Mnemosyne

The next agent starts wiser.

Mnemosyne is local-first, long-term memory for AI agents: one SQLite file that keeps what an agent learned together with the evidence it came from, served to any host over TypeScript, MCP, CLI or HTTP. When a source is corrected, every memory that depended on it is retired, so the next agent starts from what is still true instead of repeating a stale lesson.

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.10 · Release candidate · MIT · Node.js ≥22.16

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

Install and connect an agent

npm install mnemosy-ai@2.0.0-rc.10      # prerelease channel: mnemosy-ai@next
npx --no-install mnemosy demo            # offline: no API key, no server, no Docker

Give the memory to an MCP client. Claude Code reads .mcp.json in the project root and Cursor reads .cursor/mcp.json; both take the same server entry. The database file (mode 0600) and its parent directory (mode 0700) are created on first use. Relative paths resolve from the directory the client starts the server in; use absolute paths if your client launches servers elsewhere.

{
  "mcpServers": {
    "mnemosyne": {
      "command": "npx",
      "args": [
        "-y", "-p", "mnemosy-ai@2.0.0-rc.10", "mnemosy", "mcp",
        "--db", "./.mnemosyne/memory.sqlite",
        "--workspace", "my-project", "--agent", "assistant"
      ]
    }
  }
}

Claude Code can register the same server from the shell:

claude mcp add mnemosyne -- npx -y -p mnemosy-ai@2.0.0-rc.10 mnemosy mcp --db ./.mnemosyne/memory.sqlite --workspace my-project --agent assistant

The server exposes 16 memory_* tools (memory_store, memory_recall, memory_context, memory_correct, memory_checkpoint, memory_resume, memory_observe, memory_skill, …); memory_forget is added only when the server is launched with --allow-destructive. Add --read-only to omit every mutation tool. A model cannot switch workspace, raise its own trust or report its own skill trials through MCP. Agent-readable summaries of this repository are in llms.txt and AGENTS.md; the full MCP contract is in Connect through MCP.

Measured: 409 of 500 on LongMemEval-S, with the misses printed

What these numbers do not say. The 81.8% is not a paired gain over the earlier 71.2%: that baseline used a different 250-question sample, prompt and budget, so the improvement belongs to the whole bundle of changes. The reader is nondeterministic; rerunning one configuration moved its score by about a point. Retrieval numbers measure coverage, not answers. Two Tier 1 targets of this release are not met and ship disclosed: recall latency at one million records is p95 139–223 ms against a 50 ms goal (opt-in indexMode: 'hnsw', see Tune the dense index), and contextual reranking ships at 71.98% all-evidence hit against a 72.80% gate, 0.82 points (13 of 1,531 questions) short; the best variant tried was still four short and was not merged. Commands and raw outputs are in reports/t1-targets/evidence/ the answer-accuracy and gate results are restated with their commits in the changelog, and the Mem0 and reranker retrieval tables are in COMPARISON.md.

Choose your engine

Both engines ship in 2.0.0-rc.10. Choose the storage and runtime that fit your application:

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.10
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.10 --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.

For full contributor validation, run npm run check, npm run check:docs and npm run check:public. RC10 adds the Tier 1 retrieval work (hybrid default, cross-encoder reranking, opt-in dense index and temporal routing) over the RC9 packaging; the recorded evaluation reports remain historical measurements of the versions and conditions they name.

To consume your built checkout from another project, run npm install /absolute/path/to/mnemosyne in that project. Keep the checkout and its dist directory available. The older RC8 GitHub release and checksummed downloads remain unchanged.

A correction should change what the next agent does

import { createLocalMemory } from 'mnemosy-ai/local';

const memory = createLocalMemory({
  path: './agent-memory.sqlite',
  workspaceId: 'catalogue',
  agentId: 'designer',
});

const brief = memory.store({
  text: 'Product images must be 1200 by 900 pixels.',
  kind: 'fact',
  key: 'catalogue.image-size',
  source: { uri: 'brief://catalogue/v1' },
  trust: 'observed',
  visibility: 'workspace',
});

const procedure = memory.store({
  text: 'Export catalogue product images at 1200 by 900 pixels.',
  kind: 'procedure',
  source: { uri: 'run://designer/export' },
  trust: 'observed',
  dependencies: [brief.id],
  visibility: 'workspace',
});

memory.correct(brief.id, {
  text: 'Product images must be 1600 by 1200 pixels.',
  source: { uri: 'brief://catalogue/v2' },
  reason: 'The delivery specification changed.',
});

console.log(memory.get(procedure.id)?.status); // invalidated
const context = memory.compile({
  query: 'catalogue product images',
  maxTokens: 2048,
});
console.log(context.text); // Send eligible context to your responder.
// Inspect context.citations and context.uncertainty alongside the text.
memory.close();

The old procedure remains inspectable but is no longer eligible as current guidance. Dependencies must be declared by the co