If you work with more than one AI agent — running Claude Code for coding, OpenClaw for everyday tasks, Codex for documentation — you’ve almost certainly noticed the frustrating problem: none of them remember what the others learned about you.

Start a session in Claude Code and it has no idea you told OpenClaw your preferences last week. Switch to Codex and you’re explaining your project structure from scratch, again. Every agent is an island.

Memmy (from MemTensor) launched in late July/early August 2026 to solve exactly this problem: a local-first, open-source shared memory layer that gives all your AI agents the same persistent context, preferences, and project history.

What Memmy Is

Memmy is two things at once: a memory hub and a dedicated agent. As a memory hub, it provides a shared, structured store that multiple AI agents can read and write. As an agent itself, it serves as your most personalized AI — one that has accumulated everything other agents have learned about how you work.

The core architecture is built on MemOS (Memory Operating System), a companion repository from MemTensor that defines how memories are structured, retrieved, and updated. Retrieval uses a hybrid approach: FTS5 full-text search for quick keyword lookups combined with vector similarity search for semantic recall.

Key properties from the README and project page:

  • Local-first by default — all data stays on your device unless you opt into sync
  • Bring Your Own Key (BYOK) — connect your own model API keys; model routing is automatic
  • Desktop app + CLI/TUI + OpenAI-compatible API — multiple surface areas for different workflows
  • History import — scans existing agent conversation history to bootstrap your memory store from day one
  • Agent integrations — explicitly supports Claude Code, OpenClaw, Cursor, Codex, and Hermes Agent

How It Works with OpenClaw and Claude Code

The core idea is that each AI agent, instead of maintaining its own isolated context, queries and updates a shared memory layer through Memmy. When Claude Code learns something about your codebase — a naming convention you prefer, a library you’re standardizing on — it writes that to Memmy. When OpenClaw starts a session later, it can pull that same context.

This isn’t automatic for all agents out of the box. Integration varies by agent and typically involves:

  1. Installing Memmy from the official website or GitHub releases
  2. Running the history import to seed your memory store from existing conversations
  3. Configuring agent-side integration — each supported agent has its own setup path

For Claude Code, integration is direct through its project memory system. For OpenClaw, Memmy exposes an OpenAI-compatible API that OpenClaw can connect to, letting the agent read and write shared context entries.

⚠️ Important: The specific integration commands and config file formats vary between agents and Memmy versions. For exact steps, follow the official Memmy documentation at memmy.bot/docs — the setup guides are maintained per-agent and updated with each release. Don’t infer config paths from this article; they change.

The History Import Feature

One of Memmy’s most compelling features for existing users is history import: rather than starting with an empty memory, Memmy scans your existing agent conversation logs to extract and structure what each agent has already learned about you.

This is the cold-start problem for personal AI agents, and it’s a real one. If you’ve been using Claude Code for six months, there’s enormous value in that conversation history — patterns in how you work, projects you care about, tools you rely on. History import turns that raw history into structured memory entries that every agent can then access.

Why This Matters for Agentic Workflows

The memory problem is one of the most under-discussed pain points in multi-agent setups. You can have excellent individual agents, but if they share no context, the overall system is less than the sum of its parts.

Memmy’s bet is that the right layer for shared memory is infrastructure — not baked into any single agent, but a dedicated service that sits beneath all of them. This is architecturally sensible: it’s the same reason we use databases instead of having every app maintain its own file system.

For practitioners running OpenClaw alongside Claude Code or Codex, this is the first mature open-source attempt to solve this specific problem with a local-first approach. Previous solutions either required cloud services or were tightly coupled to specific platforms.

Getting Started

Memmy is free to try with trial tokens at signup (no credit card required initially). After the trial period, you can switch to BYOK mode using your own model API keys.

To get started:

📝 A note on commands: Specific CLI commands, config file paths, and integration steps are intentionally omitted here because they vary by agent version and platform. The official docs at memmy.bot/docs are the reliable source — this article may go out of date faster than they will.

Sources

  1. MemTensor/memmy-agent on GitHub — project repository, README, and release notes
  2. Memmy Official Website — installation downloads and documentation
  3. MemOS companion repository — underlying Memory Operating System architecture
  4. Product Hunt listing — launch announcement

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260802-2000

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