Memory

Enhance your agents with persistent memories.

Added in version 0.10.0

Within an agentic workflow, information is naturally added to the thread history over time, making available to all agents who participate in the workflow. However, that information is not accessible from other threads, even if they relate to the same objective or resources.

ControlFlow has a memory feature that allows agents to selectively store and recall information across multiple interactions. This feature is useful for creating more capable and context-aware agents. For example:

• Remembering a user’s name or other personal details across conversations
• Retaining facts from one session for use in another
• Keeping details about a repository’s style guide for later reference
• Maintaining project-specific information across multiple interactions
• Enabling “soft” collaboration between agents through a shared knowledge base

Memory modules provide this functionality, allowing agents to build up and access a persistent knowledge base over time.

How Memory Works

ControlFlow memories are implemented as context-specific vector stores that permit agents to add and query information using natural language. Each memory object has a “key” that uniquely identifies it and partitions its contents from other vector stores for easy retrieval. For example, you might have a different memory store for each user, agent, project, or even task, used to persist information across multiple interactions with that object. Agents can be provided with multiple memory modules, allowing them to access different sets of memories simultaneously.

Creating Memory Modules

To create a memory object, you need to provide a key and instructions. The key uniquely identifies the memory module so it can be accessed later. The instructions explain what kind of information should be stored, and how it should be used.

ControlFlow does not include any vector database dependencies by default to keep the library lightweight, so you must install and configure a provider before creating a memory object.

To run the examples with minimal configuration, run pip install chromadb to install the dependency for the default Chroma provider. To change the default, see the default provider guide.

import controlflow as cf

# Create a Memory module for storing weather information
memory = cf.Memory(
    key="weather",
    instructions="Stores information about the weather."
)

Assigning Memories

Like tools, memory modules can be provided to either agents or tasks. When provided to an agent, it will be able to access the memories when executing any task. When provided to a task, the memories will be available to any assigned agents. The choice of where to assign a memory module depends entirely on your preference and the design of your application; when the workflow is compiled the behavior is identical.

Assigning to an Agent

agent = cf.Agent(
    name="Weather Agent",
    memories=[memory]
)

Assigning to a Task

task = cf.Task(
    name="Weather Task",
    memories=[memory]
)

Assigning Multiple Memories

You can assign multiple memories to an agent or task. When this happens, the agent or task will have access to all of the modules and be able to store and retrieve information from each of them separately.

Sharing Memories

Remember that you can provide the same memory module to multiple agents or tasks. When this happens, the memories are shared across all of the agents and tasks.

Memories are partitioned by key, so you can provide different instructions to different agents for the same module. For example, you might have one agent that you encourage to record information to a memory module, and another that you encourage to read memories from the same module.

Configuration

Key

The key is crucial for accessing the correct set of memories. It must be provided exactly the same way each time to access an existing memory. Keys should be descriptive and unique for each distinct memory set you want to maintain.

Instructions

The instructions field is important because it tells the agent when and how to access or add to the memory. Unlike the key, instructions can be different for the same memory key across different Memory objects. This allows for flexibility in how agents interact with the same set of memories.

Good instructions should explain:

• What kind of information the memory is used to store
• When the agent should read from or write to the memory
• Any specific guidelines for formatting or categorizing the stored information

For example:

project_memory = cf.Memory(
    key="project_alpha",
    instructions="""
    This memory stores important details about Project Alpha.
    - Read from this memory when you need information about project goals, timelines, or team members.
    - Write to this memory when you learn new, important facts about the project.
    - Always include dates when adding new information.
    """
)

Provider

The provider is the underlying storage mechanism for the memory. It is responsible for storing and retrieving the memory objects.

The default provider is “chroma-db”, which uses a local persistent Chroma database. Run pip install chromadb to install its dependencies, after which you can start using memories with no additional configuration.

Installing provider dependencies

To configure a provider, you need to install its package and either configure the provider with a string value or create an instance of the provider and pass it to the memory module.

