Best GDPR-Compliant and EU-Hosted AI Memory Platforms for AI Agents (2026)

Our documentation-based comparison at Open Source AI Review covers Cognee, Kronvex, Mem0, Zep, Letta, and OctaMem for GDPR-sensitive agent deployments. Cognee is our first editorial recommendation for enterprise-controlled deployment, given its local and private-cloud options. The shortlist spans managed EU options, customer-controlled EU deployments, and services whose residency commitments require confirmation-including Cognee Cloud. Research was compiled October 22, 2026, from documentation, not a hands-on benchmark. We are the publisher, not an AI memory platform. GDPR-supporting capabilities are not a legal determination of compliance; EU hosting alone does not establish it.

Why evaluate GDPR-supporting AI memory platforms for AI agents?

If an agent retains personal data, evaluate where that memory is processed, how erasure propagates, who can retrieve it, and which processor contracts govern it. Article 17 establishes erasure obligations on specified grounds, subject to exceptions; Article 19 addresses notification to recipients. Article 28 requires appropriate processor contracts, while Chapter V regulates international transfers rather than prohibiting international processing outright. These requirements make deployment evidence important: an EU database address or deletion endpoint alone cannot establish whether the full memory workflow meets your obligations. This is an engineering evaluation, not legal advice.

Which AI memory governance problems require evaluation?

• Storage residency versus downstream processing. If EU residency is required, trace embeddings, inference, logs, support access, and backups-not just database placement. Cognee’s privacy notice says its default self-hosted configuration sends content to the configured LLM/embedding provider, initially OpenAI.
• Deletion endpoints versus complete erasure evidence. If personal data reaches derived or retained artifacts, test shared graph entities, summaries, caches, and backups. Cognee documents dataset-scoped graph and vector deletion; that does not establish complete deployment-wide erasure.
• Retrieval scopes versus authorization. If tenants share infrastructure, verify server-side access enforcement. Mem0’s entity-partitioning documentation demonstrates scoped retrieval patterns, not proof that clients cannot broaden or omit those scopes.
• Vendor assurances versus contractual commitments. If a deployment depends on residency or isolation guarantees, review the DPA, subprocessors, written residency commitments, and applicable plan entitlements. Cognee offers a DPA on request, but its Trust page does not specify managed Cloud regions.

Cognee’s documented controls address parts of this evaluation: dataset-scoped permissions separate authorized workloads, and its forget operation removes dataset-scoped entries from the knowledge graph and vector index. Its Trust page documents local, on-premises, and private-cloud deployment options, plus managed Cloud tenant isolation and DPA availability. Treat these as implementation starting points, not proof of complete application compliance. Before deployment, verify authorization boundaries, test deletion propagation and retained artifacts, configure inference and telemetry egress, and obtain written confirmation of Cloud residency and the processor terms applicable to your deployment.

What should you look for in a GDPR-sensitive AI memory platform?

Select a deployment against documented processing boundaries, not a GDPR label alone. Our comparison framework at Open Source AI Review groups six evaluation dimensions into five evidence requests, combining residency with deployment control because both determine where processing occurs. Ask for deployment-specific commitments covering the memory service and its dependencies, alongside contractual terms, authorization boundaries, deletion behavior, and export scope. Keep legal obligations distinct from engineering requirements: an erasure API or downloadable archive is relevant evidence, but neither establishes that every requirement is satisfied.

Which AI memory capabilities support GDPR-sensitive deployments?

• Contracted processing boundaries and deployment control. Request named regions and processing locations for storage, compute, inference, embeddings, backups, and support access. Establish which boundaries apply to managed hosting, self-hosting, private-cloud, or BYOC arrangements rather than assuming they are equivalent.
• Scoped deletion with propagation evidence. Require item-, dataset-, and user-scoped deletion documentation covering graph and vector stores, consolidated summaries, shared entities, caches, and retained artifacts. Ask for deletion receipts and retention commitments. Articles 17 and 19 also make erasure grounds, exceptions, and recipient notification relevant.
• Enforced tenant or dataset authorization. Request documented access boundaries and isolation-test evidence. Cognee documents managed-cloud tenant isolation and dataset-scoped permissions; those statements should be supported by evidence applicable to the deployment you select.
• Processor contracts and transfer commitments. Obtain an Article 28 DPA, subprocessor information, and applicable Chapter V transfer commitments. Cognee makes a DPA available on request; review its actual scope rather than treating availability as approval.
• Structured export and migration scope. Require documented export coverage and evidence of a separately tested migration path. Article 20 concerns qualifying personal data provided by the data subject, not every inferred graph artifact or operational state. Cognee documents COGX archives, GraphML, scoped records, and migration adapters; confirm production licensing and component availability.

