Andrii Bidochko

• Updated: March 23, 2026
• 5 min read

Getting Started with OpenClaw’s Memory Layer on UBOS: A Step‑by‑Step Guide

OpenClaw’s memory layer can be configured on a self‑hosted UBOS instance by installing the OpenClaw package, setting up a vector store, and initializing both short‑term and long‑term memory components through a few concise configuration files and API calls.

Introduction

OpenClaw is a powerful open‑source framework that provides vector‑based storage and multi‑stage memory management for generative AI applications. When paired with UBOS homepage, developers gain a unified platform for deploying, scaling, and monitoring AI services without relying on third‑party SaaS providers.

This guide walks you through the entire lifecycle— from prerequisites to best‑practice tips—so you can get the memory layer up and running on your own UBOS server.

Prerequisites

• Ubuntu 22.04 LTS (or compatible Debian‑based distro) with sudo privileges.
• Docker Engine ≥ 20.10 and Docker Compose ≥ 2.0 installed.
• Access to the UBOS CLI (ubos) – see the UBOS platform overview for installation steps.
• OpenAI API key (for optional ChatGPT integration) and a PostgreSQL instance for persistent storage.
• Basic knowledge of Python 3.10+ and YAML configuration files.

Installing OpenClaw on UBOS

UBOS simplifies application deployment through its Web app editor on UBOS. Follow these steps to add OpenClaw to your environment:

1. Log in to the UBOS dashboard and navigate to Apps → Add New App.
2. Search for “OpenClaw” in the marketplace. If it’s not listed, you can pull the official Docker image directly:

docker pull ubos/openclaw:latest
docker run -d \
  --name openclaw \
  -p 8000:8000 \
  -e OPENCLAW_DB_URL=postgres://user:pass@db:5432/openclaw \
  ubos/openclaw:latest

After the container is running, register the service with UBOS so it can be managed alongside other apps:

ubos service register openclaw \
  --url http://localhost:8000 \
  --health /healthz

Configuring the Vector Store

The vector store is the backbone of OpenClaw’s memory layer. UBOS supports multiple back‑ends; for this guide we’ll use Chroma DB integration because of its low latency and easy scaling.

Create a vector_store.yaml file in the /etc/openclaw directory:

store:
  type: chroma
  host: localhost
  port: 8001
  collection: openclaw_vectors
  embedding_model: text-embedding-ada-002

Restart the OpenClaw service to apply the configuration:

ubos service restart openclaw

Initializing Short‑Term Memory

Short‑term memory (STM) holds the most recent interaction context. It is stored in‑memory and expires after a configurable TTL.

Update memory.yaml to enable STM:

memory:
  short_term:
    enabled: true
    ttl_seconds: 300   # 5 minutes
    max_entries: 50

Load the configuration via the OpenClaw CLI:

openclaw memory init --config /etc/openclaw/memory.yaml

Setting Up Long‑Term Memory

Long‑term memory (LTM) persists embeddings in the vector store and is queried using cosine similarity. Define the LTM schema in the same memory.yaml file:

  long_term:
    enabled: true
    persistence: true
    retrieval_k: 10
    similarity_threshold: 0.78

Run the migration script to create the necessary tables in PostgreSQL:

openclaw db migrate --target ltm

Code Snippets for Each Step

Below are ready‑to‑copy snippets that you can embed in your Python application.

Initialize the OpenClaw client

import openclaw

client = openclaw.Client(base_url="http://localhost:8000", api_key="YOUR_API_KEY")

Add a document to the vector store

doc = {
    "id": "doc-123",
    "text": "OpenClaw enables seamless memory management for LLMs.",
    "metadata": {"source": "internal-wiki"}
}
client.vector_store.upsert([doc])

Store short‑term context

session_id = "session-abc"
client.memory.short_term.store(session_id, {"role": "user", "content": "Explain vector stores."})

Retrieve long‑term memory

query = "How does OpenClaw handle embeddings?"
results = client.memory.long_term.search(query, top_k=5)
for r in results:
    print(r["text"])

Troubleshooting Common Issues

• Vector store connection refused: Verify that Chroma DB is running on the configured host/port. Use docker ps to confirm the container status.
• STM entries disappearing too quickly: Check the ttl_seconds value; increase it if your use case requires longer retention.
• Low similarity scores: Ensure the embedding model matches the one used during indexing. Mismatched models produce incompatible vectors.
• Database migration errors: Run openclaw db reset only on a development environment; in production, apply incremental migrations.

Best Practices and Tips

Adopting the following practices will keep your memory layer performant and secure:

1. Version‑lock your Docker images. Pin the exact tag (e.g., ubos/openclaw:1.4.2) to avoid breaking changes.
2. Monitor vector store latency. Use UBOS’s Workflow automation studio to trigger alerts when query times exceed 200 ms.
3. Encrypt API keys. Store secrets in UBOS’s built‑in vault rather than plain text files.
4. Leverage the partner ecosystem. The UBOS partner program offers pre‑built connectors for popular LLM providers.
5. Scale horizontally. Deploy multiple OpenClaw replicas behind a load balancer; the vector store can be sharded across nodes.

Conclusion

By following this step‑by‑step guide, you’ve equipped your self‑hosted UBOS instance with a fully functional OpenClaw memory layer—complete with a high‑performance vector store, short‑term cache, and durable long‑term embeddings. This foundation enables sophisticated context‑aware AI applications while keeping data under your control.

Ready to explore more UBOS capabilities? Check out the Enterprise AI platform by UBOS for advanced orchestration, or dive into the AI marketing agents that can automatically generate campaign copy using the memory layer you just built.

For a deeper technical dive, refer to the official OpenClaw documentation and the original news article.

Host OpenClaw on UBOS to start leveraging the memory layer in production today.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.