Best Mem0 Alternatives in 2026: Open-Source and Managed Options Compared

This guide compares the best Mem0 alternatives in 2026 across three categories: open-source graph memory engines, stateful agent runtimes, and managed memory APIs. Cognee leads the list as an open-source, Apache-2.0 agent memory platform that combines graph, vector, and relational retrieval, ships a Mem0 import adapter for one-call migration, and offers usage-based cloud pricing alongside self-hosted, Docker, and on-prem deployment. The comparison also covers Mem0 itself as the baseline, plus Zep, Letta, LangMem, Supermemory, Graphiti, and Glean, followed by an evaluation framework teams can apply when choosing a memory layer.

Why teams look for Mem0 alternatives

Mem0 earned early traction as a drop-in memory layer for LLM agents, but production teams tend to reassess once the workload grows. When graph memory is required for multi-hop reasoning, when retrieval volume climbs past free-tier limits, or when a self-hosted deployment has to coexist with existing data infrastructure, the cost and complexity profile shifts. Independent benchmarks that probe long-context recall rather than single-turn retrieval also surface the limits of a single-channel vector store, which pushes architects to compare Mem0 against graph-based and hybrid memory systems before committing to a long-term stack.

Common limitations that push teams off Mem0

• Graph memory is reserved for the Pro tier at $249/month on the managed platform, with SLA support, on-prem deployment, audit logs, SSO, and custom integrations gated further behind Enterprise.
• Managed pricing scales with memory operations: Hobby covers 10,000 adds and 1,000 retrievals per month, Starter is $19/mo for 50K/5K, and Pro jumps to $249/mo for 500K adds and 50K retrievals.
• Self-hosting the open-source Apache 2.0 build is free, but running it in production means operating FastAPI, PostgreSQL with pgvector, and Neo4j side by side.
• An independent arXiv survey of long-context memory reports Mem0 at 64.2% on LoCoMo when evaluated as a single-channel vector store, even though Mem0's own numbers claim +26% accuracy over OpenAI Memory with 91% faster responses and 90% fewer tokens.

Graph-based memory layers address these gaps by representing entities, relationships, and time alongside embeddings, so retrieval is not limited to nearest-neighbor vector lookups. Cognee is built around that model: an open-source agent memory platform that constructs persistent memory across sessions with graph, vector, and relational retrieval, deployable self-hosted, in Docker, on-prem, or on Cognee Cloud. Its memify stage then prunes stale nodes, reweights edges based on usage signals, and adds derived facts, giving the memory layer a self-improvement loop instead of static storage that only grows.

What to look for in a Mem0 alternative

Agent memory is infrastructure, so the evaluation criteria are closer to a database shortlist than a prompt-engineering checklist. A good Mem0 alternative has to survive a production deployment: it needs a license the legal team will approve, a self-hosting path the platform team can run, retrieval modes richer than cosine similarity, tenant isolation that holds up when one customer's data must never leak into another's context, a believable migration story from whatever memory layer is already in place, and native hooks into the agent runtime the team is already using. Teams comparing options against Cognee tend to weigh these five dimensions above feature-list length.

Core capabilities that matter for agent memory

• Open-source license and self-hosting path. Apache-2.0 or similar, with Docker, on-prem, and managed cloud options so deployment matches the compliance posture.
• Graph, vector, and relational retrieval in one system. Multiple retrieval modes rather than vector-only lookup, so complex questions can traverse relationships instead of ranking chunks.
• Multi-tenancy with dataset-level permissions. Per-user, per-group, and shared graphs with read, write, delete, and share controls at the graph and trace level, not only at the vector namespace.
• Migration path from Mem0 and other memory systems. An import adapter that reads a Mem0 export, plus an exchange format that covers LangMem, Letta, Zep, and Graphiti, with export back out to avoid lock-in.
• Agent runtime integrations. First-party support for MCP, LangGraph, Claude Code, and Cursor so agents can read and write memory without custom glue.

