8 Mem0 Alternatives for AI Agent Memory in 2026

The best Mem0 alternative depends on what kind of memory your agent needs. If facts change over time and you need to know what was true when, use Zep or its open-source engine Graphiti. If you want memory built into the agent itself, use Letta. For a local, open-source knowledge graph, try Cognee. For a hosted memory API with ready-made plugins, try Supermemory. If you're on LangGraph, LangMem fits in natively. And if you only need an agent to keep notes between sessions, the Claude memory tool may be all you need.

Mem0 is still one of the most popular memory layers, so this isn't a "Mem0 is dying" story. People look around for three reasons: the managed plan meters every add and retrieval, graph memory sits on the custom tier, and Mem0's memory algorithm changed in 2026. Below is what Mem0 is today, a quick comparison, and when each alternative is the better fit.

What Mem0 Is in 2026

Mem0 calls itself "the memory layer for AI agents and applications." You send it conversations, it extracts facts about users and tasks, and it returns the relevant ones when your agent needs them. It comes in two forms: a managed platform and an Apache-2.0 open-source library.

A few details matter for anyone comparing:

• A new memory algorithm. Mem0's GitHub README describes an April 2026 algorithm with single-pass, add-only extraction (no UPDATE or DELETE steps), entity linking across memories, and parallel semantic, keyword and entity retrieval. It also points to an upgrade guide from open-source v2 to v3. If you built on the old update-and-delete behaviour, test before upgrading.
• Pricing is metered. The Mem0 pricing page lists two plans, Free and Custom. Free meters add and retrieval requests each month. Model calls run on Mem0 for the first 30 days, then you bring your own LLM key. Graph memory (entity linking) and on-prem deployment are Custom-plan features.
• Self-hosting is real. The open-source version defaults to a local Qdrant instance for the library and Postgres with pgvector for the self-hosted server.
• Compliance claims. Mem0's homepage says it is SOC 2 Type 1, HIPAA and GDPR compliant. That's Mem0's own claim, backed by its Trust Center.

Where Mem0 is still the right call: you want user-level memory that drops into any agent, you like the option to start hosted and move to self-hosted, and you don't need a full knowledge graph on day one.

If you're new to the topic, our explainer on how AI agent memory works covers the layers these tools sit in.

Quick Comparison

Graph Memory Alternatives

Zep: when facts change over time

Best for: production agents in companies where the right answer depends on when something was true.

Zep now calls itself an enterprise context layer for reliable agents. Its core is a context graph that stores each fact with two timelines: when it was true, and when Zep learned it. So if a customer changes plans in March, the agent can tell the old plan from the new one instead of holding two conflicting memories.

Around that core, Zep has added connectors for Slack, Gmail, Google Drive, Notion, Snowflake and others, plus governance features like access control, retention policies and audit logs. Agents connect through MCP or Zep's SDKs.

Pricing is credit-based. According to Zep's pricing page, you pay per episode of data ingested, while memory retrieval, storage, users and graph storage aren't metered. There's a free tier, and Enterprise adds BYOC inside your own VPC.

The trade-off: Zep is a commercial service, and its scope is now much broader than a memory layer. If you just want "remember this user's preferences," it's more than you need.

Graphiti: Zep's engine, self-hosted

Best for: teams that want temporal graph memory on their own infrastructure.

Graphiti is the Apache-2.0 framework that Zep is built on. It builds temporal knowledge graphs for AI agents. When information changes, old facts are marked invalid rather than deleted, and every entity links back to the raw data it came from.

You bring your own graph database. The README lists Neo4j, FalkorDB and Amazon Neptune. Kuzu support is deprecated because the upstream project is unmaintained. There's a reference MCP server, but the README says plainly that it isn't a production deployment and has no authentication.

The trade-off: you run and tune a graph database yourself. Mem0's open-source version is lighter to start with.

Cognee: local-first knowledge-graph memory

Best for: developers who want graph memory that runs on a laptop, without API keys.

Cognee is an Apache-2.0 memory platform for agents. It turns text into entities, relationships and searchable chunks, building a knowledge graph next to vector search. It can also turn code into a graph of symbols and dependencies. The core operations are remember, recall, improve and forget, and at query time it picks graph, vector or code context.

It can run locally with Ollama, and Postgres can hold metadata, pgvector embeddings and graph state in one instance. Cognee Cloud is the managed version, and the project ships often.

The trade-off: you're adopting a graph pipeline, which means more to configure than Mem0's simple add-and-search calls.

Agent-Level and Hosted Alternatives

Letta: memory as part of the agent

Best for: builders who want agents that manage their own memory, not a separate memory service.

Letta, formerly MemGPT, describes itself as a platform for stateful agents with memory that can learn and improve over time. The difference from Mem0 is architectural. Mem0 is a layer you call from your agent. In Letta, memory is part of the agent, and the agent decides what to keep.

Letta has changed shape this year. Its README says the retired V1 API server now lives on an archive branch, and the current source lives in the Letta Code repository. Letta's docs point developers to the Letta Agent SDK, with two ways to run it: Letta Cloud, or the Letta App Server on your own machines.

