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Evaluation Memory Framework

This repository provides tools and scripts for evaluating the LoCoMo, LongMemEval, PrefEval, personaMem dataset using various models and APIs.

Installation

  1. Set the PYTHONPATH environment variable:

    export PYTHONPATH=../src
cd evaluation

  2. Install the required dependencies:

    poetry install --extras all --with eval

Configuration

Copy the .env-example file to .env, and fill in the required environment variables according to your environment and API keys.

Setup MemOS

local server

# modify {project_dir}/.env file and start server
uvicorn memos.api.server_api:app --host 0.0.0.0 --port 8001 --workers 8

# configure {project_dir}/evaluation/.env file
MEMOS_URL="http://127.0.0.1:8001"

online service

# get your api key at https://memos-dashboard.openmem.net/cn/quickstart/
# configure {project_dir}/evaluation/.env file
MEMOS_KEY="Token mpg-xxxxx"
MEMOS_ONLINE_URL="https://memos.memtensor.cn/api/openmem/v1"

Supported frameworks

We support memos-api and memos-api-online in our scripts. And give unofficial implementations for the following memory frameworks:zep, mem0, memobase, supermemory, memu.

Evaluation Scripts

LoCoMo Evaluation

⚙️ To evaluate the LoCoMo dataset using one of the supported memory frameworks — run the following script:

# Edit the configuration in ./scripts/run_locomo_eval.sh
# Specify the model and memory backend you want to use (e.g., mem0, zep, etc.)
./scripts/run_locomo_eval.sh

✍️ For evaluating OpenAI's native memory feature with the LoCoMo dataset, please refer to the detailed guide: OpenAI Memory on LoCoMo - Evaluation Guide.

LongMemEval Evaluation

First prepare the dataset longmemeval_s from https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned , and save it as data/longmemeval/longmemeval_s.json

# Edit the configuration in ./scripts/run_lme_eval.sh
# Specify the model and memory backend you want to use (e.g., mem0, zep, etc.)
./scripts/run_lme_eval.sh

PrefEval Evaluation

Downloading benchmark_dataset/filtered_inter_turns.json from https://github.com/amazon-science/PrefEval/blob/main/benchmark_dataset/filtered_inter_turns.json and save it as ./data/prefeval/filtered_inter_turns.json. To evaluate the Prefeval dataset — run the following script:

# Edit the configuration in ./scripts/run_prefeval_eval.sh
# Specify the model and memory backend you want to use (e.g., mem0, zep, etc.)
./scripts/run_prefeval_eval.sh

PersonaMem Evaluation

get questions_32k.csv and shared_contexts_32k.jsonl from https://huggingface.co/datasets/bowen-upenn/PersonaMem and save them at data/personamem/

# Edit the configuration in ./scripts/run_pm_eval.sh
# Specify the model and memory backend you want to use (e.g., mem0, zep, etc.)
# If you want to use MIRIX, edit the the configuration in ./scripts/personamem/config.yaml
./scripts/run_pm_eval.sh