Pillar comparison · Updated May 2026

OpenClaw Memory Plugins Compared: Every Option for 2026

Ten persistent-memory options for OpenClaw, evaluated on a single checklist with every factual claim sourced from the vendor's own docs. Maximem Vity (formerly MemoryPlugin) leads on cross-AI coverage; specialists like Hindsight and ByteRover lead on raw published accuracy. Pick by workflow, not vibes.

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Why OpenClaw needs a memory plugin

OpenClaw is a powerful coding agent, but agentic context management is not part of the box. Every new session is a blank slate. Markdown files written to local disk are not semantically searchable. Long conversations get compacted, silently dropping earlier instructions and architectural decisions. Switching from your desktop to your laptop resets your agent's understanding of your project. And every other AI you use — ChatGPT, Claude, Gemini — maintains a completely separate memory store that OpenClaw can't see.

These are the five failure modes the /openclaw landing page calls out in its problem section — stateless sessions, markdown files on disk, context compaction, device-locked memory, and siloed AI tools. Solving them is what every plugin on this page is trying to do, with different trade-offs.

The right plugin depends on which of those five failure modes hurts you most. A solo dev on one machine for a one-week project feels none of them. A senior engineer who jumps between OpenClaw, Claude, and ChatGPT across two laptops and a desktop feels all five. The rest of this page maps the failure modes to the plugin that addresses them best.

What to evaluate

A persistent memory plugin for OpenClaw lives or dies on six properties. Before installing anything, ask each candidate these questions and write the answers down. Anything a vendor won't answer in their docs, treat as a "no."

  • First-party OpenClaw plugin
    Is there a turnkey integration, or do you write an adapter against a generic SDK?
  • Semantic vs. keyword retrieval
    Can the agent find a memory by meaning, or only by exact filename / header match?
  • Cross-device sync
    Does memory follow you from laptop to desktop, or is it pinned to one filesystem?
  • Cross-AI bridge
    Does the same store serve ChatGPT, Claude, and Gemini, or is OpenClaw a silo?
  • Privacy posture
    Encrypted at rest? Excluded from model training? Exportable on demand?
  • Verifiable accuracy
    Is there a LongMemEval score or third-party benchmark, ideally on the open-source harness?

We score every plugin below against this checklist, citing vendor sources. The LongMemEval harness is the most widely-used open-source memory benchmark and a useful apples-to-apples reference where vendors publish numbers against it.

The 10 plugins, one by one

Ordered roughly by fit for the typical OpenClaw user, not by raw benchmark numbers. Pros and cons are sourced from vendor docs and, where called out, third-party benchmarks. Each section is short on purpose — the goal is a usable shortlist, not an encyclopedia.

#1

Maximem Vity

Recommended for cross-AI usersmaximem.ai/openclaw ↗

Persistent memory for OpenClaw — and every other AI you use

Maximem Vity (formerly MemoryPlugin) is the only persistent-memory layer built to follow you across OpenClaw, ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok from a single encrypted vault (maximem.ai/cross-app-memory). For OpenClaw, install the plugin from npm — @maximem/memory-plugin — wire a free API key, and your coding agent immediately has long-term memory across sessions and machines.

What sets Vity apart for OpenClaw users is the cross-AI surface. The context your OpenClaw agent captures during a refactor is queryable from ChatGPT the next morning; the architectural rule you taught Claude last week is automatically available the moment OpenClaw spins up a new plan. Auto-Recall surfaces the right memory before you have to ask, and Auto-Capture saves new facts without manual tagging (maximem.ai/vity). Memory is encrypted at rest, never used for model training, and fully exportable.

Trade-offs: Vity is intentionally agent-flavored rather than RAG-shaped, so if your only need is a vector store over a markdown corpus, a narrower tool is fine. For OpenClaw users who already use multiple chatbots, Vity is the only option that unifies all of them.

