threadline-sdk
v0.1.9
Published
Persistent memory and context layer for AI agents. inject() before your LLM call, update() after. Works with OpenAI, Anthropic, Vercel AI SDK, and LangChain.
Maintainers
Readme
threadline-sdk
The persistent context layer for AI agents.
Every agent you build starts from zero. Threadline changes that.
Install
npm install threadline-sdkUsage
import { Threadline } from "threadline-sdk"
const tl = new Threadline({ apiKey: process.env.THREADLINE_KEY })
// Before your AI call — inject user context
const { injectedPrompt, cacheHint } = await tl.inject(userId, "You are a helpful assistant.")
// After your AI call — update user context
await tl.update({ userId, userMessage, agentResponse })That's it. Your agent now remembers every user, across every session.
Why Threadline?
Think of it like OAuth — but for context. You don't build auth from scratch. You shouldn't build memory from scratch either.
- One SDK, any LLM (OpenAI, Anthropic, Gemini, Mistral)
- User-owned context — users can view and delete their data
- < 50ms context retrieval via Redis
- Privacy-first by design
API
tl.inject(userId, basePrompt)
Returns { injectedPrompt, cacheHint? }. Use injectedPrompt as your system message. When cacheHint.recommended is true, pass cacheHint.openaiParam as extra_body on supported OpenAI models for 24h prompt cache retention (~50% lower cached input token cost).
tl.update({ userId, userMessage, agentResponse })
Extracts and stores context updates from a conversation turn.
Get started
- Sign up at threadline.to
- Create an agent and get your API key
- Drop in the two lines above
