LLM Memory API: Choosing and Connecting a Memory Layer

An LLM memory API is a set of calls that let an AI application store facts between sessions, retrieve them when needed and delete them when they are no longer accurate. Without one, a language model (LLM) begins every conversation with no record of previous work. Tools like fenbs give you a ready-made project memory layer: tasks, decisions, rules and handoff state that any connected assistant reads through a standard MCP connection, without you building storage from scratch.

Memory APIs range from simple key-value stores to full vector databases for semantic search. The right choice depends on what you need to store, how you retrieve it and whether you are building a consumer product or managing shared project work. The agent harness topic covers more on the software that surrounds a working AI model.

What an LLM memory API does

A memory API sits outside the model and gives it a persistent place to write and read information. The model itself does not retain facts after a session ends. The API fills that gap: it accepts a write during one run and returns that same fact when the next session asks for it.

This is different from in-context memory, which is text included in the current prompt. In-context memory lasts only as long as the conversation and uses tokens from the context window. A memory API stores facts outside that window and retrieves only what is relevant, so an assistant working on a large project does not carry the full history in every message.

The four operations any memory layer needs

Check for all four of these before committing to a layer. Missing one creates maintenance problems later.

• Store: write a fact, a decision or a task update to the memory layer during an agent run.
• Retrieve: read back the relevant facts at the start of a session, filtered by project, topic or recency rather than returning everything at once.
• Delete or expire: remove facts that are no longer true. Without this operation, stale information accumulates and an assistant may act on outdated rules.
• Scope or namespace: keep memory for one project separate from another, so facts from a financial review do not surface in an unrelated project's context.

Read access patterns matter as much as write access. A layer that accepts writes easily but requires a full scan to retrieve anything useful becomes slow as a project grows. Design for the retrieval pattern first.

How storage types compare

Most real applications combine storage types. A vector store handles unstructured documents; a relational layer holds tasks and decisions; a key-value store keeps preferences. The question is which combination fits your retrieval patterns, not which single type to choose.

Scope and access control

A memory layer without scope isolation is a risk. One agent run should not read or overwrite another project's facts. Define a namespace for every project, user or session when you design the schema. When multiple people share the same layer, add access roles that limit what each person's assistant can read or write.

Also decide who can correct a stored fact. An agent that writes incorrect information should not be the only actor that can remove it. Give the people running the work a direct path to review and fix stored facts, independent of the agent's own write path.

Connecting a memory layer to your LLM application

1. Define your schema or namespace first. Decide what facts to store, how they are keyed and when they expire, before writing any retrieval code.
2. Add a read step at the start of each agent run. Retrieve facts scoped to the current project, filtered by recency or topic. Do not return the entire store at once.
3. Add a write step at natural checkpoints: after completing a task, confirming a decision or ending a session. Not every model turn needs a write.
4. Add a delete or update path. When a fact changes, update or remove the old entry. A layer with no way to correct stored facts drifts from reality over time.
5. Test the round-trip: write a known fact, start a fresh session, retrieve it and confirm it looks correct. Check that earlier test data does not appear.

What is an LLM explains how language models work, and what is agent memory covers the broader memory landscape. For the shape and limits of specific vendor surfaces, Claude's memory API and ChatGPT's memory API each cover their own approach.

When to build versus when to use an existing layer

Build a custom memory layer when the facts belong to a product you are shipping: per-user conversation history, personal preferences or application-specific context. An existing purpose-built layer fits better when the memory is project work: tasks, decisions, progress and handoff state that a team and its AI apps share.

If you are managing memory at scale, you will also face questions about what to prune and how to avoid contradictions between stored facts. The LLM memory management guide covers pruning and expiry strategies for larger stores. For teams with shared access, AI memory for teams covers roles and maintenance.

How to do this with fenbs

1. Create a free account and add your first project. Write a short goal, add the active tasks and record any rules that every session must follow.
2. Connect your AI app over MCP from Integrations. Approve access through the browser sign-in. The connection takes a few minutes to set up.
3. Ask the assistant to read context at the start of each session. It reads Rules first, then Where we left off, current tasks and notes.
4. At the end of each session, ask the assistant to save progress: what it completed, what it checked, what remains and the next step.
5. Add Decisions and rules for anything the next assistant must know before starting work. These appear first in the context, before task lists and notes.

fenbs does not vector-search your documents or import chat history from other tools. It is a project memory layer for shared work, not a personal memory app. For moving context between tools, see portable AI memory.