Local AI Assistants: Chat, Task Automation and Privacy

A local AI assistant can mean a chat window running a model on your laptop, or an agent that remembers work and uses connected tools. Choose the category before comparing products. For persistent tasks and coding-agent supervision, OpenAGI is a self-hosted personal agent built by Spencer Shulem, the author of this publication. For a private conversation with no actions, you may only need a local chat application. Neither category guarantees privacy without checking the model and connections you enable.

This guide explains how to choose and test a local assistant for a specific job. It does not present a performance benchmark or claim that every feature works offline. The OpenAGI examples follow its current public website and source documentation, reviewed October 30, 2026. Installation and feature availability can differ between a published release and the latest source.

What do you want the assistant to do?

Start by writing one sentence about the outcome: “Answer questions about this document,” “Remember my project preferences,” or “Show me which coding sessions need attention.” These are different jobs. A model runner supplies inference. A chat application manages a conversation. A persistent agent adds state, connected tools and a process for carrying work across sessions. Buying more model capability does not automatically supply the missing workflow.

Think about the work you currently repeat. If you paste the same context into every conversation, persistent memory may help. If you continually open coding threads to discover which need a decision, a supervision view may help. If you just want to write without sending a document to a cloud model, task automation may add setup without improving the result. A smaller tool can be the more useful choice.

Local state and local inference are different

A self-hosted agent can keep its memory on your computer while sending selected prompts to a cloud model. That is a valid configuration when you want it, but it is not an offline configuration. The same distinction applies to connected tools: reading a cloud calendar or repository uses that service even when the agent process runs on your own hardware.

OpenAGI describes its state as living on the host you choose. Cloud models receive selected prompts and tool context, and connected services receive the data needed for their actions. A supported local model is an option for on-device inference. Do not translate “self-hosted” into “nothing ever leaves the machine.” Describe the actual configuration, including any optional observation, integrations and remote devices.

Write a simple data map

• State: where conversation history, memories and task records are stored.
• Inference: which model receives prompts and whether it is local or remote.
• Tools: which services receive requests, documents or action payloads.
• Devices: which paired clients can reach the host and what they can do.

Make the map before connecting real accounts. A harmless sample document and a test task are enough to exercise the first workflow. Include backups in the map: storing state locally does not tell you who can read a synchronized backup, shared folder or exported conversation. Your device access and storage policies still matter.

What OpenAGI adds beyond a chat window

OpenAGI is a persistent personal agent available at openagi.sh, with source at Spshulem/openAGI. It is distinct from the academic project and other products that share the name. Its public documentation describes persistent conversations, memories and corrections, reviewed skills, optional observation, and tools you choose to connect. These are workflow capabilities, not a promise that the agent will complete every task reliably.

Its Supervisor brings supported coding sessions into one view, including Codex, Claude Code, Conductor and Antigravity. The useful question is which session needs attention, with the thread, pull request and verification evidence behind it. Observe mode lets you learn what the system sees before granting broader authority. Optional automatic nudging is a separate configuration choice; installing an assistant should not be treated as approving every future action.

Another use is turning repeated work into a routine. You can first write the routine yourself, then compare an agent's proposed version with it. A morning review might read a few approved sources, prepare a draft and point out missing evidence. Keep sending the draft, changing an account or publishing a release as explicit decisions. The value is less repeated assembly, with a clear record of what happened.

Run the workflow manually before automating it

A useful first task is one you can describe and check in a few minutes. Avoid starting with “manage my life.” Choose a bounded task such as preparing a status note from two repositories you can read. Write down what counts as a result, which sources may be used, and what the assistant must leave for your decision. This creates an acceptance test you can reuse across tools.

1. Record the inputs. Name the two repositories, the time window and the fields you need.
2. Describe the output. For example, a draft note with links to merged changes and unresolved questions.
3. Set the boundary. Reading is allowed; commenting, merging and sending are separate actions.
4. Do one manual pass. Save the note and the source links you used.
5. Try the assistant. Compare its note with the manual one and inspect every claimed action.

A missed item gives you something concrete to repair: a connection, a filter, a task instruction or a model limitation. A confident paragraph without source evidence does not establish completion. Correct the routine before scheduling it. If the manual method is unclear, an agent is likely to automate that ambiguity rather than remove it.

Install from the path that fits your computer

The current OpenAGI README links a Mac download and source-install instructions for Windows, Linux and Raspberry Pi. The Mac application includes its runtime and setup flow. Source installs require Node.js 22 or newer. Check the install guide for the platform and release you are using instead of assuming every platform has the same installer or native capabilities.

