@dakera-ai/ai-sdk

Vercel AI SDK integration for Dakera — a self-hosted memory server that adds persistent, decay-weighted vector recall across sessions. Memories are importance-scored and decay over time, so stale context stops competing with fresh, relevant facts.

It plugs into the AI SDK's two standard extension points — language model middleware and tools — so you can add cross-session memory to any AI SDK app without changing your model or provider code.

Install

npm install @dakera-ai/ai-sdk ai @dakera-ai/dakera zod

Run a Dakera server first (self-hosted, no external dependencies): see dakera-ai/dakera-deploy for the Docker Compose setup (server + MinIO). Point the integration at it with DAKERA_URL (default http://localhost:3000) and DAKERA_API_KEY.

Pattern 1 — Memory middleware (transparent)

createDakeraMemoryMiddleware wraps a model so that relevant memories are recalled and injected as system context before every call, and the new exchange is stored afterwards. No changes to your generation code.

import { generateText, wrapLanguageModel } from "ai";
import { openai } from "@ai-sdk/openai";
import { createDakeraMemoryMiddleware } from "@dakera-ai/ai-sdk";

const model = wrapLanguageModel({
  model: openai("gpt-4o"),
  middleware: createDakeraMemoryMiddleware({
    apiUrl: "http://localhost:3000",
    apiKey: process.env.DAKERA_API_KEY,
    agentId: "user-1234",
  }),
});

// First session
await generateText({ model, prompt: "I'm building a Rust vector database." });

// A later session — the model recalls the earlier context automatically
const { text } = await generateText({ model, prompt: "What am I working on?" });
// → "You're building a Rust vector database."

Under the hood the middleware uses transformParams to prepend recalled memories as a system message, and wrapGenerate to persist the exchange. Storage is best-effort: a memory-server error never breaks generation.

Options

| Option | Default | Description | | --- | --- | --- | | agentId | — | Identifier that scopes stored/recalled memories (required) | | apiUrl | $DAKERA_URL / http://localhost:3000 | Dakera server URL | | apiKey | $DAKERA_API_KEY | Dakera API key (dk-...) | | client | — | A pre-built DakeraClient (overrides apiUrl/apiKey) | | recallK | 5 | Memories to recall per call | | minImportance | 0 | Minimum importance to recall | | importance | 0.7 | Importance assigned to stored memories | | store | true | Store the exchange after generation |

Pattern 2 — Memory tools (model-driven)

createDakeraTools gives the model explicit recallMemory and storeMemory tools, so it decides when to look something up or remember it.

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { createDakeraTools } from "@dakera-ai/ai-sdk";

const tools = createDakeraTools({ agentId: "user-1234" });

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  maxSteps: 4,
  prompt: "Remember that I prefer metric units, then convert 5 miles to km.",
});

The two patterns compose — use the middleware for automatic continuity and the tools when you want the model to manage memory deliberately.

License

MIT © Dakera