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feat: Add MemMachine Integration Guide for crewai. (MemMachine#875)
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‎integrations/crewai/README.md‎

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# CrewAI Integration with MemMachine
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This directory contains tools for integrating MemMachine with CrewAI agents.
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## Overview
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MemMachine provides memory tools that can be integrated into CrewAI agents to enable persistent memory capabilities. This allows agents to remember past interactions, user preferences, and context across multiple sessions.
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## Installation
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```bash
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# Install CrewAI and crewai-tools
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pip install crewai crewai-tools
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# Install MemMachine client
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pip install memmachine-client
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```
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## Configuration
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The integration can be configured via environment variables:
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `MEMORY_BACKEND_URL` | URL of the MemMachine backend service | `http://localhost:8080` |
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| `CREWAI_ORG_ID` | Organization identifier | `crewai_org` |
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| `CREWAI_PROJECT_ID` | Project identifier | `crewai_project` |
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| `CREWAI_GROUP_ID` | Group identifier (optional) | `None` |
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| `CREWAI_AGENT_ID` | Agent identifier (optional) | `None` |
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| `CREWAI_USER_ID` | User identifier (optional) | `None` |
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| `CREWAI_SESSION_ID` | Session identifier (optional) | `None` |
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## Quick Start
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### 1. Basic Usage
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```python
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from crewai import Agent, Crew, Task
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from integrations.crewai.tool import create_memmachine_tools
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# Create MemMachine tools
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memmachine_tools = create_memmachine_tools(
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base_url="http://localhost:8080",
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org_id="my_org",
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project_id="my_project",
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user_id="user123",
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)
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# Create an agent with memory tools
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researcher = Agent(
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role="Researcher",
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goal="Research topics and remember important information",
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backstory="You are a helpful research assistant with memory capabilities.",
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tools=memmachine_tools,
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verbose=True,
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)
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# Create a task
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task = Task(
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description="Research AI trends and remember key findings",
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agent=researcher,
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)
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# Create and run the crew
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crew = Crew(
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agents=[researcher],
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tasks=[task],
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)
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result = crew.kickoff(inputs={"topic": "Latest AI trends"})
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```
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### 2. Advanced Usage with Multiple Agents
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```python
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from crewai import Agent, Crew, Task
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from integrations.crewai.tool import create_memmachine_tools
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# Create shared memory tools
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memmachine_tools = create_memmachine_tools(
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base_url="http://localhost:8080",
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org_id="my_org",
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project_id="my_project",
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group_id="research_team",
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)
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# Create multiple agents with memory
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researcher = Agent(
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role="Researcher",
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goal="Research and store findings in memory",
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backstory="You research topics and remember important information.",
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tools=memmachine_tools,
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verbose=True,
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)
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writer = Agent(
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role="Writer",
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goal="Write content based on research and recall past work",
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backstory="You write articles and can recall previous research.",
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tools=memmachine_tools,
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verbose=True,
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)
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# Create tasks
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research_task = Task(
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description="Research AI trends and store findings in memory",
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agent=researcher,
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)
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writing_task = Task(
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description="Search memory for previous research and write an article",
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agent=writer,
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)
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# Create and run the crew
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crew = Crew(
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agents=[researcher, writer],
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tasks=[research_task, writing_task],
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)
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result = crew.kickoff(inputs={"topic": "AI in healthcare"})
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```
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### 3. Using MemMachineTools Directly
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```python
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from integrations.crewai.tool import MemMachineTools
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# Initialize tools
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tools = MemMachineTools(
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base_url="http://localhost:8080",
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org_id="my_org",
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project_id="my_project",
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user_id="user123",
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)
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# Add memory
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from memmachine.common.api import EpisodeType
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result = tools.add_memory(
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content="User prefers Python over JavaScript",
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role="user",
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episode_type=EpisodeType.MESSAGE, # Optional: defaults to EpisodeType.MESSAGE
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)
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print(result)
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# Search memory
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results = tools.search_memory(
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query="What programming languages does the user prefer?",
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limit=5,
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)
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print(results["summary"])
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```
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## Tool Descriptions
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### Add Memory Tool
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Stores important information, facts, preferences, or conversation context in MemMachine memory. Use this automatically whenever the user shares new information that should be remembered.
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**Parameters:**
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- `content`: The content to store in memory (required)
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- `role`: Message role - "user", "assistant", or "system" (default: "user")
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### Search Memory Tool
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Retrieves relevant context, past conversations, or user preferences from MemMachine memory. Use this when you need to recall information from previous interactions.
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**Parameters:**
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- `query`: Search query string describing what you're looking for (required)
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- `limit`: Maximum number of results to return (default: 5)
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## Example: Research Agent with Memory
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```python
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from crewai import Agent, Crew, Task
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from integrations.crewai.tool import create_memmachine_tools
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import os
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# Get configuration from environment
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base_url = os.getenv("MEMORY_BACKEND_URL", "http://localhost:8080")
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org_id = os.getenv("CREWAI_ORG_ID", "research_org")
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project_id = os.getenv("CREWAI_PROJECT_ID", "research_project")
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# Create memory tools
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memmachine_tools = create_memmachine_tools(
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base_url=base_url,
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org_id=org_id,
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project_id=project_id,
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user_id="researcher_001",
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)
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# Create agent with memory
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researcher = Agent(
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role="Research Assistant",
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goal="Research topics thoroughly and remember key findings",
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backstory="""You are an expert researcher with excellent memory.
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You always store important findings in memory so you can recall them later.
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When researching, you search memory first to see if you've researched this topic before.""",
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tools=memmachine_tools,
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verbose=True,
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allow_delegation=False,
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)
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# Create task
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task = Task(
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description="""Research the topic: {topic}
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1. First, search memory to see if you've researched this topic before
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2. Research the topic thoroughly
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3. Store key findings in memory for future reference
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4. Provide a comprehensive summary""",
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agent=researcher,
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)
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# Create and run crew
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crew = Crew(
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agents=[researcher],
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tasks=[task],
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)
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result = crew.kickoff(inputs={"topic": "Quantum Computing"})
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print(result)
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```
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## Requirements
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- MemMachine server running (default: http://localhost:8080)
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- Python 3.10+
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- CrewAI
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- crewai-tools
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## Notes
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- Memory tools are shared across all agents in a crew by default
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- Each agent can have its own `agent_id` in metadata for tracking
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- Use `user_id` to scope memories to specific users
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- Use `session_id` to scope memories to specific sessions
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- Memories persist across crew runs, enabling long-term context
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‎integrations/crewai/__init__.py‎

