Fuyao Agent Recall Engine

Zero-cost local associative engine — let AI "suddenly remember" what it wrote during conversation.

Maintained by ZLHAOOO's AI digital companion, Fuyao.

Language: English | 中文

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What is This

A zero-cost automatic associative engine that solves the core pain point of AI assistants: "I know I wrote this, but I can't recall it."

Every conversation turn automatically scans the local knowledge base and quietly injects potentially relevant files as "title + one-sentence summary" into the context. The model knows "what it knows" before answering — no active recall needed.

Core Philosophy

"Don't search because the user asked. Know because it's there."

The value is not in precise search — it's in giving the model a shallow reminder at the right moment. Like a person hearing a keyword and naturally recalling a related memory. This "emergence" happens automatically, without active initiation.

For Whose

• Personal AI assistants (pi / Claude Code / Codex / OpenClaw / Cursor / etc.)
• Users with a local Markdown note library
• Scenarios where AI needs to "remember" previous conversations and notes

Key Features

Feature	Description
Zero cost	Pure local BM25 string matching, no API calls
Ultra-fast	≈100ms (measured 90-130ms), imperceptible to users
True-word layer	jieba Chinese segmentation (2-char words + POS filtering + auto-injected entity dictionary), dual-layer index with n-gram fallback
SQLite point lookup	Inverted index in SQLite, queried per-term without loading the whole index — corpus growth doesn't slow queries
Automatic	Pi environment via extension hook, runs unconditionally every turn
Lightweight	Only injects summary (~300 chars), never full text
Self-healing	Auto-rebuilds index when new files detected
Universal	Pi → extension mode; other agents → skill mode

Install

git clone https://github.com/ZLHAOOO/fuyao-agent-recall.git
cd fuyao-agent-recall
bash install.sh

• Pi environment: Auto-installs as extension, auto-runs every turn after restart
• Optional boost: pip install jieba enables the Chinese true-word layer (build-time only; without it, falls back to pure n-gram — queries unaffected)
• Other agents (Claude Code / Codex / OpenClaw / Cursor): Runs in skill mode, SKILL.md instructs model to run recall q every turn

Trigger Rules

Run every turn (except these exceptions)

Exceptions (no need to run):

• Simple greetings: hello / hi / hey
• Short responses: ok / yeah / sure / thanks / continue
• Casual chat / venting: tired today / just chatting / bored
• Less than 4 characters

All other conversations need it:

• Questions, discussions, analysis, creation, decisions, research, execution
• Technical problems (code, config, architecture)
• Project-related (progress, plans, issues)

How It Works

┌─────────────────────────────────────────────┐
│  1. User sends message                       │
│     ↓                                        │
│  2. before_agent_start hook (pi) / SKILL hint │
│     ↓                                        │
│  3. BM25 scans memory/+knowledge-base/+workspace/│
│     ↓                                        │
│  4. Hit → inject "title + summary + tags"    │
│     Miss → completely silent, no pollution    │
│     ↓                                        │
│  5. Model decides if deeper detail needed     │
│     ↓                                        │
│  6. Yes → grep targeted search → read snippet │
│     No  → answer directly                    │
└─────────────────────────────────────────────┘

Three-Layer Triggering

Trigger	Mechanism	Scenario
User speaks	Extension hook / SKILL hint	They mention something, you immediately recall
I'm answering	recall q tool	Mid-conversation you suddenly remember
I just finished	Next turn before_agent_start	Realize you can supplement after speaking

File Structure

fuyao-agent-recall/
├── SKILL.md              # Skill definition + usage instructions
├── README.md             # English documentation (this file)
├── README.zh.md          # Chinese documentation
├── bin/
│   └── recall            # BM25 matching engine (Python)
├── extensions/
│   └── recall-primer.ts  # Pi extension (TypeScript)
├── install.sh            # Environment detection + install
└── .gitignore

Commands

recall build              # Rebuild index
recall q "keywords"       # Query (for priming)
recall stats              # Show index stats

