---
title: "OpenClaw Local vs Cloud Memory Setup with Hindsight"
authors: [benfrank241]
date: 2026-04-15
tags: [comparison, openclaw, memory, setup]
description: "Compare OpenClaw local vs cloud memory setup with Hindsight, including setup, privacy, shared memory, maintenance, and when each option fits best."
image: /img/blog/comparison-openclaw-local-vs-cloud-memory-setup-with-hindsight.png
hide_table_of_contents: true
---

![OpenClaw Local vs Cloud Memory Setup with Hindsight](/img/blog/comparison-openclaw-local-vs-cloud-memory-setup-with-hindsight.png)

If you are deciding between **OpenClaw local vs cloud memory setup with Hindsight**, the real question is not just where memory runs. It is what kind of operational tradeoff you want. Local mode gives you a self-contained memory stack on the same machine as OpenClaw. Cloud mode gives you a managed Hindsight endpoint that OpenClaw talks to over the network. Both give you automatic retention and automatic recall. The difference is where the infrastructure lives, how credentials are managed, and how easy it is to share memory across machines.

This decision matters because it changes more than deployment. It affects startup behavior, reliability patterns, privacy boundaries, and whether one OpenClaw instance can share memory with another. A solo user on one laptop may prefer local mode because it is simple and keeps everything on device. A team running multiple OpenClaw gateways will usually prefer cloud mode because shared memory becomes straightforward.

This comparison walks through what each mode does, how the setup differs, when local mode wins, when cloud mode wins, and how to switch later if your needs change. Keep the [OpenClaw integration docs](https://hindsight.vectorize.io/docs/integrations/openclaw), the [docs home](https://hindsight.vectorize.io/docs), and the [quickstart guide](https://hindsight.vectorize.io/docs/quickstart) open if you want the full reference while you compare.

<!-- truncate -->

> **Verdict**
>
> Use **local** if you want one-machine memory, minimal external dependencies, and strong local control. Use **cloud** if you want the fastest setup, simpler multi-instance sharing, and less infrastructure work. If you are unsure, start with cloud for team or multi-device use, and start with local for a single personal deployment.

## What each setup does

At a high level, both modes give OpenClaw the same product behavior:

- conversations are retained automatically after each turn
- relevant memories are recalled automatically before each response
- the agent gets context without explicitly calling a memory tool

What changes is where Hindsight lives.

### Local mode

In local mode, the plugin runs an embedded Hindsight service through `hindsight-embed`. That means the memory API and its PostgreSQL storage live on the same machine as OpenClaw.

You configure an extraction model for Hindsight, start OpenClaw, and the local daemon handles memory operations nearby.

A typical embedded setup command looks like this:

```bash
npx --package @vectorize-io/hindsight-openclaw hindsight-openclaw-setup \
    --mode embedded --provider openai --api-key-env OPENAI_API_KEY
```

This is a strong fit when the machine running OpenClaw is also the machine that should store and process memory.

### Cloud mode

In cloud mode, OpenClaw talks to a managed Hindsight endpoint at `https://api.hindsight.vectorize.io` using an API token. The local gateway no longer needs to host the memory service itself.

A typical cloud setup command looks like this:

```bash
npx --package @vectorize-io/hindsight-openclaw hindsight-openclaw-setup \
    --mode cloud --token-env HINDSIGHT_CLOUD_TOKEN
```

This is the fastest way to get working memory across one or more OpenClaw instances without managing the Hindsight server locally.

## Side by side comparison

| Dimension | Local mode | Cloud mode |
|---|---|---|
| Setup path | embedded daemon on the OpenClaw machine | managed Hindsight endpoint with API token |
| Infra you manage | OpenClaw plus local Hindsight runtime | OpenClaw only |
| Data locality | memory service stays on the machine | memory service runs remotely |
| Shared memory across devices | awkward by default | easy |
| Startup behavior | may need daemon startup on first use | no local memory daemon startup |
| Best for | one machine, local-first preference | teams, multi-instance, fastest time to value |
| Key credentials | LLM provider key for extraction | Hindsight cloud token |
| Failure pattern | local daemon or local config issues | network or remote endpoint issues |

That table gets you most of the way there, but the practical differences are easier to see in concrete workflows.

## When to use local mode

Local mode is the better choice when control and locality matter more than centralization.

Choose local mode if:

- you run one OpenClaw instance on one machine
- you want the memory service to stay on that same machine
- you are comfortable giving Hindsight an extraction-model credential locally
- you do not need multiple OpenClaw instances to share a common bank right away

A local deployment feels clean because OpenClaw and Hindsight live together. There is no separate memory server to think about. For a personal assistant setup, that is attractive.

It is also nice for iterative experimentation. You can change plugin settings, restart the gateway, inspect logs, and keep the entire memory stack close to hand.

The tradeoff is that the memory service is no longer something you can naturally share across devices. If you later want your laptop OpenClaw and your server OpenClaw to share one memory bank, local mode stops feeling like the obvious answer.

### Local mode example

A direct `openclaw.json` style example for embedded mode looks like this:

```json
{
  "plugins": {
    "entries": {
      "hindsight-openclaw": {
        "enabled": true,
        "config": {
          "llmProvider": "openai",
          "llmApiKey": {
            "$ref": {
              "source": "env",
              "provider": "default",
              "id": "OPENAI_API_KEY"
            }
          },
          "apiPort": 9077,
          "daemonIdleTimeout": 0
        }
      }
    }
  }
}
```

The details can vary, but the important part is that OpenClaw is hosting the Hindsight path locally rather than calling a remote Hindsight API.

