Memori Review (2026): Pricing, Features & Honest Verdict

TLDR

Memori is agent-native memory infrastructure that turns agent execution traces and conversations into structured, persistent state: facts, preferences, and rules, with targeted recall and hybrid semantic search. It treats memory as schema-backed data in a relational database, not loose vector chunks. The approach is differentiated and benchmarks well, but it is brand new. Best for: teams building production AI agents. Price: Free, or Starter $19/mo. Rating: 6.3/10.

What is Memori?

Memori is an agent-native memory layer built by Memori Labs Inc. The problem it solves is that AI agents forget. By default, an agent has no durable memory of what a user told it last week or what it did in a prior run, so every session starts cold and context has to be stuffed back into the prompt at high token cost. Memori sits between your agent and your model (it is LLM-agnostic) and turns the agent’s conversations and execution traces into structured, persistent state. It classifies what it captures into facts, preferences, rules, and summaries, and you control how long memories live and where they are stored. When the agent needs context, Memori does targeted recall plus hybrid semantic search, pulling back only the relevant pieces rather than dumping everything into the prompt.

The architectural bet is what makes Memori interesting. Instead of treating memory as a pile of similar text chunks in a vector store, Memori treats it as data with schema, constraints, and history, using relational databases as the primary store. That design is meant to make recall explainable, with traceable lineage by entity, time, and source, and an interactive memory graph that shows how people, places, and interests connect. Memori Labs launched it out of stealth in late May 2026 and cites partners and integrations including DigitalOcean, CockroachDB, MongoDB, and SK Networks. It also publishes strong benchmark numbers, which we cover below. We found the relational approach genuinely different from the crowd, but it is worth holding the skepticism a brand-new infrastructure product deserves: this is days old, and memory is exactly the kind of foundational layer you do not want to gamble on lightly.

What Are Memori’s Key Features?

Automatic Memory Classification

Memori captures chat turns and agent execution and automatically organizes them into facts, preferences, rules, and summaries, rather than leaving you to structure raw logs yourself. You retain control over how long each memory persists and where it is stored, which matters for both cost and data residency. This automatic structuring is the core of the pitch: it is what turns a transcript into something an agent can reliably reason over later. How accurate the classification is across messy, real-world conversations is the open question that only production use will answer.

Targeted Recall and Hybrid Semantic Search

When an agent needs context, Memori retrieves only the relevant memories across conversations and documents instead of reloading everything. Its hybrid semantic search enriches fuzzy, natural-language queries with semantic context to improve accuracy while keeping token usage down. The explicit goal is controlling token cost, since the usual alternative (stuffing full history into every prompt) is expensive and slow. On Memori’s internal LoCoMo benchmarking, the company reports a 95% reduction in token usage versus full-context retrieval, though that figure is self-reported and not independently verified.

Explainable Results and Memory Graph

Memori provides clear reasoning for why a given memory was included in a recall, with traceable lineage by entity, time, and source. There is also an interactive memory graph that visualizes connections between people, places, interests, and experiences. This explainability is a real differentiator against vector-store memory, where it is often a black box why a particular chunk surfaced. For teams that need to debug agent behavior or satisfy auditors, being able to trace exactly what the agent remembered and why is a meaningful advantage.

Schema-Backed Storage and Analytics

Because Memori stores memory in relational databases as data with schema, constraints, and history, it fits naturally into existing data infrastructure and supports air-gapped or privacy-sensitive deployments, which is reflected in partnerships with CockroachDB and MongoDB. Built-in analytics track memory creation, recall usage, and cache hit rates so you can see how the memory layer is actually performing and where token spend is going. For an infrastructure product, that observability is the difference between a tool you trust and one you cannot reason about.

How Much Does Memori Cost?

Memori starts free. The Free plan is $0 per month and includes 5,000 memories created and 15,000 recalled per month, unlimited end users, advanced augmentation, a 30-day usage reset, and community support. It is aimed at builders exploring Memori or running lightweight workflows, and it is genuinely enough to prototype an agent’s memory before paying. The usage model is two-sided (memories created and memories recalled), so heavy-recall workloads will hit limits differently than write-heavy ones, which is worth modeling before you commit.

The paid tiers scale those limits. Starter is $19 per month for 25,000 created and 100,000 recalled, aimed at growing apps and agents stepping into production. Pro is $99 per month for 150,000 created and 500,000 recalled, adds the ability to buy add-on packs, and includes a private Slack channel, suited to teams running consistent production or multi-agent loads. Enterprise is custom-priced and brings custom plans, forward-deployed engineers, and dedicated integration and maintenance support, which signals Memori Labs will get hands-on for larger or air-gapped deployments. No annual discount is listed, so these appear to be flat monthly prices.

