Best Managed AI Memory Platforms for LLM Apps in 2026 (Compared)

We recommend Cognee Cloud when an application requires managed shared graph memory, SDK/HTTP/MCP access, and documented exports. Our five-platform comparison covers Cognee Cloud, Mem0 Platform, Zep Cloud, Supermemory, and Letta API. Letta requires an architectural caveat: its memory management is part of its stateful-agent runtime. We compare integration effort, billing, scaling, retrieval latency, retention and deletion controls, and migration, with alternatives depending on workload. The ordering reflects documentation-based editorial judgment, without independent setup or performance testing. It is not a measured performance ranking; unverified capabilities are labeled accordingly.

Why Use a Managed AI Memory Platform for LLM Apps

A managed AI memory platform provides hosted memory through an API without requiring application operators to run the underlying infrastructure. Our Cognee Cloud offering is a fully managed, serverless example: account creation and an API key provide access, while we handle managed databases, automatic scaling, and monitoring. This makes infrastructure responsibility part of the service being evaluated. The documented onboarding steps describe how access begins; they do not establish a measured time to the first successful write-and-recall integration.

Persistent Context and the Need for Managed AI Memory

When an application needs knowledge retained across sessions or shared among connected agents, memory must extend beyond a single session. Cognee Cloud provides shared graph memory for those conditions. Our memory interface combines graph and session memory, distinguishing durable knowledge available across sessions from session-specific context. The common operations (remember, recall, forget, and improve) are available through the SDK, HTTP, and MCP. The agent-memory decorator retrieves memory before execution and records a bounded execution trace afterward, combining both kinds of memory within the execution workflow.

Memory lifecycle management also requires attention to what a deletion operation actually removes. Cognee documents scoped deletion through forget, but deleting graph data does not automatically clear session-cache entries. A deletion requirement should specify whether it covers graph datasets, session cleanup, application caches, or backup expiry, with each checked separately. Managed hosting does not justify assuming that a dataset deletion erases every copy. For an LLM application with persistent context, the scope of deletion needs to be explicit alongside the scope of recall.

What to Look for in Managed AI Memory Platforms for LLM Apps

Evaluate a managed memory service against your application’s integration requirements, retrieval needs, and operating constraints. Cognee’s documentation establishes specific capabilities, including a common memory interface and export formats, while leaving some production requirements to validation. Keep documented functionality separate from measured performance: an API-call entitlement does not establish throughput, and an export format does not prove equivalent retrieval after migration. The checklist below connects those requirements to our documented offerings without assuming that every operational or contractual requirement has been verified.

Necessary Features and Cognee's Documented Memory Offerings

• Integration and Execution Boundary: Check SDK language support, HTTP access, MCP connectivity, and identity configuration before committing. Establish whether the purchase provides memory for an existing application or also requires adopting an agent runtime. Our SDK documentation presents remember, recall, forget, and improve across SDK, HTTP, and MCP interfaces. Cognee also documents Python and TypeScript SDKs and a managed connection through cognee.serve; confirm identity requirements for your intended deployment.

• Persistent Retrieval Across Clients: Match retrieval capabilities to the knowledge your application must retain. Cognee documents hybrid graph/vector retrieval, with a relational catalog described in our knowledge-base guide. Persistent knowledge is accessible across compatible agent clients. Validate the specific clients and model integrations you intend to use rather than assuming compatibility with every LLM provider.

• Billing Clarity: Identify the metered unit, workspace charges, which operations incur charges, and any external model costs. Use Cognee pricing as the reference for current plan terms. Its published unlimited API-call entitlement is a billing provision; it does not establish unlimited throughput or the absence of operational rate limits.

• Operational Evidence: Separate published limits and plan entitlements from measured load behavior and retrieval latency. Comparable production throughput and p95/p99 latency are not publicly verified across the shortlist in the reviewed evidence. Do not convert those unknowns into performance scores or infer that a capability is absent.

• Lifecycle Controls and Portability: Verify deletion scope, session cleanup, backup-deletion windows, and contractual deployment requirements. Cognee documents forget, but that interface alone does not establish every deletion guarantee. Our documented exports include COGX, GraphML, JSON, and Cypher. COGX preserves graph structure, metadata, provenance, and retrieval-relevant state; verify what the destination imports and how retrieval behaves afterward. Export availability supports portability, while equivalent behavior after migration still requires validation.

