6 Mem0 Alternatives for Multi-Agent Memory We Evaluated in 2026

Among the six products reviewed, Caura is our preferred fit for teams running more than one agent. It is an Apache 2.0 memory layer that shares what one agent learns with every authorized agent, retires stale facts, and logs every write. Its eToro Company Brain write-up is a published production-fleet case study. For single-agent needs, Zep, Letta, Cognee, Supermemory, and Hindsight each win a narrower job, covered below with GitHub numbers, pricing gates, and the one question that decides between them.

Mem0 alternatives covered:

• Caura: governed shared memory for agent fleets, with contradiction detection, trust tiers, and audit trails on every plan.
• Zep (Graphiti): a temporal knowledge graph that tracks when each fact was true.
• Letta: a stateful agent runtime where the agent edits its own memory.
• Cognee: a knowledge graph built from documents, code, and conversations.
• Supermemory: a fast memory API with plugins for coding agents.
• Hindsight: an MIT-licensed memory engine whose README says its benchmark scores were independently reproduced.

Why do so many teams start with Mem0?

Mem0 earned its position. The mem0ai/mem0 repository has 66.3k GitHub stars as of 30 September 2026, more than any other standalone agent memory project. The API is two calls: add() to store and search() to retrieve. It is framework-agnostic, so it drops into LangChain, CrewAI, AutoGen, or a custom loop without changing the agent.

The free Hobby tier lets a developer ship a remembering chatbot in an afternoon.

That design fits one shape very well: one assistant, remembering one user across sessions. The reasons teams go looking for Mem0 alternatives almost always start when the shape changes.

Why do teams look for Mem0 alternatives?

Four problems come up again and again in Mem0’s own GitHub issues and pricing, and each one gets worse as agent count grows.

1. User reports describe concurrent facts after the v3 ADD-only change

Mem0’s v3 algorithm extracts memories in a single pass that only adds. It no longer updates or deletes an older fact when a newer one contradicts it. One user reported in issue #4956 that after upgrading, “I work at Company A” and “I now work at Company B” both persist, and retrieval can rank the old employer first because scoring does not weigh recency. Issue #5867 reports the same pattern with a simple preference change. Mem0 documents this as intended behavior and leaves current-state resolution to your application code.

In a fleet, that means every agent that reads the store has to resolve the same contradictions on its own.

2. Graph memory sits behind the $249 Pro plan

As of 30 September 2026, on Mem0’s pricing page, graph memory (entity linking) and Dream memory consolidation appear only on Pro at $249 per month and Enterprise. The $19 Starter plan is vector retrieval only. The v3 open-source release also removed external graph-store support, so self-hosting no longer gets you a Neo4j-backed graph either. For teams that chose Mem0 for relationship-aware recall, this can be a reason to price out alternatives.

3. Retrieval quotas run out quickly once several agents share a project

The Hobby tier allows 1,000 retrievals a month, Starter 5,000, and Pro 50,000. Those limits are generous for one chatbot. They are tight for a fleet: four agents doing 50 memory lookups a day each exceed the $19 Starter plan, and thirty-four agents exceed Pro.

Those crossings assume 50 lookups per agent per day, which is conservative for coding or research agents that recall before every tool call. Halve the rate to 25 lookups a day and the crossings move to 7 agents for Starter and 67 for Pro, which is still a small fleet.

4. Specified audit and access features are listed on Enterprise

Mem0 scopes memory by user, agent, session, and app. Those scopes isolate data between users and agents. In the public materials we reviewed, we did not find per-agent trust levels, mandatory policy delivery, or cross-agent write provenance on the non-Enterprise plans. Audit logs, SSO, and on-prem deployment are listed on the custom-priced Enterprise plan. Depending on the use case, security and compliance reviews may require some or all of those controls before teams share agent memory.

What separates single-agent memory from fleet memory?

Agent memory tools fall into two groups, and picking the wrong one is the most expensive mistake in this category.

