TypeScript quickstart

pip install "llmslim[server]" && llmslim serve     # the server: one process, one SQLite file
npm install llmslim                                 # the client

import { LLMSlim } from "llmslim";

const slim = new LLMSlim({ baseUrl: "http://127.0.0.1:8080", apiKey: process.env.LLMSLIM_API_KEY });

await slim.memories.add({
  userId: "asha",
  messages: [
    { role: "user", content: "I'm allergic to peanuts, so never suggest satay." },
    { role: "assistant", content: "Understood: no peanuts." },
  ],
});

const context = await slim.memories.search({ userId: "asha", query: "Am I allergic to anything?", budget: 1500 });
console.log(context.text); // the selected turns under their dates; use it as data, not instructions

import { generateText, stepCountIs, wrapLanguageModel } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { LLMSlim } from "llmslim";
import { memoryMiddleware, memoryTools } from "llmslim/ai-sdk";

const client = new LLMSlim();
const model = wrapLanguageModel({
  model: anthropic("claude-haiku-5-5"),
  middleware: memoryMiddleware({ client, userId: session.userId }),
});

const result = await generateText({
  model,
  tools: memoryTools({ client, userId: session.userId }),
  stopWhen: stepCountIs(5),
  messages,
});

import { Agent } from "@mastra/core/agent";
import { MASTRA_RESOURCE_ID_KEY } from "@mastra/core/request-context";
import { LLMSlim } from "llmslim";
import { memoryProcessor, memoryTools, type MemoryOptions } from "llmslim/mastra";

const memory: MemoryOptions = {
  client: new LLMSlim(),
  // Whose memory: the signed-in user, from your server, for each request.
  scope: (requestContext) => ({ userId: requestContext.get(MASTRA_RESOURCE_ID_KEY) }),
};

export const assistant = new Agent({
  id: "assistant",
  name: "Assistant",
  instructions: "You are a helpful assistant.",
  model: "anthropic/claude-haiku-5-5",
  tools: { ...memoryTools(memory) },
  outputProcessors: [memoryProcessor(memory)], // after any guardrails
});