arriadne 0.3.0

AI agent memory system — sub-millisecond hybrid search (FAISS + FTS5 + RRF), knowledge graph, temporal awareness, LLM-powered extraction, entity resolution, conversation memory. Zero infrastructure. OpenAI/Anthropic/Ollama support.

Ariadne

Memory for AI agents. Sub-millisecond search. Zero infrastructure.

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Quick Start

from arriadne import AriadneMemory

mem = AriadneMemory(db_path="memory.db")
# Auto-detects ONNX embeddings — zero config

mem.remember("VPS has 4 cores, 8GB RAM", importance=0.8)

results = mem.recall("server specs", k=5)

pip install arriadne

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Why

	Ariadne	Mnemosyne	Mem0	ChromaDB
Vector search	0.83ms	153ms	12ms	8ms
Hybrid search	✅ RRF	❌	❌	⚠️ basic
Knowledge graph	✅ BFS	⚠️ basic	❌	❌
Auto-embeddings	✅ ONNX	❌	✅ cloud	❌
Auto-dedup	✅ MinHash	❌	❌	❌
Runs locally	✅	✅	❌	✅
No daemon	✅	✅	❌	❌

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Features

0.83ms Vector Search

FAISS-powered. 12× faster than sqlite-vec. Auto-upgrades from exact to approximate search as your data grows.

Engine	10K vectors
FAISS (Ariadne)	0.83ms
sqlite-vec	10.5ms

Hybrid Retrieval

Vector similarity + BM25 keywords fused with Reciprocal Rank Fusion.

results = mem.recall("how to deploy to production", k=5)
# Searches both keyword and semantic similarity, fuses with RRF
for r in results:
    print(f"[{r['search_type']}] {r['content'][:80]}")

Zero-Config Embeddings

Auto-downloads a quantized ONNX model on first use (~90MB). No API keys, no cloud, works offline.

# Just works — no embedding_provider parameter needed
mem = AriadneMemory("memory.db")
mem.remember("Paris is the capital of France")  # auto-embedded

Falls back to keyword matching if ONNX is unavailable.

Knowledge Graph

BFS graph traversal with typed, weighted edges. Bidirectional — edges are traversed in both directions.

mem.add_edge("Paris", "France", "located_in")
mem.add_edge("Nginx", "VPS", "runs_on")
g = mem.graph("VPS", hops=2)
# Returns: VPS ↔ Nginx, VPS ↔ France (via Paris)

Auto-Deduplication

MinHash LSH near-duplicate detection. Catches paraphrases, not just exact matches.

mem.remember("The server runs Ubuntu 24.04")
mem.remember("Ubuntu 24.04 is running on the server")
# Second store detects near-duplicate (LSH similarity > threshold)

Conversation Memory

Track conversations and extract structured facts automatically.

mem.sync_turn("user", "Deploy the app to production")
mem.sync_turn("assistant", "Deploying now via GitHub Actions")

context = mem.get_context("deployment")  # relevant past turns

Agent Tools

OpenAI function-calling compatible tool definitions for any LLM.

tools = AriadneMemory.get_tools()  # 6 tools: remember, recall, graph, link, forget, stats
# Plug into any agent framework that supports function calling

Memory Lifecycle

Ebbinghaus forgetting curve + priority-based eviction. Memories that matter survive; noise gets cleaned up.

mem.consolidate()  # merge similar memories
mem.evict()        # remove low-priority noise

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Benchmark

Measured on a 4-core 8GB VPS with 10K memories and ONNX embeddings (all-MiniLM-L6-v2, 384-dim):

Operation	p50	p95
Vector search	0.83ms	0.95ms
FTS search	1.56ms	2.12ms
Hybrid search	5.07ms	6.78ms
Graph traversal (2 hops)	0.06ms	—
Store (with ONNX embedding)	42ms	—

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Install

pip install arriadne

Optional (for faster dev):

pip install "arriadne[dev]"

Requirements: Python 3.10+, SQLite (built-in). ONNX model auto-downloads on first use.

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Hermes Integration

hermes plugin install arriadne
hermes config set memory.provider ariadne

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Links

• Docs: ariadne.mantes.net
• PyPI: pypi.org/project/arriadne
• GitHub: github.com/kyssta-exe/Ariadne