OpenContext

The agentic context runtime, powering applications that act on your behalf.

A temporal context graph, a memory API, retrieval primitives, and a multi-platform integration mesh — designed to be embedded into any host process or agents.

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What is OpenContext?

OpenContext is the agentic context runtime that sits underneath an agentic application — and the substrate you build your own agent on top of. It is not a UI, a chat surface, or a model provider — it is the glue between the things that make an agent useful: durable memory, retrieval, context correction, multi-platform connectivity, scheduled awareness, and a deterministic loop engine, all behind one dependency.

→ Read docs/architecture.md for the full data model, the lifecycle of a fact, and the transport surface map.

Who is it for?

OpenContext fits teams who need to engineer their context — that is, teams whose day-to-day work runs straight into the problems OpenContext was built to solve. Each bullet spells out the pain and how OpenContext addresses it:

• Software engineering teams. Decisions scatter across GitHub PRs, Linear tickets, Slack threads, and Notion docs — across people, tools, and quarters. New hires ask "why did we pick X?" and no one can answer. OpenContext's temporal graph stores every fact with valid_from / valid_until, so "what did we believe last quarter?" is a real, citable query — not a guess.
• Efficiency / productivity engineering teams. The people building internal automation for the rest of the company. They don't want another SaaS — they want a runtime they can drop into a CLI, an MCP server, or a daemon. OpenContext is library-first, and the deterministic Loop engine only invokes the LLM when there is real work, so it does not become a token-burning always-on loop.
• Office-assistant products. Assistants that live inside Telegram, iMessage, WhatsApp, Lark/Feishu, and friends. Same agent code, same context across channels. IntegrationRecord hides credentials, rate-limits, and reconnect logic, while platform + messageId is the natural audit trail for personal and work data.
• Financial trading teams. Every order, rebalance, and risk decision needs to be traceable and auditable. The temporal graph plus append-only corrections mean "what was the strategy in April?" is a queryable fact, not a buried guess — and the trail lines up with MiFID II / SEC retention rules.
• Legal, healthcare and other audited domains. Law firms, hospitals, and similar teams where every judgement needs per-fact provenance, append-only corrections, and exportable compliance evidence.
• Multi-agent and autonomous-workflow authors. Need scheduled, deterministic wake-up instead of an LLM loop all the way down. packages/loop ships exactly that separation.

Features

Benchmarks

Third-party memory and long-context recall benchmarks (numbers current as of 2026-08):

Scores above are opencontext self-evaluations on the public datasets, produced by our own harnesses under benchmark/. They are not Agent Memory Leaderboard (AML) results — AML evaluates against private refined datasets on its own platform. Official AML scores, once our hosted submission completes, will appear on the leaderboard card; see benchmark/aml-local/ for the submission.

Quick Start

There are several ways to get opencontext into your project. Pick the one that matches what you're building.

1. Embed the runtime into your own app

pnpm add @melandlabs/opencontext

A 30-second example of the memory API:

import { createMemoryStore, getRawMessageManager } from "@melandlabs/opencontext";

// The store defaults to SQLite at MEMORY_STORE_DB_PATH (./memory.db by
// default). Each call returns an awaitable handle.
const store = await createMemoryStore();
const messages = await getRawMessageManager();

// A message is one fact: a single piece of content attributed to a user.
// `messageId` makes the call idempotent across re-ingest.
const now = Date.now();
await messages.storeMessages([
	{
		messageId: "msg-1",
		userId: "u-42",
		content: "User prefers dark mode in all tools",
		platform: "test",
		botId: "bot-1",
		timestamp: now,
		createdAt: now,
	},
]);

// Unified search fans out to memory + insights