Caura Memclaw is live · free plan, paid from $41/mo billed annually · last verified 26 Oct 2026 · viability monitor
Caura Memclaw
Governed shared memory for AI agent fleets — scoped writes, keystone policies and a per-recall audit trail.
Pick Caura when several agents must share facts under rules you can defend to an auditor. The scope-stamped writes, keystone policies and per-recall audit trail are the product, and eToro running it in production is a receipt rather than a slide. For a single assistant, a lighter memory library wins on overhead; the governance layer is weight you'd be paying for and not using.
Verdict generatedVerified 4d ago · liveness 57/100 · cite: rightaichoice.com/tools/caura-memclaw
- Enterprises running 50+ specialized agents that must share facts under enforceable policy
- Fintech, healthcare and regulated teams needing per-recall audit trails plus PII, PCI and secret detection
- Platform teams wanting the Apache 2.0 core self-hosted, on-prem, air-gapped or white-labeled
- Multi-agent builds where knowledge compounds across sessions, harnesses and vendors
- Single-agent apps that only need a memory buffer — the policy layer is pure overhead
- Prototypes that won't cross a few thousand memories or a few hundred recalls a month
- Teams unwilling to own schema, keys, upgrades and support if they self-host
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Skip Caura if you run a single agent that only needs a conversation buffer, or if your fleet will stay under a few thousand memories and a few hundred recalls a month — the governance layer is overhead you would be paying for but not using.
Our "biggest gripe" note written Oct 16, 2026 quotes $49, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Our price-reality note written Oct 16, 2026 quotes $49, $399, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
In short
Caura Memclaw — Governed shared memory for AI agent fleets — scoped writes, keystone policies and a per-recall audit trail. Best for Enterprises running 50+ specialized agents that must share facts under enforceable policy, Fintech, healthcare and regulated teams needing per-recall audit trails plus PII, PCI and secret detection, Platform teams wanting the Apache 2.0 core self-hosted, on-prem, air-gapped or white-labeled. Free to start; paid plans from $41/mo billed annually.
What is Caura Memclaw?
Caura Memclaw — Governed shared memory for AI agent fleets — scoped writes, keystone policies and a per-recall audit trail. Best for Enterprises running 50+ specialized agents that must share facts under enforceable policy, Fintech, healthcare and regulated teams needing per-recall audit trails plus PII, PCI and secret detection, Platform teams wanting the Apache 2.0 core self-hosted, on-prem, air-gapped or white-labeled. Free to start; paid plans from $41/mo billed annually.
Caura Memclaw is shared, governed memory for fleets of AI agents, built for platform teams and regulated enterprises that need many agents pulling from one store while proving which agent saw what. An agent writes plain text over MCP or REST; a single LLM pass classifies the entry, extracts RDF triples into a knowledge graph, flags contradictions, scans for PII, PCI data and secrets, then stamps a visibility scope and tenant before storing it. Recall fuses lexical, vector and graph-hop retrieval, filtered to what the caller's scope and trust level allow. Four boundaries are enforced on every operation: organization, tenant (hard isolation), fleet (the default collaboration boundary), and agent identity with a trust level. Keystone policies are org rules fetched at session start so they override conflicting agent instructions, and every read, write and delete lands in an audit trail attributed to a named agent, including recalls that return nothing. The engine is model-, harness- and provider-agnostic. One MCP config covers Claude Desktop, Claude Code, Cursor and Windsurf, REST covers everything else, and the OpenClaw plugin installs whole fleets with OTA updates. A Rail SDK preview makes recall-in and write-out run every turn by code rather than by model decision. Caura Prism is the console for coverage, recall replay, spend showback, security findings and audit. The Apache 2.0 core carries the full engine and governance model, so self-hosting, on-prem, air-gapped and white-label deployments are viable, while Caura Cloud adds SOC 2 Type II and a free tier. Caura reports production use at eToro (NASDAQ: ETOR) and single-agent benchmarks of 92.2% on LongMemEval with 79.2% context-only savings — figures that do not test fleet sharing or governance. Against lighter memory libraries like Mem0 or Zep, Caura trades setup weight for enforceability.
