Memori – Agent-native Memory Infrastructure

Description

Memori provides a robust memory infrastructure designed to enhance AI applications by transforming agent execution and conversation into a structured, persistent state. As an LLM-agnostic layer, Memori captures each chat turn, classifying it into facts, preferences, rules, and summaries, allowing users to control what is stored, for how long, and where. This ensures that only relevant information is recalled when a prompt requires context, eliminating the need for additional services.

Memori's selective semantic search enriches searches with semantic context, improving accuracy without increasing token costs. Each result includes a clear explanation of its inclusion, allowing teams to trace relevance by entity, time, and source. Tested on LoCoMo, Memori achieves 87% accuracy while reducing token usage by 97% compared to full-context retrieval.

The platform offers easy integration with existing systems, requiring just one line of code to drop the SDK into existing codebases. Memori Cloud further simplifies storage and search capabilities, enabling instant memory access without additional setup. The memory graph feature allows users to explore relationships between people, places, interests, and experiences, providing a dynamic view of the memory network.

Memori is trusted by top developers and enterprises for its strong recall accuracy and significant inference-cost savings. It integrates seamlessly with databases like MongoDB and CockroachDB, offering a compelling memory abstraction paired with flexible document models and integrated vector search. Memori's security features include role-based access control, data retention policies, and audit trails, ensuring data ownership and compliance with organizational policies.

Overall, Memori is an ideal solution for developers and enterprises looking to enhance their AI agent workflows with a reliable and efficient memory infrastructure.

Memori's Core Features

• LLM-agnostic memory layer

• Automatic memory capture

• Targeted context recall

• Selective semantic search

• Explainable results

• High accuracy with reduced token usage

• Easy SDK integration

• Memori Cloud for instant access

How to use Memori?

1. 1

   Integrate: Drop the SDK into your code

2. 2

   Configure: Set up memory capture and retention

3. 3

   Use: Enable targeted recall and semantic search

4. 4

   Optimise: Monitor analytics and adjust settings

Memori's Use Cases

• AI Workflow Enhancement
• Semantic Search
• Data Security
• Memory Analytics
• Cloud Integration

FAQ from Memori

• What is Memori's primary function?

  Memori provides a memory infrastructure that transforms agent execution and conversation into a structured, persistent state for AI applications.

• How does Memori enhance AI applications?

  It captures and classifies chat turns, enabling targeted recall and semantic search, improving accuracy and efficiency.

• Is Memori compatible with existing systems?

  Yes, Memori can be integrated with existing systems using a simple SDK, requiring minimal configuration.

• What are the security features of Memori?

  Memori offers role-based access control, data retention policies, and audit trails to ensure data security and compliance.

• Can Memori be used with cloud storage?

  Yes, Memori Cloud enables instant storage and search capabilities without additional setup.

• What is the accuracy rate of Memori?

  Memori achieves 87% accuracy while reducing token usage by 97% compared to full-context retrieval.

• Who can benefit from using Memori?

  Developers and enterprises looking to enhance their AI agent workflows with efficient memory management can benefit from Memori.

• Does Memori support semantic search?

  Yes, Memori enriches searches with semantic context, improving accuracy without increasing token costs.