Local AI Memory: Keep Context on Your Own Machine

To keep AI memory local, save the context you want to reuse in files or a database on your machine, then configure your assistant to read the relevant records in each session. If processing must also stay local, check the model, search, extensions, and every other component that receives those records. A local file can still be sent to an online model.

fenbs is a hosted option for project information you deliberately choose to share across connected assistants. It is not local storage. Start by separating what must stay on the machine from what you want available elsewhere. The memory tools hub covers those different approaches.

How do you set up a simple local memory workflow?

1. Choose one project and create a dedicated local folder. Keep it outside folders that automatically sync elsewhere if your goal is machine-only storage.
2. Write a short project record: the goal, approved rules, current work, unresolved questions, and the next action. Add references to supporting files instead of copying every document.
3. Choose the assistant and model that will read the folder. Check where inference happens, meaning where the model processes your request, and whether other enabled features contact outside services.
4. Start a fresh session with harmless test information. Ask the assistant to read the project record, summarize the current rules, and identify where it will begin.
5. At the end, review a proposed progress update before saving it. Restart the application and confirm the next session can recover that update from disk.

Begin with manual reading and saving if your app has no file tools. Copying an approved short record into a session can prove the structure works before you automate it. When the destination is an online service, however, that copy leaves the local boundary. Make the destination part of your decision, not an assumption.

What should you put in the local files?

Save facts that change the next action. An assistant needs to know that a design was approved, which version applies, and what remains unverified. It rarely needs an unfiltered transcript of every discussion. Keep source material separate from the concise starting record so a session can check detail without reading everything.

Project: Studio contact form
Goal: Make inquiries easier to submit.
Approved decision: Use the short form in design-v3.pdf.
Source: Approval note, October 12, 2026.
Rule: Ask a person before changing collected information.
Current state: Form built; delivery check unfinished.
Next action: Test message delivery with fictional details.
Unknown: Whether the confirmation email arrives.
Last reviewed: October 20, 2026.

Use stable filenames and explicit dates. Label a proposal as a proposal, and say when a later decision replaces an earlier one. If you cannot support a statement with a source or recent check, mark it uncertain. That makes the file easier for both a person and an assistant to interpret.

For records that need to remain understandable when you change tools, the guide to portable AI memory explains the value of a clear handoff. Local storage solves location; readable records solve continuity.

When does a local database help more than files?

Use files while a person can comfortably review the record and find the right entry. Consider a database when you need structured filtering, stable identifiers, consistent updates, or many records that must remain linked. Database storage does not automatically make an assistant remember; your application still needs to select and supply the right records.

SQLite's documentation↗ (opens in a new tab) explains that its database engine reads and writes files directly without a separate server process. That can suit a local application. A simple record design might include the project, statement, source, approval state, and relevant dates, with an explicit link from a replaced record to its replacement.

Keep search optional at first. Looking up records by project and status may be sufficient. If you later add embeddings, numerical representations used to find related text, check where they are generated and stored. A local database paired with an online embedding service is still sending information outside your machine. The AI memory database guide covers the wider storage decision.

How do you check that processing stays local?

Follow the data through the whole workflow. Identify which program reads the file, which model receives it, which search features run, and where logs and backups go. A desktop interface can offer both local and hosted processing. Read the selected configuration rather than inferring behavior from the application being installed on your computer.

For example, Ollama's FAQ↗ (opens in a new tab) distinguishes local model use from its cloud-hosted models. It documents a local-only configuration that disables its cloud models and web search. Follow its current instructions for the operating system you use, restart after the change, and verify the configuration before supplying private material.

Test with fictional records while disconnected from the network after obtaining the software and model files you need. Ask a question that requires the saved record, restart, and ask again. This checks whether that workflow works offline; it does not establish that every program on the computer never transmits information. Review enabled connectors and sync separately.

What usually goes wrong after the first session?

The most common design failure is an unclear update rule. A session appends a new summary while the old starting file remains unchanged, so the next session reads an obsolete answer. Give each current fact one maintained home. Preserve older decisions as history with a clear replacement reference instead of leaving two competing instructions.

Another failure is mistaking a running process for durable storage. Verify that the record survives closing the program and restarting the machine. Back up the reviewed source files and test a restore. For an active SQLite database, use a supported backup method; its Online Backup API documentation↗ (opens in a new tab) explains why copying a live database requires care.

Finally, keep the current record short enough to use. If every session must read a growing archive, important instructions become harder to find. Ask the assistant to report which sources it read and which facts remain uncertain. You can catch a missing record before the next action depends on it.

How to do this with fenbs

Use this route only for project information you have chosen to put in a hosted service. Keep machine-only records local. For a hybrid workflow, you might retain private research files on disk while sharing the approved task, its public source link, and the next action in fenbs.

1. Create a free account and add the project information you intend to share.
2. Add current tasks, then record human-approved choices in Decisions and rules. Keep private source files out of the shared text.
3. Connect a supported AI app through Integrations. MCP is a standard connection for an assistant to use an outside tool.
4. Ask the assistant to read the shared context before working and save Where we left off afterward, including what remains only in your local files.

fenbs is not the right fit when every project record must stay on your own machine. When sharing is appropriate, its work context can give supported assistants the same agreed starting point. The AI memory for teams guide covers maintaining that shared record with colleagues.