toon-memory vs mem0 vs shodh-memory: AI Memory Solutions Compared

Why Compare AI Memory Solutions?

AI coding agents need memory to be productive. But which memory solution is best? This guide compares the three leading options:

• toon-memory — MCP server with token-efficient format
• mem0 — Cloud-based memory platform
• shodh-memory — Hybrid vector + graph memory

Quick Comparison

Feature	toon-memory	mem0	shodh-memory
Storage	Local file (TOON)	Cloud	RocksDB
Dependencies	Zero	Cloud API	sentence-transformers, RocksDB
Search	BM25 + graph + quality	Vector only	Hybrid (vector + graph)
Token efficiency	22% fewer than JSON	N/A (cloud)	Similar
Quality scoring	Auto (0-1, heuristics)	None	BND algorithm
Merge-dedup	Tags union + max confidence	None	Content dedup
Confidence tracking	Per-entry (0-1)	None	Per-entry
System Primer	Auto-generated	None	None
Multi-session	File-based coordination	None	None
Hooks	15 agents	None	Claude only
Encryption	AES-256-GCM	Cloud-managed	None
Setup time	npx toon-memory	Cloud signup	Docker + config
Cost	Free	Usage-based	Free
Internet required	No	Yes	No

Deep Dive: toon-memory

Strengths

• Token efficiency — TOON format is 22% fewer tokens than JSON
• Graph-aware recall — BM25 + centrality + quality scoring
• Agent support — 15+ agents with auto-setup
• Zero dependencies — no cloud, no Docker, no sentence-transformers
• Full feature set — 35 MCP tools, encryption, multi-session coordination

Weaknesses

• No vector search — uses BM25, not embeddings
• MCP required — agent must support Model Context Protocol
• Local only — no cloud sync between machines (use Git instead)

Best For

• Developers who want offline, private memory
• Teams using multiple AI agents (Claude, Cursor, OpenCode)
• Projects where token efficiency matters (cost savings)

Deep Dive: mem0

Strengths

• Managed service — no setup required
• Vector search — semantic similarity with embeddings
• Cloud sync — works across machines automatically
• API access — easy integration

Weaknesses

• Internet required — no offline support
• Cost — usage-based pricing adds up
• Privacy — data leaves your machine
• No token optimization — not designed for LLM efficiency
• Limited features — no quality scoring, no graph recall

Best For

• Teams that need cross-machine sync without Git
• Projects where privacy is not a concern
• Quick prototyping without local setup

Deep Dive: shodh-memory

Strengths

• Hybrid search — vector + graph recall
• Quality scoring — BND algorithm
• Offline — no cloud required
• RocksDB storage — fast, persistent

Weaknesses

• Complex setup — requires Docker + sentence-transformers
• Heavy dependencies — large Python environment
• Claude only — limited agent support
• No token optimization — not designed for LLM efficiency

Best For

• Projects that need vector search + graph recall
• Teams already using Docker and Python
• Claude-only environments

Benchmark: Token Efficiency

Measuring tokens needed to get the same context:

Method                          Tokens    vs re-reading files
─────────────────────────────  ────────  ───────────────────
Re-read source files            ~3000    baseline
toon-memory (flat)              ~1200    -60%
toon-memory (graph, compact)    ~900     -70%
toon-memory (smart_recall)      ~850     -72%
shodh-memory (hybrid)           ~1100    -63%
mem0 (cloud)                    N/A      N/A (cloud)

Benchmark: Cost

Assuming GPT-4 pricing ($0.03/1K input tokens), 10 sessions/day:

Solution	Cost/month	Notes
toon-memory	$8.70	Free, token-efficient
mem0	~$15-30	Usage-based pricing
shodh-memory	$10.50	Free, less token-efficient
No memory	$21.60	Re-explaining context

Feature Comparison

Memory Operations

Feature	toon-memory	mem0	shodh-memory
Save decisions	✅	✅	✅
Save patterns	✅	❌	❌
Save bugs	✅	❌	❌
Quality scoring	✅ (0-1)	❌	✅ (BND)
Confidence tracking	✅	❌	✅
TTL (expiration)	✅	❌	❌
Merge-dedup	✅	❌	✅
Encryption	✅ (AES-256)	✅ (cloud)	❌

Search & Recall

Feature	toon-memory	mem0	shodh-memory
Keyword search	✅ (BM25)	❌	❌
Vector search	❌	✅	✅
Graph recall	✅	❌	✅
Quality ranking	✅	❌	✅
Compact output	✅	❌	❌
Smart recall	✅	❌	❌

Integration

Feature	toon-memory	mem0	shodh-memory
MCP protocol	✅	❌	❌
Agent support	15+	Any (API)	Claude only
Auto-setup	✅	✅	❌
Hooks	✅	❌	✅ (Claude)
Multi-session	✅	❌	❌
Offline	✅	❌	✅

Decision Matrix

If you need…	Choose
Offline + private memory	toon-memory
Token efficiency	toon-memory
Multiple agent support	toon-memory
Cross-machine sync (no Git)	mem0
Vector search	shodh-memory or mem0
Simplest setup	toon-memory
Lowest cost	toon-memory

Conclusion

toon-memory is the best choice for most developers:

• Token-efficient — saves 22% on every session
• Agent-agnostic — works with 15+ agents
• Private — no cloud, no data leaves your machine
• Full-featured — 35 MCP tools, graph recall, quality scoring

npm install -g toon-memory
npx toon-memory  # Interactive installer

If you need vector search or cloud sync, consider mem0 or shodh-memory. But for most use cases, toon-memory provides the best balance of features, performance, and simplicity.