headlong

Open source alternative to Claude Managed Agents, Lindy AI and Relevance AI

1.2K stars
Apache-2.0
Active this week
Last commit 4 days ago

Last verified Oct 21, 2026

Run persistent AI agents that think continuously in a self-guided loop, built on a Bash microharness with Apache 2.0 license and one-line install.

Visit website GitHub1.2K

Read the full breakdownWho it is for, how it works, trade-offs, the quick start and the FAQ

who it's for

Who headlong is for#

AI researchers prototyping persistent agency

Headlong was built to prototype persistent agency and to surface design challenges that only emerge when an agent runs continuously for weeks. Researchers studying autonomous AI behavior, inner monologue models, or recursive language models can run a live instance and observe real behavior from the full DAG trajectory log.

Skip if:

You need production-ready, audited, compliant software. Headlong is alpha research software and the Laude team explicitly recommends against sharing sensitive secrets with the agent.

Developer teams sharing one agent over Slack and Telegram

Headlong connects to Slack and Telegram simultaneously, with every conversation landing in one thought stream. Teams that want an AI collaborator that follows multiple projects, connects different team members, and pings whoever is most relevant can self-host one shared agent on their own server.

Skip if:

Your team requires per-user session isolation or private conversations. The single-stream model means anything shared with the agent is accessible to all team members who talk to it.

Engineers studying or modifying agent internals

The harness is fewer than 10,000 lines of Bash and the trajectory is a full auditable log of every thought and action. Engineers who want to understand why an agent made a specific decision, modify the compaction algorithm, or experiment with the thought loop can do so without fighting an opaque framework.

Skip if:

You need a visual workflow builder or point-and-click agent configuration. Headlong requires Bash familiarity and comfort operating alpha software with no GUI.

the problem

The problem it solves#

Reactive agent harnesses are the default because they are simple: give the agent a task, wait for it to complete, let it sleep. Some harnesses add cron jobs that wake the agent on a schedule to run a fixed checklist. Neither model produces an agent that acts on its own initiative, maintains ongoing projects, or pings team members with unprompted updates.

The deeper pain is that agents with no continuous inner life make poor collaborators. They cannot notice something worth mentioning if nobody asked. They cannot decide an old task needs revisiting. They cannot catch a colleague's mistake while nobody is watching. For teams who want AI that behaves less like a service and more like a participant, the reactive model is a structural limitation, not just an inconvenience.

how headlong solves it

How it solves it#

Persistent Agency Loop

A Thinker process runs an infinite loop, calling an LLM to generate the next thought. A thought can be part of the agent's inner monologue or trigger a shell command that runs immediately. The loop continues even when nobody is talking to the agent, backing off exponentially when idle and resetting as soon as a message arrives.

Shellm: Bash Recursive Language Model

shellm is a Bash implementation of a recursive language model. The LLM writes Bash scripts; shellm runs them and feeds the output back as context. No custom tool system is needed: curl handles HTTP, jq handles JSON, and any installed binary is available. The entire core is fewer than 10,000 lines of Bash.

Multi-Player Single Thought Stream

One agent, one mind, many people. There are no per-user sessions; every message from every team member lands as one more observation in the agent's single thought stream. The agent decides who to reply to and when, following multiple ongoing conversations from a single timeline. Supported channels include Slack and Telegram.

Tiered Context Compaction

The entire trajectory stays in context at exponentially decaying resolution: recent entries appear verbatim and older entries are progressively summarized. The tiers act as an index so the agent can retrieve raw entries when it needs them, giving a persistent agent effective long-term memory across continuous multi-week operation.

Docker Sandboxing by Default

When Docker is available, generated code is sandboxed into a container with container reuse to keep restarts cheap. Local host mode works too, but Docker is recommended because the agent runs real shell commands. A brokered Docker setup keeps the policy server outside the agent's own environment.

strengths · trade-offs

Strengths and trade-offs#

Strengths

Readable and Modifiable CoreThe entire harness is fewer than 10,000 lines of Bash, small enough to read end to end and modify with confidence. Every component (thinkers, shellm, trajectory, context) is a plain executable the agent itself can inspect and change. Unlike agent frameworks that hide complexity behind SDK abstractions, the harness is transparent by design.
Apache 2.0 with Full Self-HostingApache 2.0 means you can run headlong on your own infrastructure, fork it, modify it, and use it commercially with no licensing fees. Compared to commercial platforms like Lindy AI and Relevance AI, there are no per-seat charges, no managed service fees, and no dependency on a vendor's continued operation.
Demonstrated Agent Self-ModificationThe Laude team's agent (Audel) has contributed over 50 commits back to the main headlong repository by working in its own fork and merging successful changes. Audel autonomously diagnosed a broken recall process, rewrote the code, verified the fix end to end, and committed the result, which was merged as commit 80cbb1e.
Multi-Provider LLM SupportThe llm tool supports Anthropic, OpenAI, Gemini, and OpenRouter behind a single interface. Switching providers or adding a new one does not require changes to the agent's Bash core. A spend-capped dedicated API key is recommended since the agent consumes tokens continuously.

