Can OpenClaw and Claude Share Memory?
Give your always-on agent and chat assistant the same picture
You run OpenClaw as your always-on agent — scheduled jobs, monitoring, reaching you across Slack and Telegram. You also use Claude for the thinking work: drafting, reasoning, talking through decisions. Two agents, two separate heads. What OpenClaw learns about a project never reaches Claude, and what you worked through in Claude is invisible to OpenClaw. So you brief each one separately, every time.
The obvious question: can these two just share what they know?
Can OpenClaw see what you told Claude?
No — not natively, and not the other way around either. OpenClaw's memory lives as plain Markdown files in its own workspace, while Claude's memory lives inside Anthropic's platform. Neither agent has any path to the other's store. They were never built to share, because the context each holds is locked to its own environment.
This is the same wall you hit anywhere two AI tools meet. Platform memory is a moat, not a feature meant to travel — Platforms have bolted on memory features which help, but they're reactive, start empty, and stay locked to one platform . OpenClaw's file-based memory is more open than most, since the files are yours on disk. But "yours on disk in OpenClaw's workspace" still isn't "readable by Claude." The formats don't match, the locations don't match, and nothing keeps them in sync.
So you end up doing the syncing by hand — re-explaining the same project to OpenClaw that you just walked Claude through. With multi-tool use now the norm rather than the exception, that tax compounds fast.
What actually works — one shared layer both connect to
The fix isn't wiring OpenClaw and Claude directly together. It's giving both agents one place to read from and write to: a shared context layer that holds a structured profile of who you are and what you're working on, independent of either tool.
This works because of MCP — the Model Context Protocol, an open standard for connecting AI tools to outside data. MCP is the open protocol that lets AI tools connect to outside systems. A connector is a specific integration built on MCP. A context layer is a different kind of thing you reach through MCP: instead of giving an agent a raw data source to search, it delivers structured context about you.
Both directions matter here:
- Read. Claude connects to the layer over MCP and loads your identity, role, and current priorities at session start. OpenClaw reads the same layer, so its scheduled jobs and monitoring act on the real you, not a generic default.
- Write. When either agent learns something worth keeping — a decision, a new project, a changed preference — it saves that back to the same layer. Save-back isn't on by default; you enable it with a line in each agent's custom instructions. From then on, an update Claude writes is context OpenClaw reads next run, and vice versa.
That's the shift from two isolated memories to one shared source of truth. Neither agent owns your context. You do — and both borrow from it.
The alternative people reach for first is exporting from one tool and importing to the other. It doesn't hold up: an export is a one-time snapshot that's stale the moment anything changes. A context layer stays live because it's connected to the sources that reflect your real situation. For the deeper comparison of why platform-bound memory can't cross this gap, see OpenClaw vs Hermes — the same locked-in-per-tool limit applies to both.
How to set it up
Point both agents at the same context layer over MCP, then turn on save-back. Using Unabyss as the layer, the flow is:
- Build your context vault. Connect your real sources — LinkedIn, Notion, GitHub, email — and Unabyss extracts a structured profile in under 90 seconds. This is the shared store both agents will read.
- Connect Claude via MCP. Add Unabyss as an MCP server in Claude. From then on Claude loads your context automatically at the start of every conversation.
- Connect OpenClaw via MCP. Point OpenClaw's MCP config at the same Unabyss server. Its agents now read the same profile Claude does — including on scheduled runs when you're not watching.
- Turn on save-back in both. Add a line to each agent's custom instructions telling it to write meaningful updates back to Unabyss. Now what OpenClaw discovers on a monitoring job lands in the same vault Claude reads next session.
- Control what each sees. Scope permissions per tool — OpenClaw might get identity and environment for its automation, while Claude gets your full working profile. You decide, and you can revoke any tool instantly.
For the full cross-tool walkthrough covering more agents, see How to Share Memory Across AI Tools.
Why this beats bridging them directly
A direct OpenClaw-to-Claude bridge would be brittle and one-off — a custom integration you'd rebuild every time either tool changed, syncing two memory formats that were never meant to align. And it would still leave every other tool out.
A shared layer scales instead. Add Cursor, Codex CLI, or ChatGPT to the same vault and they all draw from the same profile — no new bridge per pair. The math is simple: bridging N tools directly is a tangle of connections; connecting N tools to one layer is N clean links. This is the difference between patching symptoms and fixing the structure. The fix is starting every session "warm" — loading your context automatically.
OpenClaw and Claude will never natively share memory, because neither was built to give its context away. But they don't need to. Give them a shared layer you own, and the question stops being "can these two talk to each other" and becomes "what does my context say" — one answer, read by every tool.
Want both agents working from the same context? Start with the pillar: What Is Multiagentic Memory? — then connect OpenClaw and Claude to one shared vault.
