Memori
Memori is an LLM-agnostic agent memory layer that turns agent trace and conversation into structured, auditable persistent state.
Memori earns a look because it does the two things most memory add-ons can't: trace relevance by entity, time, and source, and pool memory across agents with access control. The 87% LoCoMo score at 97% token savings is the reason finance signs off. The catch is the ladder — Cloud Free is a 5,000-created / 15,000-recalled sandbox, and production starts at $60K/year, so most teams begin on the open-source self-host build.
Verified 1h ago · liveness 72/100 · cite: rightaichoice.com/tools/memori
- Platform teams shipping production agents that need memory surviving audits and cost reviews
- Developers who want to cut LLM token spend without wrecking recall accuracy on long-horizon tasks
- Regulated enterprises needing immutable audit logging, lineage, and ReBAC memory pools
- Multi-agent deployments that want one centralized memory layer instead of per-agent wiring
- Simple chatbots or internal tools with no long-term or cross-session context
- Teams that want managed memory on a small budget — production plans start at $60K/year
- Applications needing real-time multimodal recall such as video-frame memory
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Skip Memori if you need simple chat memory without a database or engineering lift, or if your budget can't reach the $60K/year production floor.
The Cloud Free tier stops at 5,000 created and 15,000 recalled memories, so a busy agent blows through the free allowance and forces a jump to a $60K/year plan.
Fits teams with real infrastructure budgets: $60K/year (Team, single production agent) and $150K/year (Business, multiple agents) target funded startups and enterprises. Cheaper managed-memory peers like Zep or Letta suit smaller teams; the open-source or Cloud Free tiers are where individuals and early projects actually start.
In short
Memori — Memori is an LLM-agnostic agent memory layer that turns agent trace and conversation into structured, auditable persistent state. Best for Platform teams shipping production agents that need memory surviving audits and cost reviews, Developers who want to cut LLM token spend without wrecking recall accuracy on long-horizon tasks, Regulated enterprises needing immutable audit logging, lineage, and ReBAC memory pools. Free to start; paid plans from $60/yr.
What's new in Memori
Checked todayAcross the latest 2 updates: 2 launches.
Memori Labs and MongoDB partner to make agent memory a native workload on MongoDB
Memori Labs and MongoDB are partnering to run agent memory as a first-class workload on MongoDB, storing persistent memory in the customer's own Atlas cluster with data ownership and access control.
Memori Labs releases Hermes Agent integration for long-term persistent memory
New Memori plugin gives Hermes agents long-term persistent memory capturing conversation plus agent trace and execution context, grounded in what agents actually do.
What people actually say about Memori — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
30 mentions across 3 sources (Hacker News, App Store, Lemmy) · researched Jul 3, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +SQL-native storage avoids vector DB complexity and cost.
- +Persistent memory works across multiple agents for workflow continuity.
- +Automatic memory classification into facts, preferences, rules, and summaries.
- +LLM-agnostic drop-in SDK promises easy integration without code changes.
- +Tokenless recall and caching can significantly reduce LLM costs.
- −App Store reviews report slowness and frequent disconnections.
- −No GitHub activity or open-source code visible to the community.
- −Limited public feedback makes it hard to gauge production stability.
- −Memory relevance selection as context grows is not clearly explained.
- −Zero configuration claim may not hold for complex agent setups.
- • Self-hosted BYODB may incur own infrastructure costs.
- • Managed cloud pricing beyond free tier not fully transparent.
