Agent-native database with causal memory, composite-scored recall, and a tamper-evident integrity chain
Reason this release was yanked:
0.2.10 available with recalls across all LLM Sessions;
Project description
meta-agents-db
Agent-native database with causal memory, composite-scored recall, and a tamper-evident integrity chain.
The first database designed for autonomous AI agents.
Install
pip install meta-agents-db
Quick start
from meta_agents_db import MetaAgentsDB
db = MetaAgentsDB("./data")
event_id = db.write(content="SPY broke below 200-day MA", tags=["signal", "SPY"])
results = db.recall("SPY bearish signals", top_k=10)
chain = db.trace_cause(event_id, direction="backward", max_depth=5)
Features
- Causal DAG — every memory links to its causes; trace full decision chains
- 5-signal composite recall — vector similarity + recency + causal proximity + importance + tag overlap
- Storage-layer policy (roadmap) — risk, budget, and rate caps at write time; the policy engine is not enabled by default in this release
- Optional HNSW vector index — approximate nearest-neighbor search (opt-in; brute-force is the default backend)
- Session caching — deduplicates repeated recalls within a pipeline cycle
- MVCC — snapshot isolation for concurrent readers/writers
- WAL + SSTables — crash-safe append-only log with background compaction
License
Meta-Agents.AI Proprietary License v1.0. Install-and-use is permitted for end-users of the published wheels; source access, OEM embedding, and hosted-service rights are reserved for separately-negotiated agreements. Patent pending.
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