Mem0 is an open-source, production-grade memory layer for AI agents and chatbots that turns stateless LLM calls into context-aware experiences. We rate it 83/100 — if you are shipping an agent that needs to remember user preferences, prior decisions or long-running tasks across sessions, Mem0 is the most battle-tested option in the category right now, with a free Apache 2.0 core and benchmark numbers no competitor has matched in 2026.

What is Mem0?

Mem0 was founded in 2023 by Taranjeet Singh (CEO) and Deshraj Yadav (CTO), graduated from Y Combinator's Summer 2024 batch, and has raised $24M total — a $3.9M seed led by Kindred Ventures and a $20M Series A in October 2025 led by Basis Set Ventures with Peak XV, GitHub Fund and Y Combinator participating. The mem0ai/mem0 repository has crossed 54,900 GitHub stars and 13M+ Python package downloads, and the company says API calls grew from 35M in Q1 2025 to 186M in Q3 2025 — roughly 30% month over month.

The thesis is simple: every production agent eventually hits the same wall — the LLM does not remember anything between sessions, so engineers stuff ever-larger chunks of chat history into the prompt and watch token costs explode. Mem0 sits in front of the LLM as a drop-in memory store: it extracts durable facts ("user is vegetarian, allergic to nuts"), updates them as conversations evolve, and retrieves only the relevant memories on each turn. Less context, lower latency, lower cost.

mem0ai/mem0 on GitHub — 54,900+ stars, Apache 2.0 — The mem0ai/mem0 repository on GitHub: an Apache 2.0 universal memory layer with 54.9K stars and 6.2K forks.

Key Features of Mem0

New 2026 memory algorithm: Single-pass hierarchical distillation with multi-signal retrieval (semantic + BM25 + entity matching). On the LoCoMo long-conversation benchmark Mem0 jumped from 71.4 to 91.6, and on LongMemEval from 67.8 to 93.4 — with median latency around 0.88s at ~7K tokens per call.
Drop-in SDKs: Official clients for Python, Node.js and the Vercel AI SDK, plus AGENTS / CLAUDE / Cursor plugin manifests in the repo. Add a memory in three lines, search it in two.
Open core, Apache 2.0: The full memory engine is OSI-approved and self-hostable for free; the managed Platform sits on top for teams that don't want to run their own vector store.
Compliance built in: SOC 2 and HIPAA on the managed Platform, BYOK encryption, audit logs of every read and write, plus on-prem and air-gapped deployment options for enterprise.
Hybrid search: The optional mem0ai[nlp] install adds BM25 keyword matching and spaCy entity extraction in parallel with the vector pipeline.

Mem0 home page — The Memory Layer for your AI Apps — Mem0's positioning: drop-in memory infrastructure for AI agents and apps, built for production.

What Users Say About Mem0

On Hacker News and Reddit, the consensus is that Mem0 is the easiest way to bolt persistent memory onto an existing agent stack — the original Show HN thread has hundreds of upvotes and the Series A discussion highlights how quickly the open-source SDK has been adopted in production. The recurring complaints are equally consistent: the $19 to $249 jump from Starter to Pro is the single most-cited frustration, graph-style retrieval is paywalled to the Pro tier, and Mem0 stores explicit facts but does not infer behavioural patterns from repeated implicit signals — one independent benchmark put implicit-preference accuracy around 30–45% versus 77–90% for raw long-context approaches. Founders Taranjeet and Deshraj are unusually responsive on GitHub issues, which softens those rough edges.

Mem0 Pricing

Mem0 has a Free Hobby tier on the managed Platform, plus unlimited free use of the open-source core when you self-host.

Plan	Price	Key Limits
Self-Hosted Open Source	$0	Apache 2.0 core, unlimited memories, run on your own infra
Hobby (Cloud)	$0	10,000 add requests / 1,000 retrieval requests per month, community support
Starter	$19/month	50,000 add requests / 5,000 retrieval requests, community support
Pro	$249/month	500,000 add / 50,000 retrieval, private Slack, advanced analytics, multi-project
Enterprise	Contact sales	Unlimited usage, on-prem, SSO, audit logs, SLA, custom integrations

Eligible startups under $5M in funding can also apply for 3 months of free Pro access via Mem0's Startup Program.

Who Should Use Mem0?

Best for: Engineering teams shipping production AI agents — customer support copilots, sales assistants, healthcare and education tutors — that need persistent, auditable memory across thousands of users without rebuilding a vector store and ranking layer from scratch. The Apache 2.0 core also makes it a strong choice for regulated teams that need to self-host.

Not ideal for: Weekend chatbots and one-off prototypes — if you only need session-level memory, the LLM's native context window will do. Teams that need behavioural pattern inference over implicit signals should benchmark Mem0 against long-context models before committing.

Pros and Cons

Pros:

Best-in-class benchmark numbers in 2026 — 91.6 on LoCoMo, 93.4 on LongMemEval, sub-second p50 latency.
Apache 2.0 core that you can self-host indefinitely with no per-seat tax.
SOC 2 + HIPAA + BYOK on the managed Platform out of the box.
Strong, well-maintained SDKs across Python, Node.js, Vercel AI SDK, and major agent frameworks.
Founders are very active on GitHub and Hacker News.

Cons:

The $19 → $249 pricing cliff between Starter and Pro is steep with no middle ground.
Graph-style memory retrieval is gated to the Pro tier.
Mem0 captures explicit facts but does not learn from repeated implicit corrections.
Documentation has improved but still trails the rate of API churn.

Alternatives to Mem0

The closest open-source competitor is Zep, which leans on a temporal knowledge-graph approach and tends to win on time-sensitive recall, although Mem0's 2026 algorithm closes most of that gap. LangMem from the LangChain team is a lighter wrapper if you are already deep in LangGraph. For pure long-context approaches, Anthropic's 200K+ token Claude windows or Google's 1M-token Gemini context can occasionally outperform a memory store on implicit-preference recall — at the cost of much higher per-call token spend.

Verdict: Is Mem0 Worth It?

Yes, for almost any team building production AI agents that need to feel personal across sessions. The free self-hosted core is genuinely usable, the managed Platform is priced reasonably at the bottom and top of the stack (it is only the Starter–Pro middle that stings), and the new 2026 algorithm is currently the benchmark to beat. 83/100 — the rough pricing gap and the lack of implicit-pattern learning are the main reasons it is not in the 90s yet.

Frequently Asked Questions

Is Mem0 free?: Yes. The Apache 2.0 open-source core (mem0ai/mem0) is free to self-host with no usage caps, and the managed Platform has a free Hobby tier with 10,000 add requests and 1,000 retrievals per month.
What platforms does Mem0 support?: Mem0 ships first-party SDKs for Python and Node.js, an integration with the Vercel AI SDK, and plugins for Cursor and Claude-style coding assistants. The managed Platform runs as a hosted REST API; the open-source version runs anywhere you can run Python.
How does Mem0 compare to Zep?: Both are open-source memory layers for AI agents. Zep emphasises a temporal knowledge graph and historically led on time-sensitive recall; Mem0's April 2026 algorithm closes most of that gap and pulls ahead on the LoCoMo and LongMemEval benchmarks, while keeping a simpler add/search API.
Is Mem0 open source?: Yes — the core memory engine is published under the Apache 2.0 license at github.com/mem0ai/mem0 with 54,900+ stars and 6,200+ forks as of May 2026.
Is Mem0 SOC 2 and HIPAA compliant?: The managed Mem0 Platform is SOC 2 and HIPAA compliant, supports BYOK encryption and offers on-prem and air-gapped deployments for enterprise customers.

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