Memori

Product Introduction

1. Definition: Memori is an agent-native memory infrastructure, specifically a middleware layer for AI agent systems. It is an LLM-agnostic service designed to transform the unstructured execution traces, tool results, workflow steps, and conversational history of AI agents into structured, persistent, and queryable long-term memory.
2. Core Value Proposition: Memori exists to solve the critical challenge of state management and context retention in production-grade AI agent applications. Its primary value is enabling agents to learn from their own actions, avoid repeating mistakes, and deliver personalized, contextually-aware responses while dramatically reducing LLM inference costs and latency. Key keywords: agent memory, structured memory from trace, persistent agent state, LLM cost reduction, production AI infrastructure.

Main Features

1. Automated Memory Structuring and Classification: Memori automatically ingests each agent interaction (chat turn, tool call, execution step) and classifies the content into distinct, structured categories such as facts, user preferences, business rules, and conversation summaries. This process uses machine learning models to parse and tag data, transforming raw agent trace into a searchable knowledge graph. Users retain granular control over data retention policies and storage location.
2. Targeted, Explainable Recall with Semantic Search: When an agent requires context, Memori performs a hybrid retrieval. It uses metadata filtering (by entity, time, source) combined with semantic search that is automatically enriched with context to find only the most relevant memory snippets across all conversations and documents. Every retrieved result includes a clear lineage, explaining why it was included based on relevance scores and metadata, ensuring transparency and auditability.
3. Memori Cloud & Integrated Analytics: Memori Cloud is a hosted service providing instant memory storage and search without infrastructure management. It includes a visual memory graph to explore relationships between entities (people, places, topics) across the memory network. Comprehensive analytics dashboards track key operational metrics like memory creation rate, recall usage patterns, cache hit rates, and token savings, providing full observability into the memory layer's performance.
4. Enterprise-Grade Security and Data Controls: The platform offers role-based access control (RBAC) that integrates with existing identity providers (SSO, OAuth), ensuring memory operations respect user permissions. It features configurable data retention and automatic deletion policies with audit trails. Crucially, Memori operates with a "bring your own database" principle, where memory data can reside entirely within the customer's own secure database environment, with Memori's layer providing the intelligence on top.

Problems Solved

1. Pain Point: The high cost and inefficiency of using full-context window retrieval with LLMs, where entire conversation histories are re-submitted, leading to exorbitant token usage, slow response times (latency), and context window limits.
2. Pain Point: The lack of persistent, structured memory in AI agents, causing them to operate statelessly, forget past interactions, repeat errors, and fail to provide personalized or consistent experiences across sessions.
3. Target Audience: AI Agent Developers and Engineers building production systems with frameworks like LangChain, LlamaIndex, or Swarms; Enterprise DevOps and MLOps Teams needing scalable, secure, and observable AI infrastructure; Product Teams creating customer-facing AI assistants, support bots, or personalized recommendation engines.
4. Use Cases: Customer Support Agents remembering a user's past issues and preferences across multiple tickets; E-commerce Shopping Assistants recalling a customer's size, style, and purchase history; Robotics and IoT systems where a robot needs persistent memory of its environment and past interactions; Internal Enterprise Copilots that learn company processes and individual user workflows over time.

Unique Advantages

1. Differentiation: Unlike simple vector databases or caching layers, Memori is purpose-built for agent trace, meaning it structures memory from what an agent does (tool calls, execution paths, decisions), not just what it says. Compared to naive full-context retrieval, it delivers superior accuracy (81.95% on LoCoMo benchmark) while using ~95% fewer tokens, translating to direct and substantial cost savings.
2. Key Innovation: Its agent-native architecture treats the agent's execution trace as a first-class data source. The integration of selective semantic search with automatic query enrichment and metadata filtering ensures high-recall accuracy without bloating token counts. The explainable results and lineage feature provides necessary transparency for debugging and trust in production systems, a feature often missing in black-box retrieval systems.

Frequently Asked Questions (FAQ)

1. What is Memori and how does it work with AI agents? Memori is a memory infrastructure layer that integrates directly into your AI agent's code. It captures the agent's execution trace—including conversation turns, tool results, and decision logic—structures this data, and stores it as persistent memory. When the agent needs context, Memori retrieves only the relevant, structured memories instead of the entire history, making the agent more efficient and context-aware.
2. How does Memori reduce LLM inference costs by 95%? Memori reduces costs through tokenless recall and targeted retrieval. Instead of re-submitting an entire conversation history (full-context) to the LLM, which consumes thousands of tokens, Memori's intelligent search pulls only concise, relevant memory snippets. As per their LoCoMo benchmark, this approach uses an average of only 1,294 tokens per query compared to full-context, achieving over 95% savings on inference spend.
3. Is my data secure with Memori? Can I use my own database? Yes, Memori prioritizes data security and ownership. It offers a "bring your own database" model where your memory data never leaves your chosen environment (e.g., your own PostgreSQL, MongoDB, or CockroachDB instance). Memori's service layer adds the intelligence for search, graph, and analytics on top. It also includes enterprise features like RBAC, SSO integration, data retention policies, and full audit trails.
4. What is the difference between Memori and a vector database? A vector database is a general-purpose storage and similarity search engine for embeddings. Memori is a higher-level, application-specific memory system for AI agents. It uses vector search as one component but adds critical layers: automatic structuring/classification of agent trace, explainable hybrid search (semantic + metadata), memory graph relationships, analytics, and cost-optimized recall logic specifically designed for agent workflows.
5. How do I integrate Memori into my existing AI project? Integration is designed for developer velocity. You can start with Memori Cloud for a hosted solution by adding their SDK with one line of code, requiring zero configuration for model calls and callbacks. For more control, you can deploy the Memori layer to work with your own database. The SDK is compatible with popular AI agent frameworks, allowing you to add persistent memory to your existing agent codebase in minutes.