Open-source cross-platform memory for AI agents
agents claude cursor open-source
| Source: HN | Original article
An open-source memory layer called Beacon enables AI coding agents to retain and share context across Claude Code, Cursor, Codex, OpenCode and more than 20 other platforms.
Open‑source, cross‑harness memory for AI agents
A new GitHub project, **Beacon**, announced on 12 May 2026, adds a shared memory layer that works across dozens of AI‑coding harnesses such as Claude Code, Cursor, Codex and OpenCode. The library records full session histories, extracts recurring workflows, corrections and debugging patterns, and makes that distilled knowledge available to future agents regardless of the underlying tool. By persisting what developers have taught an agent in one environment and re‑using it in another, Beacon aims to turn fragmented “one‑off” interactions into a cumulative knowledge base.
The development matters because AI coding agents have long suffered from siloed learning. Earlier this month we reported on **LayerFS**, an experimental filesystem for coding agents that began to address data persistence, but it focused on raw file storage rather than semantic workflow capture. Beacon goes a step further, providing a higher‑level abstraction that can recognise and replay useful patterns across heterogeneous harnesses. This could reduce repetitive prompting, speed up debugging, and lower the engineering effort required to integrate new agents into existing developer toolchains.
Watchers should monitor how quickly the community adopts Beacon and whether it is incorporated into broader runtimes such as **Exo**, an open‑source agent‑plus‑harness platform, or the **OpenHarness** SDK that already supports multiple model providers. Benchmarks from the recently published AI Agent Memory Framework index will likely be used to gauge Beacon’s impact on task efficiency and cost. If the cross‑harness memory proves effective, it could become a de‑facto standard for building more reusable, user‑centric AI agents.
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