piia-engram
Your AI knows your code. It doesn't know you. piia-engram fixes that.
A local identity layer that makes every AI tool start from the same understanding of who you are.
(Originally released as engram; renamed to piia-engram to avoid confusion with same-name projects. The CLI command engram still works as an alias.)
Claude Code | Codex | Cursor | MCP compatible | local-first
ENGLISH | 中文
TL;DR: piia-engram is a local identity layer for AI tools — not session memory, not an agent framework, not a hosted database. It stores who you are (profile, preferences, lessons learned, key decisions) as local JSON files on your machine, and exposes them through MCP so every AI tool reads the same you. One write, every AI reads. Local-first, Apache 2.0.
AI coding tools are powerful, but they don't know you.
Every time you open a new chat window, switch from Claude Code to Codex, update your AI tool, or move into a different project, you're back to zero:
- your communication preferences — gone
- your code standards and quality bar — forgotten
- which mistakes you've already learned from — lost
- why you made that architecture decision last month — erased
This happens because AI memory today is locked inside each platform. It belongs to the tool, not to you. The tool updates, resets, or gets replaced — and your context disappears with it.
piia-engram gives you a personal identity layer that lives on your machine, independent of any AI tool. You tell it once who you are, how you work, and what you've learned. Every MCP-compatible tool reads the same context. New chat, new tool, new version — your identity persists.
piia-engram is not an agent memory database. Tools like Mem0, Zep, and Letta store task context and session history for AI agents. piia-engram stores who you are as a person — your identity, preferences, hard-won lessons, and key decisions. It's a different layer: not what happened in a task, but who is behind every task.
Why piia-engram?
| Without piia-engram | With piia-engram |
|---|---|
| New chat window = start from zero | Every conversation already knows you |
| AI tool updates and your preferences vanish | Your identity lives on your machine, survives any update |
| Switching tools loses accumulated context | Claude Code, Codex, and Cursor read the same memory |
| Past mistakes get repeated | Lessons learned follow you across tools and sessions |
| Memory is locked inside one product | Data stays local, editable, and portable |
Who Uses piia-engram
piia-engram is built for developers who use multiple AI coding tools and are tired of re-explaining themselves.
If you switch between Claude Code, Codex, and Cursor — your code standards, architecture decisions, and hard-won lessons reset every time. piia-engram makes every tool start from the same understanding of who you are.
If you open 10+ AI chat windows a week — each one starts from zero. piia-engram gives every conversation your full context from the first message.
If you've lost preferences after a tool update — your identity lives on your machine, not inside any platform. Updates, resets, and migrations don't touch your memory.
Other use casesInvestment analystsDecisions get made but reasoning gets lost. piia-engram stores the full reasoning chain so six months later, "why did I pass on that?" has a real answer — and your analytical framework travels with you across every new analysis.
System architectsArchitecture decisions need context: what you chose, what you ruled out, and why. piia-engram keeps living Architecture Decision Records that travel with you across companies and projects, queryable by any AI tool.
Backend developersAPI quirks, integration gotchas, performance trade-offs — tacit knowledge that normally lives in your head and resets when you change jobs. piia-engram turns it into a searchable library that persists across everything.
Frontend and designDesign philosophy rarely gets documented in a way AI tools can use. piia-engram stores your real standards, UX lessons from real users, and the reasoning behind component decisions — so every project starts where your last one ended.
Vibe codersYou build with AI and move fast. The problem: every new session your AI starts from scratch — different style choices, inconsistent patterns, re-explaining the same preferences. piia-engram makes every tool consistent from session one: your stack, your patterns, your voice, already there.
What piia-engram Stores
All data lives under ~/.engram/ as plain JSON and Markdown files you can open, edit, back up, or migrate yourself.