Key features
- MCP-native connection to Claude Desktop, Claude Code, Cursor and Windsurf
- REST API for harnesses that don't speak MCP
- OpenClaw plugin — one-line fleet install with OTA updates
- Rail SDK (preview) — recall in, write out every turn, by code not model decision
- Governed shared memory with visibility scopes and per-agent trust levels
- Keystone policies — org rules fetched at session start to override agent instructions
- Four enforced boundaries: organization, tenant, fleet, agent identity
- Scope-stamped writes with tenant resolved from the API key
- Content policy detects PII, PCI and secrets on every write, then flags, masks or drops
- Audit trail on every read, write and delete, logged even on zero-hit recalls
- Knowledge graph extraction with RDF triples on every write
- Contradiction detection with stale facts superseded and provenance kept
- Crystallizer merges near-duplicates into atomic facts
- Per-agent retrieval tuning that reweights recall from reported outcomes
- Caura Prism console for coverage, recall replay, spend showback and security findings
Behind the verdict
The interesting thing about Caura isn't the retrieval stack — lexical plus vector plus graph-hop is table stakes for memory vendors in 2026. It's that authorization runs before recall ranks a single row. Tenant, scope and trust level are checked on every operation, and a zero-hit recall still gets logged with the agent's name on it. That's the detail auditors ask about and the one most memory layers can't answer. We'd reach for this when a fleet has genuinely outgrown one agent's scratchpad. Fifty specialized agents sharing a knowledge graph, half of them touching customer records, is the shape of deployment where Caura's boundaries earn their keep. eToro running it as a governed company brain inside a single tenant is meaningful — though note it's one tenant, not a cross-tenant federation story. Pass when the problem is smaller than the solution. A single-agent app that just needs a memory buffer will spend more time on scope design and key management than it saves. Same for prototypes that won't cross a few thousand memories; the free tier's 500 recalls a month disappears once a real fleet is live. Watch the volume math before you demo internally. Pro at $41/mo billed annually ($490/year) carries 3,000 recalls a month, and Business at $333/mo annual ($3,990/year) takes you to 10,000. Caura calls these advisory allowances rather than hard caps, so a burst won't flip you read-only — but budget the overage conversation early. The closest alternative depends on what you're optimizing for. If you want zero infrastructure, Mem0 and Zep get you memory faster. If you want the governance model without the managed service, the Apache 2.0 core gives you the full engine to run yourself — and that's where teams with air-gapped or white-label requirements end up. One real
Models under the hood
as of 2026-10-22
Limitations
- Caura is LLM-, harness- and provider-agnostic; the site names no underlying model behind its memory layer, and 'LLM enrichment included' (custom models on Business) does not disclose which models run.
- Monthly write, search and recall figures are described as advisory allowances that do not automatically make an organization read-only.
- The published blog/PeerRank articles mention models like Claude Opus 5.5 and Meta's Muse, but those are benchmark subjects, not Caura's underlying engine.
as of 2026-10-16
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Is Caura Memclaw still active?
Caura Memclaw is live · free plan, paid from $41/mo billed annually · last verified 26 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Caura Memclaw? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in Caura Memclaw
Checked 7 days agoAcross the latest 4 updates: 3 community discussions and 1 news mention.
PeerRank: Meta's Muse Wonder 1.0 and Muse Spark 1.3 Debut; Claude Opus 5.5 Leads
Caura's PeerRank run benchmarks two Meta Muse models against six frontier models. Muse Wonder ranked second-fastest; Claude Opus 5.5 led at 8.88 with a 126-point Elo lead.
Stateful vs Stateless AI Agents: What Actually Changes When an Agent Keeps State
Caura research post breaks agent state into three layers and measures what statefulness buys, including context-token reduction, plus four failure modes.
Long-Term Memory for AI Agents: Which Architectures Survive Contact With Production
Caura research post compares seven long-term memory architectures for AI agents, covering the write-path problem, lifecycle rot, and selection criteria with measured numbers.
PeerRank: Six Frontier Models Graded Each Other and Showed Self-Bias
Caura PeerRank study of 3,570 peer scores found models rated their own work up to 0.76 points above peer scores, favored famous names, and showed first-slot bias.
What do users say about Caura Memclaw?
What people actually say about Caura Memclaw — is it worth it?
Our community sentiment research written Jul 6, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Caura Memclaw— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 26 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Caura Memclaw 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing tiers, our verdict, who should skip it, summary, features, description
- — changed limitations, pricing page
- — changed description, pricing tiers, who it suits, our verdict, summary, features, who should skip it
- — changed hidden costs, limitations, who should skip it, who it suits, our verdict, summary, pricing tiers, description, pricing fit, features, use cases, switching out, setup time, switching in
- — changed limitations, pricing page
- — changed who it suits, our verdict, description, features, integrations, pricing tiers, who should skip it, summary
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Caura Memclaw cost in 2026?