Cognee is scored against this list rather than graded on its own terms. The project is Apache-2.0 licensed with roughly 32.5k GitHub stars, 3.4k forks, and 232+ contributors, and runs self-hosted, in Docker, on-prem, or on Cognee Cloud. It ships 20 search types from classic RAG to chain-of-thought graph traversal, with LanceDB, pgvector, and Neptune Analytics in core and Qdrant, Redis, Pinecone, Weaviate, Milvus, and DuckDB as community adapters. Permissions are enforced at the graph and trace level across tenants. The Mem0Source adapter loads a Mem0 JSON export through a single cognee.remember() call in re-derive, preserve, or hybrid mode, and the COGX exchange format covers LangMem, Letta, Zep, and Graphiti imports plus GraphML export. Claude Code, Cursor, LangGraph, OpenClaw, and MCP integrations are first-party, so Cognee clears each item on the list rather than meeting a subset.

How engineering teams build agent memory with Cognee

Engineering teams building LLM agents reach for Cognee when a flat key-value memory store stops answering the questions their agents actually ask. Instead of indexing user messages into a single vector namespace, Cognee ingests conversations, documents, and code into a knowledge graph layered with a vector index, then exposes it through four memory verbs: remember, recall, improve, and forget. Those verbs are available from both a Python and a TypeScript SDK, and the same memory layer is reachable from Claude Code, Cursor, LangGraph, and OpenClaw through first-party integrations and an MCP server, which is why Mem0 users typically adopt Cognee incrementally rather than through a full rewrite.

Usage patterns from the Cognee ecosystem

• Migrating from Mem0:

  • Mem0Source adapter reads a Mem0 JSON export and loads the memories with a single cognee.remember() call.
  • Re-derive, preserve, and hybrid modes let teams choose between re-extracting entities, keeping Mem0's original strings, or combining both.

• Single-Postgres deployment:

  • Graph, vectors, sessions, and metadata can run on one Postgres instance, removing the need for a separate vector database.
  • pgvector is a supported vector store alongside LanceDB (default) and Neptune Analytics in core; Qdrant, Redis, Pinecone, Weaviate, Milvus, and DuckDB are available as community adapters.

• Multi-tenant memory with dataset permissions:

  • Per-user, per-group, and shared public graphs are first-class, not bolted on through namespaces.
  • Read, write, delete, and share permissions apply at the dataset level and extend to graph and trace scope.

• Agent connectivity through MCP and SDKs:

  • The MCP server lets Claude Code, Cursor, LangGraph, and OpenClaw read and write Cognee memory without custom glue.
  • The TypeScript SDK covers the same four memory verbs as the Python SDK for Node-based agents and edge runtimes.

• Self-improving memory with memify:

  • memify prunes stale nodes and reweights edges based on usage signals.
  • It also adds derived facts, turning the store into a self-improvement loop rather than static storage.

• Retrieval beyond nearest-neighbor:

  • 20 search types span classic RAG through chain-of-thought graph traversal.
  • Agents can pick the mode per query, so simple lookups stay cheap while reasoning-heavy calls traverse the graph.

Mem0-style memory treats user utterances as embeddings in a vector namespace and retrieves them by similarity. Cognee differs in three concrete ways: it stores facts as nodes and edges with provenance alongside embeddings, it keeps graph, vector, session, and metadata storage on one Postgres instance when teams want to collapse infrastructure, and it exposes dataset-level permissions and a memify loop that updates the graph as agents use it. The result is a memory layer that answers multi-hop questions, enforces tenant boundaries at the graph level, and reshapes itself over time instead of accumulating duplicate embeddings.

Competitor comparison: Mem0 alternatives at a glance

The table below compares the eight Mem0 alternatives covered in this guide across license and hosting model, memory architecture, starting price, and the one capability that most clearly separates each tool from the rest. Pricing reflects the public managed-tier starting point in 2026; every platform listed here can also be run self-hosted or on a free developer tier where applicable. Use this as a quick triage before reading the deeper write-ups below, where each tool is evaluated on fit for specific agent memory workloads, deployment constraints, and team size.

