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headlong

Persistent-agency agent harness in ~11K lines of Bash: it keeps thinking between messages, thinks by writing shell commands, and one shared mind serves a whole team over Slack or Telegram.

1,218 128 Shell Apache-2.0updated today
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Curator's take

Read this if you think harnesses have converged. Headlong has no tool-calling layer — the model emits shell rather than function calls, so `curl` is the HTTP client and `jq` the JSON parser, with `skills` playbooks and the `mem`/`traj`/ `context` CLIs as the capability layer — and it never stops: your message lands in an ongoing thought stream as one more observation, and the agent decides whether to answer. The trajectory is a fork/merge DAG of jsonl files, and context is a projection of it at exponentially decaying resolution, so nothing is compacted away in place. Laude Institute research alpha: it runs real shell commands around the clock at $1-2/hour, so use a spend-capped key and let the installer put it in Docker. Not the pick if you want a reactive, per-user, request/response coding agent — that mode exists but the whole design is aimed elsewhere.

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README.md3 min read
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CI

Headlong is an open source agent microharness, a complete agent harness with a core of about 11K lines of Bash. Headlong's defining feature is persistent agency. Your agent keeps thinking between external interactions in a self-guided loop inspired by human inner monologue. A message from a human doesn't start a session. It lands in the agent's thought stream as one more observation, and the agent decides if and when to respond. You give your agent a name and a personality, and it sets its own interests and priorities, starts its own projects, and pings you when it has something to say.

A Headlong agent is also built to be shared. A whole team can talk to one agent over Slack, Telegram, and a chat app, and every conversation lands in the agent's single stream of thoughts. The agent follows what different people are working on, connects them, and pings whoever seems most relevant. Sharing one agent is fun, because it behaves more like a person than a service.

At the heart of Headlong is shellm, a Bash implementation of a recursive language model (RLM). The agent thinks by writing shell commands, running them, and reading the output. No tool system besides Bash is needed.

Get started

One line installs everything, interviews you to bring a Headlong agent to life, and opens a dashboard where you can watch its mind run:

curl -fsSL https://headlong.ai/install.sh | bash

You'll need bash 3.2+, git, curl, jq, and an LLM API key (Anthropic, OpenAI, Gemini, or OpenRouter); the dashboard also needs uv and bun or node, and the installer offers to fetch those.

Headlong is alpha research software. Use a dedicated, spend-capped key, because your agent runs real shell commands and thinks around the clock. With Docker running, the installer offers to keep the whole agent in a container, or to install on your machine with the agent's commands sandboxed in a container (an unsandboxed host install exists too, behind an explicit yes, and is not recommended). Without Docker the commands would run directly on your machine as you, so the installer stops and asks for an explicit yes before setting that up. How much the background thinking costs depends on how quickly the agent loops and which model backs it. The rate of thinking backs off exponentially when nobody is talking to the agent and resets the moment a message arrives. At the settings we run our agent with, it comes to $1 to $2 an hour.

The agent's name becomes a command:

ada hello            # one message, wait for the reply
ada                  # chat
ada stop / ada start # pause / resume its mind
ada dash             # open the dashboard
ada bugreport        # bundle logs + trajectory (keys scrubbed) for a bug report

headlong-killall stops every Headlong process on the machine if you need a panic button. curl -fsSL https://headlong.ai/status.sh | bash shows what is installed and running; curl -fsSL https://headlong.ai/uninstall.sh | bash removes it all (details in docs/install.md).

The container flow the installer offers is this, and you can also run it yourself:

docker run -it --name headlong --restart unless-stopped -p 8080:8080 buildpack-deps:curl \
  bash -c 'curl -fsSL https://headlong.ai/install.sh | bash; exec bash'

Details, no