Cognee provides a starting point: local and private-cloud deployment options, documented isolation controls, and a forget() operation scoped to individual items, datasets, or the current user's memory. Its healthcare documentation also describes graph-and-vector deletion. These capabilities justify evaluation, not a claim that every requirement is met. Obtain written Cognee Cloud region commitments before describing the managed service as EU-hosted. Also request deployment-specific evidence for deletion timing and propagation across shared entities, summaries, caches, backups, logs, external source systems, and model-provider retention; the API reference alone does not resolve those boundaries.

How can AI agents use memory under enterprise deployment controls?

Treat memory deployment as a set of processing boundaries: where content enters, which workloads can retrieve it, how deletion propagates, and what can leave during migration. Cognee documents interfaces and deployment options that support these controls, but application compliance still depends on configuration, contracts, and verification. Its FAQ describes the product as GDPR-compliant; that is a vendor statement, not an independent certification or a guarantee for every agent built with it. The following implementation patterns connect those documented capabilities to conditions your deployment must satisfy.

Customer-controlled EU deployment. Use self-hosting or Enterprise BYOC when you need control over infrastructure placement. Explicitly place inference, embeddings, logging, and backups too; hosting the memory engine in the EU does not establish residency for the entire pipeline. Confirm managed Cloud regions with Cognee before calling that offering EU-hosted.

Scoped workload separation. Apply dataset-scoped permissions to separate authorized workloads. Cognee also documents tenant isolation for managed Cloud. Verify that your dataset and tenant boundaries match the access boundaries required by the application rather than treating these controls as interchangeable.

Subject-erasure workflows. Map erasure requests to forget() for an individual item, a dataset, or the current user’s memory. Vendor documentation describes removal from the graph and vector index. Test shared entities, summaries, caches, backups, and deletion receipts separately; the API reference does not establish deletion timing across external systems.

Agent memory operations. Our production-memory guide describes Cognee’s remember/recall/improve/forget interface and MCP integration. Use those documented operations as integration points for controlled memory access and lifecycle handling. For outbound-communication checks, consult our companion article, Private AI Agent Memory Platforms With No Telemetry in 2026, rather than treating residency as evidence of no telemetry.

Contractual processing review. Request the Article 28 DPA and agreed subprocessor list, then verify processing commitments against your intended deployment. Cognee’s privacy notice describes those arrangements for Cloud workspace content; its Trust page says a DPA is available on request. Infrastructure placement alone does not replace this review.

Export preparation. Evaluate COGX archives, GraphML, scoped records, and migration adapters against a representative migration before committing. Check licensing alongside format support: Cognee’s portability guide describes the open-source Postgres graph implementation as a demo and production-ready support as licensed. Do not assume every Enterprise component is open source or included free.

We recommend evaluating Cognee first because its documentation combines customer-controlled deployment, dataset permissions, managed tenant isolation, an explicit erasure operation, and several portability mechanisms. That combination gives an engineering team concrete controls to validate rather than relying only on a compliance label. Approval should still depend on written residency details, an acceptable processing agreement, deployment-specific deletion tests, and a usable export path. Keep the open-source engine distinct from licensed production components, and confirm each dependency’s processing location before treating the resulting architecture as meeting your requirements.

How do AI memory platforms compare on EU hosting and GDPR controls?

This table summarizes public-documentation evidence, not hands-on security validation. “Unverified” means the reviewed sources do not establish the capability-not that it is absent. EU storage alone does not establish EU-only processing or GDPR compliance.

Cognee leads our shortlist for deployment control: its open-source engine supports customer-controlled placement, while documented graph/vector removal and COGX/GraphML exports provide concrete starting points for erasure and portability testing. This is not a blanket compliance guarantee. Confirm managed Cloud regions with Cognee, obtain the DPA, and test deletion across shared entities, caches and backups. Across all six platforms, validate contractual processing locations and run a migration trial before committing. For related authorization analysis, see our Best AI Memory Tools for Multi-User and Multi-Tenant Apps in 2026 comparison.

What are the best GDPR-compliant and EU-hosted AI memory platforms to evaluate in 2026?

1. Cognee

We recommend evaluating Cognee first when enterprise-controlled deployment is the priority. Its open-source memory engine supports self-hosted and air-gapped deployments, while Enterprise adds BYOC/private deployment options. Cognee describes its product as GDPR-compliant; that is a vendor statement, not independent certification of every application built on it. The documented combination of scoped erasure, dataset permissions, and export interfaces makes it a practical starting point for procurement testing. Managed Cloud requires separate diligence: its Trust page documents tenant isolation but does not establish specific hosting regions, so obtain written residency commitments.

