Give Any LLM Long-Term Memory Without Bloating the Context Window
LLMs are stateless by design — every session starts from zero. MemoryLake changes that by providing a structured memory layer that any model can read from and write to, with millisecond retrieval and zero context window inflation.
Give Any LLM Long-Term Memory Without Bloating the Context Window
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The Memory Problem
LLMs don't forget because of a bug. They forget because the transformer architecture has no persistent state — each inference call is independent. Workarounds like stuffing previous conversations into the context window hit token limits fast, degrade response quality, and add latency. You need memory outside the model, not inside it.
What MemoryLake Does Differently
Typed memory categories, not a flat knowledge dump
MemoryLake organizes memory into six structured types: Background (identity, read-only), Fact (versioned, conflict-checked, source-attributed), Event (timeline), Conversation (permanent session history), Reflection (behavioral patterns), and Skill (reusable workflows). Retrieval is precise because storage is structured.
#1 retrieval accuracy on LoCoMo benchmark
MemoryLake scores 94.03% on LoCoMo, the standard benchmark for long-term conversational memory. That means when your LLM asks for what a user said three months ago, it gets the right answer.
10,000x scale vs direct context injection
Injecting memory directly into context doesn't scale. MemoryLake's retrieval architecture handles the same workload at 10,000x the scale, with millisecond latency suitable for real-time applications.
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How It Works
- Connect — Integrate MemoryLake via REST API, MCP (Model Context Protocol), or the Python SDK. Works with ChatGPT, Claude, Gemini, Qwen, AutoGPT, and any model reachable via API endpoint.
- Structure — As your LLM session runs, relevant outputs — user facts, decisions, learned patterns, recurring workflows — are written to the appropriate typed memory category with source attribution and timestamps.
- Reuse — On the next session (or any future session), the model retrieves relevant memory at millisecond speed. Context stays lean; the model stays informed.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Session continuity | Every session starts cold | Background + Conversation memory surfaces prior context instantly |
| Context window usage | Grows with every workaround | Memory lives outside the window; context stays focused |
| Retrieval accuracy | Degrades with scale | 94.03% LoCoMo benchmark accuracy at any scale |
| Conflicting facts | Model accepts the latest silently | Conflict detection flags and versions every Fact update |
| Multi-session workflows | Rebuilt from scratch each time | Skill Memory stores reusable workflows, available across runs |
Built For
MemoryLake is designed for developers building LLM-powered products where continuity matters: AI assistants, coding agents, research tools, customer-facing chatbots, and multi-step automation pipelines. If your users interact with an LLM more than once, they need persistent memory.
Try this use case on your own account
An assistant's memory of one user grows from 20 facts to 100 to 400. At each point the same four questions are asked three ways: with no memory, with the whole memory pasted into the prompt, and through MemoryRouter with one boundary. Every reply is graded against what is stored, so you can see what pasting costs and where it starts to go wrong.
What you will see
- The pasted prompt carries 395, then 1,822, then 6,911 tokens of memory on every question (
router chat --body); MemoryRouter attaches 78 to 158 tokens at every size (--boundary) - Look-ups (a door code, a passport date) stay right either way. Lists are where pasting breaks: at 400 facts, 0 of 18 birthday lists were exact over 9 runs, each time with contacts listed under the wrong month
- MemoryRouter answered 108 of 108 questions exactly, and Claude on the Anthropic API listed the same five birthdays through the same boundary
- Every wrong name is looked up with
fact list --query: the memory holds the right date. Plusask(any question, three ways) and a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/long-term-llm-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
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Frequently asked questions
Does MemoryLake work with any LLM?
Does MemoryLake work with any LLM?
Yes. MemoryLake is model-agnostic. It supports ChatGPT, Claude, Gemini, Qwen, OpenClaw, AutoGPT, Manus, Perplexity, and any model accessible via a standard API endpoint. Memory is stored and retrieved independently of the model.
How does MemoryLake avoid bloating the context window?
How does MemoryLake avoid bloating the context window?
Memory is stored externally and retrieved selectively — only the relevant memory items for a given session are surfaced. Your context window contains focused, relevant information rather than a full conversation history dump.
What is LoCoMo and why does it matter?
What is LoCoMo and why does it matter?
LoCoMo (Long-term Conversational Memory benchmark) is the standard evaluation for how accurately AI systems retrieve information from long-term interaction history. MemoryLake's 94.03% score is the current top result on the benchmark, meaning it retrieves the right memory more reliably than alternatives.