| name | memories-sdk |
| description | Build against the memories.sh SDK packages in application code. Use when working with `@memories.sh/core` or `@memories.sh/ai-sdk`, including: (1) Initializing `MemoriesClient`, (2) Reading, writing, searching, or editing memories from backend code, route handlers, workers, or scripts, (3) Integrating memories with the Vercel AI SDK via `memoriesMiddleware`, `memoriesTools`, `preloadContext`, or `createMemoriesOnFinish`, (4) Choosing and applying `tenantId` / `userId` / `projectId` scoping, (5) Managing SDK skill files or management APIs, or (6) Debugging memories SDK usage in TypeScript or JavaScript applications. Use `memories-cli` for CLI workflows, `memories-mcp` for MCP setup, and `memories-dev` for monorepo internals. |
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
- 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.
- 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.
- 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
- 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