Memori
Agent-native memory infrastructure that cuts LLM token costs by up to 97%.
Memori is a serious contender for production agents that need persistent, explainable memory. The 97% token savings and 87% LoCoMo accuracy are hard numbers, and the trace-native, audit-ready design matters in regulated industries. However, production pricing starts at $60K/year, so it's for teams with real budgets—not hobbyists. If you're cost-sensitive, you might start with the free tier or open-source, but for enterprise reliability, Memori is worth the investment.
Verified 22h ago · liveness 72/100 · cite: rightaichoice.com/tools/memori
- Developers building production AI agents needing persistent, structured memory
- Teams aiming to cut LLM token costs by over 95% while improving recall accuracy
- Enterprises requiring explainable AI with audit trails and lineage
- Open-source contributors looking for a self-hosted memory solution
- Simple chatbots that don't need long-term context
- Teams without engineering resources to integrate an SDK
- Applications requiring real-time multi-modal memory (e.g., video frames)
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Skip Memori if you need a simple chatbot with no long-term memory, lack engineering resources to integrate an SDK, or expect a no-code solution—it's a developer-focused memory infrastructure, not a plug-and-play product.
Going beyond 5,000 created memories on the free tier requires upgrading to a paid plan, which starts at $60K/year for Team—a big jump for small projects.
Memori's pricing starts free (open-source or Cloud Free with limited quotas) but production plans jump to $60K/year (Team) and $150K/year (Business), making it a fit for enterprises with substantial AI budgets. Compared to cheaper alternatives like simple vector DBs or DIY memory, Memori's cost is justified by its structured memory and 97% token savings, but small teams may find it expensive—consider open-source or free tier for experimentation.
In short
Memori — Agent-native memory infrastructure that cuts LLM token costs by up to 97%. Best for Developers building production AI agents needing persistent, structured memory, Teams aiming to cut LLM token costs by over 95% while improving recall accuracy, Enterprises requiring explainable AI with audit trails and lineage. Free to start; paid plans from $60/mo.
What's new in Memori
Checked todayAcross the latest 5 updates: 3 feature updates, 1 launch and 1 news mention.
Memori Labs and MongoDB Partner to Bring Production Memory to MongoDB
Memori partners with MongoDB to offer agent memory on Atlas clusters, with data ownership, retention, and access control built in.
Memori Integrates with Hermes Agent for Long-Term Persistent Memory
Memori releases plugin for Hermes Agent, capturing conversation, trace, and execution context for persistent memory.
Memori Labs Launches Agent-Native Memory Infrastructure
Memori releases agent-native memory infrastructure that automatically creates structured memory from agent trace.
Memori Labs Surpasses 13,000 GitHub Stars
Memori Labs passes 13,000 GitHub stars, indicating rapid adoption of its agent-native memory infrastructure.
Memori Achieves 81.95% Accuracy on LoCoMo at 4.97% of Full Context Cost
Memori reaches 81.95% overall accuracy on LoCoMo benchmark using 1,294 tokens per query, just 4.97% of full context.
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.
- +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: August 2026
How we score →Key Features
- Automatic memory classification into facts, preferences, rules, summaries
- Targeted recall across conversations and documents
- Selective semantic search
- Tokenless recall with cached snippets
- Explainable results with entity, time, and source provenance
- Interactive memory graph visualization
- Observability dashboard: memory creation, recall usage, cache hit rate
- LLM-agnostic—works with any model provider
- Drop-in SDK (Python, TypeScript) with zero configuration
- SQL-native storage (BYODB or Memori Cloud)
- Trace-native memory—captures agent execution
- Immutable audit logging
- Memory pooling with ReBAC access control
- MCP server access
- PCI and SOC 2 compliant payments vault
About Memori
Memori is an LLM-agnostic memory layer that turns agent execution and conversation into structured, persistent state for production AI systems. It automatically classifies each chat turn into facts, preferences, rules, and summaries, then uses targeted recall and selective semantic search to pull only what's relevant, dramatically cutting token usage. On the LoCoMo benchmark, Memori scores 87% accuracy while reducing token costs by 97% compared to full-context retrieval, and a more recent paper reports 81.95% accuracy at 4.97% cost using 1,294 tokens per query. Every result includes a clear "why this was included," tracing relevance by entity, time, and source. Memori is designed for developers building production AI agents, offering an interactive memory graph, analytics dashboards, and a drop-in SDK that works with zero configuration. Memori Cloud provides fully hosted SQL-native storage, while the open-source version lets you bring your own database. With partnerships including MongoDB, CockroachDB, and DigitalOcean, Memori positions itself as a trace-native, structured memory solution with audit logging and ReBAC access control, making it a strong fit for regulated or high-stakes environments. It also includes a PCI and SOC 2 compliant payments vault for securely handling cards and PII. Beyond the core memory layer, Memori now extends to MongoDB Atlas, Hermes Agent, and other integrations, underscoring its role as a centralized memory backbone for multi-agent systems.