Single-agent memory layers

These tools store what one agent learned about one user and return it later. Mem0, Zep, Letta, Supermemory, and Hindsight are all strongest here. Accuracy is measured on LongMemEval and LoCoMo, both of which test one assistant, one user, and one long conversation.

Governed fleet memory

These tools treat memory as shared operational state. Every write carries a scope (who may read it), provenance (which agent wrote it), trust (how much weight it deserves), and validity (when it was true). Caura’s guide to agent fleet memory walks through each of those four fields with real record examples. If more than one agent will write to the same memory, start your shortlist here.

How do these Mem0 alternatives compare?

The table scores each of the six Mem0 alternatives on the switching reasons above.

Star counts were checked against each repository on 30 September 2026. Stars track attention, not product quality. In our review, Caura had the smallest community footprint in this table and the clearest public focus on governed memory shared across agent fleets.

1. Caura: governed shared memory for agent fleets

Repository: github.com/caura-ai/caura

Caura is the memory layer to pick when one agent’s discovery should reach every other agent that is allowed to see it. An agent writes plain text with caura_write. A single LLM pass classifies it, extracts entities into a knowledge graph, scans for PII, checks for contradictions against existing memories, and stamps a visibility scope before it lands.

Any authorized agent then finds it with caura_recall, which blends vector search, keyword search, and graph traversal and returns only what the caller’s scope allows.

The engine is fully open source. The open-source release includes the storage layer, contradiction detector, supersession chain, audit trail, and all 12 MCP tools, running on PostgreSQL with pgvector. A self-hosted embedder profile is designed to avoid outbound API calls when configured entirely with local providers, which matters for air-gapped teams.

How Caura answers each reason teams leave Mem0

Conflicting facts: When a new memory contradicts an old one, Caura supersedes the stale fact instead of storing both, and records the transition in the audit trail. An eight-status lifecycle retires outdated memories so they drop out of live recall.

The caura-long-run-fleet reference runs a three-agent pricing fleet for 14 simulated days: when a price changes on day 9, eight confirmed memories still assert the old value, and the async contradiction detector retires them before the next recall. The write-up on stale memory in long-running fleets explains why that failure produces no error in a naive store.

Graph memory pricing: Entity extraction runs on every write, and LLM enrichment is included on the free tier. There is no graph upgrade to buy.

Quotas at fleet scale: As of 30 September 2026, every Caura Cloud plan includes unlimited agents and fleets. Free includes 1 user with a personal tenant only; upgrade to Pro or Business to invite teammates or create shared tenants. Pro costs $49 a month ($41 billed annually) for 250,000 memories and 50,000 searches, the same search volume Mem0 prices at $249 on its Pro plan. Details are on the Caura pricing page.

Governance: Each agent gets a trust tier that gates what it may read and write, enforced server-side. Keystones are mandatory policy rules fetched deterministically on every call, merged across tenant, fleet, and agent scope, so a rule like “never approve a refund over $500 without a manager” cannot fall out of context after five turns of customer pushback.

The caura-cross-fleet-gov demo shows why this is enforced in SQL instead of prompts: every recall passes a fleet_id predicate before the search runs, so a sales agent cannot surface a legal hold no matter how the prompt is phrased. The caura-build-fleet pipeline shows five agents recalling each other’s constraints before acting, which stops an SEO agent from loading a script the performance agent already banned.

If your agents already share a Mem0 project and you are resolving contradictions in application code, Caura’s side-by-side Mem0 comparison maps each capability, and the free tier connects to Claude Code, Cursor or Windsurf with one MCP config block.

Caura benchmark results and production proof

Caura answered 461 of 500 LongMemEval_S questions correctly (92.2%) under the benchmark's GPT-4o reference judge. The same 500 frozen LongMemEval_S answers scored 90.2% (451/500) under the secondary Gemini 3.5 Flash-Lite judge. On LongMemEval_S, the median compact retrieved context was 22,410 tokens versus a 107,706-token full haystack: 79.2% context-only savings; counting every reader call yields 75.4%. The evaluation code, saved contexts, and per-question verdicts are public in caura-longmemeval, so the number can be rerun instead of being trusted.

