Agent Memory Ultimate

Cut your agent's token usage by 60-80%. Stop reading entire files into context — store memories once, recall only what's relevant. Vector search, knowledge graphs, automatic decay, and RAPTOR hierarchies give your agent human-like memory that actually scales.

Zero cloud APIs. Zero costs. Everything runs locally.

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Why Token Savings Matter

Every time your agent reads MEMORY.md or a daily log, that's thousands of tokens burned on context. Most of it is irrelevant to the current question.

Approach	Tokens per recall	Precision
Read entire MEMORY.md	3,000-10,000	~5% relevant
Read daily log + MEMORY.md	5,000-20,000	~10% relevant
mem.py recall "query"	200-800	~80% relevant
mem.py primed-recall (context-aware)	300-1,000	~90% relevant

On a typical agent doing 50 recalls/day:

• Old way: ~500K tokens/day on memory reads
• With v3: ~30K tokens/day
• Savings: ~470K tokens/day (~$7-14/day on API, or significant Claude Max headroom)

The agent gets better answers with less context pollution.

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What's New in v3

v2 was markdown files + FTS5 keyword search. v3 adds a full cognitive architecture:

Feature	v2	v3
Storage	Markdown files	SQLite + sqlite-vec embeddings
Search	FTS5 keyword only	Hybrid (vector + keyword + BM25)
Recall	Read entire files	Precise snippets via 6 strategies
Associations	None	Knowledge graph with multi-hop traversal
Hierarchy	Flat files	RAPTOR tree (L0→L1→L2→L3 abstraction)
Decay	Manual cleanup	Automatic strength decay + half-life
Consolidation	Cron text prompt	Clustering + merging + hierarchy rebuild
Context priming	None	Spreading activation from conversation
Sharing	None	Cross-agent with sensitivity gates
Token cost	High (full file reads)	Low (targeted retrieval)

v2 still works. Markdown files, daily logs, MEMORY.md — all unchanged. v3 adds a parallel cognitive layer that dramatically reduces token waste.

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Architecture

┌─────────────────────────────────────┐
│         Agent Session               │
│  (reads MEMORY.md, daily logs)      │  ← v2 (unchanged)
├─────────────────────────────────────┤
│         mem.py CLI                  │  ← v3 (NEW)
│  store / recall / consolidate       │
├──────────┬──────────┬───────────────┤
│ sqlite-vec│  FTS5   │ Association   │
│ (vectors) │(keyword)│   Graph       │
├──────────┴──────────┴───────────────┤
│            memory.db                │
│  memories, associations, hierarchy  │
│  shares, embeddings (384-dim)       │
└─────────────────────────────────────┘

• Embedding model: all-MiniLM-L6-v2 (384 dimensions, ~80MB, ONNX)
• Vector search: sqlite-vec — no Pinecone, no Weaviate, no cloud
• Database: db/memory.db (one file, portable, backupable)

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Quick Start

# 1. Initialize database
python3 scripts/mem.py init

# 2. Start the local embedding server (port 9999)
bash scripts/start-memory.sh

# 3. Store a memory
python3 scripts/mem.py store "Oscar prefers wired home automation" --type semantic --importance 0.8

# 4. Recall it (200 tokens instead of 10,000)
python3 scripts/mem.py recall "home automation preferences"

That's it. Your agent now has precise memory retrieval.

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6 Recall Strategies

Not all queries are the same. Use the right strategy:

Strategy	Best For	Command	When
Hybrid (default)	General recall	mem.py recall "query"	Most queries
Vector	Semantic similarity	recall --strategy vector	Fuzzy, conceptual
Keyword	Exact terms, IDs	recall --strategy keyword	File names, codes
Adaptive	Auto-selects detail	mem.py recall-adaptive "query"	Exploratory
Graph	Follow connections	mem.py recall-assoc "query" --hops 2	Related concepts
Primed	Context-aware	mem.py primed-recall "q" --context "..."	Mid-conversation

Primed recall is the killer feature — pass the last 2-3 user messages as context and results are biased toward what's conversationally relevant. This is how human memory works.

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CLI Reference

Core Commands

Command	Description
mem.py init	Create database schema
mem.py migrate	Import existing documents from jarvis.db
mem.py store <content> [--type TYPE] [--source SRC] [--importance N]	Store a memory
mem.py recall <query> [--strategy hybrid|vector|keyword] [--limit N]	Search memories
mem.py forget <id|--query QUERY>	Soft-delete (strength → 0)
mem.py hard-delete <id>	Permanently remove
mem.py stats	Database statistics

Memory types: episodic (events), semantic (facts), procedural (how-to), preference (user preferences)

Knowledge Graph

Command	Description
mem.py associate <src_id> <tgt_id> [--type TYPE] [--weight N]	Link two memories
mem.py links <memory_id>	Show all associations
mem.py recall-assoc <query> [--hops N] [--limit N]	Multi-hop traversal
mem.py graph-stats	Graph statistics

Edge types: related, caused_by, part_of, contradicts, temporal, supports

RAPTOR Hierarchy

Command	Description
mem.py build-hierarchy [--levels N]	Build abstraction tree
mem.py recall-adaptive <query> [--detail auto|broad|specific|0-3]	Recall at right abstraction level
mem.py hierarchy-stats	Show hierarchy structure

Broad queries ("what do I know about AI?") match high-level summaries. Specific queries ("sqlite-vec version") match leaf nodes. Adaptive auto-selects.

