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.
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
Compress millions of tokens into ranked, retrievable memory. Pull only what each turn needs.
Native MCP support
Claude Desktop and Claude Code can read MemoryLake directly via Model Context Protocol. No glue code required.
Six memory types preserve nuance
Background, Facts, Events, Conversation, Reflection, Skill. Better than collapsing everything into one summary chain.
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
- Connect — Use the Python SDK, REST API, or MCP server. Authenticate once.
- Structure — As users interact, MemoryLake stores each turn and document as typed memory.
- 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 MemoryLake | With MemoryLake | |
|---|---|---|
| Year-long user history | Truncated or summarized | Retrieved on demand |
| Context window utilization | Bloats over time | Compact, relevant block |
| MCP-based tool integrations | Custom state plumbing | MemoryLake as native MCP server |
| Migrating to a new Claude version | Manual prompt rework | Same 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.
Related use cases
Frequently asked questions
Does this work with Claude's prompt caching?
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?
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?
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.