StructMem: Structured Memory for Long-Horizon Behavior in LLMs
---
**StructMem**, a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. By temporally anchoring dual perspectives and performing periodic semantic consolidation, StructMem improves temporal reasoning and multi-hop performance.
## Key Contributions
### 1. Event-Level Extraction Mode
StructMem introduces an **event extraction mode** that goes beyond flat factual entries by capturing:
- **Factual components**: Core information units (who, what, when, where)
- **Relational components**: Interpersonal dynamics, causal influences, and temporal dependencies.
- **Temporal binding**: Event sequences and causal relationships
This mode is particularly effective for narrative-heavy conversations and time-sensitive scenarios.
**Usage:**
```python
config = {
# ... other configs ...
"extraction_mode": "event", # 'flat' or 'event'
# ... other configs ...
}
```
### 2. Cross-Event Summarization
StructMem implements a **hierarchical summarization mechanism** that:
- **Semantic Event Connections**: Buffers related events within time windows
- **Memory Consolidation through Synthesis**: Generates cross-event summaries stored separately from detailed memories
**Usage:**
```python
# Generate summaries after building memories
summary_result = lightmem.summarize(
retrieval_scope="global",
time_window=3600, # seconds
top_k=15, # number of seed memories
process_all=True
)
```
**Configuration:**
```python
config = {
# ... other configs ...
"summary_retriever": {
"model_name": "qdrant",
"configs": {
"collection_name": "my_summaries",
"embedding_model_dims": 384,
"path": "./my_summaries",
}
},
# ... other configs ...
}
```
## Usage Example
See the complete example in [`experiments/locomo/`](./experiments/locomo/) for building and evaluating StructMem on the LoCoMo dataset.
### Quick Start
```python
from lightmem.memory.lightmem import LightMemory
# Configure StructMem
config = {
# ... base LightMem configs ...
# StructMem-specific settings
"extraction_mode": "event",
"summary_retriever": {
"model_name": "qdrant",
"configs": {
"collection_name": "my_summaries",
"embedding_model_dims": 384,
"path": "./my_summaries",
}
},
# ... other configs ...
}
# Initialize
lightmem = LightMemory.from_config(config)
# Add memories with event extraction, providing both factual and relational prompts.
lightmem.add_memory(
messages=turn_messages,
METADATA_GENERATE_PROMPT={
"factual": LoCoMo_Event_Binding_factual,
"relational": LoCoMo_Event_Binding_relational
},
force_segment=True,
force_extract=True
)
# Generate cross-event summaries
lightmem.summarize(
retrieval_scope="global",
time_window=3600,
top_k=15,
process_all=True
)
```
## Experimental Results
StructMem demonstrates improved performance on long-context conversation benchmarks. For complete results and configurations, see [LoCoMo Results](./experiments/locomo/readme.md#structmem-results)