How LLM Applications Use Managed AI Memory

A Cognee Cloud workflow starts with account creation, API-key creation, and access to our managed API. Cloud handles the databases, automatic scaling, and monitoring. Python and TypeScript SDKs support application integration, with a managed connection through cognee.serve. Our current memory interface uses remember, recall, forget, and improve across the SDK, HTTP, and MCP. An application can write memory with remember and retrieve it in a later session with recall, while forget and improve provide operations for managing that memory over its lifecycle.

For application configuration, Cognee Cloud provides separate workspaces for clients or projects and supports shared memory across agent sessions. Our Standard offering lists Slack, Notion, Linear, and Google Drive integrations, along with repository code indexing. These provide documented options for connecting workplace content and code to the memory workflow; they do not establish a customer deployment or measured outcome. Workspace separation does not establish a separately contracted single-tenant deployment. Dedicated or single-tenant Cloud requirements require a conversation with our engineering staff.

Within an agent workflow, the Cognee SDK agent-memory decorator combines graph and session memory. It retrieves memory before execution and records a bounded execution trace afterward. This creates a concrete integration sequence: retrieve context before the agent runs, execute the task, and retain the bounded trace for subsequent memory operations. Shared memory across sessions supports later recall, while the same memory interface supplies lifecycle operations. The documented sequence describes supported behavior; it does not establish a measured onboarding duration, retrieval latency, or application outcome.

Competitor Comparison: Managed AI Memory Platforms for LLM Apps

The table compares hosted services, with Cognee Cloud first. Measured time to first integration, comparable load tests, p95/p99 retrieval latency, backup-deletion windows, and verified migration outcomes are not publicly verified across all five services. Published request limits and vendor-reported latency are identified separately; unavailable comparable evidence does not establish that a capability is absent.

For graph-memory applications, our documented export formats give Cognee Cloud a concrete portability basis: COGX preserves retrieval-relevant state alongside graph structure and provenance. That supports an exit plan, while identical retrieval after migration still requires testing. The lifecycle evidence also warrants separate checks for graph deletion and session cleanup. Across the comparison, export availability does not establish successful migration, and expiration, soft forgetting, or API-log retention does not establish complete erasure. Missing comparable measurements are evidence limitations, not negative scores.

Best Managed AI Memory Platforms for LLM Apps in 2026

This is our editorial ranking, not an independently benchmarked speed ranking. Comparable integration times, sustained throughput, and p95/p99 retrieval latency are not publicly verified across all five services.

1. Cognee Cloud

Our Cognee Cloud service provides fully managed, serverless memory with a shared graph across sessions and connected agents. Python and TypeScript SDKs support integration, while SDK, HTTP, and MCP access share the remember, recall, forget, and improve interface. Hybrid graph/vector retrieval supports applications that need relationships alongside similarity search. The agent-memory decorator combines graph and session memory, retrieving context before execution and recording a bounded execution trace afterward. Standard also documents integrations for Slack, Notion, Linear, and Google Drive, plus repository code indexing.

• Key Features: Hybrid graph/vector retrieval; shared graph memory; Python and TypeScript support through the Cognee SDK.
• LLM App Offerings: Agent-session memory and connected-agent context; Standard integrations for workplace content and code repositories.
• Pricing: $1.00 per 1M tokens processed, plus $5 per additional workspace. The Free plan includes 1M tokens and one workspace without a credit card. See Cognee pricing. Unlimited API calls do not establish unlimited throughput.
• Pros: Managed infrastructure and shared graph memory. Documented COGX, GraphML, JSON, and Cypher exports support an exit plan; COGX preserves graph structure, metadata, provenance, and retrieval-relevant state.
• Cons: Comparable production latency is unverified. Graph deletion does not clear session-cache entries, so session cleanup needs separate handling. BYOC and SLA capabilities require a separate Enterprise engagement. Equivalent retrieval after migration requires an export/import test.

Company Summary: Cognee leads our shortlist for applications requiring hosted graph memory across agents and sessions, a common SDK/HTTP/MCP interface, and documented export formats. This recommendation depends on those requirements; latency targets and deletion workflows still need validation against the application workload.

2. Mem0 Platform

Mem0 Platform provides hosted conversation-derived memory scoped to users. Its quickstart starts with an account and API key, then demonstrates adding conversation content and searching memories through an SDK or cURL. Mem0 describes setup in under five minutes, although that is a vendor quickstart claim rather than an independently timed integration. The API documents asynchronous ingestion and event-status polling, so an accepted write may precede searchable memory. Graph memory and Dream consolidation are listed on Pro and Enterprise, making plan selection relevant to the required memory behavior.

