---
title: Memory
description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
icon: database
---

## Introduction to Memory Systems in CrewAI

The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. 
This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember, 
reason, and learn from past interactions.

## Memory System Components

| Component            | Description                                                                                                             |
| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |

## How Memory Systems Empower Agents

1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.

2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.

3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.

## Implementing Memory in Your Crew

When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. 
The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model. 
It's also possible to initialize the memory instance with your own instance.

The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG.
The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
The data storage files are saved into a platform-specific location found using the appdirs package,
and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.

### Example: Configuring Memory for a Crew

```python Code
from crewai import Crew, Agent, Task, Process

# Assemble your crew with memory capabilities
my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True
)
```

### Example: Use Custom Memory Instances e.g FAISS as the VectorDB

```python Code
from crewai import Crew, Agent, Task, Process

# Assemble your crew with memory capabilities
my_crew = Crew(
    agents=[...],
    tasks=[...],
    process="Process.sequential",
    memory=True,
    long_term_memory=EnhanceLongTermMemory(
        storage=LTMSQLiteStorage(
            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
        )
    ),
    short_term_memory=EnhanceShortTermMemory(
        storage=CustomRAGStorage(
            crew_name="my_crew",
            storage_type="short_term",
            data_dir="//my_data_dir",
            model=embedder["model"],
            dimension=embedder["dimension"],
        ),
    ),
    entity_memory=EnhanceEntityMemory(
        storage=CustomRAGStorage(
            crew_name="my_crew",
            storage_type="entities",
            data_dir="//my_data_dir",
            model=embedder["model"],
            dimension=embedder["dimension"],
        ),
    ),
    verbose=True,
)
```

## Integrating Mem0 for Enhanced User Memory

[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 

To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.


```python Code
import os
from crewai import Crew, Process
from mem0 import MemoryClient

# Set environment variables for Mem0
os.environ["MEM0_API_KEY"] = "m0-xx"

# Step 1: Record preferences based on past conversation or user input
client = MemoryClient()
messages = [
    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
    {"role": "user", "content": "I am more of a beach person than a mountain person."},
    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
    {"role": "user", "content": "I like Airbnb more."},
]
client.add(messages, user_id="john")

# Step 2: Create a Crew with User Memory

crew = Crew(
    agents=[...],
    tasks=[...],
    verbose=True,
    process=Process.sequential,
    memory=True,
    memory_config={
        "provider": "mem0",
        "config": {"user_id": "john"},
    },
)
```

## Memory Configuration Options
If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.

```python Code
from crewai import Crew

crew = Crew(
    agents=[...],
    tasks=[...],
    verbose=True,
    memory=True,
    memory_config={
        "provider": "mem0",
        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
    },
)
```

## Additional Embedding Providers

### Using OpenAI embeddings (already default)
```python Code
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "openai",
        "config": {
            "model": 'text-embedding-3-small'
        }
    }
)
```
Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.

Example:
```python Code
from crewai import Crew, Agent, Task, Process
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
)
```

### Using Ollama embeddings

```python Code
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "ollama",
        "config": {
            "model": "mxbai-embed-large"
        }
    }
)
```

### Using Google AI embeddings

```python Code
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "google",
        "config": {
            "api_key": "<YOUR_API_KEY>",
            "model_name": "<model_name>"
        }
    }
)
```

### Using Azure OpenAI embeddings

```python Code
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder=OpenAIEmbeddingFunction(
        api_key="YOUR_API_KEY",
        api_base="YOUR_API_BASE_PATH",
        api_type="azure",
        api_version="YOUR_API_VERSION",
        model_name="text-embedding-3-small"
    )
)
```

### Using Vertex AI embeddings

```python Code
from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder=GoogleVertexEmbeddingFunction(
        project_id="YOUR_PROJECT_ID",
        region="YOUR_REGION",
        api_key="YOUR_API_KEY",
        model_name="textembedding-gecko"
    )
)
```

### Using Cohere embeddings

```python Code
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "cohere",
        "config": {
            "api_key": "YOUR_API_KEY",
            "model_name": "<model_name>"
        }
    }
)
```
### Using VoyageAI embeddings

```python Code
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "voyageai",
        "config": {
            "api_key": "YOUR_API_KEY",
            "model_name": "<model_name>"
        }
    }
)
```
### Using HuggingFace embeddings

```python Code
from crewai import Crew, Agent, Task, Process

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "huggingface",
        "config": {
            "api_url": "<api_url>",
        }
    }
)
```

### Using Watson embeddings

```python Code
from crewai import Crew, Agent, Task, Process

# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.

my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True,
    embedder={
        "provider": "watson",
        "config": {
            "model": "<model_name>",
            "api_url": "<api_url>",
            "api_key": "<YOUR_API_KEY>",
            "project_id": "<YOUR_PROJECT_ID>",
        }
    }
)
```

### Resetting Memory

```shell
crewai reset-memories [OPTIONS]
```

#### Resetting Memory Options

| Option             | Description                      | Type           | Default |
| :----------------- | :------------------------------- | :------------- | :------ |
| `-l`, `--long`     | Reset LONG TERM memory.          | Flag (boolean) | False   |
| `-s`, `--short`    | Reset SHORT TERM memory.         | Flag (boolean) | False   |
| `-e`, `--entities` | Reset ENTITIES memory.           | Flag (boolean) | False   |
| `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False   |
| `-a`, `--all`      | Reset ALL memories.              | Flag (boolean) | False   |


## Benefits of Using CrewAI's Memory System

- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
- 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
- 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.

## Conclusion

Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
