Ship LLM Apps Without Building Memory Infrastructure From Scratch
Every LLM developer ends up writing the same memory layer twice — a vector store, a summary chain, a session table, a deduper. MemoryLake replaces that stack with a single memory API that handles persistence, conflict resolution, versioning, and cross-model retrieval out of the box.
Ship LLM Apps Without Building Memory Infrastructure From Scratch
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The problem: every LLM app rebuilds the same memory stack
You wire up Pinecone for retrieval, Redis for sessions, Postgres for user facts, and a custom dedupe pipeline to keep them in sync. Three months later you swap models and most of that plumbing breaks. The memory API for LLM developers should be one HTTP call, not five subsystems.
How MemoryLake solves memory infrastructure for developers
One SDK, six memory types
Background, Fact, Event, Conversation, Reflection, Skill. Stop writing custom schemas for each kind of context your app needs to remember.
REST, MCP, and Python SDK
Use it from any backend, any framework, any agent runtime. MCP support means Claude Desktop, Cursor, and Windsurf can read your app's memory natively.
Built-in conflict resolution
When new facts contradict old ones, MemoryLake flags the conflict and applies your chosen strategy: latest-source, confidence-weighted, or manual review.
Git-style version control
Branch, commit, merge, and roll back memory state. Every change has an immutable audit trail. Critical for regulated industries.
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How it works for LLM developers
- Connect — Install the Python SDK or hit the REST endpoint. Authenticate with an API key.
- Structure — Send raw user turns, documents, or events. MemoryLake routes them into the right memory type and resolves duplicates.
- Reuse — Call
retrieve()at inference time. Get a ranked, token-budgeted context block for your prompt.
Before vs. after: LLM developer workflow
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Memory infra to build | 4–6 subsystems wired together | One SDK call |
| Schema design for user facts | Custom tables per app | Six built-in memory types |
| Switching the underlying model | Rewrite retrieval pipeline | Same API, any model |
| Audit log of memory changes | Build it yourself | Built in, exportable |
Who this is for
Backend engineers, agent builders, and indie founders shipping LLM products who want to spend their time on UX and model orchestration — not on debugging memory pipelines. Especially valuable for solo and small-team developers who can't justify a dedicated infra hire.
Try this use case on your own account
Brindle Lane Deliveries' dispatch assistant reads six recipients' gate codes through MemoryRouter and keeps its conversations on the Responses API (store: true, then previous_response_id). Turn 1 asks for the code and gets it. Then every recipient phones in a new code: fact update changes the fact, fact trace keeps the old text, and a fresh call answers the new code. Turn 2 asks again on the same chain. The note MemoryRouter attached on turn 1 was stored in the chain with it, and no memory operation reaches that copy. Across 12 runs: whole conversation in the request 144/144 new code; chain without the boundary 0/144 (always the old code); chain with the boundary 15/72 when asked to confirm the code, 69/72 when asked which code the driver should punch in; chain plus one sentence in instructions (notes on the newest message are current) 143/144.
What you will see
- Six gate codes stored with
fact add, one boundary over the project; every model call ismemorylake router responses --body … --boundary - Between the turns,
fact updatechanges all six codes;fact traceshows the old text in the history andfact listthe new codes - Turn 2 is sent four ways from the same stored turn 1, with two phrasings of the question; every reply is graded old code / new code
ask --recipient "Ruth Abara"replays one parcel with the code change in between;factslists the notes; 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/frozen-chain-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Is MemoryLake a vector database?
Is MemoryLake a vector database?
No. Vector databases retrieve embeddings. MemoryLake stores structured, typed memory with conflict resolution, versioning, and provenance. Use both together if you need document chunk retrieval plus user state.
How does the Python SDK compare to using REST directly?
How does the Python SDK compare to using REST directly?
The SDK adds typed memory objects, batching, and cluster-level operations. REST is fine for simple integrations; the SDK is faster to ship with for production apps.
Can I self-host MemoryLake?
Can I self-host MemoryLake?
Enterprise tiers support deployment in your VPC. End-to-end AES-256 encryption applies in both cloud and self-hosted modes — even MemoryLake cannot read your data.