On price, Memori is competitive with the field. Mem0, the most widely adopted memory layer, has a free Hobby tier (10,000 memories, 1,000 retrieval calls) and a $19 Starter, then jumps to $249 for its Pro, so Memori’s $99 Pro undercuts Mem0’s Pro significantly on headline price. Zep starts free and its Flex tier is $25 per month with full temporal-graph features. Letta offers an open-source path and a $20 per month API plan. So Memori’s pricing is reasonable rather than cheap or expensive, sitting right in the band these tools occupy. The real cost question is not the monthly fee, it is the switching cost: memory is foundational, and migrating your agents’ accumulated state from one provider to another later is painful, which is the strongest argument for testing thoroughly on the free tier first.

Who is Memori Best For?

Use Memori if you are building production AI agents that genuinely need persistent, structured memory and you care about token cost, data privacy, and being able to explain what the agent remembered. The relational-database design is a strong fit for teams that already run that infrastructure, or that have air-gapped and compliance requirements where a hosted vector black box will not pass review. The explainable recall and memory graph also help teams that need to debug agent behavior rather than just hope the right context surfaced. If structured, auditable memory is a hard requirement, Memori’s whole architecture is built around exactly that.

Skip Memori, at least for now, if you just want a quick drop-in memory call for a side project and do not care how it works under the hood. A simpler hosted memory tool may be less to think about, and Memori’s structured approach is more machinery than a hobby agent needs. More importantly, weigh the risk before betting production infrastructure on it: Memori launched in late May 2026, its benchmark and token-saving figures are self-reported, and the category already has established players in mem0, Zep, and Letta. For anything where reliability over the long haul is critical, the prudent path is to prototype on the free plan, validate the recall quality and the benchmark claims against your own data, and keep an eye on how the platform matures before you make it load-bearing.

Best Memori Alternatives

Mem0

Mem0 is the most widely adopted agent memory layer and the default comparison. It extracts memories from interactions, stores them, and retrieves them for personalization and long-term coherence. It has a free Hobby tier (10,000 memories, 1,000 retrieval calls) and a $19 Starter, with Pro at $249 per month. On Memori’s own LoCoMo benchmark, Memori scored 81.95% to Mem0’s 62.47%, but Mem0’s maturity, ecosystem, and adoption are real advantages a brand-new competitor cannot match. If you want the safe, established choice, Mem0 is it.

Zep

Zep emphasizes episodic and temporal memory, structuring interactions into meaningful sequences using its Graphiti knowledge-graph engine rather than flat logs. It starts free and its Flex tier is $25 per month with temporal reasoning and entity resolution. Zep benchmarks strongly (79.09% on the same LoCoMo test, just behind Memori) and is the pick if temporal reasoning, knowing how facts changed over time, is central to your agent. It is a more proven option than Memori today.

Letta

Letta takes an OS-inspired approach where the LLM manages memory tiers (core context, recall, archival) and decides what to retrieve and when. It is open-source, with a $20 per month API plan and a free tier, which makes it the natural choice for teams that want to self-host or avoid vendor lock-in entirely. It is a framework as much as a memory store, so it is a different shape of tool than Memori’s focused infrastructure, but it competes for the same agent-memory budget.

Final Verdict: Is Memori Worth It?

Memori earns a 6.3. The core idea is genuinely differentiated: treating agent memory as structured, schema-backed data with constraints and history in a relational database, rather than a pile of vector chunks. That design buys explainable, traceable recall and a natural fit for air-gapped and compliance-heavy setups, and Memori’s LoCoMo numbers (81.95% accuracy, ahead of Zep and well ahead of Mem0) suggest the approach is not just theory. The free plan and fair pricing make it cheap to evaluate, and the focus on cutting token cost addresses a real production headache.

The reasons it sits here, not higher, are about maturity and risk. Memori launched out of stealth in late May 2026, so its persistence layer is unproven over long production timelines, and the standout accuracy and token-saving figures are self-reported rather than independently verified. The category is also crowded with established names, and memory is foundational infrastructure where switching later is costly. Our recommendation: if structured, explainable agent memory is what you need, prototype Memori on the free plan and validate the recall quality and benchmarks against your own data. For anything mission-critical right now, lean on a more proven option like Mem0 or Zep until Memori has earned a longer track record.