人工智能工作流

Requirements

• Node.js 18+
• 2GB RAM minimum — the embedding model (ONNX runtime) needs ~1GB for installation. On servers with less RAM, embeddings are skipped and search falls back to BM25 keyword matching.

Install

Manual setup (Claude Code)

One command sets up everything — storage, MCP config, and Claude Code hooks:

npx @plur-ai/mcp init

This creates ~/.plur/ for storage, adds PLUR to your .mcp.json, and installs Claude Code hooks for automatic engram injection. PLUR is installed globally — one MCP server, one store, available in every project. You only run init once.

For multi-project setups, use domain/scope to separate knowledge:

cd ~/projects/my-app
npx @plur-ai/cli@0.9.4 init --domain myapp --scope project:my-app

This creates a .plur.yaml in the project with defaults that hooks apply automatically. Engrams learned in that project are tagged; recall filters by scope but always includes global knowledge.

Global install (faster startup)

npm install -g @plur-ai/mcp
plur-mcp init

Usage

import { Plur } from '@plur-ai/core'

const plur = new Plur()

// Learn from a correction
plur.learn('toEqual() in Vitest is strict — use toMatchObject() for partial matching', {
  type: 'behavioral',
  scope: 'project:my-app',
  domain: 'dev/testing'
})

// Recall (hybrid: BM25 + embeddings, zero cost)
const results = await plur.recallHybrid('vitest assertion matching')

// Inject relevant engrams into agent context
const { engrams } = plur.inject('Write tests for the user service', {
  scope: 'project:my-app',
  limit: 15
})

// Feedback trains the system
plur.feedback(engram.id, 'positive')

// Capture an event (episode)
plur.capture('Fixed CrashLoopBackOff on bee-3-4 by increasing memory limits', {
  agent: 'claude-code',
  channel: 'terminal'
})

// Query timeline
const incidents = plur.timeline({ agent: 'claude-code' })

// Sync across machines
plur.sync('git@github.com:you/plur-memory.git')

Packages