Auto Memory

Auto Memory is an experimental feature that mines your past Gemini CLI sessions in the background and proposes durable memory updates and reusable Agent Skills. You review each candidate before it becomes available to future sessions: apply memory updates, promote skills, or discard anything you do not want.

[!NOTE] This is an experimental feature currently under active development.

Overview

Every session you run with Gemini CLI is recorded locally as a transcript. Auto Memory scans those transcripts for durable facts, preferences, workflow constraints, and procedural patterns that recur across sessions. It can draft memory updates as unified diff .patch files and draft reusable procedures as SKILL.md files. All candidates are held in a project-local inbox until you approve or discard them.

You'll use Auto Memory when you want to:

• Capture team workflows that you find yourself walking the agent through more than once.
• Preserve durable project context such as repeated verification commands, local constraints, or personal project notes.
• Codify hard-won fixes for project-specific landmines so future sessions avoid them.
• Bootstrap a skills library without writing every SKILL.md by hand.

Auto Memory complements—but does not replace—the save_memory tool, which captures single facts into GEMINI.md when the agent explicitly calls it. Auto Memory infers candidates from past sessions, writes reviewable patches or skill drafts, and never applies them without your approval.

Prerequisites

• Gemini CLI installed and authenticated.
• At least one idle project session with 10 or more user messages. Auto Memory ignores active, trivial, and sub-agent sessions.

How to enable Auto Memory

Auto Memory is off by default. Enable it in your settings file:

1. Open your global settings file at ~/.gemini/settings.json. If you only want Auto Memory in one project, edit .gemini/settings.json in that project instead.

2. Add the experimental flag:

   jsonCopy

   {
     "experimental": {
       "autoMemory": true
     }
   }
   

3. Restart Gemini CLI. The flag requires a restart because the extraction service starts during session boot.

How Auto Memory works

Auto Memory runs as a background task on session startup. It does not block the UI, consume your interactive turns, or surface tool prompts.

1. Eligibility scan. The service indexes recent sessions from ~/.gemini/tmp/<project>/chats/. Sessions are eligible only if they have been idle for at least three hours and contain at least 10 user messages.
2. Lock acquisition. A lock file in the project's memory directory coordinates across multiple CLI instances so extraction runs at most once at a time. A state file records processed session versions, and extraction is throttled so short back-to-back CLI launches do not repeatedly scan history.
3. Candidate extraction. A background extraction agent reviews the session index, reads any sessions that look like they contain durable memory or repeated procedural workflows, and drafts candidates. It defaults to creating no artifacts unless the evidence is strong, so many runs produce no inbox items.
4. Safety boundaries. Auto Memory writes candidates to a review inbox. It cannot directly edit active memory files, settings, credentials, or project GEMINI.md files.
5. Patch validation. Skill update patches are parsed and dry-run before they are surfaced. Memory patches are parsed, target-allowlisted, and applied atomically only when you approve them from the inbox.
6. Notification. When a run produces new candidates, Gemini CLI surfaces an inline message telling you how many items are waiting.

How to review extracted items

Use the /memory inbox slash command to open the inbox dialog at any time:

Command: /memory inbox

The dialog groups pending items into new skills, skill updates, and memory updates. From there you can:

• Read the full SKILL.md body before deciding.
• Promote a skill to your user (~/.gemini/skills/) or workspace (.gemini/skills/) directory.
• Discard a skill you do not want.
• Apply or reject a .patch proposal against an existing skill.
• Review memory diffs before they touch active files.
• Apply or dismiss private and global memory patches. Private patches target the project memory directory; global patches target only your personal ~/.gemini/GEMINI.md file.

Promoted skills become discoverable in the next session and follow the standard skill discovery precedence. Applied memory patches update the underlying memory files and reload memory for the current session.

How to disable Auto Memory

To turn off background extraction, set the flag back to false in your settings file and restart Gemini CLI:

{
  "experimental": {
    "autoMemory": false
  }
}

Disabling the flag stops the background service immediately on the next session start. Existing inbox items remain on disk; you can either drain them with /memory inbox first or remove the project memory directory manually.

Data and privacy

• Auto Memory only reads session files that already exist locally on your machine.
• Auto Memory uses model calls to analyze selected local transcript content during extraction. No candidates are applied automatically, but transcript excerpts may be sent to the configured model as part of those calls.
• The extraction agent is instructed to redact secrets, tokens, and credentials it encounters and to never copy large tool outputs verbatim.
• Drafted skills and memory patches live in your project's memory directory until you promote, apply, dismiss, or discard them. They are not automatically loaded into any session.

Limitations

• The extraction agent runs on a preview Gemini Flash model. Extraction quality depends on the model's ability to recognize durable patterns versus one-off incidents.
• Auto Memory does not extract memory or skills from the current session. It only considers sessions that have been idle for three hours or more.
• Project or workspace shared instructions in project GEMINI.md files are not auto-extractable. Auto Memory can propose private project memory, global personal memory, and skills.
• Inbox items are stored per project. Skills extracted in one workspace are not visible from another until you promote them to the user-scope skills directory.

Next steps

• Learn how skills are discovered and activated in Agent Skills.
• Explore the memory management tutorial for the complementary explicit-memory and GEMINI.md workflows.
• Review the experimental settings catalog in Settings.