Mengram is an AI memory API with human-like architecture featuring semantic, episodic, and procedural memory types. It enables AI systems to remember facts, events, and learned workflows through a single API call.

Mengram serves as the foundational memory layer for AI agents, enabling them to learn from every interaction and retain knowledge across sessions. It is designed for developers building AI applications that require persistent, evolving memory, such as coding assistants, autonomous workflows, multi-agent systems, and voice agents. The core purpose is to replace complex RAG pipelines with a simple API that automatically extracts and organizes three distinct types of human-like memory, providing AI systems with deep, contextual understanding of users and tasks without manual configuration. This transforms agents from stateless tools into intelligent entities that accumulate experience and improve over time, making every interaction smarter than the last.

Traditional AI systems suffer from severe amnesia between sessions, forcing users to repeat context, preferences, and past decisions. Developers face the burden of building and maintaining custom retrieval pipelines involving chunking, embedding, and vector storage, which are error-prone and require multiple API keys and manual tuning. This results in AI agents that cannot learn from failures, remember user-specific workflows, or provide personalized experiences without extensive engineering effort. The problem extends to multi-user applications where data isolation is crucial, and to multilingual contexts where English-only solutions fail. Mengram directly addresses these pain points by offering a unified, zero-config memory system that works out of the box.

The first major feature group is its three memory types, which mimic human cognitive architecture. Semantic memory captures facts, preferences, and skills—like a user's preferred database or coding style. Episodic memory records events, discussions, and decisions, such as the details of a recent DevOps standup or a project planning session. Procedural memory learns workflows, processes, and habits, for example, the steps a user consistently follows to deploy code. Each memory type is automatically extracted from conversation history via a single `add()` call, eliminating the need for manual annotation. This structured approach allows AI agents to recall not just isolated facts but the full context of past interactions, enabling truly personalized and context-aware responses.

The second major feature group comprises advanced capabilities that differentiate Mengram from simple memory storage. Smart Triggers enable memory that proactively raises its hand, generating reminders from conversations, contradiction alerts, and workflow pattern detection so your AI can tell you what it remembers without being asked. Experience-Driven Procedures allow workflows to self-improve; failures auto-evolve procedures to new versions, and three or more similar successes auto-create new workflows, with full version history and evolution logs. Native Multilingual support delivers equal retrieval quality across 23 languages including Russian, Chinese, Spanish, Japanese, Korean, and Arabic, with cross-lingual search where an English query finds Russian documents, built on Cohere multilingual models rather than English-only alternatives.

Mengram

Mengram

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