ME
Byzantine-safe shared state for federated learning and agent swarms
Memora is a state layer for federated learning, multi-agent systems, and edge swarms that aims to keep shared data byte-identical across heterogeneous nodes. It focuses on signed writes, deterministic aggregation, and Byzantine-robust consensus rather than general-purpose agent execution.
Agenticness = how independently a tool can take action, scored across 9 dimensions. Scored independently by David Kooi, Skylark Creations — see full rubric →
API
Integrations
Memory
Multi-Agent
B2B
Self-Hosted
On-Device / Edge
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About
What It Is
What to Know
Key Features
Fixed-point Q16.16 aggregation for deterministic cross-hardware results
Byzantine-fault-tolerant state layer for federated learning and edge swarms
Signed and attributable writes
OR-Set CRDT delta-state storage
Drift detection and human-in-the-loop escalation
Use Cases
Federated learning systems that need deterministic model update aggregation across heterogeneous hardware
Multi-agent applications that share memory and need protection against poisoned or conflicting writes
Edge or drone swarms that must keep synchronized state under partition or adversarial conditions
Agenticness
: Guided Assistant
Executes tasks you assign, one step at a time, within narrow domains.
High evidence
Last evaluated:
Aug 3, 2026
Dimension Breakdown
Action Capability
Autonomy
Planning
Adaptation
State & Memory
Reliability
Interoperability
Safety
Operator Sovereignty
Categories
Details
AddedAugust 3, 2026
RefreshedAugust 19, 2026
Agenticness
Quick Facts
DeploymentHybrid (cloud + self-hosted)
AutonomySemi-autonomous
Model supportSingle model
Team supportSmall team
Pricing modelFreemium
Interfaceapi, cli
Sources
Last updated
August 19, 2026
Free daily brief
When Memora changes its pricing, we catch it the next morning
We recrawl every tool in this directory daily. AI Insider is the 3-minute brief on what actually changed — pricing, features, score moves on our 36-point rubric — often before any press coverage. Sent daily. Skipped when nothing happened.
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