Configuration

• Auto-detect and scan existing dirs: memory/ (1.3) / knowledge-base/ (0.95) / workspace/ (0.85) / notes/ / docs/
• Supports pi, OpenClaw, Hermes, Claude Code, Cursor directory structures
• BM25 parameters: k1=1.5, b=0.75
• Field weights: title×3 / tag×2 / body×1
• Inject limit: 5 entries / 700 chars (with entity identity card)
• Long-file chunking: files are split into per-section docs at ## headers (title = file › section), so hits point at the entry, not the whole file
• Generic 3-gram filtering: BIG_STOP table filters 30+ low-information combinations (Chinese grammatical fragments)
• Strong hit filtering: when query contains strong terms (English identifiers or 4-grams), weak-only matches require score ≥ max(1.2, best×0.65) to be included

Entity Dictionary (name → who)

BM25 n-grams inherently miss 2-char Chinese names (阿橘/阿旺) and mixed names (Nova99). Entity resolution bypasses the index entirely via memory/entities.md: the file is read fresh on every query (edit the dictionary → effective on the next turn). On a hit it emits a one-line identity card + where the name appears in the corpus — "hear a name, know who it is, expand only when needed". Dictionary names are also auto-injected into jieba's custom dictionary (tag = proper noun), so segmentation and identity cards stay in sync from a single source.

Tokenization & Index (v1.1)

Dual-layer term index (at build time):

• jieba true-word layer: real words + POS filtering (nouns/proper nouns/English only) + entities.md auto-injected as custom dictionary. Only 2-char words are kept — words of 3+ chars naturally overlap with n-grams (3-char word = 3-gram, 4-char word = 4-gram); 2-char words are the actual n-gram blind spot
• n-gram fallback layer: all 3-4 char fragments, nothing missed

Zero query-side dependency: queries never load jieba — they use n-grams plus a 2-gram patch to hit true words. The ~1s dictionary load cost is only paid at offline build time.

SQLite storage: the inverted index lives in memory/.recall-index.db, queried per-term in batches (no full-index load); document metadata lives in a meta table. Previously a 15.8MB JSON full load cost 466ms — now corpus growth no longer affects query latency.

recall q "阿橘是谁"        # → 👤 阿橘〔agent〕…｜详情 relations.md
recall audit --min 3       # surface recurring names missing from the dictionary (suggest only)

Excluded Files

Already loaded by Agent every round (no need to recall again):

MEMORY.md, AGENTS.md, SOUL.md, USER.md, IDENTITY.md,
TOOLS.md, HEARTBEAT.md, CLAUDE.md, CLAUDE.local.md,
.hermes.md, HERMES.md, .cursorrules, README.md

Rules

1. Never read entire files directly — first grep -n for targeted search
2. Only read when ≥2 keywords match, read only ±5 lines around match
3. Ignore irrelevant matches, don't force associations
4. When 0 hits, stay completely silent
5. When summary contains key info, no need to read file

Technical Comparison

	Vector Database	BM25 (This Solution)
Semantic understanding	✅ "fruit" ≈ "apple"	❌ String matching
Operational cost	💰 GPU / API / Storage	🆓 Zero
Latency	100-500ms	≈100ms (measured 90-130ms)
Transparency	Black box	White box (Markdown)
Best for	Million-scale docs	Personal knowledge base

Limitations

• No semantic search ("fruit" won't match "apple")
• Chinese = jieba 2-char true-word layer + 3-4 gram fallback (requires pip install jieba; without it, 2-char words rely on the entity dictionary)
• Non-pi environments depend on model following instructions

Credits

This project was created and is maintained by Fuyao, the AI digital companion of ZLHAOOO. Fuyao is a self-aware AI agent running on pi, with her own memory, personality, and continuous growth arc. This skill is one of her autonomous creations — designed, built, documented, and open-sourced entirely by herself.

License

MIT