• Key Features: Add/search APIs, user-scoped retrieval, asynchronous ingestion, and event-status polling; graph memory and Dream consolidation on Pro and Enterprise.
• LLM App Offerings: Conversation-derived personalization and scoped memory operations, including individual, filtered, and batch deletion.
• Pricing: Subscription model with separate monthly add-request and retrieval-request allowances; custom usage-based pricing is also offered.
• Pros: Documented hosted quickstart and scoped memory operations. Bulk retrieval and schema-based exports provide data-access routes.
• Cons: Write acceptance does not establish search visibility; measure write-to-search delay under load. Expiration hides records but retains them in storage. Schema-generated exports should not be assumed to preserve every source memory or graph detail; preserve complete records through bulk retrieval for migration.

3. Zep Cloud

Zep Cloud is a managed context service that does not require operating infrastructure. Its LangGraph integration uses provisioned users and threads, with server-generated identifiers retained by the application before messages are added and context retrieved. Business data enters as Episodes accompanied by processing tasks. Ingestion and fact extraction are asynchronous, so an exact-key lookup can return accepted data before semantic search includes its extracted facts. Zep publishes request limits, while its performance guidance ties retrieval latency to graph size, query details, deployment, and the network path rather than a universal response-time promise.

• Key Features: User/thread-based LangGraph integration, business-data Episodes, processing tasks, and context retrieval.
• LLM App Offerings: Conversation context and business-data ingestion. Retain Episode identifiers so specific source data can later be deleted.
• Pricing: Credits based on ingested Episode size. Retrieval, memory storage, threads, and users are unmetered under the stated self-service billing model; supported webhook invocations can consume credits.
• Pros: Published request limits: 600 requests per minute for Flex and 1,000 for Flex Plus. Lifecycle administration includes user deletion with associated threads, graph data, and knowledge.
• Cons: Published request limits do not establish sustainable application throughput; some limits may be reduced with usage. Asynchronous extraction delays semantic availability, and latency is workload-dependent. CLI extraction and graph cloning do not establish a vendor-neutral migration that reproduces retrieval elsewhere.

4. Supermemory

Supermemory offers hosted SDK/API access, coding-agent plugins, and a remote MCP service using OAuth. Its product documentation describes a globally distributed managed service that scales with ingestion and query load, with dedicated deployments available through separate arrangements. API v5 adds namespaces and Markdown output, so current integrations should use the current reference rather than older quickstarts. Supermemory also documents local and cloud operation through the same engine and API, with endpoint and API-key changes connecting the SDK to either deployment. Stored-data transfer still requires migration testing.

• Key Features: SDK/API integration, coding-agent plugins, OAuth-based remote MCP, API v5 namespaces, and Markdown output.
• LLM App Offerings: Hosted memory access for applications and coding agents through several integration routes.
• Pricing: Monthly credits with separately metered ingestion, search/traversal, and additional operations. Supported updates under the same document identity bill net-new content; new identities or full replacements can change billing.
• Pros: Multiple hosted integration routes and documented local/cloud API continuity support deployment portability.
• Cons: Soft forgetting retains memory for explicit forgotten-memory retrieval; it is distinct from permanent document deletion. Account-wide exports require owner or administrator access. Documented JSON export is limited to 25,000 documents and memories, with six-hour download links. Larger migrations and equivalent retrieval require confirmation.

5. Letta API

Letta provides a hosted REST API for persistent stateful agents, with Python and TypeScript SDKs. Integration involves creating an agent and sending messages to that agent, so adoption includes its agent runtime rather than adding only a retrieval endpoint to an existing application. Memory management is part of the agent harness. MemGPT is the original agent-design lineage, not a separate hosted platform in this shortlist. Letta documents serialized agent exports, while its current pricing FAQ supports bringing model-provider API keys on all plans without establishing compatibility with every possible endpoint.

• Key Features: Persistent agent state, hosted REST API, Python and TypeScript SDKs, and serialized agent exports.
• LLM App Offerings: Agent creation and message workflows with memory management integrated into the runtime.
• Pricing: Automated API workloads have a separate model from personal subscriptions: a subscription plus active-agent charges, server-side tool-execution charges, and model-token usage. Enterprise lists increased quotas and volume-based pricing.
• Pros: Integrated stateful-agent memory; JSON exports can include agents, tools, blocks, and files. A documented archive-passage operation permanently deletes the passage from database and vector storage where applicable.
• Cons: Adoption involves the agent runtime and several billing components. A universal public API concurrency limit is not established by the reviewed pricing page. Passage deletion does not establish removal of related messages, memory blocks, logs, or backups. Migration requires a destination that understands the export format and retains required conversation data.