- Identity: who you are, how you communicate, what languages you prefer
- Quality standards: your code review bar, test coverage expectations, what you refuse to ship
- Preferences: coding style, AI behavior, how you like explanations
- Trust boundaries: which fields to keep private, what tools can access
- Project snapshots: context for ongoing work, captured and reloadable
- Lessons learned: mistakes, surprises, things that worked and didn't
- Key decisions: what you chose, what you ruled out, and why
- Domain knowledge: reusable insights across projects and tools
What piia-engram Does (Beyond Storage)
Most memory tools are passive — you put things in, they give them back. piia-engram is also active.
Knowledge inheritance across projects Describe a new project in plain text. get_knowledge_inheritance returns a curated starter pack of the most relevant lessons and decisions from everything you have ever worked on. Your tenth project benefits from all nine before it — one tool call away.
Passive knowledge capture Paste a session summary into extract_session_insights and piia-engram extracts and stores the lessons and decisions. No manual note-taking. Knowledge accumulates through normal AI conversations.
Works with tools that do not support MCP ChatGPT, Gemini, Kimi — get_identity_card exports a ready-to-paste Markdown identity card. Your context travels even to tools that cannot connect directly.
Knowledge health and discovery get_knowledge_overview surfaces stale lessons (not reviewed in 30+ days), gives a health score, and flags gaps worth revisiting. find_similar_knowledge finds overlapping items to merge. link_knowledge connects related lessons and decisions into a navigable knowledge graph.
Quick Start (30 seconds)
pip install piia-engram
engram setup
The setup wizard will:
- Detect your Python environment
- Find and configure your AI tools (Claude Code, Cursor, Claude Desktop)
- Walk you through seed knowledge (role, tech stack, language)
- Smart-import rules from your existing
CLAUDE.md/.cursorrulesfiles - Show your privacy preferences (cross-tool sync, anonymous statistics — both optional)
- Preview your AI identity card — immediate proof of value
Restart your AI tool after setup. The first conversation will call get_user_context automatically — your AI already knows you.
If you prefer to configure manually, add to your AI tool's MCP config:
{
"mcpServers": {
"piia-engram": {
"command": "python",
"args": ["/path/to/piia-engram/src/piia_engram/mcp_server.py"]
}
}
}
Upgrading
pip install --upgrade piia-engram
After upgrading, piia-engram automatically migrates any stale MCP configs the next time its server starts (stdio mode). If your AI tool still shows an "MCP disconnected" error after restarting, run:
piia-engram doctor # show what's wrong
piia-engram doctor --fix # auto-repair and fix in one step
Then restart the affected AI tool. The doctor command checks Claude Code, Cursor, and Claude Desktop configs and removes any outdated server entries.
Remote Deployment
Run piia-engram on your own server and connect from anywhere.
Server Setup
# Install with remote support
pip install piia-engram[remote]
# Generate an auth token
python -c "import secrets; print(secrets.token_urlsafe(32))"
# Save the output, e.g. "abc123..."
# Start in SSE mode
ENGRAM_AUTH_TOKEN=abc123... python -m piia_engram.mcp_server --transport sse --host 0.0.0.0 --port 8767
Client Config (Claude Code)
{
"mcpServers": {
"piia-engram": {
"url": "http://your-server:8767/sse",
"headers": {
"Authorization": "Bearer abc123..."
}
}
}
}
Client Config (Cursor)
{
"mcpServers": {
"piia-engram": {
"url": "http://your-server:8767/sse",
"headers": {
"Authorization": "Bearer abc123..."
}
}
}
}
Security notes:
- Always use HTTPS in production, behind nginx or caddy with TLS.
- The auth token protects your identity data. Keep it secret.
- Default bind is
127.0.0.1for localhost only. Use0.0.0.0only behind a reverse proxy. - Set
ENGRAM_CORS_ORIGINSto restrict cross-origin access (e.g.https://your-domain.com). - Data stays on your server and never touches third-party clouds.