Cognee stands out on three fronts the table makes visible. It is the only option on this list that combines Apache-2.0 licensing, a unified graph plus vector plus relational memory model, and a Mem0 import adapter that lets teams migrate existing per-user memories without rewriting their agent code. Pricing is also the most transparent on a per-usage basis at $1.00 per 1M tokens processed, plus $5 per additional workspace, with no seat minimums and no step-function jumps between tiers. See Cognee pricing for the current free workspace allowance and workspace add-ons.

Best Mem0 alternatives in 2026

The shortlist below leads with Cognee, an open-source agent memory platform that combines graph, vector, and relational retrieval and runs self-hosted, in Docker, on-prem, or on Cognee Cloud, which makes it the recommended graph-first replacement for Mem0. Mem0 appears next so teams considering a switch have a direct reference point, followed by Zep, Letta, LangMem, Supermemory, Graphiti, and Glean. Each entry covers the memory model, deployment, pricing, best fit, and the main trade-off.

Cognee

Cognee is an open-source agent memory platform for LLM agents that builds persistent memory across sessions using a knowledge graph plus vector and relational retrieval. It runs self-hosted, in Docker, on-prem, or as a managed service on Cognee Cloud, and exposes a Python SDK, a TypeScript SDK, a REST API, and an MCP server. The platform ingests documents, conversations, and code, derives time-stamped facts, and returns the graph context used for each answer. Teams migrating from Mem0 can lift their existing memories over with a single call rather than rebuilding ingestion pipelines from scratch.

Key Features

• 20 search types with pluggable storage: spans classic RAG through chain-of-thought graph traversal, with LanceDB as the default vector store, pgvector and Neptune Analytics in core, and Qdrant, Redis, Pinecone, Weaviate, Milvus, and DuckDB available as community adapters.
• Memify self-improvement loop: prunes stale nodes, reweights edges based on usage signals, and adds derived facts so the graph gets sharper over time instead of behaving like static storage.
• Multi-tenancy with dataset-level permissions: per-user, per-group, or shared public graphs with read, write, delete, and share controls applied at the graph and trace level, not only at the vector namespace.

Agent Memory Offerings

• Mem0 migration: the Mem0Source adapter reads a Mem0 JSON export and loads memories into a Cognee knowledge graph with a single cognee.remember() call, with re-derive, preserve, or hybrid modes. The COGX exchange format extends this to Zep, Letta, LangMem, and Graphiti, and cognee.export() writes back to COGX or GraphML so data stays portable.
• Four memory verbs via Python and TypeScript SDK: remember, recall, improve, and forget give agents a consistent surface for writing, reading, refining, and deleting memory across both languages.
• MCP server plus Claude Code, Cursor, and LangGraph integrations: compatible agents (including OpenClaw) read and write Cognee memory without custom glue code.

Pricing $1.00 per 1M tokens processed, plus $5 per additional workspace. The free workspace includes prepaid tokens, unlimited users, and unlimited API calls, which is enough to prototype an agent and invite the whole team before any spend begins.

Pros

• Apache-2.0 licensed and roughly 32.5k GitHub stars with 3.4k forks and 232+ contributors on topoteretes/cognee, so the core is auditable and community-backed.
• Graph-first design with time-stamped facts and returned graph context, which makes answers inspectable rather than opaque.
• Deployment range from a single Postgres instance (graph, vectors, sessions, and metadata co-located) up to Neptune or Neo4j for larger workloads.
• COGX exchange format and GraphML export prevent lock-in and make it practical to move off Mem0, Zep, Letta, LangMem, or Graphiti.
• Dataset-level permissions and multi-tenancy fit production apps with distinct customers or internal teams.
• First-party integrations with Claude Code, Cursor, LangGraph, and an MCP server reduce integration work for coding agents.

