mnemoverse 0.1.0

Python SDK for Mnemoverse Memory API — persistent memory for AI agents

Mnemoverse Python SDK

Persistent memory for AI agents. Not vector search — statistical learning.

Installation

pip install mnemoverse

Quick Start

from mnemoverse import MnemoClient

client = MnemoClient(api_key="mk_live_YOUR_KEY")

# Store a memory
result = client.write(
    "Retry with exponential backoff fixed the timeout issue",
    concepts=["retry", "backoff", "timeout"]
)

# Query — Hebbian associations expand "timeout" → "retry", "backoff"
memories = client.read("how to handle timeouts?")

# Report outcome — the system learns what works
client.feedback(
    atom_ids=[item.atom_id for item in memories.items],
    outcome=1.0,
    query_concepts=memories.query_concepts
)

Async Client

from mnemoverse import AsyncMnemoClient

async with AsyncMnemoClient(api_key="mk_live_YOUR_KEY") as client:
    result = await client.write("async memory", concepts=["async"])
    memories = await client.read("what about async?")

Features

• Circuit breaker — 5 failures → open → 30s half-open → probe
• Retry with backoff — 3 attempts, rate-limit-aware
• Sync + async — MnemoClient for scripts, AsyncMnemoClient for FastAPI
• Type-safe — Pydantic models, full type hints

Methods

Method	Description
write(content, concepts, domain, metadata)	Store a memory
write_batch(items)	Store up to 500 memories
read(query, top_k, domain)	Query with Hebbian expansion
feedback(atom_ids, outcome)	Report success/failure
stats()	Memory statistics
health()	API health check

Documentation

• Getting Started
• API Reference
• Python SDK Docs

License

MIT