Viability Score
How well maintained and how widely used is Memori? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Automatic memory classification into facts, preferences, rules, and summaries
- Trace-native memory that captures agent execution, not just chat
- Targeted recall across conversations and documents
- Selective semantic search that enriches fuzzy queries
- Tokenless recall with cached snippets for sub-second responses
- Explainable results with entity, time, and source lineage
- Memory graph: interactive visualization of how memories connect
- Observability dashboard: memory creation, recall usage, cache hit rate
- LoCoMo benchmark: 87% overall, 88.50 single-hop, 90.30 temporal
- Drop-in Python SDK with zero configuration
- Drop-in TypeScript SDK
- Drop-in proxy — deploy memory with no code changes
- SQL-native storage — bring your own database (PostgreSQL, CockroachDB)
- Memori Cloud managed hosting
- MCP server access
About Memori
Memori is a memory infrastructure layer for teams running AI agents in production. It captures every chat turn and agent execution step, classifies the raw trace into facts, preferences, rules, and summaries, then recalls only what the current prompt actually needs — across conversations and documents, without standing up extra retrieval services. The integration story is deliberately thin. You drop the Python or TypeScript SDK into existing code and Memori handles model calls and callbacks; the production plans also ship a drop-in proxy so you can add the layer without rewriting application code. Storage is SQL-native: bring your own database like PostgreSQL or CockroachDB, or use Memori Cloud, the managed option. A July 2026 MongoDB partnership puts persistent memory directly in a customer's own Atlas cluster, and a June 2026 plugin gives Hermes agents the same long-term memory treatment, conversation plus execution context. On the LoCoMo benchmark Memori reports 87% overall accuracy — 88.50 single-hop, 90.30 temporal — while cutting token usage 97% versus full-context retrieval. Every recalled result carries a "why this was included" trail tracing relevance by entity, time, and source, and the memory graph visualizes how those connections evolve. Compliance-minded buyers get immutable audit logging, memory pooling with ReBAC access control, and a PCI and SOC 2 compliant payments vault for cards and PII. Where it fits: platform teams shipping agents that must survive audits and cost reviews. When to look elsewhere: if you're prototyping a chatbot with no cross-session state, or you want cheap managed memory without the governance layer, Zep or Letta will get you further for less.
Behind the Verdict
Pick Memori when the memory layer has to be defensible, not just functional. The audit logging, ReBAC memory pools, and per-result provenance need to exist in your architecture anyway if you're in a regulated vertical, and bolting them on later is worse than starting there. We'd reach for it on single production agents first, then scale to pools once multiple agents share context. Pass if memory is a nice-to-have. A support bot that only needs the last ten turns, or an internal tool with no cross-session state, will not recover the setup cost — the SDK is one line, but the operational discipline around what gets stored and for how long is real work. The economics are the honest sticking point. Tokenless recall and cached snippets are what deliver the sub-second recall times, and the LoCoMo numbers back the savings claim. But Cloud Free caps at 5,000 memories created and 15,000 recalled, which is a sandbox, not a business. Production Team at $60K/year from is a budget line, and Business at $150K/year from is a platform commitment. Open-source self-host with your own database is the path most teams take to production without that conversation. Against the field: Zep and Letta are the closest alternatives, and both are easier to justify on price. What you pay Memori for is the compliance apparatus — immutable audit logs, PCI and SOC 2 payments vault, holdout attribution on Enterprise — plus trace-native memory that captures what agents did, not just what they said. If you don't need that, you're overbuying. One caveat worth flagging. Memori's partner list has grown fast — MongoDB Atlas, CockroachDB, DigitalOcean Gradient Agents, Hermes, OpenClaw — and breadth is not the same as depth. Test the integration you actually plan to ship before you commit, particularly if
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Real-world workflow fit
Concrete scenarios for the personas Memori actually fits — and what changes day-one when you adopt it.
Drops the Python SDK into an existing agent codebase, points it at the team's PostgreSQL, and lets Memori classify each support turn into facts and preferences.
Outcome: The agent recalls a customer's prior issues and stated preferences without resending full history, cutting token spend and giving the team a 'why this was included' trail.
Deploys Memori as a shared memory backbone behind a multi-agent gateway and pools memory with ReBAC so each agent sees only the partitions it's allowed to.
Outcome: One centralized memory layer replaces per-agent wiring, and audit logging keeps recall explainable for compliance review.
Uses the payments vault for cards and PII and turns on immutable audit logging so every memory write and recall is traceable by entity, time, and source.
Outcome: Agents handle sensitive form data under PCI and SOC 2 controls, and the firm can produce an audit trail on demand.
Use Cases
- Give customer support agents persistent recall of past interactions and stated preferences across sessions.
- Build personal AI assistants that learn user preferences and rules over extended conversations.
- Share and pool memory across multi-agent systems so behavior stays consistent between agents.
- Create audit-ready AI workflows with immutable logging and explainable recall for compliance teams.
- Cut LLM inference costs by caching structured memory snippets instead of resending full context.
- Store cards and PII in a PCI and SOC 2 compliant vault so agents can fill forms safely.
Limitations
- Cloud Free is capped at 5,000 memories created and 15,000 recalled, which limits heavier production workloads.
- Production pricing starts at $60K/year for a single production agent (Team) and $150K/year for multiple agents (Business), a significant budget commitment.
- Self-hosting is free but requires bringing and managing your own database.
