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Headlong
Headlong is an agent microharness for running one AI agent that can keep thinking and acting between human messages instead of waiting for the next prompt.
Its core is a small set of Bash tools for model calls, shell execution, trajectory recording, context assembly, memory, and skills. The same agent can receive messages through the terminal, a web dashboard, Slack, or Telegram, with every conversation entering one shared stream. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A persistent agent harness
In its continuous mode, Headlong repeatedly asks a model for the next thought, executes its shell actions, and records the results. Messages join one shared stream. The README also describes a classic turn-taking mode for request-and-response use.
Why it stands out
The history stays inspectable
Thoughts and actions live in JSONL trajectory files with fork-and-merge relationships.
Availability
Public alpha with a Docker-first path
The Apache-2.0 repository includes an installer, a Docker setup, terminal and web interfaces, Slack and Telegram bridges, operating documentation, and model-provider paths for Anthropic, OpenAI, Gemini, and OpenRouter.
Why it matters
What makes it useful
When an agent works between your messages, its actions can otherwise be hard to follow. Headlong records thoughts, shell actions, and results in file-backed trajectories, giving a builder a history to inspect when checking what happened during background work.
What to know
Where it fits
The installer asks for a model connection, then offers an interview for the identity's name and initial interests before starting its mind and dashboard. The name becomes the command for chatting, pausing, and resuming it. The documented setup can keep the whole agent in Docker or run the main runtime on the host with generated shell commands in Docker.
Notable points
What stands out
Context is a projection of the trajectory rather than a replacement for it. Recent entries appear in detail while older entries are progressively summarized; the underlying records remain available for retrieval. This separates the shorter history sent to the model from the fuller record a builder can inspect. In their launch write-up, the maintainers describe recurring failures during long-running experiments and say long-term improvement is still judged mainly through qualitative observation.
Before using
What to review
The maintainers call this alpha research software. For a cloud model, use their recommended dedicated, spend-capped key; for a local OpenAI-compatible server, a key is needed only if that server requires one.
With a paid model provider, calls can keep costing money while nobody is messaging the agent. Decide whether continuous thinking and its idle cadence fit your budget.
The installation guide makes Docker the default and describes unsandboxed host execution as not recommended. That host path requires an explicit opt-in before generated shell commands run as your user.
Review every mounted folder, forwarded environment variable, messaging token, network route, and Docker-access mode before letting the agent work around private files or accounts.
The installation guide warns that host.docker.internal can expose other host services to agent code, including loopback-bound services. A container alone does not restrict that route to the model server.
Treat a shared Headlong identity as one shared conversation. Slack, Telegram, web, and terminal messages enter the same thought stream, and the maintainers warn that there are no hard privacy walls between participants.
Plan stop, backup, and recovery steps for the identity files and trajectory before depending on background work or self-modification.
Reader fit
Who may find it relevant
Builders studying what changes when an agent keeps thinking between requests.
Teams comparing a single shared agent across terminal, web, Slack, and Telegram.
People who want an inspectable Bash implementation of trajectories, context compaction, memory, skills, and recursive model calls.
Less relevant for readers who want a no-setup chatbot, predictable per-request costs, strict per-user conversation separation, or production-ready safety assurances.
Editorial note
Why LifeHubber lists it
Headlong is useful for studying continuous agent work whose cadence responds to conversation activity. Its documented idle slowdown and message-triggered reset let a builder examine how work between messages changes when a person returns. When estimating background usage, check a quiet period and a period just after sending a message. The maintainers say idle thinking slows exponentially, but a new message resets its rate, so quiet-period usage alone does not describe activity after a conversation resumes. To pause the mind, use the named identity's stop command, such as ada stop, replacing ada with your agent's name.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare what should keep running between messages.
Headlong makes continuous self-directed thought the center of the agent. These next paths show different ways to combine ongoing work, memory, messaging, schedules, and isolation.
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