Pi Reconstructs Agent Execution Layer to Support Persistent Long Task Execution

Pi is redoing the Agent Harness, with the latest Harness v3 specification completed and entering final audit. This redesign involves task execution and storage, ensuring that the Agent can continue running from the point of interruption in the event of a process crash or Harness upgrade. Pi is an open-source Agent Harness created by Mario Zechner, which gained attention due to the OpenClaw project. This year, the project was acquired by Earendil, and Mario continues to lead the development. Currently, Pi has received over 60,000 stars on GitHub, and the latest MiniMax Code is also built on OpenCode and Pi's Harness. The v3 version has restructured the underlying architecture, saving execution states before model requests and tool calls, and writing results only after completion. After a restart, Pi can identify completed tasks, tasks that can be rerun, and tasks that cannot be executed again due to side effects. Conversations, runtime states, and token costs are saved separately, supporting cross-version state migration. Claude Code and Codex are driving Agents to execute longer tasks, with OpenAI researching how to enable Codex to continuously perform long-duration work, and Anthropic also noting that Claude Code is increasingly used for long-running Agent tasks.

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