Run LoCoMo benchmark for long-term conversational memory
Scanned 9/2/2026
Install to Claude Code
npx -y skills add majiayu000/claude-skill-registry --skill locomo-benchmark --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: locomo-benchmark
description: Run LoCoMo benchmark for long-term conversational memory
execution: inline
model: inherit
aliases: [locomo, benchmark-memory]
---
# LoCoMo Benchmark
Evaluate cc-soul's memory against the [LoCoMo benchmark](https://github.com/snap-research/locomo) (ACL 2024) for long-term conversational memory.
## Quick Start
Run the benchmark script:
```bash
# Test one conversation (default: conv-26)
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py
# Test specific conversations
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py conv-26 conv-30
# Full benchmark (all 10 conversations)
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py --full
# Limit QA pairs per conversation
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py --max-qa 20
```
Where `$PLUGIN_DIR` is `/maps/projects/fernandezguerra/apps/repos/cc-soul` (or installed plugin path).
## What the Script Does
1. **Downloads** LoCoMo data from GitHub to `/tmp/locomo/` (if not present)
2. **Ingests** conversations into cc-soul memory:
- Extracts session summaries as observations
- Creates triplets for speaker facts
- Tags with sample_id for retrieval
3. **Evaluates** QA pairs:
- Retrieves context using `chitta recall --tag {sample_id}`
- Calculates F1 score vs ground truth
4. **Reports** results by category
## Categories
| Cat | Name | Description |
|-----|------|-------------|
| 1 | Multi-hop | Requires connecting multiple facts |
| 2 | Single-hop | Direct fact retrieval |
| 3 | Temporal | Date/time questions |
| 4 | Open-domain | General knowledge |
| 5 | Adversarial | Should answer "no information" |
## Baseline Scores (from paper)
| Model | F1 |
|-------|-----|
| Human ceiling | 87.9% |
| AutoMem | 90.5% |
| GPT-4 | 32.1% |
| GPT-3.5 | 23.7% |
| Mistral-7B | 13.9% |
## Data
- Repository: `https://github.com/snap-research/locomo`
- Local cache: `/tmp/locomo/data/locomo10.json`
- 10 conversations, ~200 QA pairs each, ~35 sessions per conversation
## Manual Execution
If you prefer to run manually:
```bash
# Ensure data exists
git clone https://github.com/snap-research/locomo /tmp/locomo
# Run benchmark
python3 /maps/projects/fernandezguerra/apps/repos/cc-soul/scripts/locomo-benchmark.py conv-26
```
## Expected Output
```
=== LoCoMo Benchmark Results ===
Total QA Pairs: 50
Overall F1: XX.X%
By Category:
Multi-hop (n=XX): XX.X%
Single-hop (n=XX): XX.X%
Temporal (n=XX): XX.X%
Open-domain (n=XX): XX.X%
Adversarial (n=XX): XX.X%
Per Conversation:
conv-26: XX.X% (50 QA)
Comparison (from paper):
Human ceiling: 87.9%
GPT-4 baseline: 32.1%
cc-soul: XX.X%
```
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