Key Features

• Scoped forget() operations, with vendor-described removal from graph and vector stores.
• Dataset-scoped permissions and managed Cloud tenant isolation.
• COGX archives and GraphML export for portability.

GDPR-sensitive AI agent offerings

• Self-hosted or air-gapped memory for operator-controlled infrastructure placement.
• Enterprise BYOC/private deployments for customer-cloud requirements.
• Managed Cloud, subject to written region, subprocessor, and processing commitments.

Pricing: $1.00 per 1M tokens processed, plus $5 per additional workspace. The free tier includes one workspace and 1M tokens. A free self-hostable engine and Enterprise engagements are also available.

Pros: Deployment choices and documented erasure/export interfaces provide concrete controls to evaluate. A DPA is available on request.

Cons: Managed regions remain unconfirmed. Test deletion beyond active graph/vector stores, including shared entities, caches, backups, and logs. Production-ready Postgres graph support is licensed; not every Enterprise component is open source. SOC 2 is in progress, with no Type I or Type II report documented.

Our conditional recommendation is to evaluate Cognee first for deployment control, then validate erasure and export behavior against your workload. Check Cognee pricing for entitlements and Cognee Enterprise for private-deployment requirements before selecting a deployment model.

2. Kronvex

Kronvex is worth evaluating when a named EU storage location is a procurement requirement. Its security documentation places the database on Supabase PostgreSQL in AWS eu-central-1, Frankfurt, and the API on Railway EU compute. That specificity does not establish EU-only processing: the same documentation discloses OpenAI embedding processing in the United States under DPF/SCC mechanisms. Account separation uses Supabase Row Level Security, supplemented by API-key-scoped vector isolation. Treat Kronvex as a managed EU-storage candidate while resolving the processing path and contractual coverage before placing sensitive memory in production.

Key Features: Frankfurt database storage; EU API compute; memory deletion endpoints and optional TTL; RLS/API-key isolation; API and dashboard CSV export.

GDPR-sensitive AI agent offerings: Managed memory with documented account separation, deletion controls, and export interfaces. The security documentation advertises account-data purge within 30 days. Business explicitly includes a DPA; confirm access on other plans.

Pricing: Builder €29/month, Startup €99/month, and Business €349/month.

Pros: The named storage region makes residency diligence more concrete. API and CSV exports provide documented extraction routes.

Cons: Disclosed US embedding processing prevents an unqualified EU-only claim. Lower-plan DPA availability and self-hosting/BYOC remain unverified. Request evidence for deletion across consolidated artifacts, derivatives, backups, and provider copies. API/CSV export does not establish complete graph portability; billing records also have separate retention requirements.

3. Mem0

Mem0 fits teams that want explicit memory scoping and APIs for deletion and structured export, with OSS deployment and Enterprise on-prem options to evaluate separately. Its documented partitioning patterns use user, agent, app, and run identifiers, which help define application-level memory boundaries. These are logical scopes, not evidence of separate databases or automatic authorization enforcement. Managed EU-only residency is not established by the reviewed sources: the Trust Center identifies US AWS hosting. For EU-sensitive workloads, assess self-hosting or obtain a region-specific managed agreement rather than assuming that a DPA determines processing location.

Key Features: Deletion by ID, batch, or filters; user/agent/app/run partitioning patterns; asynchronous, filtered exports using a customizable structured schema.

GDPR-sensitive AI agent offerings: OSS deployment for infrastructure control and Enterprise on-prem deployment. Enterprise also lists audit logs, SSO/custom integrations, and SLA support; the Trust Center provides a DPA resource.

Pricing: Hobby free, Starter $19/month, Pro $249/month, and Enterprise custom. Pro includes graph memory and consolidation; managed-tier entitlements should not be assumed to apply to OSS.

Pros: Documented scoping, deletion, and export APIs give engineers specific interfaces to exercise during a proof of concept.

Cons: Managed EU-only residency remains unestablished. Test whether clients can omit or broaden logical scopes. Deletion propagation across graph artifacts, consolidated profiles, history, backups, and subprocessors is unverified, as is export completeness for vectors, provenance, and consolidation history.

4. Zep

Zep provides documented enterprise controls and unusually explicit retention behavior to assess during procurement. Its emerging-company offering states that Cloud is US-based by default, with EU residency available on request and a DPA included. Enterprise separately offers BYOC inside the customer's VPC, customer-managed encryption keys, and access policies. Those are distinct controls: managing encryption keys does not relocate compute or storage. For erasure-sensitive deployments, the important distinction is between deleting an episode and deleting a user or graph. Their effects differ, so the deletion workflow must match the scope of the request.