Pros
  • +Cross-app memory: ChatGPT + Claude + Gemini + OpenClaw share one vault
  • +Auto-Recall and Auto-Capture remove manual tagging
  • +Encrypted at rest, no model training on user data
  • +Free API key — 30-second install
Cons
  • −Cross-AI sync requires the Chrome extension for chatbot side
  • −Optimized for personal/developer memory, not enterprise RAG

Best for: Developers who use OpenClaw plus at least one chatbot and want one continuous memory across them.

#2

Mem0

mem0.ai ↗

Open-source memory layer for LLM apps

Mem0 is an open-source memory layer for LLM applications, designed primarily for agent developers rather than end-users (github.com/mem0ai/mem0). It exposes a Python and JavaScript SDK that lets your agent call add() / search() against a hosted or self-hosted vector + graph store, and it ships an OpenMemory MCP server you can wire into MCP-aware editors and agents (mem0.ai/openmemory-mcp).

For OpenClaw specifically, there is no first-party plugin — you either write a small adapter that pipes OpenClaw tool calls into Mem0's SDK or you route through Mem0's MCP server. On the published LongMemEval accuracy track Mem0 scores 57.5%, well below SOTA systems like Synap (90.2%) and Zep (63.8%) measured on the same open-source harness (source).

Trade-offs: powerful API surface and an active OSS community, but the OpenClaw experience is DIY, and accuracy on long, evolving conversations lags the front-runners.

Pros
  • +Open-source core with active community
  • +OpenMemory MCP server bridges into MCP clients
  • +Self-hostable
Cons
  • −No first-party OpenClaw plugin — adapter or MCP only
  • −57.5% on LongMemEval, below Synap and Zep

Best for: Teams already building on Mem0 who want to extend the same store to OpenClaw via MCP.

#3

Supermemory

supermemory.ai ↗

Universal memory API with a hosted plan and Chrome extension

Supermemory ships a universal memory API plus a Chrome extension that captures your AI chats and surfaces them across tools (supermemory.ai). For OpenClaw users, the practical integration path is their REST API or MCP server — there is no dedicated OpenClaw plugin listed in their docs as of April 2026 (docs.supermemory.ai).

The hosted product is paid (Pro tier required for cloud sync across devices) and supports encrypted-at-rest storage. Auto-Capture works on the chatbot side via the extension, but the OpenClaw side requires you to wire writes from your agent yourself. Supermemory's biggest strength is breadth: documents, tweets, web clips, and chat messages all land in the same store.

Trade-offs: solid product if your workflow is document-heavy and chatbot-oriented; weaker fit if you specifically need a turnkey OpenClaw plugin with first-party hooks. Pricing is opaque relative to per-seat tools — check current plan limits before committing.

Pros
  • +Captures across chatbots, documents, tweets
  • +Encrypted at rest
  • +REST and MCP integration paths
Cons
  • −Cloud sync gated to Pro tier
  • −No first-party OpenClaw plugin

Best for: Users who want one store for chat history, documents, and web clips and are comfortable wiring OpenClaw to it themselves.

#4

OpenClaw native memory

openclaw.ai ↗

Built-in markdown memory files on local disk

OpenClaw ships with a built-in memory feature that writes flat markdown files to a local .openclaw/ directory in your project (openclaw.ai/docs/memory). It's free, requires zero setup, and the format is human-readable — you can open the files in any editor and edit them by hand.

The trade-offs are significant. Markdown files are not semantically searchable — OpenClaw scans them by filename and section header at load time, not by meaning, so a memory phrased as "use TypeScript strict mode" won't surface when you ask about "type safety." There is no cloud sync (your laptop and desktop have separate memory), no cross-AI bridge (ChatGPT and Claude can't read these files), and no protection against context compaction silently dropping earlier instructions.

Trade-offs: an honest free baseline that works for single-machine, single-tool, short-horizon projects. For anything cross-device, long-running, or shared across teammates, a dedicated plugin removes most of the pain.

