@oh-my-pi/pi-mnemopi

@oh-my-pi/pi-mnemopi

Local SQLite memory engine for Oh My Pi agents.

This package is the Bun/TypeScript port of the Mnemosyne memory engine. It provides:

• Mnemopi, a small facade for remember/recall/stats/sleep workflows.
• BeamMemory, the lower-level working/episodic memory engine.
• MCP tool definitions and a dispatcher for host integrations.
• Optional local ONNX embeddings through fastembed and optional OpenAI-compatible embedding/LLM endpoints.

The package does not bundle or download a local GGUF LLM. LLM paths are host-backend or OpenAI-compatible remote only; when no LLM is configured, deterministic heuristic paths are used.

Basic use

import { Mnemopi } from "@oh-my-pi/pi-mnemopi";

const memory = new Mnemopi({ dbPath: "./mnemopi.db", bank: "project" });
const id = memory.remember("The deployment target is stable-cluster.", {
	source: "notes",
	importance: 0.8,
	veracity: "true",
});

const results = memory.recall("deployment target", 5);
console.log(id, results[0]?.content);

memory.close();

Configuration

Mnemopi accepts LLM and embedding options directly. MNEMOPI_* environment variables remain fallbacks/defaults when the matching constructor option is omitted.

import { Mnemopi } from "@oh-my-pi/pi-mnemopi";
import type { Model } from "@oh-my-pi/pi-ai";

const ftsOnly = new Mnemopi({ noEmbeddings: true });

const remoteEmbeddings = new Mnemopi({
	embeddingModel: "text-embedding-3-small",
	embeddingApiUrl: "https://api.openai.com/v1",
	embeddingApiKey: process.env.OPENAI_API_KEY,
});

const remoteLlm = new Mnemopi({
	llm: {
		baseUrl: "https://api.openai.com/v1",
		apiKey: process.env.OPENAI_API_KEY,
		model: "gpt-4.1-mini",
	},
	// Equivalent aliases: llmBaseUrl, llmApiKey, llmModel.
});

declare const smolModel: Model;
const piAiLlm = new Mnemopi({ llm: smolModel });
const dynamicLlm = new Mnemopi({
	llm: async (prompt, opts) => {
		const token = await getFreshOauthToken();
		return await completeWithPiAi(prompt, {
			token,
			maxTokens: opts?.maxTokens,
			temperature: opts?.temperature,
		});
	},
});

Banks and host scoping

Mnemopi itself exposes banks directly through constructor options such as bank; it does not hard-code coding-agent project scoping.

The Oh My Pi coding-agent wrapper adds mnemopi.scoping on top of those constructor options:

• global: one shared bank
• per-project: isolated project memory
• per-project-tagged: project-local writes plus global recall visibility

In per-project-tagged, the wrapper is responsible for combining project-local retention with global recall visibility. The package still just exposes banks plus constructor-level LLM and embedding options.

Common environment fallbacks:

• MNEMOPI_DATA_DIR / MNEMOPI_DB_PATH: default storage location.
• MNEMOPI_NO_EMBEDDINGS=1: force FTS-only recall.
• MNEMOPI_EMBEDDING_MODEL: defaults to BAAI/bge-small-en-v1.5.
• MNEMOPI_EMBEDDING_API_URL and MNEMOPI_EMBEDDING_API_KEY: OpenAI-compatible embedding endpoint.
• MNEMOPI_LLM_ENABLED=1, MNEMOPI_LLM_BASE_URL, MNEMOPI_LLM_API_KEY, MNEMOPI_LLM_MODEL: OpenAI-compatible LLM endpoint.

Local embeddings use the fastembed npm package. Its default BGESmallENV15 model is 384-dimensional and uses the package's CLS pooling plus vector normalization path. Local GGUF LLMs are not available in this package.

Commands

mnemopi remember "Use stable-cluster for production deploys"
mnemopi recall "production deploy target"
mnemopi stats
mnemopi sleep

Tests

bun --cwd packages/mnemopi test
bun --cwd packages/mnemopi run check