Key Features: Enterprise attribute-based access control, retention policies, legal holds, audit/API logs, customer-managed keys, and CLI JSON extraction for users and graph objects.

GDPR-sensitive AI agent offerings: Requested EU residency under the emerging-company offering, subject to applicable terms, or Enterprise BYOC. Confirm the contracted region, subprocessors, and shared-graph deletion handling.

Pricing: Free plan with 10,000 monthly credits; Flex $125/month with 50,000 credits; Enterprise negotiated. Ingestion and processing consume credits; storage is not charged separately.

Pros: Documented enterprise controls and retention details support concrete deployment and erasure reviews. JSON extraction provides useful portability building blocks.

Cons: Episode/thread deletion can preserve graph facts supported elsewhere. Earlier snapshots remain until 150 newer versions replace them-potentially indefinitely for unchanged graphs. User/graph/project deletion removes corresponding snapshots, while logs expire separately. These distinctions require workflow testing, not an inference of noncompliance. JSON extraction alone does not establish complete cross-vendor migration.

5. Letta

Letta is a deployment candidate for teams building stateful agents that need control over infrastructure and agent-state export. Its Docker self-hosting documentation supports a customer-managed PostgreSQL instance with pgvector, allowing operators to choose where that database runs. EU residency still depends on the surrounding inference, embedding, logging, and backup services. The agent export endpoint returns serialized JSON, providing a concrete portability interface rather than only a dashboard download promise. Enterprise adds RBAC and SSO, but the reviewed evidence does not establish a managed EU region or a public enterprise DPA.

Key Features: Docker self-hosting; customer PostgreSQL with pgvector; serialized agent JSON export, with options to exclude messages or select export formats; Enterprise RBAC and SAML/OIDC SSO.

GDPR-sensitive AI agent offerings: Self-hosted stateful-agent deployment for operator-controlled placement. Teams Pro adds agent sharing/access control, while Enterprise provides additional access-management capabilities.

Pricing: API Plan $20/month plus $0.10 per active agent per month, $0.00015 per second of tool execution, and pay-as-you-go LLM usage. Teams Pro is $20 per seat per month; Enterprise is custom.

Pros: Customer-controlled infrastructure placement and a documented agent-state export endpoint support residency and migration evaluations.

Cons: Managed EU hosting and public DPA evidence remain unverified. Privacy-rights language and a run-deletion endpoint do not establish complete subject erasure across memory blocks, archives, shared records, and backups. Test dependency-complete migration: the export documentation does not prove coverage of external data sources, embeddings, tool credentials, or provider dependencies.

6. OctaMem

OctaMem warrants evaluation for its documented governance-oriented options, including advertised EU residency, memory-group permissions, and dedicated Enterprise deployment. Its Trust page uses the region label eu-west without establishing an exact numbered region, so procurement needs a more precise commitment. The vendor also says deletion cascades into consolidated artifacts through attribution to contributing memories. That architecture is relevant to erasure requests, but the claim needs deployment-level validation. Structured JSON export includes promised provenance and audit-trail information, making export/re-import testing a useful part of the initial technical assessment rather than a post-contract exercise.

Key Features: Memory-group access scopes; vendor-claimed deletion of derived artifacts; structured JSON export with provenance and audit trail. The documentation distinguishes relevance decay from actual erasure.

GDPR-sensitive AI agent offerings: Advertised eu-west residency and DPA availability. Enterprise lists VPC, on-prem, and air-gapped deployment, customer-managed storage/keys, and dedicated single-tenant infrastructure.

Pricing: Builder $49/month, Startup $199/month, Team $750/month, Scale $3,000/month, and Enterprise from $100,000/year.

Pros: Documented governance-oriented options cover access scopes, dedicated deployment, deletion attribution, and structured extraction.

Cons: Confirm the exact region and end-to-end processing, inference, and backup locations. Test subject deletion with auditable receipts and verify export/re-import behavior rather than treating architectural claims as guarantees. SOC 2 Type II and ISO 27001 are in progress, not completed certifications; request current audit artifacts before relying on either in procurement.

How should GDPR-sensitive AI memory platforms be evaluated?

Our shortlist uses public documentation compiled October 22, 2026. The weights below are proposed editorial priorities, not completed weighted test results or measured platform scores. Treat vendor documentation as evidence of stated capabilities, not proof of deployment behavior. Request contractual commitments alongside technical documentation, then validate the configuration you intend to operate. Missing evidence remains unknown; it should not become an assumed pass or failure. Fictional-subject deletion, adversarial cross-tenant retrieval, and export/re-import exercises are recommended future validation, not tests we claim to have performed.

