Package Exports

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@samfp/pi-memory) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

pi-memory

Persistent memory for pi. Learns corrections, preferences, and project patterns from sessions and injects them into future conversations.

Features

Automatic learning — Extracts preferences, project patterns, and corrections from conversations at session end via LLM consolidation
Context injection — Automatically adds relevant memory into every new session's system prompt
Corrections stick — Mistakes you correct once become permanent lessons (e.g. "use sed for daily notes, not echo >>")
Complements session-search — session-search finds what you did, pi-memory remembers what you learned

Install

pi install pi-memory

Or add to ~/.pi/agent/settings.json:

{
  "packages": ["npm:pi-memory"]
}

Memory Types

Type	Key prefix	Example
Preferences	`pref.*`	`pref.commit_style` → "conventional commits"
Project patterns	`project.*`	`project.rosie.di` → "Dagger dependency injection"
Tool preferences	`tool.*`	`tool.sed` → "use for daily note insertion"
User identity	`user.*`	`user.timezone` → "US/Pacific"
Lessons	(table)	"DON'T: use echo >> for vault notes, use sed"

Tools

Tool	Description
`memory_search`	Search semantic memory by keyword
`memory_remember`	Manually store a fact or lesson
`memory_forget`	Delete a fact or lesson
`memory_lessons`	List learned corrections
`memory_stats`	Show memory statistics

Commands

Command	Description
`/memory-consolidate`	Manually trigger memory extraction from current session

How It Works

session_start — Opens the SQLite store, shows memory stats briefly in the status bar
before_agent_start — Builds a <memory> context block from stored facts and lessons, appends it to the system prompt
agent_end — Collects conversation messages for later consolidation
session_shutdown — Runs LLM consolidation (via pi -p --print) to extract structured knowledge, then closes the store

Consolidation

At session end, if there were ≥3 user messages, the extension sends the conversation to an LLM and asks it to extract:

Preferences — coding style, workflow habits, tool choices
Project patterns — languages, frameworks, architecture decisions
Corrections — things you corrected, mistakes to avoid

Only facts with confidence ≥ 0.8 are stored. Lessons are deduplicated using exact match and Jaccard similarity (≥ 0.7 threshold).

Injection

At session start, stored memory is organized into sections (preferences, project context scoped to cwd, tool preferences, lessons, user identity) and injected as a <memory> block in the system prompt. The block is capped at 8KB.

Selective lesson injection — By default, all lessons are injected into every session. When you have many lessons across different domains, this can waste context. Enable selective mode to filter lessons by relevance:

{
  "memory": {
    "lessonInjection": "selective"
  }
}

Add this to ~/.pi/agent/settings.json. In selective mode, lessons are filtered by:

Prompt relevance — FTS search against the user's first message
Project context — lessons matching the current working directory's project
Category inference — keywords in the prompt trigger relevant categories (e.g. "pentest" pulls in bug-bounty lessons, "blog post" pulls in writing lessons)
General lessons — always included regardless of prompt

The result is capped at 15 most relevant lessons instead of all of them.

Mode	Behavior
`"all"` (default)	Every lesson injected into every session
`"selective"`	Only relevant lessons based on prompt, project, and category

Storage

SQLite database at ~/.pi/memory/memory.db (WAL mode). Three tables:

semantic — key-value facts with confidence scores
lessons — learned corrections with dedup
events — audit log of all memory operations

License

MIT