Mnemosyne

The next agent starts wiser.

Persistent memory for AI agents that need to carry work forward, recover original evidence, and stop following a lesson when its source changes.

Mnemosyne connects memory to its consequences: a corrected requirement can retire a dependent procedure, a failed trial can withhold a skill, and a new agent can resume from an explicit handoff. Run the local engine in one SQLite file; connect your host through TypeScript, MCP, CLI, or authenticated HTTP with a Python client and live inspector.

2.0.0-rc.9 · Release candidate · MIT · Node.js ≥22.16

Website · Quickstart · Documentation · Examples · Migration · Measured results · npm · Contributing

Choose your engine

Both engines ship in 2.0.0-rc.9. Choose the storage and runtime that fit your application:

Engine	Best starting point	SDK entry point	Guide
Vector Engine	An application using the existing service-backed memory architecture. Requires configured external services.	createMnemosyne from mnemosy-ai	Working example · Version 2 changes
Local Engine	A self-contained SQLite memory store with evidence, corrections and agent lifecycle tools.	createLocalMemory from mnemosy-ai/local	Local API · Agent lifecycle

The MemoryAgent adapter uses the Local Engine's runtime. These engines have separate schemas and do not automatically migrate or synchronize data. To transition gradually, explicitly connect your existing store through the read-only migration bridge; it leaves that store unchanged.

Engine choice is separate from release channel: stable mnemosy-ai@latest remains on version 1.x, while mnemosy-ai@next selects the version 2 candidate containing both engines. Review the migration notes before upgrading an existing Vector Engine application.

Start with a working demonstration

Install the exact release candidate in your project. Node 24 is recommended; the local memory engine needs no model API key, database server, or Docker.

npm install mnemosy-ai@2.0.0-rc.9
npx --no-install mnemosy demo
npx --no-install mnemosy learning-demo

The prerelease channel is mnemosy-ai@next. Pin the exact version for reproducible deployments; unqualified npm install mnemosy-ai follows the stable latest channel and still selects version 1.x. Evaluate this candidate on your own workload before production. Python registry publication is separate; the bundled Python client can be installed from the checkout.

To build and run the same candidate from source:

git clone --branch v2.0.0-rc.9 --depth 1 https://github.com/28naem-del/mnemosyne.git
cd mnemosyne
npm ci --ignore-scripts
npm run build
npm run demo
npm run demo:learning

The first demo uses a temporary database to show a handoff, correction, private scope and forgetting. The learning demo exercises capture, a scripted observation job, skill trials, explicit sharing and correction-driven retirement. Both run real local APIs with deterministic fixtures and no model calls. Choose your next example.