piia-engram

Piia Engram

Local-first AI work identity you can see, edit, and override — portable across your MCP coding tools.

Tell AI once who you are, how you work, and what "good" means. Claude Code, Codex, Cursor, Windsurf, and other MCP-compatible tools can start from the same AI work identity layer — local files you own, no cloud account, no hidden memory you cannot inspect.

Install · See It in Action · Supported Tools · MCP Tools · FAQ

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Also listed in: awesome-agents · Awesome-MCP-ZH · mcpservers.org · Cursor Directory · ModelScope · PulseMCP

TL;DR: piia-engram is a local-first personal AI identity layer. It helps multiple coding agents start from the same understanding of you: your preferences, quality bar, lessons learned, decisions, and project context. It is not an agent memory database; it is the user-owned layer above your tools.

Why not just use native memory? Claude Code, Codex, Cursor, and Windsurf are adding their own memories and rules. Those are useful, but they are scoped to one tool or workspace. piia-engram gives you one portable identity layer above them: local files you own, AI-proposed knowledge you review, and context that can follow you across tools.

Trust model in four lines:

• No cloud account: install with pip, keep the core store on your machine.
• Local files: identity and knowledge live under ~/.engram/ as JSON/Markdown.
• User approval: AI writes locally; high-risk items (credentials, shell commands, MCP config, permission rules) wait for your review, while low/medium writes are auto-absorbed but fully auditable and reversible. Set ENGRAM_APPROVAL=strict to gate every write.
• Documented boundaries: see Trust model, Privacy, and Security.

Want proof? See the live cross-tool continuity proof — a memory written by Claude Code, read back by Codex through one local store — or the one-command reproducible code demo.

See It in Action

You  → "Help me refactor this auth module"

# WITHOUT piia-engram: AI starts from scratch
AI   → "What language? What framework? What's your testing preference?"

# WITH piia-engram: AI can load your approved context
AI   → "Based on your preference for pytest + 90% coverage, and your
        lesson about always separating auth middleware from business
        logic (from the March incident), here's my approach..."

And you never have to take that on faith — Memory Lens (engram preview --html) shows exactly what any AI caller would receive, and what governance withheld, before anything is sent:

Above: a real report from a demo store — 4 items exposed; an unreviewed staging note and a lesson containing a credential are withheld, with the secret shown as [REDACTED].