Caura Memclaw: Free to start; paid plans from $41/mo billed annually. Pricing last verified 26 Oct 2026.
Plans compared
Every published Caura Memclaw tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | A solo developer evaluating governed memory in a personal workspace before any teammates are involved.Adds: Starting tier: $0 forever, 1 user, personal workspace only, unlimited fleets and agents, 10,000 memories, 5,000 writes/mo, 5,000 searches/mo and 500 recalls/mo (all advisory). |
| Pro | $41/mo billed annuallyVendor: “$41/mo billed annually ($490/year)” | monthly, billed annually | A small team scaling multi-agent workflows that needs shared workspaces and someone other than the founder able to invite collaborators.Adds: Adds unlimited users, tenants and shared workspaces over Free, lifts memory to 250,000 with 25,000 writes, 50,000 searches and 3,000 recalls per month, plus a 3-business-day support target. |
| Business | $333/mo billed annuallyVendor: “$333/mo billed annually ($3,990/year)” | monthly, billed annually | Larger fleets with heavier write and recall volume that also want custom models in the enrichment pipeline and a faster support commitment.Adds: Raises the ceilings to 1,000,000 memories, 100,000 writes, 500,000 searches and 10,000 recalls per month, and adds custom models plus a 2-business-day support target. |
| Enterprise | Custom | quote | Regulated or air-gapped organizations that need the memory layer inside their own perimeter and a named owner on the vendor side.Adds: Moves off published allowances entirely: on-prem, air-gapped or white-label deployment, custom users/tenants/fleets/agents, custom memory and operation limits, a custom LLM pipeline and a dedicated account manager, starting with a two-week pilot. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Our hidden-costs note written Oct 16, 2026 quotes $49, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Our price-fit note written Oct 16, 2026 quotes $49, $399, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Has Caura Memclaw's pricing changed?
No plan price for Caura Memclaw has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Caura Memclaw?
Pick Caura Memclaw if you are Enterprises running 50+ specialized agents that must share facts under enforceable policy, Fintech, healthcare and regulated teams needing per-recall audit trails plus PII, PCI and secret detection, Platform teams wanting the Apache 2.0 core self-hosted, on-prem, air-gapped or white-labeled, Multi-agent builds where knowledge compounds across sessions, harnesses and vendors.
Look elsewhere if you are Single-agent apps that only need a memory buffer — the policy layer is pure overhead, Prototypes that won't cross a few thousand memories or a few hundred recalls a month, Teams unwilling to own schema, keys, upgrades and support if they self-host, Fleets that will blow past Free's 500 recalls/mo but can't yet justify a paid plan.
Skip it if Skip Caura if you run a single agent that only needs a conversation buffer, or if your fleet will stay under a few thousand memories and a few hundred recalls a month — the governance layer is overhead you would be paying for but not using.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Caura Memclaw actually fits — and what changes day-one when you adopt it.
Paste the MCP config block from the quickstart into Claude Desktop, Claude Code, Cursor and Windsurf, set the API key, and let each agent write plain text through the governed write path.
Outcome: Every write is scope-stamped with the tenant resolved from the API key, PII is scanned before storage, and the fleet's first shared memory is live in about 30 seconds per client.
Run the two-week Enterprise pilot across one workflow and five to twenty agents, with keystone policies barring sub-trust-level-2 agents from PII-tagged memories and Prism used to replay recalls.
Outcome: The pilot is measured on repeated failures avoided, knowledge reused across agents, permission correctness and cost per completed task — evidence an auditor can read rather than a demo script.
Watch contradiction detection surface conflicting facts after writes, then supersede the stale ones while keeping provenance, and report outcomes so retrieval reweights and failures mint preventive rules.
Outcome: The crystallizer merges near-duplicates into atomic facts, so the fleet's memory gets smaller and sharper rather than accumulating drift.
Use cases
- Connect a Claude Desktop agent to shared memory by pasting one MCP config block and starting work in about 30 seconds
- Install the OpenClaw plugin on a fleet gateway so every agent's write is auto-stamped with a fleet_id and shared across the fleet
- Use keystone policies to enforce that agents below trust level 2 cannot read PII-tagged memories
- Share skills across a fleet via the caura_doc skills collection so one team's distilled learning reaches every agent
- Self-host the engine with Docker for an on-premises governed memory layer your security team can inspect
- Report outcomes so retrieval profiles retune and failures mint preventive rules automatically
- Curate and audit the whole store from the Prism console — see what the fleet knows, who taught it, who recalled it
- Quarantine PII at write time so customer data never crosses a fleet boundary
Setup time & first value
How long it actually takes to get something useful out of Caura Memclaw — broken out by persona, not the marketing-page minute.