Pros
  • +Free, zero-install, ships with OpenClaw
  • +Human-readable files
Cons
  • −Not semantically searchable
  • −Device-locked, no cloud sync
  • −Invisible to ChatGPT, Claude, Gemini

Best for: Solo developers on one machine working on short-lived projects who want zero dependencies.

#5

Cognee

cognee.ai ↗

Knowledge-graph memory with semantic layer

Cognee takes a knowledge-graph approach to agent memory: instead of storing raw chat turns, it extracts entities and relationships and builds a graph you can query semantically (cognee.ai). The OSS core lives on GitHub and ships Python bindings; a hosted tier is available (github.com/topoteretes/cognee).

For OpenClaw, integration is via Cognee's Python SDK plus a thin adapter you write yourself — there is no out-of-the-box OpenClaw plugin. The graph model shines when your memory is highly entity-rich: large codebases with many internal services, ontologies, or strong domain vocabularies. It is less ideal when memories are mostly free-form instructions or preferences, because graph extraction adds latency you don't get back.

Trade-offs: best-in-class for graph-shaped memory and explainable retrieval paths; overhead and DIY integration cost are the price. Cognee does not publish a LongMemEval score, so accuracy comparisons against Synap, Zep, and Mem0 require running the harness yourself.

Pros
  • +Knowledge-graph memory with explainable retrieval
  • +OSS core, self-hostable
Cons
  • −No first-party OpenClaw plugin
  • −Graph extraction adds latency
  • −No published LongMemEval score

Best for: Teams whose codebase or domain is heavily entity-relational and who want graph-shaped memory.

#6

ByteRover

byterover.dev ↗

Native OpenClaw plugin with LongMemEval-S 92.8%

ByteRover ships a native OpenClaw plugin focused on coding agents, with AES-256 encryption at rest and a managed cloud tier (byterover.dev). The team publishes a LongMemEval-S score of 92.8%, which is competitive with the strongest agentic memory systems on the small-scale slice of the benchmark (byterover.dev/blog).

The product is deliberately coding-agent-shaped — its primitives line up with the operations OpenClaw actually performs (file edits, tool calls, plan revisions). Integration is one config file and an API key. Cross-AI is not in scope: if you also use ChatGPT or Claude, ByteRover will not bridge to those chats.

Trade-offs: strong benchmark numbers and a turnkey OpenClaw install, but you'll need a separate solution for the chatbot half of your workflow. Verify whether the LongMemEval-S number was reproduced on the open-source harness or on an internal variant before treating it as apples-to-apples with Synap's 90.2% on the full benchmark.

Pros
  • +Native OpenClaw plugin, single-config install
  • +AES-256 encryption at rest
  • +92.8% LongMemEval-S (vendor-published)
Cons
  • −No cross-AI bridge to ChatGPT, Claude, Gemini
  • −Benchmark scoped to LongMemEval-S, not the full harness

Best for: Developers whose entire AI surface is OpenClaw and who want maximum coding-agent accuracy.

#7

Hindsight (Vectorize)

vectorize.io ↗

Cloud retrieval engine with 94.6% LongMemEval

Hindsight is Vectorize's hosted retrieval product, marketed alongside an OpenClaw plugin and a vendor-published LongMemEval score of 94.6% (vectorize.io/hindsight). It runs on Vectorize's managed infrastructure with a free tier and paid cloud tiers for higher quotas (docs.vectorize.io).

For OpenClaw users the install path is straightforward: drop in the plugin, paste an API key, and your agent gets persistent recall against Hindsight's hosted index. As with every cloud-only product, latency depends on region and network — the 94.6% accuracy number doesn't tell you the P50 retrieval cost, which matters when OpenClaw is mid-edit and waiting on context.

Trade-offs: arguably the highest published accuracy in this list, plus a working OpenClaw plugin. The story weakens if you need self-hosting, air-gapped deployment, or a cross-AI bridge. Verify the LongMemEval configuration (S vs. M vs. full) and reproduction conditions before ranking it head-to-head against other published numbers.

