MemoryLake
Engineering & Developerpersistent context for Claude API apps

Give Claude API Apps Persistent Context Beyond the 200k Window

Claude's long context window is generous — until your app needs to remember a year of user history across hundreds of sessions. MemoryLake gives Claude API apps a persistent context layer that scales 10,000x past the window, with millisecond retrieval and cross-model portability.

Day 1“Claude's long context window is generous — until your app”“needs to remember a year of user history across hundreds of…”✓ Got it, I will remember.Day 7 — new session“Same task again — can you keep the context?”× Sure — what was the context again?(forgot every detail you taught it)+ MEMORYLAKE LAYERMemory auto-loaded10,000x scale beyond the context windowNative MCP supportSix memory types preserve nuanceSESSION OUTPUT✓ Same prompt, on-brand answerNo re-briefing required.

Give Claude API Apps Persistent Context Beyond the 200k Window

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The problem: even a 200k window runs out

A power user can fill 200k tokens of relevant history in a few weeks of heavy use. A long-running agent fills it in hours. Once you blow the window, your app either summarizes (lossy) or forgets (worse). Persistent context for Claude API apps needs to live outside the window.

How MemoryLake solves persistent context for Claude API apps

10,000x scale beyond the context window

10,000x scale beyond the context window

Compress millions of tokens into ranked, retrievable memory. Pull only what each turn needs.

MEMORYNative MCP support

Native MCP support

Claude Desktop and Claude Code can read MemoryLake directly via Model Context Protocol. No glue code required.

MEMORYSix memory types preserve nuance

Six memory types preserve nuance

Background, Facts, Events, Conversation, Reflection, Skill. Better than collapsing everything into one summary chain.

Cross-model future-proofing

Cross-model future-proofing

Today Claude, tomorrow whatever beats it. Your users' memory migrates with one config change.

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How it works for Claude API apps

  1. Connect — Use the Python SDK, REST API, or MCP server. Authenticate once.
  2. Structure — As users interact, MemoryLake stores each turn and document as typed memory.
  3. Reuse — At inference, retrieve a token-budgeted memory block. Inject it as a Claude system message or tool result.

Before vs. after: Claude API persistent context

Without MemoryLakeWith MemoryLake
Year-long user historyTruncated or summarizedRetrieved on demand
Context window utilizationBloats over timeCompact, relevant block
MCP-based tool integrationsCustom state plumbingMemoryLake as native MCP server
Migrating to a new Claude versionManual prompt reworkSame memory, new model

Who this is for

Teams shipping production apps on the Claude API — long-form research assistants, coding copilots, agentic workflows — who need user context that scales past the window without sacrificing fidelity.

Runnable demoabout 3 minutes

Try this use case on your own account

Larkspan Home's returns assistant runs on Claude Haiku 4.5. Every request carries the same 6,900-token playbook, marked for Anthropic prompt caching, plus what the assistant remembers about the customer. The demo sends twelve questions four ways through MemoryRouter and reads the cache readout of every reply. With the memory as a second system block after the cache breakpoint, the layout the caching guide describes, the playbook and the memory were written to the cache on every request and read on none: 60 of 60 written, 0 read, 1.24 times the cost of no caching at all. The same memory at the top of the user turn, or attached by MemoryRouter, was read from the cache on every request after the first, at 0.23 times. A trimmed playbook with the same breakpoint was silently never cached.

What you will see

  • Six customers' facts go in with fact add; one boundary over them; prices come from memorylake router models (input, cache write, cache read)
  • Each request is a whole Anthropic Messages body sent with memorylake router messages --body; one search per question feeds three arms the same memory block, the fourth uses --boundary
  • Every reply's cache_creation_input_tokens, cache_read_input_tokens and input_tokens are printed per arm and priced into a bill
  • ask 4 sends one question and then the customer's next one, so you see the cache written once and read again; there is 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/cache-breakpoint-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py
Open on GitHubRead the walkthrough

Watch it run · real, unedited recordings

CLI demo4:08Web demo4:59

Related use cases

Engineering & DeveloperCross-Session Context for ChatGPT APIThe ChatGPT API has no built-in cross-session memory. MemoryLake adds persistent, versioned context across every API call without bloating your tokens. Free to get started.

Frequently asked questions

Does this work with Claude's prompt caching?

Yes. MemoryLake retrievals are designed to slot into cacheable system messages so you get both persistent memory and prompt-cache savings.

What about Claude Code?

Claude Code can connect to MemoryLake as an MCP server, giving the CLI access to your team's shared memory.

How is this different from summarizing old history?

Summaries lose detail and can't be queried by type or time. MemoryLake stores structured, retrievable, versioned memory with full provenance.

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