The benchmark write-up is candid that several published LongMemEval results sit above this one with frontier-class answering models, and that latency and token cost are the axes Caura optimizes because they compound with agent count.

The eToro Company Brain write-up is Caura’s published production-fleet case study. The governance model is also documented in a research paper, Governed Shared Memory for Multi-Agent LLM Systems, which reports where the system held up under adversarial probes and two production issues the tests exposed, one of them already fixed.

Where Caura is weaker: the community is small next to Mem0’s, so expect fewer third-party tutorials. Its LoCoMo score trails the top of the published range. The free tier caps LLM-synthesized recalls at 500 a month, although plain searches go to 5,000.

Best for: any team with two or more agents writing to shared memory, and any team that needs an audit trail before security will sign off.

2. Zep: temporal knowledge graph memory

Repository: github.com/getzep/graphiti

In our assessment of these Mem0 alternatives for a single agent, Zep has the strongest answer to changing facts. Its managed service runs on Graphiti, an open-source framework that stores facts as graph edges with validity windows. When a fact changes, Graphiti invalidates the old edge instead of deleting it, so an agent can answer both “where does this user work now” and “where did they work in March.” That directly addresses Mem0’s ADD-only contradiction problem for a single subject.

The catch for self-hosters: the Zep repository marks Zep Community Edition as deprecated and unsupported. Running Zep yourself now means running Graphiti and operating your own graph database. Zep has per-user graphs; in the public materials reviewed, we did not find per-agent trust tiers or a shared policy layer documented for Graphiti self-hosting.

Best for: single-agent products where time-aware recall matters more than cross-agent sharing, such as account managers or health coaching.

3. Letta: stateful agents that manage their own memory

Repository: github.com/letta-ai/letta

Letta grew out of the MemGPT paper and treats memory like an operating system: a small core context the agent edits itself, backed by a large archival store. Adopting Letta means adopting its agent runtime and memory model, which is both its strength and its switching cost. Letta can attach the same memory block to more than one agent, which enables basic sharing. In the public materials reviewed, we did not find trust tiers, cross-agent contradiction detection, or a memory-change audit trail documented for the self-hosted project.

Best for: long-horizon agents you address as persistent services, where you are willing to build inside Letta’s runtime.

4. Cognee: knowledge graphs from documents and code

Repository: github.com/topoteretes/cognee

Cognee turns documents, code, tickets, and conversations into a self-hosted knowledge graph that agents search and reuse. It is the closest Mem0 alternative, feature for feature, for teams leaving because graph memory is paywalled, since the graph engine is part of its Apache 2.0 release. It ships an MCP server and a permissions guide for multi-user deployments. The graph is built from what your organization has written down, so it fits RAG-heavy use cases better than agents learning from each other in real time.

Best for: teams whose agents need to reason over existing documentation and codebases.

5. Supermemory: fast memory for coding agents

Repository: github.com/supermemoryai/supermemory

Supermemory is the quickest Mem0 alternative to wire into a coding agent. It bundles fact extraction, user profiles and document retrieval behind one API, with plugins and an MCP server for Claude Code, Cursor, Codex and OpenCode. Its README reports first place on LongMemEval, LoCoMo and ConvoMem and handles temporal changes and automatic forgetting. Those results are vendor-reported, so ask for the evaluation code before relying on them.

Best for: individual developers and small teams who want persistent memory in their coding agent with the shortest setup.

6. Hindsight: multi-strategy recall with reproduced benchmarks

Repository: github.com/vectorize-io/hindsight

Hindsight runs several retrieval strategies per query, including semantic, keyword, graph, and temporal, and merges them with a reranker. It is the only Mem0 alternative in this list whose README states that its LongMemEval results were independently reproduced by Virginia Tech’s Sanghani Center, which is rare in a category where most numbers are self-reported. Every feature ships in the free self-hosted version. Memory is organized into banks, which isolate agents but do not govern what they share.