Pros
  • +94.6% LongMemEval (vendor-published)
  • +Free tier + managed cloud
  • +OpenClaw plugin shipped
Cons
  • −Cloud-only — no self-host
  • −Latency not published
  • −No cross-AI bridge

Best for: OpenClaw-first teams that want the highest published accuracy and don't mind a managed-only deployment.

#8

MemOS Cloud

memos.openmem.net ↗

Memory-as-a-service from the OpenMem project

MemOS is an open research effort that frames LLM memory as an operating-system primitive — discrete memory cubes with lifecycle, scheduling, and access control (memos.openmem.net). MemOS Cloud is the hosted offering; the project also publishes a paper describing the architecture (arxiv.org/abs/2505.22101).

OpenClaw integration is not first-party; you use MemOS as a backend via REST or by importing the Python SDK and writing an adapter. The upside is the cleanest abstraction in this list — memories carry explicit metadata, retention policies, and provenance — which makes MemOS attractive when you want auditable memory behavior. The downside is that an OS metaphor is more machinery than a typical OpenClaw user needs.

Trade-offs: thoughtful design and an active research roadmap, but practical OpenClaw integration is currently DIY. No published LongMemEval number on the open-source harness as of April 2026.

Pros
  • +Clean memory abstraction with retention and provenance
  • +Active research roadmap
  • +Cloud and self-host options
Cons
  • −No first-party OpenClaw plugin
  • −Heavier mental model than typical agent stores
  • −No published LongMemEval score

Best for: Teams who want auditable, policy-driven memory and are willing to write the OpenClaw adapter.

#9

Basic Memory

basicmemory.com ↗

Local-first markdown notes exposed via MCP

Basic Memory takes the opposite design path to most of this list — it stores everything as plain markdown notes on your disk and exposes them to AI tools through the Model Context Protocol (basicmemory.com). Any MCP-aware client — including OpenClaw, Claude Desktop, and Cursor — can read and write the same notes (github.com/basicmachines-co/basic-memory).

Because the source of truth is your filesystem, you own the data and can version it in Git. The MCP bridge gives you a poor-man's cross-AI story for the subset of tools that speak MCP. Retrieval is keyword/path-based by design, not vector-semantic, which keeps it fast and explainable but limits recall on long, paraphrased conversations.

Trade-offs: maximum data ownership and a clean Git-friendly substrate, but no managed cloud, no encryption-at-rest on top of the filesystem, and weaker semantic retrieval than vector-backed alternatives.

Pros
  • +Local-first, Git-versionable markdown
  • +MCP exposes the same store to OpenClaw + Claude Desktop
  • +No vendor lock-in
Cons
  • −Keyword retrieval, not vector-semantic
  • −No managed sync across devices

Best for: Engineers who want full data ownership and use multiple MCP-aware clients.

#10

OpenMemory MCP

mem0.ai/openmemory-mcp ↗

Mem0's MCP server for cross-client memory

OpenMemory MCP is Mem0's open-source MCP server: it puts Mem0's memory store behind a Model Context Protocol endpoint so any MCP client — OpenClaw, Claude Desktop, Cursor, etc. — can capture and recall memories against the same backend (mem0.ai/openmemory-mcp).

Strictly speaking, this is a transport, not a product — the underlying accuracy and retrieval characteristics are Mem0's. The value is in the wiring: install the MCP server once, point all your MCP-aware tools at it, and they share a memory pool without per-client integration work. For OpenClaw users who already standardize on MCP, that uniformity is appealing.

Trade-offs: capture-only on some clients depending on tool support, inherits Mem0's 57.5% LongMemEval score on the open-source harness, and gives you no cross-AI bridge for non-MCP chatbots like the consumer ChatGPT web app or Gemini.