Behind the Verdict
Memori is a memory infrastructure for AI agents, not a chatbot. It's built for developers who need their agents to remember across sessions, share context across agents, and cut token spend. The core differentiator is its structured memory: it doesn't just store raw conversation logs; it classifies each turn into facts, preferences, rules, and summaries. That structure powers targeted recall—only relevant memories are pulled into a prompt, which is why it can claim 97% token reduction. The explainability features are strong: each recalled memory includes entity, time, and source provenance, which is rare and valuable for compliance. The trace-native design captures execution context, not just chat, which is crucial for debugging and auditing. The SDK is drop-in, with zero configuration, and a TypeScript SDK was released in March 2026, making integration easy for modern stacks. Memori Cloud launched in March 2026, offering fully hosted, SQL-native storage with zero setup, which lowers the barrier for adoption. The open-source version (13K+ GitHub stars) lets you self-host and BYODB, appealing to teams with data residency needs. Integrations include MongoDB Atlas (announced July 2026), Hermes Agent (June 2026), OpenClaw (March 2026), CockroachDB, PostgreSQL, and DigitalOcean Gradient Agents. Weaknesses: the free tier caps at 5,000 created memories and 15,000 recalled memories, which is tight for any real usage. Production plans start at $60K/year (Team) and $150K/year (Business), which is a major commitment—this isn't for hobbyists. There's no fully no-code solution; you need engineering to integrate the SDK. Also, while it supports multi-modal memory in principle, it's optimized for text and structured data, not video frames. For teams that need persistent, explainable memory and have a budget, Memori is a top choice. For simple chatbots or low-budget experiments, it's overkill—you could start with the free tier or open-source, but for serious production, the cost is justified.
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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.
Integrate Memori SDK into a Python agent to persist customer preferences and past issues across sessions, reducing repeated context.
Outcome: Agent recalls customer history instantly, cutting token usage by 97% and improving response accuracy, with explainable recall for debugging.
Set up Memori Cloud to centralize memory for several agents, using memory pooling and ReBAC to control access.
Outcome: Agents share consistent context, and admins get observability dashboards to monitor memory usage and cache hit rates, reducing costs.
Adopt Memori Enterprise for on-prem deployment, ensuring audit logging and lineage for compliance.
Outcome: Meets audit requirements with immutable logs and explainable recall, while cutting inference costs and enabling multi-agent coordination.
Use Cases
- Deploy persistent memory for customer support agents that recall past interactions and preferences.
- Build personal AI assistants that learn user preferences and rules over extended conversations.
- Enable multi-agent systems to share and pool memory for consistent behavior across agents.
- Create audit-ready AI workflows with immutable logging and explainable recall for compliance.
- Reduce LLM inference costs by caching structured memory snippets and avoiding full context.
Limitations
- The free tier caps at 5,000 created memories and 15,000 recalled memories, which may be insufficient for heavy production use.
- The Team and Business plans are priced annually at $60K and $150K respectively, which can be a barrier for small teams.
- Self-hosted open-source version requires managing your own database infrastructure.
as of 2026-08-23
Verification history
We have re-verified Memori 5 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
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 self-hosting with their own database.
What this tier adds
Free entry point with bring-your-own-database support, full SDK and MCP server access, and community support via Discord.
Cloud Free
$0/mo
Ideal for
Builders exploring Memori or running lightweight workflows with limited memory needs.
What this tier adds
Hosted plan with quotas: 5,000 memories created and 15,000 recalled, plus advanced augmentation and intelligent recall.
Team
$60K/year
Ideal for
Teams scaling a single production agent with managed infrastructure and observability.
What this tier adds
Adds unlimited memories, memory pooling, ReBAC access control, and full observability dashboards, with deployment in a managed multi-tenant cloud.
Business
$150K/year
Ideal for
Organizations running multiple production agents that need a single-tenant, Memori-managed cloud environment.
What this tier adds
Scales to multiple agents with single-tenant deployment and includes all Team features, plus SSO/SCIM and immutable audit logging.
Enterprise
Custom
Ideal for
Large enterprises needing org-wide, unlimited agents with dedicated infrastructure and compliance features.
What this tier adds
Adds customer-VPC or on-prem deployment, advanced eval/holdout attribution, 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.
Memori's pricing starts free (open-source or Cloud Free with limited quotas) but production plans jump to $60K/year (Team) and $150K/year (Business), making it a fit for enterprises with substantial AI budgets. Compared to cheaper alternatives like simple vector DBs or DIY memory, Memori's cost is justified by its structured memory and 97% token savings, but small teams may find it expensive—consider open-source or free tier for experimentation.
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.
For developers, you can get Memori running in under a minute: install the SDK, add a few lines of code, and start using it. The cloud version requires zero database setup. For production deployment with managed cloud or enterprise, expect a few days to configure access control, monitoring, and integration with existing systems.
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 a vector database (e.g., Pinecone) to Memori: replace direct vector search with Memori's structured memory layer, using its SDK to capture and recall memories, and migrate existing vectors if needed.
- →From a custom memory layer (e.g., Redis or Postgres) to Memori: use Memori's SDK to replace manual storage, and migrate existing data by exporting to Memori Cloud or your own SQL database.
- ↗To a self-hosted open-source memory: if you need full control, migrate from Memori Cloud to the self-hosted version, using BYODB and exporting memories.
- ↗To a custom vector DB: if you need to leave Memori, you can export memories from the dashboard or API and import them into another store.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Memori
Common stack mates teams adopt alongside Memori, with the specific reason each pairing earns its keep.
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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Granica AI
Enterprise AI efficiency layer: cut data lake and agent token costs in your cloud
Memtrace Public
Shared structural memory for multi-agent coding: bi-temporal graph, MCP-native, 27x faster in v1.0.0
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