## When to use cloud mode

Cloud mode is the better choice when you want less infrastructure work and more operational flexibility.

Choose cloud mode if:

- you want the fastest path from zero to working memory
- you are running more than one OpenClaw instance
- you want a shared bank across machines
- you do not want to manage an embedded memory service locally

The big win is simplicity at scale. A second OpenClaw instance can point at the same Hindsight backend without you having to expose or synchronize a local embedded daemon.

This is why cloud mode pairs naturally with shared-memory setups. If you want one user's context or one team's context to follow them across instances, cloud mode removes the biggest operational obstacle.

The tradeoff is obvious: your memory backend is now remote. That is often worth it, but it is still a real boundary change.

### Cloud mode example

A direct cloud configuration looks like this:

```json
{
  "plugins": {
    "entries": {
      "hindsight-openclaw": {
        "enabled": true,
        "config": {
          "hindsightApiUrl": "https://api.hindsight.vectorize.io",
          "hindsightApiToken": {
            "$ref": {
              "source": "env",
              "provider": "default",
              "id": "HINDSIGHT_CLOUD_TOKEN"
            }
          }
        }
      }
    }
  }
}
```

If you want a more locked-down production setup, use a `SecretRef` from env, file, or exec rather than writing secrets inline. The [OpenClaw integration docs](https://hindsight.vectorize.io/docs/integrations/openclaw) cover those patterns in more detail.

## How setup and operations feel different

### Startup and reliability

In local mode, a fresh machine may need time for the embedded memory runtime to start. That can make the very first memory-enabled interactions feel slower or more fragile if you are watching logs closely.

In cloud mode, the main operational dependency shifts to network reachability and API health. You avoid local daemon startup, but you now depend on the remote endpoint being reachable.

### Credentials

In local mode, the main sensitive value is usually the extraction-model credential, for example `OPENAI_API_KEY` or another provider key.

In cloud mode, the critical credential is the Hindsight cloud token. You can still use `SecretRef` patterns either way, and you should for anything non-trivial.

### Shared memory

This is where cloud mode usually wins decisively. If you want multiple OpenClaw instances to learn into the same bank, cloud mode is much simpler to reason about. The existing shared-memory OpenClaw pattern depends on a common external endpoint. That is why cloud mode pairs naturally with multi-instance memory.

If that is your main goal, also read the [OpenClaw integration docs](https://hindsight.vectorize.io/docs/integrations/openclaw), the [team shared memory post](https://hindsight.vectorize.io/blog/team-shared-memory-ai-coding-agents), and [Adding memory to Codex with Hindsight](https://hindsight.vectorize.io/blog/adding-memory-to-codex-with-hindsight). They make the multi-agent and multi-tool story clearer.

## When local is better than cloud

Local mode is better when:

- you care most about keeping the memory service close to the gateway machine
- you are a single-user deployment
- you want fewer external moving parts during day-to-day use
- you are comfortable managing the local extraction-model credential yourself

In other words, local mode wins when the deployment is small and the control you gain is worth the operational responsibility.

## When cloud is better than local

Cloud mode is better when:

- you want to get from setup to useful memory fast
- you expect more than one OpenClaw instance
- you want shared memory across machines
- you want to avoid running or diagnosing a local memory service

Cloud also tends to be the easier recommendation for teams, because the moment multiple gateways need the same memory, the local-first advantage gets weaker.

## Migration notes

The nice part is that this choice is not permanent.

### Moving from local to cloud

The cleanest path is usually just rerunning the setup wizard in cloud mode:

```bash
npx --package @vectorize-io/hindsight-openclaw hindsight-openclaw-setup \
    --mode cloud --token-env HINDSIGHT_CLOUD_TOKEN
openclaw gateway restart
```

Then verify the updated config and test a real memory-enabled conversation.

### Moving from cloud to local

Likewise, rerun the wizard in embedded mode:

```bash
npx --package @vectorize-io/hindsight-openclaw hindsight-openclaw-setup \
    --mode embedded --provider openai --api-key-env OPENAI_API_KEY
openclaw gateway restart
```

The important thing in either direction is not to treat the switch like a purely cosmetic toggle. It changes the operational shape of the memory backend, and you should retest recall, retention, and bank behavior after the change.

## Next steps

- [Create a Hindsight Cloud account](https://hindsight.vectorize.io) if you want the fastest path to shared OpenClaw memory across instances.
- Read the [OpenClaw integration docs](https://hindsight.vectorize.io/docs/integrations/openclaw) for the full plugin setup and config reference.
- Keep the [quickstart guide](https://hindsight.vectorize.io/docs/quickstart) handy if you want a smaller end-to-end Hindsight refresher.
- Use the [Recall API reference](https://hindsight.vectorize.io/docs/api/recall) to understand what each mode retrieves before it reaches OpenClaw.
- Use the [Retain API reference](https://hindsight.vectorize.io/docs/api/retain) if you want to reason about what actually becomes memory.
- Compare adjacent workflows like [Adding memory to Codex with Hindsight](https://hindsight.vectorize.io/blog/adding-memory-to-codex-with-hindsight) and the [team shared memory post](https://hindsight.vectorize.io/blog/team-shared-memory-ai-coding-agents) if you are designing a broader multi-tool memory setup.