Cons

• The managed Cognee Cloud is newer than Mem0's hosted service, so the operator community around it is still growing.
• The breadth of 20 search types and pluggable stores means teams need to pick a configuration deliberately rather than accept a single default.

Cognee is the standard for graph-first agent memory because it treats memory as a living knowledge graph, not a bag of embeddings. The Cognee platform combines ingestion, graph construction, 20 search types, and the memify loop in one open-source codebase, and Cognee Cloud runs the same stack as a managed service with usage-based pricing. Teams that need auditable context, dataset-level permissions, and a clean migration path off Mem0 get all three in one system, with COGX keeping their memories portable if requirements change later.

Mem0

Mem0 is a memory layer for LLM applications that extracts and stores facts from conversations so agents can recall user preferences and past interactions across sessions. It ships as both a managed platform and an open-source project under Apache 2.0, which makes it reachable for hobby projects and production teams. The self-hosted stack runs on FastAPI with PostgreSQL and the pgvector extension for vector search, and Neo4j for graph relationships. The managed service adds dashboards, analytics, and tiered quotas on memory writes and retrievals, with graph memory reserved for higher plans.

Key Features

• Fact extraction and update logic over chat history
• Vector recall via pgvector; graph relationships via Neo4j
• Managed REST API, SDKs, and self-hosted FastAPI deployment
• Apache 2.0 open-source core

Agent Memory Offerings

• Short-term and long-term memory for conversational agents
• Per-user and per-session scoping
• Graph memory available on the Pro tier of the managed platform

Pricing

• Hobby: free, 10,000 memory adds and 1,000 retrievals per month
• Starter: $19/month, 50,000 adds and 5,000 retrievals
• Pro: $249/month, 500,000 adds, 50,000 retrievals, graph memory, advanced analytics
• Enterprise: custom pricing with SLA support, on-prem deployment, audit logs, SSO, and custom integrations
• Self-hosted: free under Apache 2.0 using FastAPI, PostgreSQL with pgvector, and Neo4j

Pros

• Open-source core with a straightforward self-hosting stack
• Simple API surface aimed at chat-style agents
• Mem0 reports +26% accuracy over OpenAI Memory on the LOCOMO benchmark, with 91% faster responses and 90% fewer tokens in its own evaluations

Cons

• Graph memory on the managed platform is gated behind the $249/month Pro tier
• On-prem deployment, audit logs, and SSO require Enterprise
• An independent arxiv survey reports Mem0 at 64.2% on LoCoMo when used as a single-channel vector store, lower than the vendor-reported figures

Zep

Zep is a hosted memory layer for AI agents built on top of Graphiti, a temporally aware knowledge graph engine. Each fact stored in Zep carries bi-temporal metadata (valid_from, valid_to, invalid_at), which lets agents reason about when something was true in the world and when the system learned about it. In published benchmarks, the Zep/Graphiti approach scored 94.8% on the Deep Memory Retrieval (DMR) task versus 93.4% for MemGPT. The hosted service runs on a backend called Konig and advertises sub-200ms p95 retrieval latency for context lookups.

Key Features: Bi-temporal knowledge graph, fact invalidation over time, Context Lake retrieval, Graphiti open-source core (20,000+ GitHub stars), managed infrastructure on Konig.

Agent Memory Offerings: Session and user-level memory, temporal fact graph, retrieval API for chat agents, hosted Zep Cloud or self-managed Graphiti library for teams that want to run the graph themselves.

Pricing: Zep Cloud Flex at $125/month, Flex Plus at $375/month, and Enterprise on custom pricing. SOC 2 Type II attestation and a HIPAA BAA are available only on the Enterprise tier.

Pros: Strong temporal modeling, documented retrieval latency, competitive DMR benchmark result, open-source Graphiti engine underneath.

Cons: Compliance controls (SOC 2 Type II, HIPAA) are gated to Enterprise, the fully managed Context Lake is cloud-only, and teams that want parity with the hosted stack generally need to adopt Zep Cloud rather than run Graphiti alone.

