memories-sdk

Use the SDK packages when an application needs memories.sh programmatically. Prefer @memories.sh/core for direct typed API access and @memories.sh/ai-sdk only when the caller already uses the Vercel AI SDK.

Workflow

1. Pick the integration surface:
   • Use @memories.sh/core for backend routes, workers, cron jobs, and non-AI-SDK agents.
   • Use @memories.sh/ai-sdk for generateText, streamText, middleware, or tool loops built on ai.
   • If the task is about the CLI or MCP configuration, switch to memories-cli or memories-mcp.
2. Set scope before writing code:
   • Keep MEMORIES_API_KEY server-side.
   • tenantId selects the tenant or workspace database.
   • userId narrows memory to a user inside that tenant.
   • projectId narrows reads and writes to a product area, repo, or feature slice.
3. Use the narrowest pattern that solves the task:
   • Direct CRUD or context lookup: MemoriesClient
   • Automatic prompt injection: memoriesMiddleware
   • Agent loops with explicit memory tools: memoriesTools and memoriesSystemPrompt
   • Fetch once and reuse: preloadContext
   • Persist after completion: createMemoriesOnFinish
4. Verify the integration:
   • Confirm the same scope is used on both reads and writes.
   • Catch MemoriesClientError.
   • Do not expose the API key to browser-only code.

Quick Start

@memories.sh/core

import { MemoriesClient } from "@memories.sh/core"

const client = new MemoriesClient({
  apiKey: process.env.MEMORIES_API_KEY,
  tenantId: "acme-prod",
  userId: "user_123",
})

const context = await client.context.get({
  query: "billing architecture",
  projectId: "dashboard",
  mode: "all",
  strategy: "hybrid",
  limit: 8,
})

await client.memories.add({
  content: "Enterprise billing uses Stripe invoices.",
  type: "fact",
  projectId: "dashboard",
  tags: ["billing"],
})

@memories.sh/ai-sdk

import { generateText, stepCountIs, wrapLanguageModel } from "ai"
import { openai } from "@ai-sdk/openai"
import {
  memoriesMiddleware,
  memoriesSystemPrompt,
  memoriesTools,
} from "@memories.sh/ai-sdk"

const model = wrapLanguageModel({
  model: openai("gpt-4o"),
  middleware: memoriesMiddleware({
    tenantId: "acme-prod",
    userId: "user_123",
    projectId: "dashboard",
  }),
})

const result = await generateText({
  model,
  system: memoriesSystemPrompt(),
  tools: memoriesTools({
    tenantId: "acme-prod",
    userId: "user_123",
    projectId: "dashboard",
  }),
  stopWhen: stepCountIs(5),
  prompt: "Summarize prior decisions about billing.",
})

console.log(result.text)

Decision Guide

• Need direct typed access from your own backend code: use MemoriesClient
• Need automatic context injection into prompts or messages: use memoriesMiddleware
• Need the model to read or write memory explicitly through tools: use memoriesTools
• Need to manage stored skill files or procedure fragments: use client.skills.* or the AI SDK skill-file tools
• Need tenant, key, or embedding usage administration: use client.management.*
• Need internals of the memories monorepo or server endpoints: use memories-dev

Reference Files

• references/core.md: direct client methods, transport choices, errors, management APIs, and skill-file APIs
• references/ai-sdk.md: middleware, tools, preload, post-finish persistence, and query extraction patterns
• references/scoping.md: tenant/user/project scoping rules, server-side safety, and debugging checklist