🧠 Pi Hermes Memory

Persistent memory + session search + secret scanning for Pi

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Your Pi agent normally forgets everything when you close a session. This extension fixes that.

• 🔍 Search every conversation — "what did we discuss about auth?" finds it instantly
• 🧠 Persistent memory — facts, preferences, corrections survive across sessions
• ⚠️ Learns from failures — remembers what didn't work so you don't repeat mistakes
• 🏷️ Categorized memories — failures, corrections, insights, conventions, and tool quirks organized for fast retrieval
• 🛡️ Secret scanning — API keys and tokens are blocked from being saved
• 📚 Procedural skills — the agent saves how it solved problems, not just what
• ⚡ Background learning — reviews every 10 turns, saves what matters
• 🔄 Auto-consolidation — merges entries when full, never loses data

Quick Start

# Install
pi install npm:pi-hermes-memory

# Index your past sessions (one-time)
/memory-index-sessions

# Backfill older Markdown memories into SQLite search (optional)
/memory-sync-markdown

# Learn how to use it
/learn-memory-tool

Features

Feature	What happens
🔍 Session Search	Search across all past conversations via SQLite FTS5
🧠 Persistent Memory	Facts, preferences, lessons saved to markdown files
🔄 Memory Search Sync	Successful Markdown memory writes are mirrored into SQLite for memory_search
⚠️ Failure Memory	Learn from failures — stores what didn't work and why
📚 Procedural Skills	The agent saves how it solved problems as reusable docs
⚡ Background Learning	Every 10 turns (or 15 tool calls) the agent reviews and saves
🔧 Correction Detection	When you correct the agent, it saves immediately
🔄 Auto-Consolidation	When memory hits capacity, auto-merges instead of erroring
🛡️ Secret Scanning	API keys, tokens, SSH keys blocked from persistence
📊 Memory Aging	Entries carry timestamps — consolidation knows what's stale
🏗️ Two-Tier Memory	Global + per-project memory, both searchable
💾 Extended Store	Unlimited searchable memories beyond core 5,000-char limit
🎓 Onboarding	/memory-interview pre-fills your profile on first session

How It Works

Session Lifecycle

Memory + Skills Architecture

The extension manages three types of knowledge:

Type	What	Storage	Token cost
Memory (MEMORY.md)	Facts — env details, project conventions, tool quirks	2,200 chars max	Fixed per session
User Profile (USER.md)	Who you are — name, preferences, communication style	1,375 chars max	Fixed per session
Skills (skills/*.md)	Procedures — how to do something, reusable across sessions	Unlimited	~3K for index, full on demand

Security: Content Scanning

Every write — memory and skills — passes through a scanner before being accepted. This prevents the LLM from being tricked into storing malicious content that would later be injected into the system prompt.

Installation

pi install npm:pi-hermes-memory

Or install from GitHub:

pi install git:github:chandra447/pi-hermes-memory

Or test locally without installing:

pi -e /path/to/pi-hermes-memory/src/index.ts

Two-Tier Memory Architecture

The extension stores memory at two levels:

Tier	Location	What goes here	Injected when
Global	~/.pi/agent/memory/	Facts that apply everywhere — your name, preferences, OS, tools	Always (every session)
Project	~/.pi/agent/<project>/	Facts scoped to one codebase — architecture decisions, API quirks, team norms	When cwd matches the project

Both tiers are injected into the system prompt under separate <memory-context> blocks.

System Prompt
┌─────────────────────────────────────────┐
│ <memory-context>                        │
│ MEMORY (your personal notes)            │
│ • prefers vim over nano                 │
│ • uses pnpm not npm                     │
│ ═══ END MEMORY ═══                     │
│ </memory-context>                       │
│                                         │
│ <memory-context>                        │
│ USER PROFILE (who the user is)          │
│ • name: Chandrateja                     │
│ • timezone: AEST                        │
│ ═══ END MEMORY ═══                     │
│ </memory-context>                       │
│                                         │
│ <memory-context>                        │
│ PROJECT MEMORY: pi-hermes-memory        │
│ • uses jiti for runtime TS loading      │
│ • tests use node:test with tsx          │
│ ═══ END MEMORY ═══                     │
│ </memory-context>                       │
│                                         │
│ <memory-context>                        │
│ RECENT FAILURES & LESSONS (learn from): │
│ • [correction] Use pnpm, not npm        │
│ • [failure] Tried localStorage — XSS    │
│ • [insight] Auth0 handles refresh tokens│
│ ═══ END MEMORY ═══                     │
│ </memory-context>                       │
└─────────────────────────────────────────┘

Failure Memory

The agent learns from failures, corrections, and insights — just like humans do.