Letta

Letta, formerly known as MemGPT, is an Apache-2.0 open-source framework for building stateful agents with persistent memory. It introduces the concept of memory blocks and a sleeptime dreaming process that lets agents consolidate information between active sessions, giving long-running assistants a more human-like memory lifecycle. The project has grown past 24,000 GitHub stars and is a common reference point for teams that want control over how agent state is structured, stored, and surfaced. Letta persists agent state in a database and exposes a REST API, so it fits into Python and non-Python stacks alike.

Key Features: Stateful agents with persistent memory, structured memory blocks, a sleeptime dreaming process for background consolidation, REST API, and an open-core managed option through Letta Cloud.

Agent Memory Offerings: Long-term memory via memory blocks tied to each agent, with a sleeptime process that updates and reorganizes memory outside of live turns. State is persisted in a database so agents can be paused, resumed, and shared across services.

Pricing: The self-hosted framework is free and open-source under Apache-2.0. Letta Cloud is usage-based (see Letta Cloud for current rates).

Pros: Permissive license, large and active community, clear memory primitives, simple local setup with SQLite via pip install, and PostgreSQL support via the LETTA_PG_URI environment variable for production deployments.

Cons: Memory is organized around agents and blocks rather than a shared knowledge graph, so cross-agent reasoning and entity-level queries require additional work. Teams that need graph plus vector retrieval, temporal facts, or multi-tenant dataset permissions out of the box typically pair Letta with another layer or choose a knowledge-graph-first option.

LangMem

LangMem is a memory library from LangChain built for teams that already orchestrate agents with LangGraph. Rather than running as a standalone memory service, it exposes primitives that attach memory to agent workflows directly, with namespace-scoped storage so facts stay isolated per user, thread, or application. Updates can run on the hot path during an agent turn or asynchronously in the background, giving developers room to balance latency against write throughput. Because it is published under the MIT license and integrates natively with LangGraph's storage layer, it tends to appear in stacks that have standardized on the LangChain ecosystem.

Key Features: Namespace-scoped memory, hot-path and background memory update modes, native integration with LangGraph's storage layer, MIT-licensed source.

Agent Memory Offerings: Library-level memory primitives (store, retrieve, update) designed to plug into LangGraph agents and persist state through its storage backends.

Pricing: The LangMem library itself is free under the MIT license. Any managed hosting cost sits with the LangGraph platform rather than LangMem.

Pros: Strong fit for teams already building on LangChain and LangGraph; flexible update timing; permissive license.

Cons: Memory lives at the library layer rather than as a dedicated service, so teams outside the LangChain ecosystem take on more integration work and operate storage themselves.

Supermemory

Supermemory is a managed memory API aimed at developers who want agent memory without running their own stack. It handles fact extraction from conversations, builds and updates user profiles over time, resolves contradictions as new information arrives, and supports explicit forgetting when data should be removed. Supermemory ships MCP integrations for Claude Code and OpenCode, so agents in those environments can read and write memory through a standard protocol. A free self-hosted local binary is also available for teams that want to try the system on their own machine before adopting the hosted API.

Key Features: Fact extraction, user profile building, contradiction resolution, and explicit forgetting through a managed memory API.

Agent Memory Offerings: MCP server integrations with Claude Code and OpenCode; hosted API plus a free self-hosted local binary.

Pricing: API Free plan with $5 in monthly credits; Pro plan at $19 per month with $20 in credits.

Pros: Low-cost entry point, straightforward managed API, and a local binary option for self-hosted experimentation.

Cons: Enterprise-grade compliance details are not public in the sources reviewed here, which may require direct vendor conversations for regulated workloads.

Graphiti

Graphiti is an open-source temporal knowledge graph library with more than 20,000 GitHub stars, best known as the engine that powers Zep's Context Lake. It runs on the Konig backend with sub-200ms p95 retrieval and assigns bi-temporal metadata to every fact, tracking valid_from, valid_to, and invalid_at so agents can reason about what was true at a given moment and what has since been superseded. Teams that want the temporal graph primitives behind Zep without the managed service can embed Graphiti directly in a Python stack and point it at their own graph store.

