> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# CrewAI

> Enable persistent, long-term memory for your CrewAI agents

For the full source code and advanced implementation details, see the official **[CrewAI Integration](https://github.com/MemMachine/MemMachine/tree/main/integrations/crewai)** in our repository.

## Overview

Integrating MemMachine with [CrewAI](https://crewai.com) 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`                  |

***

<Steps>
  <Step title="Install Dependencies">
    Install the core CrewAI framework along with the MemMachine client:

    ```bash theme={null}
    pip install crewai crewai-tools memmachine-client
    ```
  </Step>

  <Step title="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.

    ```python theme={null}
    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",
    )
    ```
  </Step>

  <Step title="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.

    ```python theme={null}
    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,
    )
    ```
  </Step>

  <Step title="Execute the Crew">
    Once the agent is equipped with the memory tools, define your tasks and kickoff the crew.

    ```python theme={null}
    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()
    ```
  </Step>
</Steps>

## 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."

```python theme={null}
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.

```python theme={null}
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"])
```

<Note> **Pro Tip:** Use clear `user_id` or `session_id` identifiers to prevent "memory bleed" between different users or unrelated tasks. </Note>

## Requirements

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