memdio

AI memory encoded in sound. Lossless, fast, and hardware-adaptive.

memdio encodes text into FLAC audio files with Reed-Solomon error correction, stores them in SQLite, and provides semantic search via MCP for Claude Desktop.

Architecture

Text  -->  zlib compress  -->  Reed-Solomon ECC  -->  Frequency encoding (200-4000Hz)
      -->  FLAC compress  -->  SQLite blob storage

Search: FTS5 (word match) + FastEmbed ONNX + sqlite-vector (semantic similarity)

Everything lives in one SQLite database per user. No external services, no separate index files.

Performance

Operation	Time
Store memory	67ms
Retrieve memory	15ms
Word search (FTS5)	<1ms
Semantic search	150ms
MCP server cold start	127ms

Installation

pip install -e .

# With semantic search support
pip install -e ".[search]"

Claude Desktop Integration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "memdio": {
      "command": "/path/to/venv/bin/python",
      "args": ["-u", "-m", "memdio.mcp"]
    }
  }
}

MCP Tools

Tool	Description
store_memory	Encode text into FLAC audio. Returns memory ID
retrieve_memory	Decode audio back to text by UUID
search_memories	FTS5 word-level search (any word matches)
semantic_search	Meaning-based search via embeddings
list_memories	List all stored memories
delete_memory	Delete a memory by UUID

CLI

# Store and retrieve
memdio encode "Meeting at 3pm with Alice" --tags "meeting,alice"
memdio decode <memory-id>

# Search
memdio search "meeting"
memdio semantic-search "schedule appointment"

# Manage
memdio list
memdio delete <memory-id>
memdio info <memory-id>
memdio stats

# Export
memdio export-flac <memory-id>
memdio export-wav <memory-id>

# Servers
memdio mcp          # MCP stdio server (Claude Desktop)
memdio serve        # REST API server

REST API

# Start server
memdio serve

# Create API key
memdio create-key alice

# Use API
curl -X POST http://localhost:8000/memories \
  -H "Authorization: Bearer memdio_xxxx" \
  -H "Content-Type: application/json" \
  -d '{"content": "Meeting at 3pm with Bob", "tags": "meeting"}'

curl http://localhost:8000/memories?query=meeting \
  -H "Authorization: Bearer memdio_xxxx"

Limits

Parameter	Value
Max memory size	1 MB per memory
Max query length	1,000 characters
Max tags length	500 characters
Embedding dimensions	384 (bge-small-en-v1.5)
Contradiction threshold	0.85 cosine similarity
Relation extension threshold	0.50 cosine similarity
Default search results	10 (configurable)
SQLite mmap cache	64 MB

LongMemEval Benchmark

Evaluated on LongMemEval — 500 questions across 6 task types. All models via OpenRouter, context window 2,000 chars per memory, Top-K 10.

Overall Results

Model	Task-Avg	Overall	Abstention
Gemma 3 27B	46.3%	38.2%	86.7%
Claude Sonnet 4	45.1%	35.2%	96.7%
Gemini 2.0 Flash	42.9%	33.2%	100.0%
Qwen 2.5 72B	42.7%	34.8%	90.0%
Grok 3 Mini	42.5%	33.2%	96.7%

Breakdown by Task Type

Task Type	Qs	Gemma 3 27B	Claude Sonnet 4	Gemini 2.0 Flash	Qwen 2.5 72B	Grok 3 Mini
Single-session (assistant)	56	82.1%	89.3%	83.9%	85.7%	87.5%
Single-session (user)	70	64.3%	61.4%	57.1%	61.4%	58.6%
Single-session (preference)	30	50.0%	53.3%	53.3%	36.7%	50.0%
Knowledge update	78	42.3%	39.7%	38.5%	43.6%	32.1%
Multi-session	133	15.8%	15.0%	12.8%	13.5%	15.0%
Temporal reasoning	133	23.3%	12.0%	12.0%	15.0%	12.0%

Key observations:

• Gemma 3 27B leads on task-averaged accuracy, driven by strong temporal reasoning (23.3% vs 12% for most others)
• Claude Sonnet 4 is the best single-session assistant (89.3%) and preference extractor (53.3%)
• All models achieve 87–100% abstention — memdio correctly signals when information is missing
• Single-session recall is strong across the board (57–89%), validating the retrieval pipeline
• Multi-session and temporal reasoning remain the hardest categories (active improvement area)

Run the benchmark yourself:

pip install -e ".[benchmark]"
export OPENROUTER_API_KEY=your-key
python -m benchmarks.longmemeval.run --model google/gemma-3-27b-it

How It Works

Encoding Pipeline

"Hello World"
    |
    v
UTF-8 bytes --> zlib compress --> Reed-Solomon ECC (14% redundancy)
    |
    v
CRC32 header (magic + version + length + checksum)
    |
    v
Frequency mapping: each byte -> 200-4000Hz tone (256 slots)
    |
    v
48kHz audio signal with Hann windowing (reduces spectral leakage)
    |
    v
FLAC lossless compression --> SQLite blob

Search

• FTS5: Word-level matching. "Alice wedding" finds "Alice is getting married" because "Alice" matches.
• Semantic: FastEmbed (ONNX) generates 384-dim embeddings, sqlite-vector does cosine similarity. "motorcycle" finds "bike" (0.837 score).

Error Correction

Reed-Solomon RS(255,223) recovers the original text even if 5% of audio samples are corrupted.

Tech Stack

Component	Technology
Audio encoding	numpy, scipy, soundfile
Error correction	reedsolo (Reed-Solomon)
Storage	SQLite (WAL mode, FLAC blobs)
Word search	SQLite FTS5
Semantic search	FastEmbed (ONNX) + sqlite-vector
MCP server	Lightweight JSON-RPC over stdio
REST API	FastAPI
CLI	Typer

Multi-tenant SaaS

Per-user isolation with API key auth:

/data/memdio/users/
  alice/index.db    <-- all of Alice's memories
  bob/index.db      <-- all of Bob's memories

Delete user = delete their folder. No shared database.

License

MIT