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Choosing LLMs | ||
==================== | ||
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Generally, the interaction with an LLM is up to the agent implementation. | ||
However, as motleycrew integrates with several agent frameworks, there is some common ground for how to choose LLMs. | ||
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Providing an LLM to an agent | ||
---------------------------- | ||
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In general, you can pass a specific LLM to the agent you're using. | ||
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.. code-block:: python | ||
from motleycrew.agents.langchain import ReActToolCallingMotleyAgent | ||
from langchain_openai import ChatOpenAI | ||
llm = ChatOpenAI(model="gpt-4o", temperature=0) | ||
agent = ReActToolCallingMotleyAgent(llm=llm, tools=[...]) | ||
The LLM class depends on the agent framework you're using. | ||
That's why we have an ``init_llm`` function to help you set up the LLM. | ||
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.. code-block:: python | ||
from motleycrew.common.llms import init_llm | ||
from motleycrew.common import LLMFramework, LLMProvider | ||
llm = init_llm( | ||
llm_framework=LLMFramework.LANGCHAIN, | ||
llm_provider=LLMProvider.ANTHROPIC, | ||
llm_name="claude-3-5-sonnet-20240620", | ||
llm_temperature=0 | ||
) | ||
agent = ReActToolCallingMotleyAgent(llm=llm, tools=[...]) | ||
The currently supported frameworks (:py:class:`motleycrew.common.enums.LLMFramework`) are: | ||
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- :py:class:`Langchain <motleycrew.common.enums.LLMFramework.LANGCHAIN>` for Langchain-based agents from Langchain, CrewAI, motelycrew etc. | ||
- :py:class:`LlamaIndex <motleycrew.common.enums.LLMFramework.LLAMA_INDEX>` for LlamaIndex-based agents. | ||
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The currently supported LLM providers (:py:class:`motleycrew.common.enums.LLMProvider`) are: | ||
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- :py:class:`OpenAI <motleycrew.common.enums.LLMProvider.OPENAI>` | ||
- :py:class:`Anthropic <motleycrew.common.enums.LLMProvider.ANTHROPIC>` | ||
- :py:class:`Groq <motleycrew.common.enums.LLMProvider.GROQ>` | ||
- :py:class:`Together <motleycrew.common.enums.LLMProvider.TOGETHER>` | ||
- :py:class:`Replicate <motleycrew.common.enums.LLMProvider.REPLICATE>` | ||
- :py:class:`Ollama <motleycrew.common.enums.LLMProvider.OLLAMA>` | ||
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Please raise an issue if you need to add support for another LLM provider. | ||
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Default LLM | ||
----------- | ||
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At present, we default to OpenAI's latest ``gpt-4o`` model for our agents, | ||
and rely on the user to set the `OPENAI_API_KEY` environment variable. | ||
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You can control the default LLM as follows: | ||
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.. code-block:: python | ||
from motleycrew.common import Defaults | ||
Defaults.DEFAULT_LLM_PROVIDE = "the_new_default_LLM_provider" | ||
Defaults.DEFAULT_LLM_NAME = "name_of_the_new_default_model_from_the_provider" | ||
Using custom LLMs | ||
----------------- | ||
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To use a custom LLM provider to use as the default or via the ``init_llm`` function, | ||
you need to make sure that for all the frameworks you're using (currently at most Langchain, LlamaIndex), | ||
the `LLM_MAP` has an entry for the LLM provider, for example as follows: | ||
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.. code-block:: python | ||
from motleycrew.common import LLMProvider | ||
from motleycrew.common.llms import LLM_MAP | ||
LLM_MAP[(LLMFramework.LANGCHAIN, "MyLLMProvider")] = my_langchain_llm_factory | ||
LLM_MAP[(LLMFramework.LLAMA_INDEX, "MyLLMProvider")] = my_llamaindex_llm_factory | ||
Here each llm factory is a function with a signature | ||
``def llm_factory(llm_name: str, llm_temperature: float, **kwargs)`` that returns the model object for the relevant framework. | ||
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For example, this is the built-in OpenAI model factory for Langchain: | ||
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.. code-block:: python | ||
def langchain_openai_llm( | ||
llm_name: str = Defaults.DEFAULT_LLM_NAME, | ||
llm_temperature: float = Defaults.DEFAULT_LLM_TEMPERATURE, | ||
**kwargs, | ||
): | ||
from langchain_openai import ChatOpenAI | ||
return ChatOpenAI(model=llm_name, temperature=llm_temperature, **kwargs) | ||
You can also overwrite the `LLM_MAP` values for e.g. the OpenAI models if, for example, | ||
you want to use an in-house wrapper for Langchain or Llamaindex model adapters | ||
(for example, to use an internal gateway instead of directly hitting the OpenAI endpoints). | ||
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Note that at present, if you use Autogen with motleycrew, you will need to separately control | ||
the models that Autogen uses, using the Autogen-specific APIs. |
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key_concepts | ||
agents | ||
knowledge_graph | ||
another_llm | ||
choosing_llms | ||
caching_observability |
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