ControlFlow is optimized for workflows that are composed of multiple tasks, each of which can be completed by a different agent. One benefit of this approach is that you can use a different LLM for each task, or even for each agent assigned to a task.

ControlFlow will ensure that all agents share a consistent context and history, even if they are using different models. This allows you to leverage the relative strengths of different models, depending on your requirements.

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The default model

By default, ControlFlow uses OpenAI’s GPT-4o model. GPT-4o is an extremely powerful and popular model that provides excellent out-of-the-box performance on most tasks. This does mean that to run an agent with no additional configuration, you will need to provide an OpenAI API key.

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Selecting a different LLM

Every ControlFlow agent can be assigned a specific LLM. When instantiating an agent, you can pass a model parameter to specify the LLM to use.

ControlFlow agents can use any LangChain LLM class that supports chat-based APIs and tool calling. For a complete list of available models, settings, and instructions, please see LangChain’s LLM provider documentation.

ControlFlow includes the required packages for OpenAI, Azure OpenAI, and Anthropic models by default. To use other models, you’ll need to first install the corresponding LangChain package and supply any required credentials. See the model’s documentation for more information.

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Automatic configuration

ControlFlow can automatically load LLMs from certain providers, based on a parameter. The model parameter must have the form {provider key}/{model name}.

For example:

import controlflow as cf

openai_agent = cf.Agent(model="openai/gpt-4o-mini")
anthropic_agent = cf.Agent(model="anthropic/claude-3-haiku-20240307")
groq_agent = cf.Agent(model="groq/mixtral-8x7b-32768")

Note that loading a model from a string is convenient, but does not allow you to configure all of the model’s parameters. For full control, see the docs on manual configuration.

At this time, supported providers for automatic configuration include:

ProviderProvider keyRequired dependencies
OpenAIopenai(included)
Azure OpenAIazure-openai(included)
Anthropicanthropic(included)
Googlegooglelangchain_google_genai
Groqgroqlangchain_groq

If the required dependencies are not installed, ControlFlow will be unable to load the model and will raise an error.

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Manual configuration

To configure a different LLM, follow these steps:

1

Install required packages

To use an LLM, first make sure you have installed the appropriate provider package. For example, to use a Google model, run:

pip install langchain_google_genai
2

Configure API keys

You must provide the correct API keys and configuration for the LLM you want to use. These can be provided as environment variables or when you create the model in your script. For example, to use an OpenAI model, you must set the OPENAI_API_KEY environment variable:

export OPENAI_API_KEY=<your-api-key>

For model-specific instructions, please refer to the provider’s documentation.

3

Create the model

Create the LLM model in your script, including any additional parameters. For example, to use Claude 3 Opus:

from langchain_anthropic import ChatAnthropic

# create the model
model = ChatAnthropic(model='claude-3-opus-20240229')
4

Pass the model to an agent

Finally, configure an agent with the model:

import controlflow as cf

# provide the model to an agent
agent = cf.Agent(model=model)

In addition to choosing a specific model, you can also configure the model’s parameters. For example, you can set the temperature for GPT-4o:

import controlflow as cf
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model='gpt-4o', temperature=0.1)
agent = cf.Agent(model=model)

assert agent.model.temperature == 0.1

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Changing the default model

ControlFlow has a few ways to customize the default LLM.

ControlFlow includes OpenAI and Azure OpenAI models by default. To use other models, you’ll need to first install the corresponding LangChain package and supply any required credentials. See the model’s documentation for more information.

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From a model object

To use any model as the default LLM, create the model object in your script and assign it to controlflow.defaults.model. It will be used by any agent that does not have a model specified.

import controlflow as cf
from langchain_anthropic import ChatAnthropic

# set the default model
cf.defaults.model = ChatAnthropic(
    model='claude-3-opus-20240229', 
    temperature=0.1,
)

# check that the default model is loaded
assert cf.Agent('Marvin').model.model_name == 'claude-3-opus-20240229'

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From a string setting

You can also specify a default model using a string, which is convenient though it doesn’t allow you to configure advanced model settings. This must be a string in the form {provider key}/{model name}, following the same guidelines as automatic LLM configuration.

You can apply this setting either by using an environment variable before you import ControlFlow or in your script at runtime. For example, to use GPT 3.5 Turbo as the default model:

The default model can only be set by environment variable before importing ControlFlow. Once ControlFlow is imported, it reads the controlflow.settings.llm_model value to create the default model object.

SettingsDefault Agent