What is a Memory API for AI agents?
Why LLM apps need a memory layer beyond the context window, what a Memory API provides, and how MyMemory fits agent workflows.
· 2 min read
Quick answer
A Memory API is the layer between an AI agent and durable knowledge: it ingests content, indexes it for retrieval, and exposes search and chat endpoints the agent calls on every turn. It replaces stuffing everything into the context window with fetching only the memories relevant to the current question.
Large language models are stateless. Every new chat starts from scratch unless you inject context. For demos, that's fine. For products people use every day, it breaks down quickly.
A Memory API is the layer that sits between your agents and durable knowledge: it ingests content, indexes it for retrieval, and exposes search or chat endpoints that agents call on every turn.
The problem with context windows alone
Even with very large context windows, putting everything into the prompt is expensive, slow and brittle:
- Cost scales with tokens. Every turn re-sends history and retrieved chunks.
- Recall degrades. Models miss facts buried in long contexts (the "lost in the middle" effect).
- There's no durable inbox. Users save articles, PDFs and notes elsewhere; the model never sees them unless you build pipelines.
Production agents need selective retrieval: fetch the right memories for this question, not the user's entire history.
What a Memory API typically provides
| Capability | Why agents need it |
|---|---|
| Ingest | Add URLs, files, notes and integration content as searchable memories |
| Chunk and embed | Turn raw content into retrieval-friendly units |
| Hybrid search | Combine semantic and keyword signals, so product names and dates still match |
| Chat and tools | Answer questions grounded in stored memories |
| Auth and quotas | Per-user isolation and rate limits for production |
MyMemory provides this as a hosted API at api.mymemory.dev, alongside a web app, a Chrome extension, and an MCP server for AI tools like Claude and Cursor.
Memory API vs vector database
A vector database stores embeddings and returns the nearest ones. A Memory API owns the whole loop:
- ingestion (parsing web pages, PDFs, tweets, podcasts),
- organization (spaces, tags, metadata),
- ranking tuned for natural-language questions,
- search and chat endpoints ready to call.
If you only need similarity search over a static corpus you control, a vector database may be enough. If users or agents continuously add content and ask natural-language questions, a Memory API saves months of glue code. See build vs buy for a fuller comparison.
Common agent patterns
- Personal copilot. A user saves research with the browser extension; Claude or Cursor recalls it through MCP while they work.
- Support assistant. Ingest help docs and past answers; the agent retrieves the relevant passages for each customer message.
- Research assistant. Hybrid search with a recency bias answers "what did I read about X last month?"
Getting started
- Create an account at mymemory.dev.
- Generate an API key in the dashboard.
- Follow the API quickstart to add and search your first memory.
MyMemory is a hosted service; self-hosting isn't available.