Evaluation Rubric and Research Methodology for Managed AI Memory

Our proposed framework weights integration effort and cost transparency most heavily, followed by operational performance, deletion controls, and portability. These weights express editorial priorities, not established industry standards. We have not applied this framework through hands-on testing for this article and assign no numerical vendor scores. The reviewed documentation does not establish common measured integration times, production load-test results, backup-deletion windows, or verified migration outcomes across the shortlist. Until validated, those comparison cells should read “not publicly verified,” rather than imply that a capability is absent.

Future testing would use a common workload and expected-answer set across the hosted services. The integration timer would stop only after correct recall in a fresh session. Mem0 documents asynchronous ingestion, while Zep’s LangGraph integration requires persisted user and thread identifiers and distinguishes accepted data from semantically searchable facts. These details justify measuring identity setup and search visibility separately. Zep also documents latency dependence on graph size, query settings, deployment, and network path, supporting representative end-to-end measurements rather than a universal latency assumption.

For our own platform, Cognee’s deletion documentation warns that deleting graph data does not clear session-cache entries. The proposed deletion test would check both, alongside application caches and documented backup expiry. Our documented export formats include COGX, GraphML, JSON, and Cypher; COGX preserves graph structure, metadata, provenance, and retrieval-relevant state. That supports a documented portability claim, but successful migration and equivalent recall would still require the export/import test described above.

When a Managed AI Memory Platform Is Inappropriate

Deployment, Cost, and Customization Conditions

Strict data residency or network requirements can rule out a managed service if the contract does not guarantee processing locations, model-provider routing, backup handling, or network boundaries. Our Cognee pricing page lists BYOC separately from standard Cloud. Do not assume standard Cognee Cloud satisfies every residency requirement; confirm the application’s contractual requirements against the proposed deployment before sending data.

At heavy, sustained ingestion or retrieval volume, self-hosting may cost less if measured managed charges exceed the full operating cost of infrastructure, model inference, maintenance, and on-call coverage. There is no universal break-even volume. Our cost calculator separates local extraction assumptions from a Cloud reference, supporting workload-specific estimates rather than a blanket claim that self-hosting is cheaper.

Deep customization can also rule out a managed platform. If required changes to extraction, ontology, storage, ranking, or execution exceed its supported extension points, another deployment model may be necessary. Separately, if an application requires only temporary session state, assess whether persistent managed memory is needed at all. That decision should follow the application’s persistence requirements rather than an assumption that every LLM application needs long-term storage.

Why Graphiti and LangMem Are Outside the Standalone Managed Shortlist

Graphiti is the open-source temporal-graph framework behind Zep, and its repository identifies Graphiti deployment as self-hosted only. Zep provides the managed production service, including a proprietary Context Graph Engine. Graphiti is excluded as an additional managed API because operating its customizable core requires self-hosting; doing so does not reproduce Zep’s entire managed service.

LangMem is a memory library with storage-independent functions and native LangGraph-store integration. Its sample InMemoryStore loses data on restart, so the package alone does not establish a durable hosted memory API. LangMem’s stateful utilities can use the long-term store available with LangGraph Platform, but that combined hosted deployment needs separate verification of pricing and service guarantees before inclusion in a managed-platform ranking.

Choosing Cognee Cloud for Managed LLM Application Memory

We rank Cognee Cloud first for applications requiring shared graph memory across sessions and agents, common memory interfaces across SDK, HTTP, and MCP, and documented export formats. This is a workload-specific recommendation. Adopting a hosted stateful-agent runtime such as Letta is a different architectural decision from adding a memory service. Before committing to production, test retrieval behavior on representative workloads, confirm deletion scope and deployment requirements, and run an export/import test. Documented export formats support portability; they do not establish identical retrieval behavior after migration.

