A Simple, Reliable Memory API for Your Agents

Memory API gives developers full control over long-term memory.

Store, retrieve, search, and manage structured memory through flexible endpoints that plug directly into your LLM workflow. Build agents that evolve over time — exactly the way you design them.

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How Memory API Works

1

You decide when your LLM should use Memory API

Applying it only in the moments and scenarios where your agent truly needs long-term memory.

2

You retrieve relevant memory for context when needed.

Before invoking your LLM, your agent can query the Memory API to fetch previously stored memory.

Why Developers Choose Memory API

Full Control Over Memory Flow
You decide when to store, what to store, how to retrieve, and when to use memory.
Model-Agnostic Integration
Works with any LLM — OpenAI, Anthropic, local models, custom pipelines — because you handle prompt + model.
Deterministic and Transparent Memory Management
Behavior is predictable: no auto-extraction, no background inference, only what you explicitly store and fetch.
Lightweight and Easy to Integrate
Drop-in API endpoints; no need for complex infrastructure. Perfect for existing LLM-based systems.
Ideal for Applications Needing Explicit Memory Logic
Use cases like user profiles, persistent preferences, long-term tracking, knowledge bases, or any context-sensitive agent logic.

Pricing

Pro

$249/moFree

Perfect for SMB & indie builders.

  • Unlimited memories
  • Unlimited end users
  • Unlimited API calls
  • Custom Categories
  • Ready-to-Use
  • Multiple projects support
  • Community Support
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Enterprise

Custom

For large-scale deployments.

  • All Pro Features
  • On-prem deployment
  • Cluster Categories
  • Theory-of-Mind Ability
  • Multimodal Memory
  • Custom Integrations
  • Private Slack Channel
  • Advanced Analytics
  • Audit Logs
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FAQ

Agent memory (also known as agentic memory) is an advanced AI memory system where autonomous agents intelligently manage, organize, and evolve memory structures. It enables AI applications to autonomously store, retrieve, and manage information with higher accuracy and faster retrieval than traditional memory systems.

MemU improves AI memory performance through three key capabilities: higher accuracy via intelligent memory organization, faster retrieval through optimized indexing and caching, and lower cost by reducing redundant storage and API calls.

Agentic memory offers autonomous memory management, automatic organization and linking of related information, continuous evolution and optimization, contextual retrieval, and reduced human intervention compared to traditional static memory systems.

Yes, MemU is an open-source agent memory framework. You can self-host it, contribute to the project, and integrate it into your LLM applications. We also offer a cloud version for easier deployment.

Agent memory can be used in various LLM applications including AI assistants, chatbots, conversational AI, AI companions, customer support bots, AI tutors, and any application that requires contextual memory and personalization.

While vector databases provide semantic search capabilities, agent memory goes beyond by autonomously managing memory lifecycle, organizing information into interconnected knowledge graphs, and evolving memory structures over time based on usage patterns and relevance.

Yes, MemU integrates seamlessly with popular LLM frameworks including LangChain, LangGraph, CrewAI, OpenAI, Anthropic, and more. Our SDK provides simple APIs for memory operations across different platforms.

MemU offers autonomous memory organization, intelligent memory linking, continuous memory evolution, contextual retrieval, multi-modal memory support, real-time synchronization, and extensive integration options with LLM frameworks.

Build Agents That Remember — Your Way

You control logic.
Memory API handles memory store and memory retrieval.

Start building in minutes.