The Memory Infrastructure Layer for AI Applications
Every AI application eventually needs persistent memory — user context, learned facts, session history, reusable workflows. MemoryLake provides this as a dedicated infrastructure layer via REST API, Python SDK, and MCP, so you don't build it yourself.
The Memory Infrastructure Layer for AI Applications
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The Memory Problem
Most AI applications start with a simple context variable and end up with a fragile custom memory system — flat JSON files, ad hoc database schemas, or overloaded vector stores pressed into service as memory. These break at scale, lack structure, and have no conflict detection, versioning, or access control. Memory is an infrastructure problem; it deserves an infrastructure solution.
What MemoryLake Does Differently
Six typed memory categories via a single API — Rather than a flat key-value store, MemoryLake exposes six structured memory types through a unified API: Background, Fact, Event, Conversation, Reflection, and Skill. Your application writes to the right type; retrieval is precise and ranked.
Python SDK with cluster-level memory operations — The Python SDK supports cluster-level reads and writes, making it practical to manage memory across large user bases, agent fleets, or multi-tenant applications without custom orchestration.
AES-256 encryption with compliance built in — Memory data is encrypted at rest and in transit with AES-256 end-to-end encryption. MemoryLake is ISO 27001 and SOC 2 Type II certified, GDPR and CCPA compliant — ready for production without additional compliance work.
The Memory Infrastructure Layer for AI Applications
Get Started FreeFree forever · No credit card required
How It Works
- Connect — Authenticate with the MemoryLake REST API using your API key, or install the Python SDK (
pip install memorylake). MCP integration available for agent frameworks. - Structure — Write memory items to typed categories with your application logic. Fact Memory includes automatic conflict detection and versioning. All writes are timestamped and source-attributed.
- Reuse — Query memory by type, semantic similarity, or structured filter. The API returns ranked results in milliseconds, ready to inject into your model's context selectively.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Memory architecture | Custom schema designed per project | Six typed categories with defined semantics, ready to use |
| Conflict handling | Manual or absent | Automatic conflict detection and versioning on every Fact write |
| Compliance | Custom encryption, audit trail required | AES-256 E2E, ISO 27001, SOC 2 Type II, GDPR/CCPA included |
| Scale | Degrades as user base grows | Tested against 1B+ complex documents in production |
| Multi-model support | Separate memory per model integration | Single API serves ChatGPT, Claude, Gemini, Qwen, and more |
Built For
MemoryLake is designed for development teams building AI applications that will run in production — SaaS products, internal AI tools, research platforms, and AI-powered data applications. It is the memory layer your application needs once user count, session volume, or data complexity exceeds what a quick custom solution can handle.
Related use cases
Frequently asked questions
What does the REST API look like for writing a memory item?
What does the REST API look like for writing a memory item?
You POST to the appropriate memory type endpoint with the content, source, and any metadata. Fact Memory writes include automatic conflict checking against existing facts before confirming the write. Full API documentation is available in the MemoryLake developer docs.
Does the Python SDK support async operations?
Does the Python SDK support async operations?
Yes. The Python SDK supports both synchronous and async operation patterns and includes cluster-level memory operations for managing memory across large user populations or agent fleets efficiently.
What models can retrieve memory via the MemoryLake API?
What models can retrieve memory via the MemoryLake API?
Any model that can make an HTTP call or connect via MCP. MemoryLake natively supports ChatGPT, Claude, Gemini, Qwen, OpenClaw, AutoGPT, Manus, and Perplexity, and works with any model accessible via a standard API endpoint.