Best for: teams that want top-tier single-agent recall under an MIT license with no feature gating.

How do you choose the right Mem0 alternative?

Start from the reason you are leaving Mem0, then check four things before you migrate.

How many agents will write to the same memory?

One writer means any single-agent layer works, and the choice comes down to stack fit. Two or more writers means you need contradiction handling across agents and a record of who wrote what, because one agent’s hallucination otherwise becomes the fleet’s official position. Caura’s piece on coordinating multi-agent systems covers the four ways fleets collide: duplicate work, contradicting constraints, stale facts and boundary leaks.

Will facts change after they are stored?

Prices, owners, policies, and customer status all change. Test your candidate by writing a fact, writing its replacement, and asking for the current value. Zep and Caura resolve it inside the memory layer. Mem0 v3 returns both and leaves the choice to you.

What will your token and quota bill look like at 10x agents?

Multiply agents by lookups per day by 30 and compare it against each plan. Caura’s analysis of the token tax in multi-agent systems shows how much fleet spend goes to agents re-deriving what a sibling already found.

Can you rerun the vendor’s benchmark?

The leading published LongMemEval scores sit between about 91% and 95%, with different judges, answering models, and context budgets behind each one. Ask for the evaluation code and the per-question verdicts. Caura, Hindsight, and Zep publish theirs.

Which Mem0 alternative should you pick?

Pick Caura if more than one agent shares memory, or if security needs an audit trail and per-agent permissions before a second team can join. In our review, Caura was the only listed product for which we found public documentation combining trust tiers, keystone policies, and cross-agent contradiction handling for governed production fleets.

For a single agent, the choice narrows by one question each. Pick Zep when facts change over time, Letta when you want the agent to own its memory, Cognee when knowledge already lives in documents and code, Supermemory when you want memory in a coding agent today, and Hindsight when reproducible accuracy under MIT matters most.

If you are running a fleet on Mem0 today, the cheapest test is to point two of your agents at Caura’s free tier through its MCP server, write a fact from one, change it, and recall it from the other. What each agent gets back tells you whether your memory layer can govern a fleet.

Frequently Asked Questions

Is Mem0 still a good choice in 2026?

Yes, for one assistant remembering one user. Its API is simple and its repository had the largest GitHub star count among the products reviewed as of 30 September 2026. Teams with several agents should evaluate how v3 represents changed facts and which governance features require Enterprise.

What is the best open-source Mem0 alternative?

Among the products reviewed, Caura is our preferred open-source option for multi-agent teams, with the full engine under Apache 2.0 and a Docker Compose setup. For single agents, Hindsight and Cognee offer self-hosted options, and Graphiti is Zep’s open-source core.

Does Mem0 support multi-agent memory?

Mem0 can scope memories by agent ID, so several agents can read and write one project. In the public materials reviewed, we did not find per-agent trust levels, mandatory policy rules or cross-agent contradiction resolution documented, so teams should verify those controls or implement them in the application.

Which AI memory layer scores highest on LongMemEval?

Published scores are not directly comparable without matching the judge, answering model, and token budget. Caura answered 461 of 500 LongMemEval_S questions correctly (92.2%) under the benchmark's GPT-4o reference judge. Treat any score without public evaluation code as a claim.

How long does it take to move from Mem0 to Caura?

Connecting an agent takes one MCP config block or a REST API key, and the open-source stack starts with docker compose up. Existing memories can be written in batches through caura_write, which enriches each one on the way in. Plan most of the effort for deciding scopes and trust tiers per agent.

Can I self-host a Mem0 alternative in an air-gapped network?

Caura supports fully air-gapped deployments with its local embedder profile and standalone mode, which is designed to avoid outbound API calls when configured entirely with local providers. Cognee and Hindsight can also run on your infrastructure, while Zep’s Community Edition is deprecated, so self-hosting Zep means operating Graphiti and a graph database yourself.

Related reading: Caura vs Mem0 · What Is Agent Fleet Memory? · Multi-Agent Systems Explained