Structured memory capture
Memori captures each chat turn and classifies it into facts, preferences, rules, and summaries, giving agents structured state instead of raw transcript storage.
Memori
Memori is an LLM-agnostic memory layer for AI agents and production systems. It captures conversation and execution traces into structured, persistent state so applications can recall relevant context without sending full transcripts every time.
Overview
Memori is an agent-native memory infrastructure product that turns agent execution and conversation into structured, persistent state for production systems. The site describes it as a LLM-agnostic layer focused on capturing useful memory from chats and traces, then making that memory available for later recall, search, and analysis.
Its core workflow centers on extracting facts, preferences, rules, and summaries from ongoing interactions, then retrieving only the relevant context when an application needs it. Memori also emphasizes explainability, with lineage that shows why a result was included, plus hosted cloud features such as instant memory storage, a memory graph, and analytics on recall usage and cache hit rate.
Core capabilities
Targeted recall
When a prompt needs context, Memori retrieves only the relevant memories across conversations and documents, reducing the amount of context passed back to the model.
Selective semantic search
Memori enriches fuzzy searches with semantic context so recall can work even when the wording is imprecise, while keeping token usage lower than full-context approaches.
Explainable lineage
Each result includes an explanation of why it was included, with lineage by entity, time, and source for review and debugging.
Hosted memory workflow
Memori Cloud is presented as a hosted option for storing and searching memories without additional setup, and the platform also includes a memory graph and analytics for recall activity.
Dual-layer memory model
The benchmark page describes a dual-layer system that combines semantic triples with conversation summaries, helping preserve both exact facts and narrative context.
Practical use cases
Persistent assistant memory
Use Memori when an agent needs to remember user preferences, prior decisions, and conversation history across sessions without sending the full transcript back into every prompt.
Agent workflow memory
Use the execution-trace approach when the important context lives in tool calls, actions, and outcomes rather than in the text of the conversation alone.
Targeted recall for long conversations
Use Memori to support long-running support, operations, or research workflows where the system should recall only the relevant facts from many prior interactions.
Operational monitoring of memory usage
Use the memory graph and analytics when a team needs visibility into what is being stored, how often memories are recalled, and whether caching is reducing repeated work.
Hosted memory storage and search
Use the hosted cloud option when you want to store and search memories quickly without adding separate infrastructure management to the application.
Pros and Cons
Pros
- Captures structured memory from both conversation and agent execution traces.
- Retrieves only the context that is relevant, which can reduce token usage versus full-context approaches.
- Provides explainable results with lineage, which helps teams review why a memory was used.
- Offers hosted memory storage and search through Memori Cloud with no additional setup stated on the site.
- Includes analytics so teams can monitor memory creation, recall usage, and cache hit rate.
Cons
- The public pages do not provide detailed setup documentation, supported framework lists, or a full integration matrix.
- Several claims are benchmark-driven, but the source material does not include broad third-party validation beyond the references shown on the site.
- Pricing is tiered and the higher-touch Enterprise option requires contacting the founders, so exact enterprise terms are not public.
FAQ
What kind of products is Memori designed for?
Memori is built to store and retrieve structured memory for agents, so it is a fit for production systems that need persistent context across sessions and conversations. The site highlights agent execution, conversation history, and long-running workflows rather than a narrow chat-only use case.
How does setup work?
The homepage says you can drop the SDK into an existing codebase and get memory handling with zero configuration. The solutions page also says Memori works with tools you already use and integrates natively with leading agent frameworks and protocols.
What does Memori store and recall?
Memori captures chat turns and also memory from agent execution traces, then classifies information into facts, preferences, rules, and summaries. The benchmark page describes a dual-layer approach with semantic triples and conversation summaries tied together for traceability.
Does Memori have paid plans?
The pricing page shows a Free plan, Starter, Pro, and Enterprise. Paid plans add higher memory limits, while Enterprise is custom and includes forward-deployed engineers plus dedicated integration and maintenance support.
Is Memori a database or an LLM training tool?
The site presents Memori as LLM-agnostic and optimized for production agents that need structured, persistent state rather than a general-purpose database. The public pages emphasize memory capture, recall, search, and analytics more than raw model training or embedding-only workflows.
Quick Facts
- Category
- Agent-native memory infrastructure
- Primary users
- Developers and teams building AI agents and production AI systems
- Platform
- LLM-agnostic; hosted Memori Cloud is available
- Website
- memorilabs.ai
- Pricing
- Free, Starter, Pro, and custom Enterprise plans
- Notable workflow
- Captures chats and traces into structured memory, then recalls only what is relevant
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