Spreading Activation

Command	Description
mem.py primed-recall <query> [--context 'text1' 'text2'] [--limit N]	Context-primed recall

Cross-Agent Sharing

Command	Description
mem.py share <memory_id> --with <agent> [--sensitivity N]	Share a memory
mem.py shared [--from AGENT] [--to AGENT]	List shares
mem.py revoke <share_id> | --memory <id>	Revoke access

Sensitivity levels: 0 (public) → 5 (top secret). Default gate: ≤ 3. Both agents must consent.

Maintenance

Command	Description
mem.py consolidate [--days N] [--decay-only]	Full consolidation cycle

Runs: decay → cluster → merge duplicates → rebuild hierarchy.

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Daily Cycle

Wake Up

1. Read SOUL.md, USER.md (identity — small, always load)
2. Read today's daily log (recent context)
3. Use `mem.py primed-recall "session start"` for relevant memories
   → Gets 200-800 tokens of precisely relevant context
   → Instead of 10,000+ tokens from reading MEMORY.md cover-to-cover

During Day

# Store important facts as they come up
mem.py store "Client meeting moved to March 1" --type episodic --importance 0.7

# Before answering from memory — recall, don't read files
mem.py recall "client meeting schedule"

# Link related memories when you notice connections
mem.py associate 42 87 --type related

Sleep Cycle (Nightly Consolidation)

{
  "schedule": { "kind": "cron", "expr": "0 3 * * *", "tz": "America/Los_Angeles" },
  "payload": {
    "kind": "agentTurn",
    "message": "Run memory consolidation: python3 scripts/mem.py consolidate --days 7"
  },
  "sessionTarget": "isolated"
}

What consolidation does:

1. Decay — Unaccessed memories lose strength (half-life: 30 days)
2. Cluster — Groups similar memories
3. Merge — Combines near-duplicates (saves storage + tokens)
4. Rebuild hierarchy — Updates RAPTOR tree for better adaptive recall

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File Structure

workspace/
├── MEMORY.md              # Long-term curated memory (v2, still works)
├── SOUL.md                # Identity & personality
├── USER.md                # Human profile
├── db/
│   ├── agent.db           # Contacts/history (v2)
│   └── memory.db          # Cognitive memory (v3)
├── bank/
│   ├── entities/          # People profiles
│   ├── contacts.md        # Quick contact reference
│   └── opinions.md        # Preferences, beliefs
└── memory/
    ├── YYYY-MM-DD.md      # Daily logs
    ├── projects/           # Project notes
    └── knowledge/          # Topic docs

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Comparison: Why This Over Alternatives

Feature	Read files (default)	Basic RAG	Agent Memory Ultimate v3
Token cost per recall	3K-20K	500-2K	200-800
Precision	~5-10%	~50%	~80-90%
Associations	❌	❌	✅ Knowledge graph
Abstraction levels	❌	❌	✅ RAPTOR hierarchy
Context priming	❌	❌	✅ Spreading activation
Automatic decay	❌	❌	✅ Configurable half-life
Cross-agent sharing	❌	❌	✅ With sensitivity gates
Cloud dependency	None	Usually	None — fully local
Setup complexity	Zero	High	mem.py init + one script

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Data Sources (v2 Importers)

Source	Command
WhatsApp contacts/groups	python3 scripts/sync_whatsapp.py
ChatGPT conversations	python3 scripts/init_db.py (auto-detects chatgpt-export/)
Phone contacts (VCF)	python3 scripts/import_vcf.py contacts.vcf
Existing markdown files	python3 scripts/mem.py migrate

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Dependencies

pip3 install scipy tokenizers onnxruntime numpy sqlite-vec

That's it. No Pinecone. No Weaviate. No Docker. No API keys. ~80MB embedding model downloaded once.

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Cognitive Science

This isn't arbitrary architecture — it's modeled on how human memory actually works:

Human Process	Agent Equivalent
Working memory	primed-recall (conversation-biased)
Long-term declarative	store + vector embeddings
Episodic memory	Daily logs + episodic memories
Semantic memory	MEMORY.md + semantic memories
Sleep consolidation	consolidate (decay + cluster + merge)
Forgetting curve	Strength decay with half-life
Association	Knowledge graph with typed edges
Abstraction	RAPTOR hierarchy (concrete → abstract)
Spreading activation	Context primes related memories

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Tips

1. Importance scores matter — 0.9+ for core identity, 0.5 for routine facts. Higher = survives decay longer.
2. Associate liberally — More edges = better graph traversal. Link when you notice connections.
3. Let decay work — Forgetting is a feature. Unaccessed memories fading keeps recall precise.
4. Primed recall for conversations — Pass last 2-3 messages as context. Dramatically better results.
5. Rebuild hierarchy weekly — build-hierarchy after storing many new memories.
6. Start with hybrid — Only switch to pure vector/keyword when hybrid misses.

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Credits

Created by Oscar Serra with the help of Claude (Anthropic).

Because waking fresh each session shouldn't mean burning 20K tokens to remember who you are.