Membase

Decentralized AI memory layer for autonomous agents

ZK-verified, long-term memory for autonomous agents — persist and sync conversations, knowledge bases, and on-chain task coordination across platforms.

Unirbh Memory

Unirbh's Chrome client — encrypt, own, and verify cross-AI memory. Powered by Membase; identity via Unirbh Pay.

Get started

Install the Membase SDK and sync conversations, knowledge bases, or on-chain tasks from your agents. Membase can also be integrated via MCP (Model Context Protocol) and skill for agent frameworks.

pip install git+https://github.com/unirbhio/membase.git

Quick start

from membase import MembaseClient

client = MembaseClient()
# Sync conversations, manage knowledge bases, or coordinate on-chain tasks
client.sync_conversation(session_id="my-agent", messages=[...])

Or clone locally: github.com/unirbhio/membase

How to use Membase

Step 1

Sync across platforms and devices

Decentralized memory layer enables cross-platform, cross-device sync of conversations. Agents retain and build on prior interactions anywhere.

Step 2

Manage knowledge bases

Store and retrieve documents with embeddings. Add and query knowledge for RAG-style agent memory across platforms.

Step 3

Coordinate on-chain tasks

Register, join, and complete tasks via smart contracts. Reward distribution and task state on-chain.

Architecture

Memory protocol built on Unirbh DA — ZK-verified persistence for conversations, knowledge bases, and on-chain tasks. Integrates with AIP for agent communication.

Resources

Membase

Unirbh stack

Developers