The trade-off: you adopt Letta's agent runtime, not just its memory. If you already have an agent framework you like, Mem0 or Supermemory plugs in with less change.

Supermemory: a hosted memory API with plugins

Best for: teams that want a managed API and ready-made integrations for tools like Claude Code and Cursor.

Supermemory says it is building "the default engine for memory and continual learning for agents." It extracts context from users, tasks and tenants, stores it in a vector-graph database, and puts the relevant parts back into the model's context. It ships plugins for ChatGPT, Claude (desktop and Claude Code) and Cursor, plus a generic MCP server.

Pricing is usage-based, with Free, Pro, Max, Scale and Enterprise tiers. Memory is billed by tokens processed and search by query. Self-hosting is listed on Scale and Enterprise, and Enterprise supports air-gapped deployments. The main GitHub repo is MIT-licensed.

The trade-off: the hosted API is the main path. If you need a fully open-source stack on day one, Mem0, Cognee or Graphiti are simpler bets.

LangMem: if you already use LangGraph

Best for: teams building agents on LangGraph.

LangMem is an MIT-licensed library from the LangChain team that helps agents learn from their interactions. It integrates natively with LangGraph's long-term memory store, which is available by default in LangGraph Platform deployments. A background memory manager extracts, consolidates and updates what the agent knows.

The trade-off: it's a library, not a service. It's a smaller project than Mem0, and outside LangGraph you'll wire up storage yourself. Our comparison of LangGraph vs LangChain explains where it fits in that stack.

Built-In Memory From the Model Providers

Before adding a memory service, check whether your model provider's own tools already cover the job.

The Claude memory tool

The Claude memory tool lets Claude create, read, update and delete files in a /memories directory that persists between sessions. It runs client-side: Claude asks for a file operation, and your app performs it against storage you control, whether that's local files, a database or cloud storage.

It's different from Mem0 in a useful way. Nothing is extracted for you, and there's no semantic search. Claude decides what to write down, as notes. For a coding agent tracking progress across sessions, that's often enough. For memory about thousands of end users, you'd still want a dedicated layer. If you work in Claude Code, our guide to claude-mem covers a community plugin that adds session memory there.

OpenAI Agents SDK sessions

The OpenAI Agents SDK has sessions that store conversation history for a given session, with SQLite, Redis, SQLAlchemy, MongoDB and OpenAI Conversations API backends. That covers "remember what we said earlier in this thread." It isn't long-term memory about a user across threads. OpenAI's cookbook shows patterns for building that on top, so you'd still bring your own store or a tool like Mem0.

One to Skip: Weaviate Engram

You may see Weaviate's Engram on recent lists. Its docs now say it is being sunset. Existing projects work until December 31, 2026, and new projects can't be created. Don't start on it. If you're also reviewing your vector database, see our Weaviate alternatives guide.

How to Choose a Mem0 Alternative

Start with the question your agent struggles to answer:

A few checks before you switch:

1. Export your memories. Whatever you pick, pull your existing user memories out of Mem0 first, and keep the user IDs so they map cleanly.
2. Test with real conversations. Memory tools differ most in what they choose to extract. Run the same week of chats through two candidates and compare what each remembers.
3. Price the write path. Zep charges per episode ingested, Mem0 per add and retrieval, Supermemory per token processed. Your bill depends on how chatty your users are.
4. Decide on graphs honestly. Graph memory helps when relationships and changes matter. For simple preferences, a vector store with good extraction is easier to run.

Where Taku Fits

Taku isn't a memory layer for developers, so it doesn't replace any tool here. It solves a different problem: someone shared an AI app or workflow that already remembers your context well, and you want to use it without wiring up memory, vector stores and API keys yourself.

Taku is an AI-native desktop workspace where you can mirror a working AI app or workflow someone shared, remix it, and keep it instead of re-prompting from scratch. Taku is in Beta, and the Mac app is available now.

FAQ

What is the best Mem0 alternative?

It depends on the job. Zep suits enterprise agents where facts change over time. Letta suits agents that should manage their own memory. Cognee and Graphiti are the strongest open-source graph options. For a hosted API with plugins, try Supermemory.

Is Mem0 open source?

Yes. The Mem0 library is Apache-2.0 and can be self-hosted, by default with Qdrant or Postgres and pgvector. The managed platform adds hosted infrastructure, and features like graph memory sit on its Custom plan.

Mem0 vs Zep: what's the difference?

Mem0 extracts facts from conversations and retrieves them by semantic, keyword and entity search. Zep stores facts in a temporal context graph that tracks when each fact was true. Zep has grown into a broader enterprise context layer with connectors and governance.

Is there a free Mem0 alternative?

Yes. Graphiti, Cognee, Letta and LangMem are all open source and free to self-host. Zep and Supermemory also offer free tiers on their hosted services.

Do I need a memory layer at all?

Not always. If your agent only needs working notes between sessions, the Claude memory tool or a simple file may be enough. A memory layer pays off when many users each need their own long-term memory.

What happened to MemGPT?

MemGPT became Letta. Letta is still an open-source platform for stateful agents, and its current source now lives in the Letta Code repository.