One API Call for LLM Generation with Long-Term Memory
Response API combines LLM output, memory store, memory retrieval, and context assembly into one request.
With Response API, you can build agents that understand users over time without managing separate steps or extra logic.
How Response API Works
You send a message with real conversational intent
- Developers send one input that includes the user’s latest message and any habits, goals, or current issues they want the agent to track.
- You don’t need separate memory calls, custom rules, or manual context fetches.
- What you send is pure conversational intent.
- The Response API handles everything else.
{
"user_id": "u123",
"message": "I moved to Tokyo recently. I'm still struggling with the morning routine here. Can you help me design a schedule that fits the habits we've discussed before, like my tendency to stay up late and my goal to exercise more?"
}The API performs the entire memory pipeline internally
You receive a memory-enhanced response
The Response API returns:
Just one Response API request that does everything an agent with memory needs.
Why Developers Choose Response API
Pricing
GPT Models
Gemini Models
Claude Models
Grok Models
DeepSeek Models
MemU ModelsThe memory model is automatically invoked during conversations, but not on every interaction. The frequency of invocation is determined by factors such as context length and time intervals to optimize performance and cost-effectiveness.
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
A single API call. A complete memory-aware agent loop.