Setup asks you to choose and verify a model. The documented options include cloud APIs, ChatGPT sign-in, a local model server, and Apple Intelligence on compatible devices. A successful connection test demonstrates that a model answered, not that all integrations are configured. Add one workflow at a time and keep the access token for your dashboard secure. Do not expose a local dashboard publicly just to make a first test easier.

OpenAGI is source-available under PolyForm Noncommercial terms. Review the repository's license before organizational or commercial use; source availability does not mean unrestricted permission. The installation guide and current release assets are the right places to check downloads. Mobile documentation describes source and QA builds rather than promising a public app-store download.

Verify the first useful result

Begin with a conversation using the configured model and no unnecessary integrations. Ask the assistant to remember a non-sensitive preference, open a separate conversation and check whether it can recall it. Then correct the preference and check the correction. This tests the experience you need without turning a private inbox into a diagnostic fixture.

A small acceptance checklist

• The configured model responds and its provider is the one you intended.
• A saved preference can be recalled and corrected across conversations.
• A sample task has a clear completion record or an honest failure.
• A tool connection only exposes the resources you intended to share.
• A proposed write waits for the authority required by your configuration.
• You can stop the workflow and find its saved progress afterward.

If you use Supervisor, compare its session states with the underlying coding applications. A process being active does not prove a check passed, and a green check does not prove a deployed user flow works. Keep those states distinct in the status note. The dashboard should help you find the evidence behind a decision, rather than replace it with an attractive summary.

Test offline operation separately

For an offline test, use a supported local model and a task whose inputs are already on the machine. Complete initial downloads while connected, then disable the network and repeat a harmless conversation. Inspect the result, the task log and any reported connection errors. Test the exact flow you plan to use; an offline chat does not establish that calendar access, repository checks or mobile pairing work without networking.

Keep a separate list of operations that need remote services. A hosted model needs its provider. A cloud inbox needs its mail service. A paired phone needs a reachable host for host-backed features. These dependencies are not defects by themselves, but they change the meaning of “local.” A useful setup makes the boundary visible and leaves you with a practical fallback when a connection is unavailable.

Choose hardware around the model and workload

The agent runtime and the inference model have different resource requirements. A device may be able to host the dashboard and task process while being too slow for the model you want. Model size, memory, context length and concurrency affect responsiveness. Do not assume a Raspberry Pi or an older laptop can deliver the same experience as a larger machine simply because the runtime installs.

Try your actual sample task before buying hardware. Measure whether the response arrives quickly enough, whether the host remains usable, and what happens when two tasks overlap. If you use remote inference, include its availability and cost in the decision. If you use local inference, include model loading and storage. No single minimum-memory number describes every useful assistant configuration.

Keep permission and spending choices explicit

Connect only the accounts and tools the first workflow needs. Review new skills and integrations before enabling them. Optional screen observation and live computer actions have their own permissions and platform requirements. Knowing that an agent can see something is different from granting it permission to act on it. Start with observation and draft preparation, then expand authority for a task you can verify.

Set a model budget before long-running work. The current documentation describes spend tracking and several limits, but estimates are not billing guarantees. Test what happens when a limit is reached: you should be able to inspect the completed work and decide whether to continue. Include a stopping condition in the task itself, such as finishing a named inventory or producing one reviewed draft.

Common mistakes when choosing a local assistant

• Confusing a runner with an agent. A model endpoint does not supply a persistent task workflow.
• Equating local storage with offline inference. Check the configured model and connected tools.
• Automating a vague routine. Write one manual pass and define evidence of completion.
• Connecting everything immediately. A small sample task is easier to verify and troubleshoot.
• Reading source documentation as a release receipt. Confirm which features are in your installed build.

Frequently asked questions

Is a local AI assistant completely private?

Its privacy depends on configuration. Check where state is stored, where inference runs and which services receive tool requests. A self-hosted process using a cloud model still sends selected context to that provider. Local inference can reduce that transfer, but it does not automatically make every connected workflow offline.

Can OpenAGI perform tasks instead of only chatting?

Its documented workflows include persistent tasks, reviewed skills, connected tools and coding-agent supervision. Test the task you need with the model, permissions and connections you actually configure. A capability listed in documentation is not evidence that a particular task has completed on your installation.

Do I need a local model?

Only if on-device inference is part of your requirement. You can self-host the agent while choosing a cloud model, provided that data flow fits your needs. For offline use, choose a supported local model and test the complete intended workflow without a network connection.

Where should I start?

Read the OpenAGI overview and the first-use guide. Choose one small job, verify the model connection and inspect the first result. You can add integrations after that. A useful assistant is a workflow you can trust and correct, not a count of enabled features.