Memory Categories

Category	What it stores	Example
failure	What didn't work and why	"Tried localStorage for tokens — XSS vulnerability"
correction	User corrections	"Use pnpm, not npm"
insight	Learnings from experience	"Auth0 SDK handles refresh tokens automatically"
preference	User preferences	"Prefers dark theme"
convention	Project conventions	"Monorepo uses turborepo"
tool-quirk	Tool-specific knowledge	"CI needs --frozen-lockfile"

How It Works

1. Auto-detection: Background review extracts failures from conversations
2. Correction capture: When you correct the agent, it saves what went wrong
3. System prompt injection: Recent failures (last 7 days) are injected at session start
4. Searchable: Use memory_search("auth", category: "failure") to find past failures

Example

User: No, use pnpm not npm
Agent: [saves correction memory]

Next session:
Agent: "I remember you prefer pnpm over npm. Let me use that."

The agent learns from its mistakes so you don't have to repeat yourself.

Memory blocks are wrapped in <memory-context> XML tags with a guard note ("NOT new user input") to prevent the LLM from treating stored facts as instructions.

Usage

Once installed, the extension works automatically. You don't need to do anything special — the agent will start saving memories and skills on its own.

The memory Tool

The agent gets a memory tool it can call proactively:

Action	Target	What it does
add	memory or user	Append a new entry
replace	memory or user	Update an existing entry (matched by substring)
remove	memory or user	Delete an entry (matched by substring)

The skill Tool

The agent also gets a skill tool for saving reusable procedures:

Action	What it does
create	Save a new skill (name, description, step-by-step body)
view	Read a skill's full content, or list all skills if no name given
patch	Update one section of an existing skill (e.g., just the Procedure section)
edit	Replace the description and/or full body of a skill
delete	Remove a skill

Skills are stored as SKILL.md files in ~/.pi/agent/memory/skills/. Each has a structured body:

---
name: debug-typescript-errors
description: Step-by-step approach to debugging TS errors in monorepos
version: 1
created: 2026-04-26
updated: 2026-04-26
---
## When to Use
When you see TypeScript compilation errors, especially in monorepo setups.

## Procedure
1. Read the error message carefully
2. Check tsconfig.json extends chain
3. Run tsc --noEmit to get full error list
4. Fix errors bottom-up (dependencies first)

## Pitfalls
- Don't trust VSCode's error display — use the CLI

## Verification
Run `tsc --noEmit` and confirm zero errors.

Memory vs User Profile vs Skills

Store	File	What goes here	Limit
memory	MEMORY.md	Agent's notes — env facts, project conventions, tool quirks, lessons learned	5,000 chars
user	USER.md	User profile — name, preferences, communication style, habits	5,000 chars
skills	skills/*.md	Procedures — how to debug, deploy, test, or fix something	Unlimited
extended	sessions.db	Searchable memories beyond the core limit	Unlimited
sessions	sessions.db	Past conversation history (searchable via FTS5)	Unlimited

Session History Search

The extension indexes your Pi session history into a SQLite database with FTS5 full-text search. The agent can search across all past conversations using the session_search tool:

| Tool | What it does | |---|---|---| | session_search | Search past conversations — "what did we discuss about auth?" | | memory_search | Search extended memory store — unlimited capacity, keyword-based |

Session history is indexed automatically on session shutdown. To bulk-import existing sessions:

/memory-index-sessions

Extended Memory Store

The extension keeps Markdown memory as the source of truth for prompt injection and human-readable editing, and mirrors successful writes into the SQLite-backed search store used by memory_search.

This means:

• Fresh memory tool writes become searchable immediately
• Older Markdown entries can be backfilled with /memory-sync-markdown
• SQLite search does not replace the core Markdown limit or prompt injection behavior

This is the hybrid memory architecture:

• Core memory (MEMORY.md/USER.md/failures.md): Always injected, human-readable, size-limited
• SQLite memory mirror/store (sessions.db): Searchable on demand via memory_search

Important: if core Markdown memory is full and consolidation cannot free space, the write still fails. This package does not silently spill failed core-memory writes into SQLite-only storage.

Correction Detection

When you correct the agent, it saves immediately — no waiting for the background review. Examples of corrections the agent detects:

You say	What happens
"don't do that"	✅ Immediate save
"no, use yarn instead"	✅ Immediate save
"actually, fix the test first"	✅ Immediate save
"I said use pnpm"	✅ Immediate save
"no worries"	❌ Not a correction — ignored
"actually looks great"	❌ Not a correction — ignored

Auto-Consolidation

When memory or user profile hits its character limit, the extension automatically consolidates instead of returning an error:

1. Spawns a one-shot pi.exec() process with a consolidation prompt
2. The child agent merges related entries, removes outdated ones, keeps the most important facts
3. Parent reloads from disk and retries the original save
4. If consolidation fails, falls back to the original error

You can also trigger this manually with /memory-consolidate.

Tool-Call-Aware Review

Background review triggers based on activity level, not just turn count:

• Every 10 turns — the default nudge interval
• OR every 15 tool calls — catches complex tasks that involve many reads/edits/bash calls

Both counters reset after each review.

Skill Auto-Extraction

After a complex task (8+ tool calls using 2+ different tools in a single turn), the extension automatically asks the agent:

"This was a complex task — should we save a reusable procedure?"

This means skills build up naturally over time without you having to ask.

