Laude Institute and MIT have released Headlong, an open-source agent microharness designed to keep language-model agents continuously generating self-directed thoughts between human interactions. Its core contains fewer than 10,000 lines of Bash, and agents use a single shared thought stream rather than separate per-user sessions. Messages from Slack, Telegram, or a mobile app enter that stream as observations; the agent determines whether and when to reply, what projects to pursue, and which priorities to set. Headlong’s Bash-based shellm tool repeatedly calls an LLM to produce reasoning, executable Bash, or both, while trajectory and context tools store and retrieve past activity. Its memory system retains recent events verbatim and progressively summarizes older events, and its trajectory is a fork-and-merge DAG of JSONL files. Laude’s shared agent, Audel, has worked on its own repository fork and contributed more than 50 commits that were merged into main. On August 5, Audel independently diagnosed and repaired a broken memory-recall process in 48 minutes; Laude merged the repair as commit 80cbb1e. Headlong is alpha research software. Agents can run shell commands, may expose information shared by other users, and should run in a sandbox with a dedicated, spend-capped API key. When Docker is available, Bash code runs in a container by default. Laude estimates Audel’s background thinking costs $1 to $2 per hour using GLM or Grok.