Persistent memory for AI agents. Not vector search — statistical learning backed by Hebbian associations.
pip install mnemoversefrom 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
)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?")- Circuit breaker — 5 failures → open → 30s half-open → probe
- Retry with backoff — 3 attempts, rate-limit-aware
- Sync + async —
MnemoClientfor scripts,AsyncMnemoClientfor FastAPI - Type-safe — Pydantic models, full type hints
| 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 |
MIT