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309 lines
9.7 KiB
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309 lines
9.7 KiB
Plaintext
---
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title: Memory
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description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
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icon: database
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---
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## Introduction to Memory Systems in CrewAI
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The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents.
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This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember,
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reason, and learn from past interactions.
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## Memory System Components
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| Component | Description |
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| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
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| **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.|
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| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
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| **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. |
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| **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. |
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## How Memory Systems Empower Agents
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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.
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2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
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3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.
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## Implementing Memory in Your Crew
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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.
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By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration.
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The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model.
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It's also possible to initialize the memory instance with your own instance.
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The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG.
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The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
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The data storage files are saved into a platform-specific location found using the appdirs package,
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and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
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### Example: Configuring Memory for a Crew
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```python Code
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from crewai import Crew, Agent, Task, Process
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# Assemble your crew with memory capabilities
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True
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)
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```
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### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
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```python Code
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from crewai import Crew, Agent, Task, Process
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# Assemble your crew with memory capabilities
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process="Process.sequential",
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memory=True,
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long_term_memory=EnhanceLongTermMemory(
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storage=LTMSQLiteStorage(
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db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
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)
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),
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short_term_memory=EnhanceShortTermMemory(
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storage=CustomRAGStorage(
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crew_name="my_crew",
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storage_type="short_term",
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data_dir="//my_data_dir",
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model=embedder["model"],
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dimension=embedder["dimension"],
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),
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),
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entity_memory=EnhanceEntityMemory(
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storage=CustomRAGStorage(
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crew_name="my_crew",
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storage_type="entities",
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data_dir="//my_data_dir",
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model=embedder["model"],
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dimension=embedder["dimension"],
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),
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),
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verbose=True,
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)
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```
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## Additional Embedding Providers
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### Using OpenAI embeddings (already default)
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```python Code
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder={
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"provider": "openai",
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"config": {
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"model": 'text-embedding-3-small'
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}
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}
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)
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```
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Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.
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Example:
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```python Code
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from crewai import Crew, Agent, Task, Process
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from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
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)
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```
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### Using Ollama embeddings
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```python Code
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder={
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"provider": "ollama",
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"config": {
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"model": "mxbai-embed-large"
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}
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}
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)
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```
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### Using Google AI embeddings
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```python Code
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder={
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"provider": "google",
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"config": {
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"api_key": "<YOUR_API_KEY>",
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"model_name": "<model_name>"
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}
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}
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)
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```
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### Using Azure OpenAI embeddings
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```python Code
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from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder=OpenAIEmbeddingFunction(
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api_key="YOUR_API_KEY",
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api_base="YOUR_API_BASE_PATH",
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api_type="azure",
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api_version="YOUR_API_VERSION",
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model_name="text-embedding-3-small"
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)
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)
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```
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### Using Vertex AI embeddings
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```python Code
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from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder=GoogleVertexEmbeddingFunction(
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project_id="YOUR_PROJECT_ID",
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region="YOUR_REGION",
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api_key="YOUR_API_KEY",
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model_name="textembedding-gecko"
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)
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)
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```
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### Using Cohere embeddings
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```python Code
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder={
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"provider": "cohere",
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"config": {
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"api_key": "YOUR_API_KEY",
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"model_name": "<model_name>"
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}
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}
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)
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```
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### Using HuggingFace embeddings
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```python Code
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from crewai import Crew, Agent, Task, Process
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder={
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"provider": "huggingface",
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"config": {
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"api_url": "<api_url>",
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}
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}
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)
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```
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### Using Watson embeddings
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```python Code
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from crewai import Crew, Agent, Task, Process
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# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
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my_crew = Crew(
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agents=[...],
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tasks=[...],
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process=Process.sequential,
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memory=True,
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verbose=True,
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embedder={
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"provider": "watson",
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"config": {
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"model": "<model_name>",
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"api_url": "<api_url>",
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"api_key": "<YOUR_API_KEY>",
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"project_id": "<YOUR_PROJECT_ID>",
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}
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}
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)
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```
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### Resetting Memory
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```shell
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crewai reset-memories [OPTIONS]
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```
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#### Resetting Memory Options
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| Option | Description | Type | Default |
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| :----------------- | :------------------------------- | :------------- | :------ |
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| `-l`, `--long` | Reset LONG TERM memory. | Flag (boolean) | False |
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| `-s`, `--short` | Reset SHORT TERM memory. | Flag (boolean) | False |
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| `-e`, `--entities` | Reset ENTITIES memory. | Flag (boolean) | False |
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| `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False |
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| `-a`, `--all` | Reset ALL memories. | Flag (boolean) | False |
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## Benefits of Using CrewAI's Memory System
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- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
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- 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
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- 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
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## Conclusion
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Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations,
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you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
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