PLUR stores corrections, preferences, and patterns as engrams that strengthen with use and decay when irrelevant. The CLI gives you direct access to the engram engine from your terminal, scripts, and automation.

Install

npm install -g @plur-ai/cli

Or use without installing:

npx @plur-ai/cli status

Quick Start

# Install Claude Code hooks + local hook binary (automatic memory injection)
plur init

# Store a learning
plur learn "Always validate user input at API boundaries"

# Search memories
plur recall "validation"

# Get relevant context for a task
plur inject "fix the auth bug"

# List all engrams
plur list

# Give feedback (trains what surfaces next time)
plur feedback ENG-2026-0329-001 positive

# Retire outdated knowledge
plur forget ENG-2026-0329-001

Commands

Command	Description
`plur learn <statement>`	Create a new engram
`plur recall <query>`	Search engrams (hybrid: BM25 + embeddings)
`plur inject <task>`	Get relevant engrams for a task (three-tier output)
`plur list`	List all engrams with optional filtering
`plur forget <id>`	Retire an engram
`plur feedback <id> <signal>`	Rate an engram (positive/negative/neutral)
`plur capture <summary>`	Record an episode to the timeline
`plur timeline [query]`	Query the episodic timeline
`plur status`	System health check
`plur sync`	Cross-device sync via git (engram data only — secrets and derived files are never committed)
`plur packs list`	List installed engram packs
`plur packs install <source>`	Install an engram pack
`plur import --from <source> --path <file>`	Import memories from another system (see below)
`plur init`	Install Claude Code hooks + local hook binary for automatic injection
`plur doctor`	Diagnose installation health (hooks, MCP, shim, embedder)

Importing from other memory systems

Bring existing memory with you — plur import migrates competitor exports into engrams, routed through the same dedup gates as plur learn (duplicates are skipped, never re-added), and prints a migration report: N imported, M skipped (dedup), K conflicts.

# Gentleman-Programming/engram (Go + SQLite memory tool)
plur import --from gp-engram --path ~/.engram/engram.db

# mem0 JSON export (Memory.get_all() shape)
plur import --from mem0 --path ./memories.json

# Any JSON / JSONL / CSV export, optionally with a field-mapping config
plur import --from generic --path ./export.csv
plur import --from generic --path ./export.json --mapping ./mapping.json

# Preview without writing
plur import --from mem0 --path ./memories.json --dry-run

Flags: --dry-run (report only), --scope <scope> (force a scope for all imported engrams), --mapping <file> (generic only: {"fields": {"statement": "text", "domain": "meta.area"}, "defaults": {...}} with dot-path support).

Note: for import, --path is the input file (per the issue spec); use --store <dir> to override the storage directory instead.

Temporal metadata is preserved where the source has it (created_at → temporal.learned_at, last access → activation.last_accessed, expiry → temporal.valid_until). Zep and Letta adapters are registered but stubbed — export to JSON and use --from generic in the meantime.

Global Flags

Flag	Description
`--json`	Force JSON output (auto-detected when piped)
`--path <dir>`	Override storage path (default: `~/.plur`)
`--fast`	BM25-only search (skip embeddings, faster)
`--quiet`	Suppress non-essential output

JSON Output

Output is automatically JSON when piped, human-readable in a terminal:

# Human-readable
plur recall "testing"

# JSON (piped)
plur recall "testing" | jq '.results[0].statement'

# Force JSON
plur recall "testing" --json

Use with AI Agents

The CLI is the universal bridge for AI agent integrations. It's used by:

plur-hermes — Hermes Agent plugin (auto-installs via npx if not found)
@plur-ai/mcp — MCP server for Claude Code, Cursor, Windsurf
@plur-ai/claw — OpenClaw plugin

How It Works

Engrams are stored as plain YAML at ~/.plur/. Search is fully local — BM25 keyword matching plus BGE-small embeddings with Reciprocal Rank Fusion. Zero API calls, zero cloud, works offline.

The --fast flag skips embedding model loading (~2s) and uses BM25 only — useful for scripts and automation where speed matters more than semantic similarity.