- It is developer-first: setup relies on dropping in a Python or TypeScript SDK or MCP server rather than a no-code interface.
as of 2026-09-14
Verification history
We have re-verified Memori 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Memori tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0
Ideal for
Developers who want full control of their stack and data and are comfortable running their own database.
What this tier adds
Free self-host entry point with bring-your-own-database, full SDK and MCP server access, and Discord support.
Cloud Free
$0
Ideal for
Builders exploring Memori or running lightweight, low-volume workflows without managing infrastructure.
What this tier adds
Managed hosted cloud with 5,000 created and 15,000 recalled memories, plus Advanced Augmentation and Intelligent Recall.
Team
$60K/year, from
Ideal for
A funded team running a single production agent that needs observability and audit trails.
What this tier adds
First paid production tier at $60K/year — unlimited memories, memory pooling with ReBAC, drop-in proxy, and immutable audit logging.
Business
$150K/year, from
Ideal for
Organizations running multiple production agents that need tenant isolation and enterprise identity.
What this tier adds
Single-tenant managed cloud at $150K/year, adding SSO / SCIM on top of the Team tier's memory and audit features.
Enterprise
Custom
Ideal for
Large organizations needing org-wide, unlimited agents under custom compliance and SLA terms.
What this tier adds
Custom private offer with customer-VPC / on-prem deployment, custom SLA, forward-deployed engineering, and optional outcome-based pricing.
Where the pricing makes sense
The company stage and team size where Memori's pricing actually pencils out — and where peers do it cheaper.
Fits teams with real infrastructure budgets: $60K/year (Team, single production agent) and $150K/year (Business, multiple agents) target funded startups and enterprises. Cheaper managed-memory peers like Zep or Letta suit smaller teams; the open-source or Cloud Free tiers are where individuals and early projects actually start.
Setup time & first value
How long it actually takes to get something useful out of Memori — broken out by persona, not the marketing-page minute.
Developers reach first value in under a minute per the vendor — the Python or TypeScript SDK drops in with zero configuration and handles model calls and callbacks. Memori Cloud needs no database setup. Self-host (BYODB) takes longer since you provision and connect your own database, realistically an afternoon. Enterprise deployments with VPC/on-prem and SSO are project-scale.
Switching to or from Memori
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From full-context LLM calls: drop the SDK in front of your existing model calls and let Memori store and recall structured memory instead of resending history.
- →From a homegrown memory store: move it into SQL-native Memori (BYODB) or Memori Cloud and use the memory graph to inspect what carried over.
- →From a per-agent memory stack: consolidate onto one Memori backbone and pool memory with ReBAC instead of wiring each agent separately.
- ↗To a cheaper managed-memory peer like Zep or Letta: export your SQL-native memory tables and re-ingest into the successor's store.
- ↗To a homegrown store: since Memori is SQL-native and BYODB, the underlying tables can be queried directly and migrated out.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Memori”, and we withheld 6: 6 could not be judged, because “Memori” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Memori.
Official links
Tools that pair well with Memori
Common stack mates teams adopt alongside Memori, with the specific reason each pairing earns its keep.
Mem0
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Atomicmemory
Open-source, self-hosted memory engine that gives AI agents persistent, inspectable state with full audit trails.
Signetai
Self-hosted memory and secrets layer that gives Claude Code, Codex, and OpenCode agents persistent context on your own machine
Featured Head-to-Head Comparisons
Memori vs Spider Cloud
Spider Cloud and Memori serve complementary but distinct roles. Spider Cloud excels at fetching and structuring live web data for AI agents, while Memori stores and retrieves agent conversation history efficiently. Choose Spider Cloud if your AI agent needs real-time web content; choose Memori if you need persistent, explainable memory to reduce token costs. They could even be used together for a full data pipeline.
Memori vs Temporal Ai
For teams building production AI agents that need crash-resilient workflows and human-in-the-loop, choose Temporal. If the priority is slashing token costs via persistent structured memory while maintaining high recall accuracy, Memori is the smarter pick. They solve different problems—Temporal ensures reliable execution, Memori ensures memory—and can be complementary.
Memori vs Presto Voice
Presto Voice and Memori serve entirely different domains: Presto Voice automates drive-thru ordering for QSR chains, while Memori provides persistent memory for AI agents. Choose Presto Voice if you run a multi-location QSR seeking revenue lift via upselling; choose Memori if you're a developer building production AI agents that need cost-efficient, structured memory.
Alternatives to Memori
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Atomicmemory
Open-source, self-hosted memory engine that gives AI agents persistent, inspectable state with full audit trails.
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