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## multi-agents memory
### Short-Term Memory
Short-term memory (InMemory) is suitable for lightweight, temporary multi-agent memory scenarios. Data is only stored in memory, making it ideal for testing and small-scale experiments.
**Usage Example:**
```python
from aworld.core.memory import MemoryConfig, MemoryItem
from aworld.memory.main import MemoryFactory
# Create InMemory config
memory_config = MemoryConfig(provider="inmemory", enable_summary=False)
# Initialize Memory
memory = MemoryFactory.from_config(memory_config)
# Add a memory item
memory.add(MemoryItem(content="Hello, world!", metadata={"user_id": "u1"}, tags=["greeting"]))
# Get all memory items
all_memories = memory.get_all()
for item in all_memories:
print(item.content)
```
### Long-Term Memory
Long-term memory (Mem0) is suitable for persistent, vectorized retrieval and summarization in multi-agent scenarios. It supports LLM-based summarization and vector storage.
**Usage Example:**
```python
from aworld.core.memory import MemoryConfig, MemoryItem
from aworld.memory.main import MemoryFactory
# Create Mem0 config (requires mem0 and related dependencies)
memory_config = MemoryConfig(
provider="mem0",
enable_summary=True, # Enable summarization
summary_rounds=5, # Generate a summary every 5 rounds
embedder_provider="huggingface", # Embedding model provider
embedder_model="all-MiniLM-L6-v2", # Embedding model name
embedder_dims=384
)
# Initialize Memory
memory = MemoryFactory.from_config(memory_config)
# Add a memory item
memory.add(MemoryItem(content="The agent visited Hangzhou.", metadata={"user_id": "u1"}, tags=["travel"]))
# Get all memory items
all_memories = memory.get_all()
for item in all_memories:
print(item.content)
```
> Note: To use mem0, you must install `mem0` and `sentence-transformers` in advance, and configure the required LLM environment variables.
### CheckPoint
TODO |