Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsCoding agents can retain context across sessions, but a memory feature in one tool does not mean Claude Code, Codex, Gemini CLI, and other agents share the same store. The documented approaches include managed memory stores, persistent context files, and reviewable updates inferred from past sessions. Each has different scope, controls, and privacy implications; no universal cross-agent format or direct interoperability is established by the documentation covered here.
What does persistent memory mean for a coding CLI?
“Memory” can refer to several different mechanisms. A context file supplies instructions whenever a tool loads it; a managed store holds documents that an agent can read or change; an extraction system looks back at earlier sessions and proposes additions. These mechanisms should not be treated as interchangeable.
| Approach | What persists | Scope and sharing | Review and write controls | Important limitation |
|---|---|---|---|---|
| Anthropic Managed Agents memory stores | Text documents addressed by paths; changes create immutable versions. | A store is attached when a session is created and mounted in the agent sandbox. Multiple stores can be attached to a session. | Read-write is the default; read-only is supported. Versions can be inspected and redacted, and writes can use a content-hash precondition. | Documented for Claude Managed Agents; this does not establish sharing with unrelated coding CLIs. Anthropic documentation. |
| Gemini CLI context files | Instructions and project context in Markdown files such as GEMINI.md. | Global, project or ancestor, and subdirectory context files can be loaded together. Configuration can include other filenames, including AGENTS.md. | Files are explicitly editable. The CLI provides /memory show, /memory refresh, and /memory add. |
Context files are not, by themselves, an automatic session-derived memory service or proof of compatibility with every agent. Gemini CLI documentation. |
| Gemini CLI Auto Memory | Draft memory updates and reusable Agent Skills inferred from past Gemini CLI transcripts. | Inbox items are project-local; promoted skills can be placed at user or workspace scope. | Experimental and off by default. Candidates are reviewable and require user action to apply or promote. | It skips the current session, requires eligible idle sessions, and is documented as experimental. Gemini CLI documentation. |
Can Claude Code, Codex, and Gemini CLI share one memory?
The documented features above do not establish a common store that these tools can all read and write. Anthropic describes memory for Claude Managed Agents; Gemini describes its own context-file and Auto Memory features. The public OpenAI Codex repository identifies Codex CLI as a locally running coding agent, but the repository page does not substantiate compatibility with the cited Anthropic or Gemini mechanisms.
A shared Markdown file can be a practical hand-maintained source of project context only when each specific CLI is configured or documented to load it. Gemini CLI’s context-file configuration can include AGENTS.md, but that fact alone does not show that other tools will load the same file, interpret it identically, or write back safely. Verify each tool’s current documentation and version before relying on a shared workflow.
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In particular, do not assume that a memory exported from one vendor will import into another, that updates synchronize automatically, or that two agents resolve conflicting edits. A real compatibility check needs to establish whether each tool supports the same storage format, read and write permissions, conflict handling, and versions.
How Gemini CLI keeps context between sessions
Use context files for instructions you maintain
Gemini CLI can find GEMINI.md files at global, project or ancestor, and subdirectory levels. It concatenates the discovered context and sends it with prompts. The documentation also allows other context filenames, including AGENTS.md, to be configured. This is a way to provide durable instructions and project facts; it is not a record automatically built from every conversation.
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To inspect or manage loaded context in the CLI, use /memory show to view it, /memory refresh to reload it after edits, or /memory add to add context. The files remain ordinary editable Markdown, so you can review and maintain their contents directly.
Use Auto Memory for proposed discoveries, not silent edits
Gemini CLI’s Auto Memory is marked experimental and was documented as last updated May 13, 2026. It examines past Gemini CLI transcripts for durable facts, preferences, workflow constraints, and recurring procedural patterns, then creates patch files or skill drafts in a project-local inbox. The documentation says it does not directly edit active memory files, settings, credentials, or project GEMINI.md files.
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- Auto Memory is off by default.
- It considers past sessions only after they have been idle for at least three hours and contain at least 10 user messages.
- It skips the current session.
- Inbox candidates require a user action before they are applied or promoted; reusable skills can be promoted to user or workspace scope.
Although the source transcripts are local, transcript analysis is not necessarily entirely local: selected excerpts may be sent to the configured model for extraction. Google says the extractor is instructed to redact secrets, tokens, and credentials; that safeguard is not a guarantee that sensitive information can never be exposed. See the Auto Memory documentation for the current behavior and eligibility rules.
How Anthropic Managed Agents memory stores work
Anthropic describes a workspace-scoped collection of text documents optimized for Claude. A store is attached when a session is created, mounted into the agent sandbox, and accessed through the agent’s ordinary file tools. You can attach multiple stores, and a store can be read-only when it provides reference material that the agent should not change.
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Every change creates an immutable version, supporting an audit trail and point-in-time recovery. Anthropic also documents inspecting and redacting versions and using a content-hash precondition when updating content, which can help avoid overwriting a memory that has changed since it was read. For self-hosted sandboxes, a worker maintains a local copy and synchronizes it; the documented default sync interval is 15 seconds.
Anthropic’s 2026 documentation lists these implementation limits: each memory can be up to 100 kB (approximately 25,000 tokens), a store can contain up to 10,000 memories, and a session can attach up to eight memory stores. Version history may be deleted after 30 days, while recent versions of a live memory are retained. These are documented product limits, not measures of retrieval quality or guaranteed recall. Check the current memory documentation for changes.
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How to set up a safer, more portable memory workflow
If you want project knowledge to survive sessions and be usable by more than one tool, start with a deliberately maintained source of truth rather than assuming automatic synchronization.
- Choose what belongs in durable context. Keep stable, useful project facts such as architecture decisions, build and test commands, and recurring workflow constraints. Leave temporary debugging details in the session unless they remain useful.
- Put shared project context in a file only where each tool can load it. For Gemini CLI, configure or use its documented context-file locations. For every other CLI, confirm its own supported file or memory mechanism rather than assuming that a filename is recognized.
- Separate reference material from agent-written memory. Where a tool supports it, attach reference-only content read-only. If agents can write, prefer proposed changes that a person can inspect before they become trusted context.
- Review changes for accuracy and sensitivity. Remove stale instructions, secrets, credentials, and claims copied from untrusted prompts or tool output. Keep consequential decisions traceable to a trustworthy source.
- Test each tool’s behavior. Confirm what it actually loads, whether it can write, how conflicts are handled, and whether the setting applies to the project, user, workspace, or organization.
This creates a portable practice, not a guaranteed portable memory service. Portability depends on each agent’s documented ability to read the chosen format and on how you govern updates.
What are the security and reliability risks?
Untrusted input can become trusted memory
Anthropic warns that prompt injection in untrusted prompts or tool output could lead an agent to write malicious content into a read-write memory store. A later session might then treat that content as trusted context. Use read-only access for shared reference material that does not need agent writes, and review proposed or completed writes before relying on them.
Transcript-derived memory can disclose excerpts to a model
Gemini Auto Memory selects transcript content for model-assisted extraction, and excerpts may be sent to the configured model. Its documented redaction instruction is a safeguard, not proof that leakage is impossible. Consider what sensitive material may appear in session transcripts before enabling it.
Persistent context can become stale or conflict
A remembered fact is only helpful while it remains accurate. Review project notes when architecture or workflow changes, and make the source and scope of important instructions clear. The cited documentation describes versioning, redaction, and review controls in specific implementations, but it does not provide comparative measurements of memory retrieval quality or a universal method for resolving conflicts across tools.
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