samples/mem0_1 — awesome-ai-stack

Example

Mem0 — store and recall a memory

A real, runnable mini-project. Download it and run with Docker.

View samples/mem0_1 on GitHub ↗

June 28, 2026

Mem0 — store and recall a memory

A tiny Mem0 script: it adds a fact for a user, then searches for it. Mem0 distils messages into compact facts (with an LLM) and recalls the relevant ones (from a vector store). The config uses OpenAI.

Configure

cd samples/mem0_1
cp .env.sample .env
# edit .env: set OPENAI_API_KEY

Mem0's config here uses OpenAI for fact extraction and embeddings. Get a key at platform.openai.com/api-keys. .env is gitignored — only .env.sample is committed.

Run with Docker

cd samples/mem0_1
docker build -t aas-mem0 .
docker run --rm --env-file .env aas-mem0 "what are alice's travel preferences?"

Run with Docker (in a devcontainer with DooD)

In a dev container that talks to the host Docker daemon (Docker-outside-of-Docker), the foreground docker run above often prints nothing and exits 0 — but the run itself succeeds. The script runs to completion and Docker captures all of its output; only the live attached stream drops it over the VM boundary. Run detached and follow the logs instead:

cd samples/mem0_1
docker build -t aas-mem0 .
docker logs -f "$(docker run -d --env-file .env aas-mem0 \
  "what are alice's travel preferences?")"

Run locally

cd samples/mem0_1
pip install -r requirements.txt
python app.py "what are alice's travel preferences?"

python-dotenv loads .env automatically.

Example run

Output varies by model and run — LLMs are non-deterministic, so the recalled phrasing differs each time. Below is one run (extraction + recall via OpenAI).

query: what are alice's travel preferences?
  - User prefers window seats when traveling and enjoys vegetarian meals.

.env.sample

# Copy this file to `.env` and add your OpenAI API key.
#   cp .env.sample .env
#
# Mem0's default config uses OpenAI for both fact extraction and embeddings.
# Get a key at https://platform.openai.com/api-keys
OPENAI_API_KEY=

app.py

"""Mem0: store a memory, then recall it.

Adds a fact for a user, then searches — Mem0 distils messages into compact
facts (with an LLM) and recalls the relevant ones (from a vector store). The
config below uses OpenAI, so set OPENAI_API_KEY.

Run:
    docker build -t aas-mem0 .
    docker run --rm --env-file .env aas-mem0 "what are alice's travel preferences?"
"""

import sys

from dotenv import load_dotenv
from mem0 import Memory

# Load .env when running locally; in Docker the key comes from --env-file.
load_dotenv()

# Pin the OpenAI models Mem0 uses (the default model rejects a custom temperature).
CONFIG = {
    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
}


def main() -> None:
    query = " ".join(sys.argv[1:]) or "what are alice's travel preferences?"
    user = "alice"

    m = Memory.from_config(CONFIG)
    m.add("I prefer window seats and vegetarian meals.", user_id=user)

    # Mem0 2.x takes the user as a filter on search.
    results = m.search(query, filters={"user_id": user})

    print(f"query: {query}")
    for r in results["results"]:
        print(f"  - {r['memory']}")


if __name__ == "__main__":
    main()

Dockerfile

# Minimal image for the Mem0 sample.
FROM python:3.13-slim

ENV PYTHONUNBUFFERED=1

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

# Provide your config at run time (.env is NOT baked into the image):
#   docker run --rm --env-file .env aas-mem0 "<query>"
ENTRYPOINT ["python", "app.py"]