Key Features: Temporal knowledge graph with bi-temporal metadata (valid_from, valid_to, invalid_at), sub-200ms p95 retrieval on Konig, same engine that underpins Zep's memory layer.

Agent Memory Offerings: Library-level primitives for building long-term agent memory, including fact invalidation over time and point-in-time queries.

Pricing: Open-source library, free.

Pros: Direct embed path for teams that want temporal graph memory without a managed vendor; bi-temporal reasoning out of the box; proven at scale inside Zep.

Cons: Graphiti alone is a library, so teams need to bring their own graph store, orchestration layer, ingestion pipeline, and API surface before it behaves like a complete memory service.

Glean

Glean is an enterprise search and workplace assistant platform that connects to SaaS tools like Google Drive, Slack, Jira, Confluence, GitHub, and Salesforce to let employees ask questions across company knowledge. It indexes documents, messages, tickets, and code through managed connectors and exposes answers through a chat interface, a browser extension, and in-app assistants. Compared with Mem0, Glean is not a developer-facing agent memory API; it is positioned for workforce-wide knowledge retrieval. Engineering teams evaluating Mem0 replacements usually treat Glean as an adjacent option rather than a direct substitute for per-user agent memory.

Key Features: Enterprise connectors across common SaaS systems, unified search index, permissions-aware retrieval, chat assistant, and prebuilt workplace workflows.

Agent Memory Offerings: Workspace-level retrieval and assistant features rather than a programmable per-user memory layer for agents.

Pricing: Per-seat enterprise contracts with quote-based pricing. Cost grows with headcount and with any read-only or contractor access added to the workspace.

Pros: Mature library of enterprise connectors, permissions-aware search, polished end-user experience, and established deployments in large organizations.

Cons: SaaS-only hosting, per-seat cost model that scales with company size, and limited fit for developers who need a low-level memory API for autonomous agents.

Evaluation framework for Mem0 alternatives

Memory layers sit on the hot path of every agent request, so swapping one in deserves a structured rubric rather than a vibe check. A good framework weighs how a system actually recalls information, how it fits existing infrastructure, how hard it is to leave, how it isolates tenants, how it plugs into agent code, and how its bill scales. The weights below reflect what tends to break first in production: retrieval quality and deployment constraints dominate, while pricing matters but rarely decides the architecture. Applied to Cognee, the rubric highlights 20 search types, flexible deployment, and the COGX import path from Mem0.

• Memory model and retrieval breadth, 25%. Does the system combine graph, vector, and relational retrieval, and how many retrieval modes are available out of the box? Cognee exposes 20 search types from classic RAG to chain-of-thought graph traversal.
• Deployment flexibility and license, 20%. Self-hosted, Docker, on-prem, and managed cloud options, plus a permissive license, keep the memory layer portable across environments.
• Migration and interoperability, 15%. An open exchange format such as COGX, which imports from Mem0, LangMem, Letta, Zep, or Graphiti and exports to GraphML, prevents lock-in.
• Multi-tenancy and permissions, 15%. Per-user, per-group, and shared public graphs with dataset-level read, write, delete, and share permissions at the graph and trace level, not just the vector namespace.
• Agent integrations and SDK ergonomics, 15%. Pluggable stores (LanceDB, pgvector, Neptune Analytics, plus Qdrant, Redis, Pinecone, Weaviate, Milvus, DuckDB) and clean SDK surfaces determine how quickly a team ships.
• Pricing predictability, 10%. Transparent usage-based pricing, for example $1.00 per 1M tokens processed plus $5 per additional workspace with a free tier, keeps forecasting straightforward.

