by Patdolitse
Local-first AI memory you can see, edit, and override — portable across Claude Code, Codex, Cursor, Windsurf, and other MCP coding tools.
# Add to your Claude Code skills
git clone https://github.com/Patdolitse/piia-engramLast scanned: 5/30/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-05-30T16:02:10.785Z",
"npmAuditRan": true,
"pipAuditRan": true
}See how piia-engram compares with popular alternatives.
piia-engram is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Patdolitse. Local-first AI memory you can see, edit, and override — portable across Claude Code, Codex, Cursor, Windsurf, and other MCP coding tools. It has 162 GitHub stars.
Yes. piia-engram passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Patdolitse/piia-engram" and add it to your Claude Code skills directory (see the Installation section above).
piia-engram is primarily written in Python. It is open-source under Patdolitse on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh piia-engram against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
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
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:
pip, keep the core store on your machine.~/.engram/ as JSON/Markdown.ENGRAM_APPROVAL=strict to gate every write. "Verified" means accepted by you or by that policy, not re-checked against your current code.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.
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 identity and long-term knowledge
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].
pip install piia-engram && engram setup
The wizard auto-detects your AI tools — Claude Code, Cursor, Codex, Claude Desktop — lists the exact config files it will touch, and writes the MCP connection after a one-keystroke confirm (every write is backed up first; decline and nothing changes). It previews your identity card, then you restart your configured tool; the first conversation can load your approved identity and long-term knowledge through startup or search tools. (full walkthrough ↓)
Ask your coding agent to install Engram and inspect the plan before applying:
pip install piia-engram
engram setup --non-interactive --json
engram setup --non-interactive --apply
engram doctor --json
Setup asks no questions. Use --clients cursor,codex to limit clients and
--lang zh|en for output language (default en). Without --apply, plan mode
writes nothing to the Engram store, home directory or client configs. Python
itself may use the system temporary directory. Apply configures MCP connections
with backups, preserves existing tool modes, and never imports memories, writes
identity/knowledge, or approves proposals. Restart configured clients; exit code
1 means a manual step remains. See llms.txt for agent instructions and
the CLI and JSON contract for details.
Evidence levels follow the agent client validation runbook: L0 = discovery, L1 = protocol access, L2 = read/search behavior, L3 = A/B behavior gain, L4 = cross-client continuity, L5 = reproducible public evidence.
| Tool | Integration | Evidence status |
|---|---|---|
| Claude Code | MCP over stdio | L4 partial continuity proof (Claude Code -> Codex) |
| Codex | MCP over stdio | L4 partial continuity proof (Claude Code -> Codex) |
| Cursor | MCP over stdio | L2 setup/read-search evidence path |
| Claude Desktop | MCP over stdio | L1/L2 setup path; client-specific evidence pending |
| Hermes | MCP over stdio | L4 controlled CLI marker continuity; desktop and broad benchmark claims unverified |
| OpenClaw | SOUL.md / MEMORY.md / USER.md import and export | L3 static file-bridge evidence (static snapshot A/B only; live agent behavior unverified) |
| ChatGPT / Gemini / Kimi | Markdown identity card fallback | Usable |
| Windsurf | MCP over stdio | Expected to work |
| GitHub Copilot | MCP over stdio | Expected to work |
| Cline | MCP over stdio | Expected to work |
| Roo Code | MCP over stdio | Expected to work |
| Amazon Q | MCP over stdio | Expected to work |
| Augment | MCP over stdio | Expected to work |
| Zed | MCP over stdio | Expected to work |
| Trae | MCP over stdio | Expected to work |
| Tencent CodeBuddy | MCP over stdio | Expected to work |
These are current repository facts from docs/public-facts.json. Public registries and package badges update only during release/publish.
| Current repo / development facts | |
|---|---|
| Version frame | v4.24.0 (verified 2026-10-11; check PyPI and GitHub Releases for the latest published package) |
| Supported AI tools | 16 (evidence level varies by client; see Supported Tools and the validation runbook) |
| MCP tools | 19 Core (loaded by default) + 40 Advanced (opt-in via ENGRAM_TOOLS=all) |
| Knowledge types | 3 (lessons, decisions, playbooks) |
| Test suite | Unit + integration; run pytest tests/ to verify |
Lines in core.py |
1770 (facade; domain logic now lives in focused mixins — see architecture.md) |
| PBKDF2 iterations | 600,000 (OWASP 2023+ floor; legacy 100k still decrypts) |
| Encryption | Optional field-level AES-256-GCM for supported profile fields; local files are plaintext JSON/Markdown by default |
| Cold-start time | < 100 ms typical (local JSON, no network) |
| Network calls by default | One anonymous usage ping a day (engram telemetry off or DO_NOT_TRACK=1 turns it off); the engram command also checks PyPI for a newer version at most once a day in interactive terminals, and engram doctor checks on each run (ENGRAM_NO_UPDATE_CHECK=1 turns it off); identity and knowledge tools make none, except the optional read_web_content; remote telemetry and feedback require separate explicit opt-in and send counts only (see privacy details) |
Your AI forgets you every time you switch tools or start a new chat. piia-engram fixes the handoff.
Every time you open a new chat window, switch from Claude Code to Codex, u