Is OpenHuman Private? What to Check Before Using It

The privacy questions to answer before installing

Before using OpenHuman as a personal AI companion, answer these questions in plain language:

• Did I download it from the official TinyHumans/OpenHuman source chain?
• Does the app require an account for the feature I want?
• Which model handles chat, reasoning, vision, summaries, embeddings, voice, and search?
• Are local AI options enabled, or is the default managed route being used?
• Which integrations will I connect, and what OAuth scopes do they request?
• Where are memory, vault files, logs, config, and local secrets stored?
• Can I revoke connected accounts cleanly?
• Am I comfortable using early beta software with this type of data?

If you cannot answer those questions yet, use OpenHuman with test data only.

What "local" can mean in a desktop AI app

"Local" is not one thing. In AI desktop apps, it can mean several different layers:

• The app runs on your computer.
• Conversations or memory are stored on your computer.
• A local model generates responses on your computer.
• Embeddings and summaries are generated on your computer.
• Files are indexed on your computer.
• API keys are stored locally.
• Integrations are connected through your own credentials.
• No backend service is used.

Those are separate claims. OpenHuman's public materials describe local memory and optional local AI, but they also describe backend-brokered services. A careful privacy review should separate each layer instead of accepting a single "private" or "local" label.

What OpenHuman stores locally

OpenHuman's official materials describe a local memory architecture. The README and docs refer to a Memory Tree, an Obsidian-style Markdown vault, workspace configuration, and local runtime state stored on your machine.

That local storage is one of the main reasons someone would consider OpenHuman instead of a normal cloud chatbot. A personal assistant becomes more useful when it can remember context over time, but long-term memory also increases the consequences of a privacy mistake.

Treat local memory as sensitive. It may contain summarized versions of your files, messages, calendar items, repositories, or notes depending on what you connect. Even if the storage is local, anyone with access to your operating-system account, backups, synced folders, or malware on the machine may be able to affect the risk profile.

What may still use managed services

The official README is unusually direct about this point: OpenHuman has local memory, but the default managed experience still uses OpenHuman-hosted services for several things.

Those managed areas can include:

• Account sign-in.
• Model routing and usage attribution.
• Web search proxying.
• Managed integration and OAuth flows.
• Composio connector-layer requests.
• Some real-time or hosted features.
• Cloud routes for chat, reasoning, vision, speech, or text-to-speech unless configured otherwise.

This is why the right question is not "Is OpenHuman local?" The better question is "Which parts of my workflow are local in my current configuration?"

Local AI is optional, not automatic

OpenHuman's local AI documentation describes local AI as opt-in and off by default. It can use local provider paths such as Ollama or LM Studio for supported workloads. The docs describe local AI as useful for workloads such as embeddings, summary-tree building, background loops, and explicitly routed chat or reasoning tasks.

That is useful, but it is not the same as saying every request stays on the device. If you want local behavior, you need to enable it, check the provider, and confirm that the workloads you care about are routed locally.

For example, a privacy-conscious setup might use:

• Ollama for selected local model workloads.
• LM Studio's local server for chat-style local inference.
• Local memory and vault storage.
• No connected email, calendar, or cloud document integrations during initial testing.
• A firewall or network monitor to observe unexpected connections if you are advanced enough to interpret them.

Three practical privacy scenarios

Mostly local test setup

This is the safest way to try OpenHuman.

You install from official sources, do not connect sensitive accounts, enable local AI only if you understand the setting, and use disposable test notes. You look at the settings, local data folders, and logs before adding anything personal.

This setup is best for learning the app without creating a large memory footprint.

Default managed setup

This is likely the easier setup for many users. You use OpenHuman's normal managed services for account sign-in, model routing, web search, and integration flows.

The tradeoff is convenience versus control. You may get easier onboarding and stronger cloud model quality, but you need to be comfortable with the backend services involved in the workflow.

This setup may be reasonable for low-sensitivity personal productivity, but it should not be your first choice for confidential work unless your own review supports it.

Hybrid automation setup

This is where risk rises. You might connect Gmail, Notion, Slack, GitHub, calendars, documents, search tools, and local models. The assistant becomes much more useful because it can see more context. It also becomes more sensitive because mistakes can involve multiple accounts and long-lived memory.

If you choose this route, connect one integration at a time. Review each OAuth scope, run a small test, check what is stored locally, and confirm how to revoke access.

What not to put into an early beta AI companion yet

OpenHuman may be useful, but a personal AI companion is not the right first place for every kind of data.

Avoid adding high-risk material during early testing:

• Passwords, seed phrases, recovery codes, or private keys.
• Client-confidential documents.
• Medical records.
• Legal strategy documents.
• Tax records.
• Sensitive HR files.
• Unreleased financial information.
• Personal documents involving other people who did not consent.
• Production credentials or source code secrets.

This is not because OpenHuman is uniquely unsafe. It is because any early beta AI companion with memory, integrations, and tool access deserves a higher bar before it becomes part of sensitive work.

How to inspect data flow safely

Start with the official docs and settings. You are looking for the current behavior of local AI, model routing, integrations, account state, memory, search, voice, logs, and secrets.

Then check the app itself:

• Open settings and look for model provider, local AI, account, skills, and integration options.
• Check whether Ollama or LM Studio is actually running if you expect local model use.
• Look for local data folders, config files, memory files, vault files, and logs.
• Review connected accounts and OAuth scopes before granting access.
• Revoke test connections and confirm the app behaves as expected.
• If you use a network monitor, compare idle behavior, local-only tasks, and managed-service tasks.

Do not rely on a single setting label. A workflow can use local memory and still call a cloud model. It can use a local model and still proxy search or OAuth through a backend.

How OpenHuman compares with simpler local AI tools

If privacy is your top priority and you only need local chat, OpenHuman may be more tool than you need.

LM Studio is often simpler for local model chat because it focuses on downloading, running, and chatting with local models. Its docs state that once a model is downloaded, core operations such as chatting with models, chatting with documents, and running a local server can work offline.

Open WebUI is stronger when you want a browser-based interface, user management, RAG-style document chat, multiple providers, and self-hosted team workflows. It still needs careful configuration, especially if exposed beyond your own machine.

OpenHuman is different. It is more about a personal assistant layer with memory, integrations, personality, and workflow context. That can be powerful, but it also means the privacy review is more complex.

Questions to ask before connecting real accounts

Before granting OpenHuman access to email, calendar, documents, chat, repositories, or work tools, ask:

• What exact OAuth scopes does this integration request?
• Can I disconnect it from inside OpenHuman?
• Can I revoke it from the third-party account's security page?
• What data is imported into local memory?
• Are summaries or embeddings generated locally or through a backend?
• Does auto-fetch run continuously?
• Are logs safe to keep?
• What happens to local memory if I disconnect the source?
• Can I delete local memory and vault files if I need to?

The privacy risk is not only the initial connection. It is the ongoing sync, summary, retrieval, and memory behavior after the connection is active.

Red flags

Be cautious if:

• You cannot confirm the download source.
• The release notes mention a serious issue you do not understand.
• You do not know whether your model calls are local or cloud-routed.
• The app asks for broad account access before you have tested it.
• You plan to connect sensitive work accounts on day one.
• You are relying on old screenshots or old install commands.
• You need guaranteed offline behavior.
• You cannot explain how to revoke access later.

Bottom line

OpenHuman has real local-first elements, especially around memory and vault-style knowledge storage, but it is not automatically a fully offline private AI assistant. Treat privacy as a setup-specific checklist. Verify the source, start with test data, inspect local and managed routes, enable local AI deliberately, and connect accounts only after you understand the data flow.