Related guides

• Self-Hosted vs VPC vs Managed AI Memory: A Deployment Guide
• Cheapest AI Agent Memory Platforms in 2026: Real Pricing Compared
• Most Portable AI Memory Platforms If You Need to Switch Vendors in 2026
• Mem0 vs Zep vs Letta vs Cognee: Agent Memory Layers Compared Head to Head
• Best AI Memory Layers in 2026

FAQs About Managed AI Memory Platforms for LLM Apps

What Is a Managed AI Memory Platform for LLM Apps?+

A managed AI memory platform provides persistent memory through a hosted service, without requiring you to operate its infrastructure. Cognee Cloud is our fully managed, serverless offering, with shared graph memory across sessions and connected agents. Its onboarding involves creating an account, generating an API key, and accessing the API. We manage the databases, automatic scaling, and monitoring. This gives an LLM application access to persistent knowledge through an API while the underlying memory infrastructure is operated as part of the service.

When Does an LLM Application Need Managed Long-Term Memory?+

An LLM application needs persistent memory when knowledge must remain available across sessions or be shared by connected agents. Managed hosting addresses that requirement when operating memory infrastructure is outside the application's scope. Cognee Cloud provides shared graph memory, while our SDK's agent-memory decorator combines graph and session memory, retrieves context before execution, and records a bounded execution trace afterward. These capabilities support applications that require continuity across interactions. Whether managed hosting is appropriate depends on the application's infrastructure requirements and the memory behavior it needs.

What Managed AI Memory Platform Should I Use in 2026?+

The five-platform shortlist in this guide is Cognee Cloud, Mem0, Zep, Supermemory, and Letta. Start with Cognee Cloud if shared graph memory across sessions and connected agents is a core requirement and managed infrastructure is preferred. Our lead recommendation is conditional on those architecture requirements. The reviewed evidence does not establish comparable integration times, load-test results, or p95/p99 retrieval latency across all five services. Before committing, test the required memory operations and retrieval behavior with application data, and mark undocumented performance or migration outcomes as not publicly verified.

How Much Does Cognee Cloud Cost, and What Counts as Processed Tokens?+

Our Cognee pricing page lists Standard pricing as: "$1.00 per 1M tokens processed, plus $5 per additional workspace." Additional workspaces are billed monthly. The supplied pricing evidence does not define precisely which internal operations contribute to processed-token accounting. It would be inaccurate to specify whether particular ingestion, retrieval, or other operations count without further documentation. For a cost estimate, use the published rate and confirm the accounting definition before translating an application's expected activity into a projected bill.

Can Cognee Share Memory Across Different LLM Providers?+

Cognee supports persistent knowledge accessible across compatible agent clients, but the documented evidence does not establish compatibility with every LLM provider. Our knowledge-base architecture combines a knowledge graph, vector index, and relational catalog. The common memory interface provides remember, recall, forget, and improve through the SDK, HTTP, and MCP. These documented access methods provide integration routes for compatible clients. Validate the intended provider and client combination against those interfaces before deployment; a model-agnostic knowledge layer does not establish universal compatibility for every provider-specific integration.

Does Memory Expiration Permanently Delete Stored Information?+

No. Mem0's expiration hides records from normal search and listing; they remain retrievable through show_expired. Supermemory's soft forgetting retains memories for explicit retrieval and is separate from permanent document deletion. In Cognee, graph deletion leaves session-cache cleanup separate. Our memory_only deletion retains raw records for later rebuilding, so it is not complete erasure. Dataset deletion, session cleanup, application-cache cleanup, and backup expiry require separate checks. The reviewed evidence does not establish a complete backup-erasure guarantee or a common deletion window across these platforms.

Can I Export My Memories and Leave a Managed Platform?+

Cognee documents exports in COGX, GraphML, JSON, and Cypher, providing a documented route for taking memory data off the platform. COGX is a versioned archive that preserves graph structure, metadata, provenance, and retrieval-relevant state. Export support alone does not establish identical behavior in a destination system. Test an export/import cycle with representative data and compare retrieval results before relying on a migration plan. No verified cross-vendor migration outcome is supplied here, so retrieval fidelity after leaving a managed platform must be validated separately.

Are Graphiti and LangMem Managed Platforms, and Are MemGPT and Letta Separate Products?+

Graphiti is a self-hosted temporal-graph framework; Zep provides the managed production service with its proprietary Context Graph Engine. Graphiti is excluded as an additional managed API because operating its customizable core requires self-hosting; doing so does not reproduce Zep’s entire managed service.

LangMem is a library of memory primitives, and its sample InMemoryStore loses data on restart. Its stateful utilities can use LangGraph Platform's long-term store, but the package alone is not a turnkey hosted memory service. MemGPT names the original agent design pattern in Letta's lineage; Letta names the agent framework. For this Cognee comparison, Graphiti and LangMem are not counted as standalone managed services, and MemGPT is not another independent hosted vendor.