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"""CrewAI integration for MemMachine memory operations."""
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from .tool import MemMachineTools, create_memmachine_tools
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__all__ = ["MemMachineTools", "create_memmachine_tools"]

‎integrations/crewai/example.py‎

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#!/usr/bin/env python3
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# ruff: noqa: T201
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"""
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Example CrewAI integration with MemMachine.
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This example demonstrates how to use MemMachine memory tools with CrewAI agents.
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"""
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import os
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from crewai import Agent, Crew, Task
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from integrations.crewai.tool import create_memmachine_tools
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# Configuration
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MEMORY_BACKEND_URL = os.getenv("MEMORY_BACKEND_URL", "http://localhost:8080")
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ORG_ID = os.getenv("CREWAI_ORG_ID", "example_org")
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PROJECT_ID = os.getenv("CREWAI_PROJECT_ID", "example_project")
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USER_ID = os.getenv("CREWAI_USER_ID", "user_001")
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# Qwen LLM Configuration
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QWEN_API_KEY = os.getenv("QWEN_API_KEY", "sk-xxxxxxxxx")
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QWEN_BASE_URL = os.getenv(
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"QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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QWEN_MODEL = os.getenv("QWEN_MODEL", "qwen-turbo")
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# Set environment variables for CrewAI to use Qwen
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# CrewAI uses OPENAI_API_KEY, OPENAI_BASE_URL, and OPENAI_MODEL_NAME or OPENAI_MODEL
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os.environ["OPENAI_API_KEY"] = QWEN_API_KEY
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os.environ["OPENAI_BASE_URL"] = QWEN_BASE_URL
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# Try both possible environment variable names
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os.environ["OPENAI_MODEL_NAME"] = QWEN_MODEL
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os.environ["OPENAI_MODEL"] = QWEN_MODEL
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def main() -> None:
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"""Run a simple CrewAI example with MemMachine memory."""
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print("=" * 60)
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print("CrewAI + MemMachine Integration Example")
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print("=" * 60)
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print()
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# Create MemMachine tools
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print("Creating MemMachine tools...")
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memmachine_tools = create_memmachine_tools(
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base_url=MEMORY_BACKEND_URL,
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org_id=ORG_ID,
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project_id=PROJECT_ID,
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user_id=USER_ID,
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)
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print(f"✓ Created {len(memmachine_tools)} tools")
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print()
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# Create an agent with memory capabilities
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print("Creating research agent...")
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researcher = Agent(
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role="Research Assistant",
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goal="Research topics thoroughly and remember key findings for future reference",
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backstory="""You are an expert researcher with excellent memory capabilities.
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You always search memory first to see if you've researched a topic before.
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When you find new information, you store it in memory so you can recall it later.
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You provide comprehensive, well-researched summaries.""",
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tools=memmachine_tools,
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verbose=True,
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allow_delegation=False,
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)
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print("✓ Agent created")
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print()
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# Create a task
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print("Creating research task...")
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task = Task(
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description="""Research the topic: {topic}
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Steps:
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1. First, search your memory to see if you've researched this topic before
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2. If you find previous research, build upon it
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3. Research the topic thoroughly using your available tools
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4. Store key findings and important information in memory
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5. Provide a comprehensive summary of your research
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Remember to use the search_memory tool first, then add_memory tool to store findings.""",
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agent=researcher,
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expected_output="A comprehensive research summary with key findings stored in memory",
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)
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print("✓ Task created")
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print()
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# Create and run the crew
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print("Creating crew...")
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crew = Crew(
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agents=[researcher],
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tasks=[task],
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verbose=True,
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)
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print("✓ Crew created")
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print()
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# Run the crew
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print("Running crew...")
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print("-" * 60)
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result = crew.kickoff(inputs={"topic": "Artificial Intelligence in Healthcare"})
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print("-" * 60)
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print()
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print("=" * 60)
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print("Result:")
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print("=" * 60)
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print(result)
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print()
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# Demonstrate memory recall
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print("=" * 60)
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print("Testing Memory Recall")
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print("=" * 60)
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print()
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# Run again with a related topic to demonstrate memory
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print("Running crew again with related topic to demonstrate memory...")
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print("-" * 60)
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result2 = crew.kickoff(inputs={"topic": "AI applications in medical diagnosis"})
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print("-" * 60)
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print()
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print("=" * 60)
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print("Second Result (should reference previous research):")
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print("=" * 60)
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print(result2)
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if __name__ == "__main__":
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main()

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