Use case

Assistants that remember.

Session chat forgets everything at close. Agent memory keeps decisions, preferences and facts in a local store, recalled when they matter.

Deploy LM-Kit One Build with LM-Kit.NET
Remembers across sessions Grounded in retrieval A store you can inspect Runs fully local

Your assistant meets you for the first time, every time.

Every session starts from zero: agreed terms, exceptions and decisions evaporate at close, so people re-brief the tool that was meant to save time.

  • The re-briefing tax. The same context gets typed again every morning, by everyone on the team.
  • Lost decisions. What the CFO approved in July is nowhere the assistant can see in August.
  • Tribal knowledge. The real context lives in veterans' heads, and it leaves when they do.
  • Hosted memory. Cloud assistant memory means your decisions accumulate on infrastructure you do not control.

Noted once, known from then on.

Memory is retrieval, not magic: facts embed locally and surface by meaning.

01

Note

The agent notes facts worth keeping as it works.

02

Store

Memories embed into a local store you can open and audit.

03

Recall

Relevant memories surface by meaning, not by session id.

04

Ground

Recall lands in context before the answer is written.

Institutional knowledge that survives the session.

The value compounds: every decision the assistant witnesses makes the next answer better.

Continuity

New session, same knowledge

Agreed terms, exceptions and preferences carry forward, so nobody re-briefs the assistant every morning.

Auditable

Memory is data, not a vibe

The store is inspectable: read what the assistant believes, correct what is wrong, delete what should not persist.

Private

As sensitive as the documents

Memories distill your decisions and terms, so they live under the same roof as the files, fully local.

One store, wired in at build.

Attach a memory to the agent and recall happens before every generation, from a store that never leaves the machine.

RememberingAgent.cs 
using LMKit.Agents;
using LMKit.Model;

var model      = LM.LoadFromModelID("qwen3.5:9b");
var embedModel = LM.LoadFromModelID("embeddinggemma-300m");

var memory = new AgentMemory(embedModel);

var agent = Agent.CreateBuilder(model)
    .WithMemory(memory)
    .Build();

var reply = await agent.RunAsync(
    "Draft the Meyer renewal with our agreed settlement terms.");

// Recall happens before generation; the store stays local.

The technology underneath.

Memory is retrieval infrastructure, and every layer of it is documented.

Store

Agent memory

The persistent store agents note into and recall from, local and inspectable.

How agent memory works

Recall

Embeddings

Meaning-based recall comes from local embedding models, never a hosted API.

How embeddings work

Persist

Vector storage

Built-in local vector storage, or Qdrant and pgvector when you already run them.

How storage works

Private Document Intelligence

Give your assistant a memory.

Deploy LM-Kit One Agent memory in depth