AgentMemory Vault | Scoped Memory Store for Agent and Assistant Systems v3.1

Description

AgentMemory Vault is a scoped memory module for AI assistants and agent systems that need to store, retrieve, and manage contextual information across tasks without creating uncontrolled long term memory risk. Many agent systems become unreliable when they either remember nothing or remember too much. This module provides a structured approach to session memory, project memory, user scoped memory, task state, retrieval metadata, and memory expiration rules. It can support internal assistants, research agents, customer support agents, coding assistants, knowledge agents, and workflow agents. A typical workflow is to store task relevant context, tag it with scope and source, retrieve it during a later step, and expire or archive it based on configured rules. The module is not a replacement for a full vector database, CRM, or enterprise knowledge base. It is a controlled memory layer designed to help agent systems use context safely and predictably. Users should define what may be stored, how long memory should persist, who can access it, and when it must be deleted. Sensitive data should be filtered or encrypted before storage. In production, memory usage should be logged and reviewed because memory can create both productivity gains and privacy risks.

Product attributes

Canonical product name: AgentMemory Vault

Module type: Scoped memory store for agent systems

Primary category: AI agents

Secondary categories: Agent memory, context management, retrieval state, session persistence

Suggested list price: £579.00

Intended users: Agent developers, AI platform teams, backend engineers, knowledge system builders

Applicable lifecycle stage: Agent system design, assistant development, context management, long running task automation

Typical inputs: Conversation context, task state, memory entries, metadata tags, user scope, expiration rules

Typical outputs: Retrieved memory objects, scoped context bundles, memory logs, expiration records, agent state references

Delivery format: ZIP package automatically delivered by email after purchase

Expected package contents: Source files, memory store examples, configuration templates, documentation, tests, sample agent memory workflows

Runtime environment: Python based backend or agent runtime environment

Integration mode: Agent memory layer, assistant context store, retrieval support module, internal task state service

Recommended skill level: Advanced

Commercial rights: Full commercial use is permitted

Modification rights: Modification, storage policy customization, internal adaptation, and proprietary integration are permitted

Open source policy: Public open sourcing is prohibited

Redistribution policy: Resale, redistribution, sublicensing, or repackaging as a standalone module is prohibited

Production readiness note: Requires privacy review, retention policy, access control, encryption review, and memory misuse testing

Validation standard: The module is considered valid when sample memory entries can be stored, scoped, retrieved, expired, and logged according to documentation