CrewAI

For the full source code and advanced implementation details, see the official CrewAI Integration in our repository.

Overview

Integrating MemMachine with CrewAI empowers your agents with a persistent memory layer. Unlike standard session memory, this integration allows your crews to remember past interactions, learn user preferences, and maintain deep context across multiple tasks and sessions.

Configuration

The integration is primarily managed through environment variables. You can set these in your .env file or export them directly in your shell:

Variable	Description	Default
`MEMORY_BACKEND_URL`	The URL of your MemMachine backend service	`http://localhost:8080`
`CREWAI_ORG_ID`	Your organization identifier	`crewai_org`
`CREWAI_PROJECT_ID`	Your project identifier	`crewai_project`
`CREWAI_USER_ID`	(Optional) Scopes memory to a specific user	`None`
`CREWAI_SESSION_ID`	(Optional) Scopes memory to a specific session	`None`

Install Dependencies

Install the core CrewAI framework along with the MemMachine client:

pip install crewai crewai-tools memmachine-client

Initialize Memory Tools

The create_memmachine_tools helper simplifies the setup. This will return a list of tools that agents can use to add and search their memory.

from integrations.crewai.tool import create_memmachine_tools

# Initialize tools with your project configuration
memmachine_tools = create_memmachine_tools(
    base_url="http://localhost:8080",
    org_id="my_org",
    project_id="my_project",
    user_id="user123",
)

Assign Tools to Agents

Pass the memmachine_tools list to your CrewAI agents. Agents will use these tools automatically when they need to store a new finding or recall a past preference.

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Specialist",
    goal="Identify emerging trends and remember key data points",
    backstory="You are an expert researcher. You prioritize long-term memory to avoid repeating work.",
    tools=memmachine_tools,
    verbose=True,
)

Execute the Crew

Once the agent is equipped with the memory tools, define your tasks and kickoff the crew.

task = Task(
    description="Research the latest 2026 AI trends and store the top 5 findings in memory.",
    agent=researcher,
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()

Advanced Usage

Shared Team Memory

If you want multiple agents to share the same knowledge pool, initialize the tools with a group_id. This is ideal for collaborative agents where a “Writer” needs to recall information stored by a “Researcher.”

memmachine_tools = create_memmachine_tools(
    org_id="my_org",
    project_id="my_project",
    group_id="research_team", # Agents in this group share memory
)

Manual Memory Management

For more granular control, you can use the MemMachineTools class directly without the CrewAI agent wrapper.

from integrations.crewai.tool import MemMachineTools

tools = MemMachineTools(org_id="my_org", project_id="my_project")

# Manually add a specific fact
tools.add_memory(content="User prefers Python for data science.", role="user")

# Manually query memory
results = tools.search_memory(query="What is the user's preferred language?")
print(results["summary"])

Pro Tip: Use clear user_id or session_id identifiers to prevent “memory bleed” between different users or unrelated tasks.

Requirements

MemMachine Server: Must be reachable at the MEMORY_BACKEND_URL.
Python: 3.10 or higher.
Framework: CrewAI and crewai-tools.

Getting Started

Install Guides

Integrations

Tools

Core Concepts

Open Source

Overview

Configuration

Advanced Usage

Shared Team Memory

Manual Memory Management

Requirements

​Overview

​Configuration

​Advanced Usage

​Shared Team Memory

​Manual Memory Management

​Requirements

Overview

Configuration

Advanced Usage

Shared Team Memory

Manual Memory Management

Requirements