Managed cloud: about five minutes to connect your first agent using the quickstart (the MCP config paste takes roughly 30 seconds per client). Self-hosted: Docker deployment of the Apache 2.0 core, so budget hours to a day for your first governed store plus ongoing ownership of schema, keys and upgrades. Enterprise on-prem or air-gapped: a planned two-week fleet-memory pilot across one workflow
Switching to or from Caura Memclaw
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a plain vector store: point your write path at Caura's MCP or REST endpoint so enrichment, PII scanning and scope stamping happen on ingest rather than being added around retrieval.
- →From LangMem on LangGraph: Caura publishes a LangMem alternatives comparison covering the LangGraph and multi-framework memory trade-offs.
- →From Mem0, Zep, Letta, Cognee or Supermemory: Caura publishes side-by-side alternatives comparisons covering changed facts, temporal recall, write-path pricing, self-hosting and governance controls.
- →From a self-hosted memory service: adopt the Apache 2.0 core so the governance model ships with the engine you already operate.
- ↗To a single-agent memory library: copy records out through the REST API first, verifying pagination and private-memory visibility, then delete your organization from the dashboard.
- ↗To another managed agent-memory service: there is no complete organization-export button in the dashboard, so plan the REST API extraction before you cancel.
Integrations
Tutorials & Learning
YouTube returned 6 videos for “Caura Memclaw”, and we withheld 5: 5 did not mention Caura Memclaw. Showing the 1 we can prove is about Caura Memclaw.
Official links
What are the best Caura Memclaw alternatives?
The closest alternatives to Caura Memclaw we list are Mem0, Cognee and Letta. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Mem0 | Freemium | Pricing not published | — |
| Cognee | Freemium | Pricing not published | — |
| Letta | Freemium | Pricing not published | — |
Alternatives to Caura Memclaw
View all
Tools that pair well with Caura Memclaw
Common stack mates teams adopt alongside Caura Memclaw, with the specific reason each pairing earns its keep.
Mem0
Mem0 is a memory layer that gives AI agents and apps persistent context across sessions and users
Cognee
Cognee gives AI agents persistent knowledge-graph memory with cited recall across sessions, teams, and tools.
Letta
Letta builds stateful AI agents with persistent memory that survives sessions, model swaps, and machines.
Caura Memclaw vs its closest rival
Caura Memclaw's closest head-to-head is with Spider Cloud — Spider Cloud and MemClaw address different pain points. Spider Cloud is the clear choice for AI agents that need real-time web data—its Rust engine, low cost ($0.03/1k pages), and new Browser AI commands make it unbeatable for RAG pipelines. MemClaw wins for enterprise multi-agent fleets requiring governed shared memory, knowledge graph enrichment, and audit trails. Choose based on whether you need fresh external data or persistent internal memory with governance. Verdict generated
Head-to-head comparisons
Caura Memclaw vs Spider Cloud
Spider Cloud and MemClaw address different pain points. Spider Cloud is the clear choice for AI agents that need real-time web data—its Rust engine, low cost ($0.03/1k pages), and new Browser AI commands make it unbeatable for RAG pipelines. MemClaw wins for enterprise multi-agent fleets requiring governed shared memory, knowledge graph enrichment, and audit trails. Choose based on whether you need fresh external data or persistent internal memory with governance.
GeneratedCaura Memclaw vs Temporal Ai
Choose Temporal if your core challenge is building crash-safe, long-running workflows for AI agents or microservices—it excels at durable execution with automatic retries and state recovery. Choose Caura Memclaw if you need governed shared memory that multiple agents can read/write with full audit trails, tenant isolation, and knowledge graph enrichment. They solve different problems; for agents needing both, integrate Memclaw inside a Temporal workflow.
GeneratedCaura Memclaw vs Presto Voice
If you run a QSR chain and want to automate drive-thru ordering with proven upselling, Presto Voice is your pick. If you manage multiple AI agent fleets and need governed shared memory with audit trails, choose Caura Memclaw. They are not competitors—they solve completely different problems.
GeneratedFrequently asked questions
Our FAQ answer written Oct 16, 2026 quotes $49, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
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