Headlong 是一款开源微 harness，提供持续运行的智能体，与传统的反应式智能体不同，能够自主学习和执行任务，非常适合对智能体技术感兴趣的开发者。
Headlong 是一款开源微 harness，提供持续运行的智能体，不断思考并执行重要任务。与传统的反应式智能体不同，Headlong 智能体在后台持续运行，根据项目/任务监控和跟踪工作，并在有实际建议时激活。它通过学习用户的偏好来改进推荐，并能够自主设置兴趣和优先级。Headlong 使用 Bash 编写，包含生成下一个想法的循环、shellm 语言模型和 DAG 存储轨迹，以及投影为轨迹的上下文。Laude 团队已内部运行 Headlong 几周，智能体在 Slack 和 Telegram 上与团队成员交流，并能够自主诊断和修复问题。
Recommended read. Proactive agents are a whole new and underexplored category. This microharness is ...
Recommended read. Proactive agents are a whole new and underexplored category. This microharness is about having a persistent, always-on agent that's always thinking and doing important work. Proactive agents are exciting because they unlock new ways to work with agents and unlock productivity. My proactive harness has a similar functionality that stays running in the background, monitoring and tracking work across projects/tasks, and then, when it has a genuinely useful idea or suggestion, it activates. It learns my preferences as I interact with it over time, so it gets better at recommendations with every interaction. Andy Konwinski @andykonwinski Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL headlong.ai/install.sh | bash Launch post: laude.org/updates/headlo… Repo: github.com/laude-institut… Headlong is a @LaudeInstitute / MIT collaboration. Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 1 🔄 0 ❤️ 12 👀 2058 📊 4 ⚡