OpenContext

Your agent forgets why it made decisions. OpenContext fixes that.

The context layer for AI agents — a temporal context graph, memory API, and self-evolving loop in one dependency.

Context is the missing layer. Retrieval answers what happened. OpenContext answers how what happened became what is.

Unlike memory libs that only retrieve, OpenContext keeps the why — temporal timelines, belief revision, and provenance.

<a href="./README.md">English</a> · <a href="./README-zh.md">简体中文</a>

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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

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