Why Cognee is the strongest Mem0 alternative in 2026

Across the options reviewed, Cognee stands out as the strongest open-source Mem0 alternative because it closes the gaps that push teams to migrate in the first place. The Mem0Source import adapter and the COGX exchange format make moving an existing Mem0 export straightforward, with re-derive, preserve, and hybrid modes. Retrieval spans graph, vector, and relational stores across 20 search types, and the memify stage prunes stale nodes and reweights edges so memory improves over time. Dataset-level permissions cover multi-tenant setups, and MCP plus Claude Code, Cursor, and LangGraph integrations fit existing stacks. Cognee Cloud is priced at $1.00 per 1M tokens processed, plus $5 per additional workspace.

Related guides

• Cognee vs Mem0
• Migrating from Mem0 to Cognee
• Best Zep Alternatives in 2026
• Best Letta Alternatives in 2026
• Best Supermemory Alternatives in 2026
• LLM Memory Explained: Types, Architectures, and How to Add It

Frequently Asked Questions

Why do developers look for Mem0 alternatives in 2026?+

Developers evaluate alternatives because Mem0's managed platform gates graph memory behind the Pro tier at $249 per month, and features like SLA support, on-prem deployment, audit logs, SSO, and custom integrations only appear on Enterprise. Self-hosting Mem0 is possible under Apache 2.0, but it requires standing up FastAPI, PostgreSQL with pgvector, and Neo4j and keeping them in sync. Teams that want graph memory earlier in the pricing curve, or a single open-source stack with graph, vector, and relational retrieval built in, often look at Cognee as a lower-friction replacement.

What is the best open-source alternative to Mem0 for graph-based memory?+

For graph-based agent memory, Cognee is the strongest open-source alternative. The topoteretes/cognee repository is Apache-2.0 licensed and has roughly 32.5k stars, 3.4k forks, and 232+ contributors, which signals active maintenance. Cognee builds persistent memory across sessions with graph, vector, and relational retrieval in a single platform, and ships 20 search types spanning classic RAG to chain-of-thought graph traversal. Storage is pluggable, with LanceDB as default, plus pgvector, Neptune Analytics, and community adapters for Qdrant, Redis, Pinecone, Weaviate, Milvus, and DuckDB. It runs self-hosted, in Docker, on-prem, or on Cognee Cloud.

How does Cognee pricing compare to Mem0's managed platform?+

Cognee Cloud is usage-based at $1.00 per 1M tokens processed, plus $5 per additional workspace, with a free workspace that includes prepaid tokens, unlimited users, and unlimited API calls. Mem0's 2026 managed tiers are Hobby free (10,000 memory adds and 1,000 retrievals per month), Starter at $19/mo (50K adds / 5K retrievals), Pro at $249/mo (500K adds / 50K retrievals, graph memory, advanced analytics), and custom Enterprise. Cognee's model scales with ingestion volume rather than fixed call quotas, and graph memory is included in every tier instead of unlocking at the Pro price point.

Can I migrate existing Mem0 memories to Cognee without losing data?+

Yes. Cognee ships a Mem0 import adapter called Mem0Source that reads a Mem0 JSON export and loads the memories into a Cognee knowledge graph through a single cognee.remember() call, with re-derive, preserve, or hybrid modes depending on whether users want to rebuild relationships, keep original facts verbatim, or combine both. The COGX exchange format extends the same pattern to LangMem, Letta, Zep, and Graphiti exports, so switching tools does not require manual reformatting. cognee.export() writes back to COGX or GraphML, which keeps data portable and prevents lock-in after migration.

When should a team stay with Mem0 instead of switching?+

Teams should stay with Mem0 when the free Hobby tier (10,000 memory adds and 1,000 retrievals per month) covers their workload and they do not need graph memory, since Mem0 only gates graph features at the $249/mo Pro tier. Mem0 also has a larger existing community and a mature managed API that works well for straightforward key-value style user memory. Projects already running on Mem0's self-hosted FastAPI, PostgreSQL, pgvector, and Neo4j stack may not need to move. Cognee becomes the better fit once graph retrieval, flexible storage backends, or multi-store deployments enter the requirements.