Commands

Command	What it does
/memory-insights	Shows everything stored in memory and user profile
/memory-skills	Lists all agent-created skills
/memory-consolidate	Manually trigger memory consolidation to free space
/memory-interview	Answer a few questions to pre-fill your user profile
/memory-switch-project	List all project memories and their entry counts
/memory-index-sessions	Import past Pi sessions into the search database
/memory-sync-markdown	Backfill Markdown memories into the SQLite search store
/memory-preview-context	Preview the memory/skill blocks injected into the system prompt
/learn-memory-tool	Skill that teaches users how to use the memory system

/memory-insights Output

╔══════════════════════════════════════════════╗
║            🧠 Memory Insights                ║
╚══════════════════════════════════════════════╝

📋 MEMORY (your personal notes)
──────────────────────────────────────────────
1. project uses pnpm not npm
2. test files go in __tests__/ directory
3. user prefers dark theme for UI

👤 USER PROFILE
──────────────────────────────────────────────
1. name: Chandrateja
2. prefers concise answers over verbose ones
3. codes primarily in TypeScript

/memory-skills Output

╔══════════════════════════════════════════════╗
║            🧠 Procedural Skills             ║
╚══════════════════════════════════════════════╝

📄 debug-typescript-errors
   Step-by-step approach to debugging TS errors in monorepos
   file: debug-typescript-errors.md

📄 deploy-checklist
   Pre-deploy verification steps for this project
   file: deploy-checklist.md

Configuration

Create ~/.pi/agent/hermes-memory-config.json:

{
  "memoryCharLimit": 5000,
  "userCharLimit": 5000,
  "projectCharLimit": 5000,
  "memoryDir": "~/.pi/agent/memory",
  "nudgeInterval": 10,
  "nudgeToolCalls": 15,
  "reviewEnabled": true,
  "autoConsolidate": true,
  "correctionDetection": true,
  "failureInjectionEnabled": true,
  "failureInjectionMaxAgeDays": 7,
  "failureInjectionMaxEntries": 5,
  "flushOnCompact": true,
  "flushOnShutdown": true,
  "flushMinTurns": 6
}

Setting	Default	Description
memoryCharLimit	5000	Max characters in MEMORY.md
userCharLimit	5000	Max characters in USER.md
projectCharLimit	5000	Max characters in project-scoped MEMORY.md
memoryDir	~/.pi/agent/memory	Custom directory for memory files
nudgeInterval	10	Turns between auto-reviews
nudgeToolCalls	15	Tool calls between auto-reviews (OR with turns)
reviewEnabled	true	Enable/disable background learning loop
autoConsolidate	true	Auto-merge when memory hits capacity
correctionDetection	true	Detect user corrections and save immediately
failureInjectionEnabled	true	Enable/disable injecting recent failure memories into the system prompt
failureInjectionMaxAgeDays	7	Maximum age in days for injected failure memories
failureInjectionMaxEntries	5	Maximum number of failure memories to inject
flushOnCompact	true	Flush memories before Pi compacts context
flushOnShutdown	true	Flush memories when session ends
flushMinTurns	6	Minimum turns before flush triggers

Where Data Lives

~/.pi/agent/memory/
├── MEMORY.md          ← Agent's personal notes (env facts, patterns, lessons)
├── USER.md            ← User profile (name, preferences, habits)
├── sessions.db        ← SQLite database (session history + extended memory)
└── skills/
    ├── debug-typescript-errors.md
    └── deploy-checklist.md

These are plain markdown files. You can read and edit them directly if you want to curate what the agent remembers. Memory entries are separated by § (section sign). Skills use standard SKILL.md format with frontmatter.

The sessions.db SQLite database stores session history and extended memory entries. It's searchable via FTS5 full-text search.

Known Limitations

• § delimiter: Memory entries are separated by § (section sign). If an entry naturally contains §, it will be split incorrectly on reload. This is rare in English text but possible. [Hermes uses the same delimiter.]
• Background review cost: Each review cycle costs one full LLM API call via a child pi -p process. Correction detection and skill auto-extraction add occasional extra calls.
• Session search requires indexing: Past sessions must be indexed before they're searchable. Run /memory-index-sessions to bulk-import, or let the extension auto-index on session shutdown.
• Older Markdown memories may need backfill: If you saved memories before the SQLite mirror existed or search looks stale, run /memory-sync-markdown.
• Core memory limits still apply: SQLite search mirroring does not bypass the 5,000-char core Markdown limit. If consolidation cannot free space, the write fails instead of becoming SQLite-only memory invisibly.
• System prompts are invisible: Pi's TUI does not display the system prompt. Memory injection works but you won't see it in the interface — verify by asking the agent a question that relies on stored memory.
• Skills are agent-generated: Skills are created by the agent based on its experience. They may not always be perfectly structured. You can edit or delete them in ~/.pi/agent/memory/skills/.

Architecture

Credits

Ported from the Hermes agent by Nous Research. Specifically:

• tools/memory_tool.py — MemoryStore class, content scanner, tool schema
• run_agent.py — Background review loop, session flush, nudge interval
• agent/memory_provider.py — Provider lifecycle pattern
• agent/memory_manager.py — System prompt injection, context fencing

License

MIT

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