ControlFlow does not include any vector database dependencies by default, in order to keep the library lightweight.

This table shows the supported providers and their respective dependencies:

Provider	Required dependencies
Chroma	chromadb

You can install the dependencies for a provider with pip, for example pip install chromadb to use the Chroma provider.

Configuring a provider with a string

For straightforward provider configurations, you can pass a string value to the provider parameter that will instantiate a provider with default settings. The following strings are recognized:

Provider	Provider string	Description
Chroma	chroma-ephemeral	An ephemeral (in-memory) database.
Chroma	chroma-db	Uses a persistent, local-file-based database, with a default path of ~/.controlflow/memory/chroma.
Chroma	chroma-cloud	Uses the Chroma Cloud service. The CONTROLFLOW_CHROMA_CLOUD_API_KEY, CONTROLFLOW_CHROMA_CLOUD_TENANT, and CONTROLFLOW_CHROMA_CLOUD_DATABASE settings are required.

For example, if chromadb is installed, the following code will create a memory module that uses an ephemeral Chroma database:

import controlflow as cf

cf.Memory(..., provider="chroma-ephemeral")

Configuring a Provider instance

For more complex configurations, you can instantiate a provider directly and pass it to the memory module.

For example, the Chroma provider accepts a client parameter that allows you to customize how the Chroma client connects, as well as a collection_name parameter to specify the name of the collection to use.

import controlflow as cf
from controlflow.memory.providers.chroma import ChromaMemory
import chromadb

provider = ChromaMemory(
    client=chromadb.PersistentClient(path="/path/to/save/to"),
    collection_name="custom-{key}",
)

memory = cf.Memory(..., provider=provider)

Configuring a default provider

You can configure a default provider to avoid having to specify a provider each time you create a memory module. Please see the guide on default providers for more information.

Example: Storing Weather Information

In this example, we’ll create a memory module for weather information and use it to retrieve that information in a different conversation. Begin by creating a memory module, assigning it to a task, and informing the task that it is 70 degrees today:

Now, in a different conversation, we can retrieve that information. Note that the setup is almost identical, except that the task asks the agent to answer a question about the weather.

Example: Slack Customer Service

Suppose we have an agent that answers questions in Slack. We are going to equip the agent with the following memory modules:

• One for each user in the thread
• One for common problems that users encounter

Since we always invoke the agent with these memories, it will be able to access persistent information about any user its assisting, as well as issues they frequently encounter, even if that information wasn’t shared in the current thread.

Here is example code for how this might work:

import controlflow as cf


@cf.flow
def customer_service_flow(slack_thread_id: str):

    # create a memory module for each user
    user_memories = [
        cf.Memory(
            key=user_id,
            instructions=f"Store and retrieve any information about user {user_id}.",
        )
        for user_id in get_user_ids(slack_thread_id)
    ]

    # create a memory module for problems
    problems_memory = cf.Memory(
        key="problems",
        instructions="Store and retrieve important information about common user problems.",
    )

    # create an agent with access to the memory modules
    agent = cf.Agent(
        name="Customer Service Agent",
        instructions="""
            Help users by answering their questions. Use available 
            memories to personalize your response.
            """,
        memories=user_memories + [problems_memory]
    )

    # use the agent to respond
    cf.run(
        "Respond to the users' latest message",
        agents=[agent],
        context=dict(messages=get_messages(slack_thread_id)),
    )

Best Practices

1. Use descriptive, unique keys for different memory sets
2. Provide clear, specific instructions to guide agents in using the memory effectively
3. Consider the lifespan of memories - some may be relevant only for a single session, while others may persist across multiple runs
4. Use multiple memory objects when an agent needs to access different sets of information
5. Leverage shared memories for collaborative scenarios where multiple agents need access to the same knowledge base
6. Regularly review and update memory instructions to ensure they remain relevant and useful

By leveraging ControlFlow’s Memory feature effectively, you can create more sophisticated agents that maintain context, learn from past interactions, and make more informed decisions based on accumulated knowledge across multiple conversations or sessions.