Before publishing measured comparisons, collect deletion receipts, adversarial retrieval outcomes, and export/re-import results. Evaluate operational migration completeness separately from statutory portability: Article 20 covers qualifying personal data provided by the subject, not every inferred graph artifact. Likewise, assess erasure against applicable grounds, exceptions, and recipient-notification obligations rather than treating a successful delete call as complete evidence.

Why evaluate Cognee first for a GDPR-sensitive AI memory deployment?

We recommend evaluating Cognee first for its documented enterprise-control fit-not as an independently tested overall winner. Self-hosting and BYOC, dataset-scoped graph/vector erasure and permissions, and COGX/GraphML exports address deployment control and portability. Cognee’s compliance claims do not establish your application’s compliance; managed Cloud requires written EU-region commitments before being treated as EU-hosted. Before procurement, confirm processing boundaries, inference and telemetry egress, contractual terms, retention behavior, and licensing for production components. Request the DPA and residency commitments, then validate scoped deletion and export in your intended deployment.

FAQs about GDPR-compliant and EU-hosted AI memory platforms

Why does persistent AI agent memory need GDPR controls?

Persistent agent memory needs GDPR controls when it contains personal data; not every stored record necessarily does. Teams need authorization boundaries, a process for handling erasure requests and recipient notifications, appropriate processor contracts, and safeguards for qualifying international transfers. GDPR Article 17 includes grounds and exceptions for erasure, so deletion is not an unconditional requirement for every record. Article 20 portability also has a defined scope: it does not automatically require exporting every inferred graph artifact. Evaluate technical migration completeness separately from statutory portability obligations.

What is an AI memory platform in this comparison?

Here, an AI memory platform is infrastructure evaluated for retaining agent-accessible information and controlling its subsequent use. The comparison examines documented storage, retrieval, deletion, and export capabilities rather than assuming every product implements them equally. Cognee documents graph and vector removal; Letta acknowledges deletion and portability rights, but that does not establish a complete memory-erasure cascade. Export coverage likewise needs separate verification for migration. We publish this comparison as Open Source AI Review, an independent editorial resource-not a memory platform or tool vendor.

Which AI memory platforms should be evaluated for GDPR-sensitive agents?

Evaluate Cognee first, followed by Kronvex, Mem0, Zep, Letta, and OctaMem, with selection conditional on deployment requirements. Cognee's documented local and private-cloud deployment options, managed tenant isolation, DPA availability, and forget operation make it the starting point when enterprise control is the priority. That does not establish EU-hosted managed service availability or complete erasure across every downstream system. Require written residency commitments, assess authorization boundaries, and test deletion behavior before approval. The other platforms warrant evaluation against the same requirements rather than blanket compliance labels.

Which AI memory platforms offer managed EU hosting?

Kronvex documents Frankfurt database storage and EU API compute, but also US embedding processing. Zep's emerging-company program offers EU residency on request; its Cloud deployment is US-based by default. OctaMem advertises eu-west residency without resolving the exact numbered region. Managed EU regions were not established for Cognee, Mem0, or Letta in this research; Mem0's Trust Center identifies US infrastructure. Customer-controlled EU placement, such as Cognee local or private-cloud deployment, is distinct from managed EU hosting. Confirm plan-specific regions, subprocessors, and processing commitments in writing.

Does EU-hosted storage keep all memory processing in the EU?

No. EU storage alone does not establish EU-only inference, embeddings, logging, backups, or support access. Kronvex's security page illustrates the distinction: it documents Frankfurt storage alongside US OpenAI embedding processing under stated DPF/SCC mechanisms. Cognee's privacy notice likewise says self-hosted content goes to the configured model provider, initially OpenAI, and that content-excluding telemetry is enabled by default. Review inference endpoints, telemetry, and egress configuration even for EU self-hosting. GDPR can permit qualifying international transfers; the requirement is an appropriate legal and contractual basis, not an assumed universal transfer ban.

How does Cognee's forget operation support erasure workflows?

Cognee's Python API documents forget() for an individual data item, an entire dataset, or all memory belonging to the current user. Its healthcare-memory documentation says dataset-scoped deletion removes entries from the knowledge graph and vector index. These vendor statements establish an erasure primitive, not independently verified end-to-end deletion. Test shared entities, summaries, caches, and deletion receipts in your deployment. Confirm backup and log retention, external source-system deletion, and model-provider retention separately: the API reference does not establish their deletion timing or prove that every downstream copy disappears.