MCP Tools
piia-engram ships 43 MCP tools. By default, only the 10 Tier-1 Core tools are loaded to keep the AI's context clean. To unlock all 43 tools, add ENGRAM_TOOLS=all to your MCP config:
{
"mcpServers": {
"piia-engram": {
"command": "python",
"args": ["-m", "piia_engram.mcp_server"],
"env": { "ENGRAM_TOOLS": "all" }
}
}
}
Tier-1 Core (10 tools — daily workflow)
| Tool | Purpose |
|---|---|
get_user_context |
Load identity + knowledge at session start |
wrap_up_session |
Save insights + sync at session end |
add_lesson |
Store a reusable lesson learned |
add_decision |
Record a key decision with reasoning |
search_knowledge |
Search lessons and decisions by weighted relevance |
get_relevant_knowledge |
Find knowledge relevant to current project |
get_identity_card |
Export Markdown identity card for non-MCP tools |
update_identity |
Update profile, preferences, or quality standards |
get_project_context |
Read a saved project snapshot |
save_project_snapshot |
Persist project state for future sessions |
Tier-2 Advanced (33 tools — knowledge management, review, import/export)
Click to expand full tool list| Tool | Purpose |
|---|---|
get_profile |
Read user profile (safe=true by default) |
get_work_style |
Read work style preferences |
get_preferences |
Read communication and workflow preferences |
get_trust_boundaries |
Read data access boundaries |
get_quality_standards |
Read quality expectations |
get_lessons |
List reusable lessons learned |
get_decisions |
List key decisions and reasons |
get_domains |
Read domain experience stats |
get_knowledge_inheritance |
Build cross-project knowledge starter pack |
list_projects |
List saved project snapshots |
extract_session_insights |
Extract lessons and decisions from session text |
bulk_add_knowledge |
Add multiple lessons or decisions in one call |
ingest_notes |
Parse free-form notes into structured knowledge |
update_knowledge |
Update a lesson or decision by ID |
archive_knowledge |
Archive a lesson or decision by ID |
review_knowledge |
Mark a knowledge item as reviewed |
merge_knowledge |
Merge a duplicate into the primary item |
link_knowledge |
Create a bidirectional link between items |
unlink_knowledge |
Remove a bidirectional knowledge link |
get_knowledge_overview |
Knowledge digest, health report, stale checks |
get_related_knowledge |
Follow links between knowledge items |
find_similar_knowledge |
Find similar items by content |
get_stale_knowledge |
List items that need review |
export_knowledge_report |
Export a readable Markdown knowledge report |
request_outline_review |
Generate an interactive HTML review page |
apply_review |
Process review results (promote/archive staging items) |
export_engram |
Export a full backup |
import_engram |
Import a backup |
export_engram_to_openclaw |
Export OpenClaw-compatible files |
import_engram_from_openclaw |
Import OpenClaw-compatible files |
read_web_content |
Read webpage via local Reader service |
get_audit_log |
Get recent audit log entries |
start_project |
Start a project with inherited knowledge |
Data Layout
~/.engram/
|-- schema_version.json
|-- identity/
| |-- profile.json
| |-- preferences.json
| |-- quality_standards.json
| `-- trust_boundaries.json
|-- knowledge/
| |-- lessons.json
| |-- decisions.json
| `-- domains.json
|-- projects/
| `-- {project_id}.json
|-- exports/
`-- compat/
`-- openclaw/
Supported Tools
| Tool | Integration | Status |
|---|---|---|
| Claude Code | MCP over stdio | Tested |
| Codex | MCP over stdio | Tested |
| Cursor | MCP over stdio | Expected to work |
| Claude Desktop | MCP over stdio | Expected to work |
| OpenClaw | SOUL.md / MEMORY.md / USER.md import and export | Tested |
| ChatGPT / Gemini / Kimi | Markdown identity card fallback | Usable |
Comparison
| Feature | piia-engram | Claude Memory | Manual CLAUDE.md |
Mem0 | Letta (MemGPT) |
|---|---|---|---|---|---|
| Primary purpose | User identity across tools | Per-conversation memory | Per-project notes | Agent vector memory | Agent self-editing memory |
| Cross-tool by design | ✅ MCP-native (43 tools) | ❌ Claude only | ❌ tool-specific | ⚠ requires per-tool wiring | ⚠ requires per-tool wiring |
| Storage | Local JSON in ~/.engram/ |
Cloud | Local | Vector DB + Mem0 Cloud | Postgres or Letta Cloud |
| Local-first by default | ✅ | ❌ | ✅ | ⚠ Cloud is the default | ⚠ Cloud is the default |
| Encryption at rest | ✅ AES-256-GCM, PBKDF2 600k (opt-in) | depends on Cloud | ❌ plain Markdown | depends on store config | depends on Postgres config |
| Knowledge tiers (user gate) | ✅ staging → verified | ❌ | ❌ | ❌ | ❌ |
| Conflict detection | ✅ | ❌ | ❌ | ❌ | ❌ |
| MCP-native | ✅ | n/a | n/a | ⚠ third-party | ⚠ third-party |
| Price | Free, Apache 2.0 | Subscription-bundled | Free | Free / Cloud tiers | Free / Cloud tiers |
📊 For the full side-by-side, including when to choose a competitor over piia-engram, see docs/comparison.md.
