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

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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.

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Configuration

The integration is primarily managed through environment variables. You can set these in your .env file or export them directly in your shell:
VariableDescriptionDefault
MEMORY_BACKEND_URLThe URL of your MemMachine backend servicehttp://localhost:8080
CREWAI_ORG_IDYour organization identifiercrewai_org
CREWAI_PROJECT_IDYour project identifiercrewai_project
CREWAI_USER_ID(Optional) Scopes memory to a specific userNone
CREWAI_SESSION_ID(Optional) Scopes memory to a specific sessionNone
1

Install Dependencies

Install the core CrewAI framework along with the MemMachine client:
pip install crewai crewai-tools memmachine-client
2

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",
)
3

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,
)
4

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()

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Advanced Usage

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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
)

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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.

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Requirements

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