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Memori

Agent-native memory for production AI agents

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What Is Memori?

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Memori is a memory infrastructure platform purpose-built for AI agents, converting their traces and conversations into long-term, structured memory on top of SQL databases. Teams can bring their own database and keep full data ownership while benefiting from intelligent recall, advanced augmentation, and trace-native execution. By making agents reason over what they actually do instead of just what they say, Memori improves reliability and decision quality in production environments.

The platform is LLM-agnostic, integrates with popular agent frameworks, and offers both open-source and managed cloud options. Production plans add observability, pooling, access control, and enterprise-grade security to support large-scale, multi-agent deployments.

Quick Snapshot

Memori gives AI builders a production-grade, SQL-native memory layer that reliably captures and recalls agent context without locking you into a specific LLM. It centralizes long-term memory with observability and governance so enterprises can deploy safer, smarter agents at scale.

Works on
  • Web
  • API
Pricing Model
Subscription
Starting at $60K/year — Memori provides a free open-source self-hosted option and a $0 managed Cloud Free tier for exploration. Production plans start at $60K per year for a single production agent, with Business and Enterprise tiers available for larger, multi-agent deployments.
Fits on
Affiliate Program
We could not identify an affiliate program.
API Availability
Memori has an API available.
Key Features
  1. Turn agent traces into persistent structured memory
  2. SQL-native, LLM-agnostic memory with full observability
  3. Production-ready governance for enterprise AI agents
Audience
  • AI engineers
  • machine learning engineers
  • backend developers
  • enterprise AI teams
  • startups building AI agents
  • platform and infrastructure teams
URL
https://memorilabs.ai

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Memori

Key Features of Memori

Agent-native memory

Captures AI agent executions and conversations as structured, persistent memory, designed specifically around agent workflows rather than generic data storage.

SQL-native storage

Runs on top of SQL databases so teams can bring their own database, preserve data ownership, and integrate memory directly into existing infrastructure.

LLM-agnostic design

Works with different language models and frameworks, preventing lock-in and allowing you to evolve your LLM stack without rethinking memory.

Intelligent recall

Provides smart retrieval and augmentation so agents can access the most relevant past context, improving reasoning and response quality.

Trace-native execution

Enables agents to reason over what they actually did in past traces, not just conversational history, leading to more accurate and auditable behavior.

Observability and governance

Managed production plans include observability, pooling, access control, and enterprise security to monitor and control memory usage at scale.

Open-source and cloud

Offers both an open-source self-hosted option and managed cloud tiers, letting teams choose the deployment model that best fits their security and operational needs.

Framework integrations

Integrates with popular agent frameworks and tools so builders can plug in memory capabilities to existing AI stacks in minutes.

Use Cases for Memori

Production AI agents

Provide agents with reliable long-term memory of past actions and conversations so they can make more consistent, context-aware decisions in production environments.

Enterprise AI platforms

Centralize agent memory across teams with governance, access control, and observability, improving compliance and control over how AI systems use and store context.

Agent analytics and debugging

Leverage trace-native execution and observability to understand how agents reason over stored memory, making it easier to debug, audit, and improve their behavior.

Multi-agent systems

Use a shared, SQL-backed memory layer to coordinate multiple agents, enabling them to read and write consistent state and collaborate more effectively.

LLM-agnostic AI stacks

Maintain a stable memory layer independent of any single LLM provider, allowing you to switch or mix models without losing long-term context or needing to rebuild memory logic.

Frequently Asked Questions

What is Memori and who is it for?

Memori is an agent-native memory infrastructure that turns AI agent traces and conversations into structured, persistent state on SQL databases. It is built for AI engineers, ML engineers, backend developers, and enterprise AI teams deploying production agents.

How does Memori store and manage AI agent memory?

Memori converts agent executions and conversations into structured records stored on top of SQL databases, enabling teams to bring their own database while gaining intelligent recall, augmentation, and governance for agent memory.

Is Memori tied to a specific LLM provider?

No. Memori is LLM-agnostic, so you can use it with different language models and frameworks without being locked into a single provider or needing to redesign your memory layer.

Does Memori offer a free version?

Yes. Memori offers a free open-source self-hosted option and a $0 managed Cloud Free tier for exploration and early experimentation.

What are Memori’s paid pricing options?

Production plans start at $60K per year for a single production agent, with Business and Enterprise tiers designed for larger, multi-agent deployments that need advanced controls and support.

Can I keep full ownership of my data with Memori?

Yes. Memori is built on top of SQL databases, allowing you to bring your own database and maintain full ownership and control of your data and memory state.

How does Memori help with observability and governance?

Managed production plans include observability, pooling, access control, and enterprise security, enabling you to monitor, audit, and govern how agents read and write memory across your organization.

Memori · Our Verdict

Memori stands out as a focused, production-grade memory layer for AI agents rather than yet another generic vector store. Its SQL-native, LLM-agnostic approach with strong observability and governance makes it especially compelling for teams that care about reliability, data ownership, and scaling serious agent workloads beyond prototypes.

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