By the numbers
These are factual claims about piia-engram itself, refreshed each minor release.
| v3.16.0 (2026-05-22) | |
|---|---|
| MCP tools exposed | 43 (10 Tier-1 default, 33 opt-in via ENGRAM_TOOLS=all) |
| Tests passing | 490 (unit + integration) |
| Code coverage | 83% total; mcp_server 86% (baseline) |
Lines in core.py |
1097 (down from 4277 pre-v3.14.1 — see architecture.md) |
| PBKDF2 iterations | 600,000 (OWASP 2023+ floor; legacy 100k still decrypts) |
| Encryption | AES-256-GCM, per-value random salt + nonce |
| Cold-start time | < 100 ms typical (local JSON, no network) |
| Network calls from core | 0 by default — except optional read_web_content and opt-in anonymous usage statistics (details) |
| External AI evaluations | 4 independent AIs evaluated the telemetry design; 3 earlier evaluations on architecture (see docs/) |
Built With
piia-engram is a human-directed, AI-assisted open-source project.
| Contributor | Role |
|---|---|
| @Patdolitse | Creator, product direction, strategy, ownership |
| Claude Code | Architecture, task planning, code review assistance |
| Codex | Implementation, testing, documentation assistance |
FAQ
What is piia-engram?piia-engram is a local-first AI identity layer — not session memory, not an agent framework. It stores who you are, how you work, what you've learned, and the decisions you've made — as local JSON files on your machine. Every MCP-compatible AI tool (Claude Code, Codex, Cursor) reads the same identity, so new chats, tool updates, and tool switches never erase your context.
How is piia-engram different from agent memory tools like Mem0, Zep, or Letta?Those tools store task context and session history for AI agents — what happened during a workflow. piia-engram stores who you are as a person — your identity, preferences, hard-won lessons, and key decisions. It's a different layer: identity persists across tools, sessions, and projects, while task memory is scoped to a single agent run. Your data is local JSON files you own and can edit directly.
Which AI tools does piia-engram support?piia-engram works with any MCP-compatible AI tool: Claude Code, OpenAI Codex, Cursor, Claude Desktop, and others. For tools without MCP support (ChatGPT, Gemini, Kimi), you can export a Markdown identity card and paste it in manually.
Why is the PyPI package called piia-engram if the product is just "piia-engram"?The project started as part of a larger personal-AI initiative called PIIA (Personal Intelligence Identity Asset) — piia-engram is the first and currently the only released tool in that line, so it kept the piia- prefix on PyPI. Day-to-day the product is just piia-engram — that's the CLI command (engram setup, piia-engram doctor), the import name (import piia_engram), and the MCP server name (engram). The piia- prefix only shows up at install time and may be retired in a future major version.