Pros
  • +One MCP endpoint serves OpenClaw + every other MCP client
  • +Open source, self-hostable
Cons
  • −Inherits Mem0's accuracy floor
  • −No bridge to non-MCP chatbots (ChatGPT web, Gemini)

Best for: MCP-standardized teams that want a single memory pool across all their MCP clients.

The Maximem cross-AI differentiator

Nine of the ten plugins on this page address one half of the problem: they give your OpenClaw agent persistent memory. The other half — what happens to that memory when you switch from OpenClaw to ChatGPT, or from Claude back to OpenClaw — is where Maximem is structurally different.

The Maximem Vity Chrome extension installs on the browser side and brokers your memory across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok. The @maximem/memory-plugin npm package installs on the OpenClaw side and gives your coding agent reads and writes against the same vault. The architectural consequence is that the architectural rule you taught Claude on Tuesday — "this codebase uses Result types, not exceptions" — is the rule OpenClaw applies on Thursday without you re-typing it into a prompt or pasting it into a markdown file.

Every other product on this list either covers chatbots and not coding agents (Supermemory, the consumer ChatGPT memory feature), or covers coding agents and not chatbots (ByteRover, Hindsight, the OpenClaw native), or covers only the subset of clients that speak MCP (Basic Memory, OpenMemory MCP). None of them bridge OpenClaw to the consumer chatbot web apps.

That bridge matters most for senior developers and prosumers who don't have a single AI tool — they have an AI stack. They draft architecture in Claude, write tests with OpenClaw, ask Perplexity to verify a library claim, and jump back to ChatGPT for a one-shot transform. If every one of those tools starts from zero, the cost is not just time — it's quality. A model with context recommends differently than a model without it.

Maximem also publishes a sibling product, Maximem Synap, for teams building their own agents — but for the OpenClaw user reading this page, Vity is the relevant SKU. Free API key, 30 seconds to install, encrypted at rest, no model training on user data.

Decision matrix: use-case → recommended plugin

Find the row that matches your workflow. The recommended plugin is the one that wins on the largest single failure mode for that scenario. The alternative is the second-best fit if the recommendation isn't available to you (cost, compliance, vendor lock-in).

Your workflowRecommendedAlternativeWhy
OpenClaw + ChatGPT + Claude + Gemini in the same workflowMaximem VityBasic Memory (MCP clients only)Only option that bridges OpenClaw and the consumer chatbots from a single vault.
OpenClaw-only, want highest published accuracyHindsight (Vectorize)ByteRover94.6% vendor-published LongMemEval, native OpenClaw plugin.
OpenClaw-only, coding-agent specialistByteRoverMaximem Vity92.8% LongMemEval-S, single-config install, AES-256 at rest.
Solo dev, one machine, short-lived projectOpenClaw native markdownBasic MemoryZero install, free, human-readable. Honest baseline for limited scope.
Self-host required (compliance, air-gapped)Mem0 (OSS) or CogneeBasic MemoryBoth ship fully self-hostable cores. Cognee adds graph structure.
Knowledge-graph-shaped memory (entities, ontologies)CogneeMemOS CloudGraph-native extraction and explainable retrieval paths.
MCP-standardized teamOpenMemory MCPBasic MemoryOne MCP endpoint, every MCP client shares the same pool.
Documents + chats + clips in one storeSupermemoryMaximem VityBroadest capture surface across content types.
Auditable memory with retention/provenanceMemOS CloudCogneeMemory-as-OS primitives include explicit lifecycle and access control.

Read next

Maximem Vity for OpenClaw
Product page with install steps, screenshots, and the five-problem breakdown referenced above.
Maximem Vity overview
How the Chrome-extension half of the cross-AI vault works.
Synap on LongMemEval
How Synap scored 90.2% on the open-source LongMemEval harness — the same harness most benchmark claims on this page reference.
Vity vs Mem0 vs Supermemory
Closer 3-way comparison of the Chrome-extension category for cross-AI memory.
Synap vs Mem0 vs Zep vs Letta
Developer-side comparison if you're building your own agent rather than installing one.
Cross-app AI memory
Concept page on the cross-AI memory pattern this entire page is organized around.

