Give LLM Applications Memory That Outlives Every Restart
Most LLM applications treat every session like a clean slate. Users repeat their goals, constraints, and history every time the conversation resets. MemoryLake adds a persistent long-term memory layer to LLM applications, so user context, preferences, and prior work flow into every future call automatically.
Give LLM Applications Memory That Outlives Every Restart
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The problem: LLM applications forget the user between sessions
A chatbot that learned your role yesterday cannot recall it today. A research assistant that processed 200 pages on Monday starts empty on Tuesday. Developers patch around this with vector stores, summary buffers, and ever-growing system prompts — none of which survive a model swap or a schema change. The result is fragile UX and ballooning token bills.
How MemoryLake solves long-term memory for LLM applications
Stateful context across every session — User identity, goals, and prior work are stored as structured memory and injected into the next prompt automatically. No more "remind me what we were doing."
Six memory types out of the box — Background, Fact, Event, Conversation, Reflection, and Skill memory let your app capture not just what the user said, but what they value and how they work.
Cross-model portability — Switch your app from GPT-4 to Claude to Gemini without losing a single byte of user history. The memory passport travels with the user, not the model.
10,000x scale over raw context stuffing — Compress millions of tokens into millisecond-retrieval memory. LoCoMo benchmark #1 at 94.03% accuracy on long-horizon recall.
Give LLM Applications Memory That Outlives Every Restart
Get Started FreeFree forever · No credit card required
How it works for LLM applications
- Connect — Drop in the Python SDK, MCP server, or REST API. Pipe every user turn and document upload into MemoryLake.
- Structure — MemoryLake classifies each piece of context into one of six memory types and resolves conflicts against prior facts.
- Reuse — Query the memory at inference time. Get back a compact, ranked context block sized to your model window.
Before vs. after: LLM application memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Returning user opens a new chat | App asks for context from scratch | App greets user with full prior state |
| Switching the underlying model | History stranded on the old vendor | Memory follows the user to the new model |
| Token cost per session | Bloated system prompts | Compact, retrieved memory blocks |
| User trust over time | Decays after each forgotten detail | Compounds as memory deepens |
Who this is for
Founders and engineers shipping LLM-powered products — copilots, research assistants, agents, chatbots, vertical SaaS — who need user state to survive sessions, model upgrades, and pricing tier changes. Especially relevant for B2B applications where users invest hours of context into each account.
Related use cases
Frequently asked questions
How is long-term memory different from a vector database?
How is long-term memory different from a vector database?
A vector database retrieves semantically similar chunks. MemoryLake structures the user's identity, facts, events, and skills as typed memory with conflict detection and version control. You can still pair it with a vector store for documents — they solve different problems.
Does this work with my existing model provider?
Does this work with my existing model provider?
Yes. MemoryLake is model-agnostic. The same memory works across ChatGPT, Claude, Gemini, Qwen, and any model with an API. No vendor lock-in.
How do I migrate existing chat history into MemoryLake?
How do I migrate existing chat history into MemoryLake?
Import past conversations through the REST API or Python SDK. MemoryLake automatically extracts facts, events, and reflections and stores them as structured long-term memory ready for retrieval.