How do I install piia-engram?
pip install piia-engram
engram setup
The setup wizard detects your AI tools and configures MCP automatically. Restart your AI tool after setup. The AI will call get_user_context at the start of each session.
After upgrading, my AI tool shows "MCP server disconnected". How do I fix it?Run piia-engram doctor --fix in a terminal, then restart your AI tool. This command scans all known MCP config files (Claude Code, Cursor, Claude Desktop), removes outdated server entries, and repairs broken paths in one step. piia-engram also runs this migration automatically the next time its server starts, so most users will never see this message.
Does piia-engram send data to the cloud?All identity and knowledge data is stored in ~/.engram/ on your local machine. Core identity and knowledge tools make no network requests. Anonymous usage statistics (tool call counts, knowledge totals, piia-engram version — never content, prompts, or file paths) can be optionally enabled during engram setup; they are off by default. You can inspect the exact payload with engram telemetry preview and disable anytime with engram telemetry off. The optional read_web_content tool makes outbound HTTP requests to a local Reader service (localhost:7890) which may in turn fetch external URLs — but only when explicitly invoked.
How many MCP tools does piia-engram provide?piia-engram exposes 43 MCP tools covering identity management, lessons learned, key decisions, project snapshots, bulk input, note ingestion, session insight extraction, weighted knowledge search, similarity discovery, merging, lifecycle review, digesting, reporting, linking, health checks, workflow shortcuts, and audit logging.
Is piia-engram free?Yes. piia-engram is free and open source under the Apache 2.0 license.
Limitations
piia-engram is functional and actively used, but some things it intentionally does not do yet:
| Area | Current State | Planned |
|---|---|---|
| File safety | Atomic JSON writes with a shared portalocker file lock | Broader stress testing |
| Access control | restricted_fields filters profile in get_user_context, get_profile (default safe=true), get_identity_card, and resource endpoints |
Per-caller ACL blocked by MCP caller identity |
| Encryption | Optional field-level AES-256-GCM encryption via ENGRAM_SECRET env var. Install pip install piia-engram[secure]. |
Full-disk encryption for all files (v4.0) |
| Audit logging | Optional access audit log via ENGRAM_AUDIT=1 env var. Logs to ~/.engram/audit.log. |
Per-caller audit (blocked by MCP spec) |
| Caller identity | MCP protocol doesn't pass tool identity | Blocked by MCP spec |
| Concurrent writes | Protected by file lock + atomic replace for piia-engram JSON writes | Network-filesystem edge cases not guaranteed |
What this means in practice:
- Don't store passwords, API keys, or client PII in piia-engram
- Any process with read access to
~/.engram/can read your data restricted_fieldsreduces what piia-engram emits in cold-start context, but it is not encryption or a true ACL
This is not a warning to avoid piia-engram — it's an honest description of what it is: a local memory layer for personal AI context. For personal use, it works well today.
Security Configuration
Field-level encryption (optional)
Encrypt sensitive profile fields (email, phone, location, etc.) at rest:
pip install piia-engram[secure]
export ENGRAM_SECRET="your-strong-passphrase"
Encrypted fields are stored as enc:v1:... in JSON files. Without ENGRAM_SECRET, piia-engram works normally with plaintext (backward compatible).
Audit logging (optional)
Track all read/write operations:
export ENGRAM_AUDIT=1
Logs are written to ~/.engram/audit.log in JSON-lines format. Query with get_audit_log tool or grep.
CLI Commands
engram setup # Interactive install wizard
piia-engram doctor # Check config health (all AI tools)
piia-engram doctor --fix # Auto-repair any issues found
piia-engram stats # Show project growth metrics (GitHub + PyPI)
piia-engram stats --log # Append stats snapshot to local log
engram telemetry # Manage anonymous usage statistics
engram privacy # Show what data piia-engram stores and where
Contributing
Contributions, issues, and feedback are welcome.
See CONTRIBUTING.md. Chinese readers can also use CONTRIBUTING.zh-CN.md.
License
Apache 2.0. piia-engram is free software. Your memory belongs to you.