Last updated: May 2026. Every factual claim cites the vendor's own docs or a public benchmark. Feature availability and pricing change — verify against the linked source before committing budget.

Frequently Asked Questions

For most developers, Maximem Vity is the best choice because it gives OpenClaw persistent memory and bridges that same memory across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok from a single encrypted vault. If your entire AI surface is OpenClaw and you only care about raw coding-agent accuracy, ByteRover (92.8% LongMemEval-S) and Hindsight (94.6% LongMemEval, vendor-published) are strong OpenClaw-only alternatives.

Yes. OpenClaw ships with a built-in memory feature that writes flat markdown files to a local .openclaw/ directory. The files are human-readable but are scanned by filename and section header rather than searched semantically, do not sync across devices, and are invisible to ChatGPT, Claude, and Gemini. For single-machine, short-lived projects this baseline is fine; for anything cross-device or cross-AI, a dedicated plugin is required.

As of April 2026, vendor-published LongMemEval scores in this category include Hindsight at 94.6%, ByteRover at 92.8% on the LongMemEval-S subset, Maximem Synap at 90.2% on the full open-source harness, Zep at 63.8%, and Mem0 at 57.5%. Synap, Zep, and Mem0 were measured on the same open-source harness; vendors who publish single numbers on different harness configurations are not directly comparable.

Yes — several. OpenClaw's built-in markdown memory is free with zero install. Maximem Vity offers a free API key for OpenClaw with no credit card required. Mem0 and Cognee are open-source and can be self-hosted at no cost. Hindsight, MemOS Cloud, and ByteRover offer free tiers on top of paid cloud plans. Supermemory's cloud-sync features sit behind a paid Pro tier.

Yes, but only a few products span all three. Maximem Vity does it natively via a Chrome extension that bridges chatbots plus the OpenClaw plugin that bridges your coding agent — one vault, all surfaces. Basic Memory and OpenMemory MCP cover the subset of clients that speak Model Context Protocol (OpenClaw, Claude Desktop, Cursor), but they don't bridge to the consumer ChatGPT web app or Gemini.

Six criteria matter: (1) does it have a first-party OpenClaw plugin or do you write an adapter; (2) is retrieval semantic or filename/keyword; (3) does it sync across devices; (4) does it bridge to other AI tools you use; (5) is data encrypted at rest and excluded from model training; (6) is there a verifiable accuracy benchmark like LongMemEval. Match the answers to your workflow, not to whoever has the loudest marketing.

OpenClaw, like every long-context agent, compacts the conversation when it nears the model's token limit. Without a persistent memory layer, the earliest instructions and architectural decisions get silently dropped during compaction. A plugin like Maximem Vity captures those decisions out-of-band so they remain queryable in future sessions even after the in-window context is gone.

Model Context Protocol is great for in-IDE tools and other MCP-aware clients, but most consumer chatbots (ChatGPT web, Gemini, Perplexity) do not yet speak MCP. If your workflow stays inside MCP-aware tools, an MCP-only solution like OpenMemory MCP or Basic Memory is enough. If you also use chatbots that don't speak MCP, you need a product like Maximem Vity that explicitly bridges them.

About 30 seconds. Get a free API key at app.maximem.ai, install @maximem/memory-plugin from npm, and add the plugin to your OpenClaw configuration. Your agent immediately has persistent memory across sessions, and if you install the Vity Chrome extension as well, that memory flows in both directions across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok.

We refresh this comparison whenever a vendor publishes a new LongMemEval score, ships a first-party OpenClaw plugin, or materially changes pricing or self-host availability. Last updated April 2026. If you spot stale data, email [email protected] and we'll re-verify against the vendor's docs and update the page.