )
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)
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print(continued_response.messages[-1]["kwargs"]["content"])