CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents

CLAG is a memory framework for small language model (SLM) agents that organizes long-horizon memory through agent-driven clustering.

CLAG employs an SLM-driven router to assign incoming memories to semantically coherent clusters and autonomously generates cluster-specific profiles—including topic summaries and descriptive tags—to establish each cluster as a self-contained functional unit.

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Overview

CLAG addresses this problem with three key components:

• Agentic Routing: routes each new memory into the most appropriate semantic cluster
• Localized Evolution: refines and consolidates memories only within the routed cluster
• Cluster-Aware Two-Stage Retrieval: first selects relevant clusters, then retrieves fine-grained evidence only inside them

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Repository Structure

.
├── CLAG_memory.py                  # CLAG memory implementation
├── test_CLAG.py                    # Main evaluation / experiment entry point
├── prepare_bioasq.py               # BioASQ preprocessing
├── prepare_bioasq_gold_context.py  # BioASQ preprocessing utilities
├── run_prepare_bioasq_all.sh       # BioASQ preprocessing pipeline
├── data/                           # Datasets and processed files
├── logs_CLAG/                      # Experiment logs
├── results_CLAG/                   # Output results
└── figure/                         # README figures

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Installation

Requirements

• Python 3.9+ recommended

Setup

git clone https://github.com/<your-org-or-user>/CLAG.git
cd CLAG

python -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt

Note
Some NLTK resources may be downloaded at runtime if they are not already installed.

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Data Preparation

Included datasets

The following datasets are already included under data/:

• HotpotQA
• LoCoMo

BioASQ

BioASQ is not distributed with this repository. Please download the required files from the official BioASQ participant area:

• training10b
• test dataset

After downloading, place the files under data/ and run:

bash run_prepare_bioasq_all.sh

This script generates processed files under data/processed/, including chunked versions used in the experiments.

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Quickstart

Run CLAG evaluation with:

python3 test_CLAG.py \
  --dataset data/locomo10.json \
  --backend sglang \
  --model gpt-4o-mini

Common arguments

• --dataset: path to the evaluation dataset JSON
• --backend: backend to use (openai, ollama, or sglang)
• --model: model name for the selected backend
• --output: path to save output JSON
• --ratio: evaluate only a subset of the dataset (0.0 to 1.0)
• --retrieve_k: retrieval top-k (default: 10)

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Backend Setup

OpenAI backend

export OPENAI_API_KEY="YOUR_KEY"

SGLang backend

By default, CLAG expects an SGLang server at:

http://localhost:30000

You can override this with:

• --sglang_host
• --sglang_port

Ollama backend

Make sure your Ollama server is running locally and the specified model is available.

Reproducibility

To reproduce the main experiments, use test_CLAG.py with the target dataset and backend configuration.

Citation

If you find this repository useful, please cite:

@article{roh2026clag,
  title={CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents},
  author={Roh, Taeyun and Jang, Wonjune and Jung, Junha and Kang, Jaewoo},
  journal={arXiv preprint arXiv:2603.15421},
  year={2026}
}