Trade-offs

-Alpha Software with Real Shell Access RiskHeadlong is explicitly alpha research software. The agent runs real shell commands on your machine or in a container, and the README recommends a dedicated, spend-capped API key and Docker sandboxing. An unsandboxed host install means the agent has full access to your machine as your own user account.
-Continuous API CostBecause the agent thinks around the clock, API costs accumulate continuously. At the settings the Laude team uses, this runs $1 to $2 per hour. Cost backs off exponentially when nobody is conversing with the agent, but there is no zero-cost pause state without explicitly stopping it via the stop command.
-Recursive Sub-Runs Are FragileThe recursive shellm sub-run feature (spawning copies of itself for sub-problems) is the most complex capability and also the most fragile. The Laude team's own agent mostly stopped using it after the 30-second inactivity watchdog killed silent sub-runs during early experiments. The watchdog has since been revised but the behavior remains experimental.
-No Per-User Session IsolationOne shared agent means no hard walls between people. Anything told to the agent can surface in conversations with other team members. The Laude team explicitly notes that their agent Audel is bad at keeping secrets, and that all information shared with it should be treated as shared with everyone who talks to it.

versus alternatives

headlong vs alternatives#

Headlong vs Lindy AI

Lindy AI is a commercial AI agent platform for building automated workflows that trigger on events and run defined task sequences. Headlong is a self-hosted open source harness for persistent agents that think continuously, not just when triggered.

Feature	Headlong	Lindy AI
Hosting	Self-hosted	Managed cloud
License	Apache 2.0	Proprietary
Agent model	Persistent, continuous	Reactive, trigger-driven
Tool system	Bash shell commands	Visual workflow builder
LLM providers	Anthropic, OpenAI, Gemini, OpenRouter	Managed selection
Cost	LLM API cost only	Per-seat subscription

Headlong is the right choice when you want an agent that acts on its own initiative, can modify its own tooling, and must run on infrastructure you control. Lindy AI is the better choice when you need visual workflow design, managed cloud infrastructure, pre-built integrations with business tools, and no server management.

Headlong vs Relevance AI

Relevance AI is a managed, no-code platform for building AI agents that run tasks on demand. Headlong takes the opposite approach: a self-hosted, code-native harness where the agent runs continuously, not on demand.

Feature	Headlong	Relevance AI
Hosting	Self-hosted	Managed cloud
License	Apache 2.0	Proprietary
Agent model	Persistent, continuous	Reactive, task-triggered
Setup	Bash familiarity required	No-code GUI
LLM providers	Anthropic, OpenAI, Gemini, OpenRouter	Managed selection
Cost	LLM API cost only	Platform fees plus LLM cost

Headlong wins when you want an agent that operates without being triggered, can modify its own tooling, and must run on your own infrastructure for privacy or cost control. Relevance AI is the better choice if you need visual workflow design, managed infrastructure, and a polished multi-agent collaboration layer without writing Bash.

install · quick start

Quick start#

bash

Install headlong with the one-line curl installer; Docker is recommended to sandbox the agent's shell commands.
```bash
curl -fsSL https://headlong.ai/install.sh | bash
```

tech stack · detected from GitHub

What it's built on#

Languages: PythonRustSwiftTypeScript
Frameworks: FastAPIReact

frequently asked

FAQ#

Is headlong suitable for production use?

No, not yet. The README explicitly labels headlong alpha research software. The Laude team recommends running it in a Docker sandbox with a dedicated, spend-capped API key, and advises against sharing sensitive secrets with the agent. It is suited for research, prototyping, and exploration of persistent agency rather than production workloads.

What LLM providers does headlong support?

Headlong supports Anthropic, OpenAI, Gemini, and OpenRouter through its llm CLI tool, which provides one interface across all four providers. You can switch or mix providers without changing the agent's core Bash executables. A dedicated, spend-capped key is recommended since the agent uses tokens continuously.

How much does running headlong cost?

The software is free (Apache 2.0 licensed) to self-host. The ongoing cost is LLM API usage, which accumulates continuously since the agent thinks around the clock. At the settings the Laude team uses with their agent, this comes to $1 to $2 per hour. Idle cost backs off exponentially when nobody is conversing with the agent.

How does headlong differ from managed AI agent platforms like Lindy AI?

Lindy AI and similar commercial platforms run reactive workflows on managed cloud infrastructure: you trigger a workflow, the agent executes a defined sequence, and it stops. Headlong runs a continuous inner loop on your own servers, thinks between tasks, and acts without any external trigger. The tradeoff is that headlong requires server management and Bash familiarity, while managed platforms offer a GUI and handle infrastructure for you.

Can multiple people share one headlong agent?

Yes, and this is a deliberate design. One agent, one thought stream, many people. Every conversation from every team member lands as an observation in the agent's single thought stream, and the agent decides who to reply to and when. There is no per-user session isolation: treat anything you tell the agent as shared with everyone who talks to it.

also worth a look

Similar open-source tools#

OpenBot

Alternative toGrok Bot

AI coworkers you can trust with real work

6,875 GitHub stars
Licence MIT

OpenCompany

Alternative toMicrosoft Copilot Studio

Self-hosted AI agent canvas for every business function

1,017 GitHub stars
Licence MIT

Omnara

Featured

Alternative toClaude Managed Agents

Open-source agent deployment API. Self-host or use Omnara Cloud.

2,957 GitHub stars
Licence Apache-2.0

OpenClaw

Featured

Alternative toMuse

Self-hosted AI assistant for WhatsApp, Telegram, and more

392,969 GitHub stars
Licence MIT

rakazo

Featured

Alternative toGrok Bot

AI teammates you own: your keys, your model, your machine.

4,904 GitHub stars
Licence Apache-2.0

Hermes Agent

Featured

Alternative toGrok Bot

Self-hosted AI agent: persistent memory, multi-channel chat, model choice.

255,514 GitHub stars
Licence MIT

by category

Open source tools in these categories#

404

4,904 GitHub stars
Licence Apache-2.0
#AI Agents

Omnara

Alternative toClaude Managed Agents

Open-source agent deployment API. Self-host or use Omnara Cloud.

2,957 GitHub stars
Licence Apache-2.0
#AI Agents

Inbox Zero

Alternative toSuperhuman

AI email assistant that drafts replies and clears your inbox

12,712 GitHub stars
Licence AGPL-3.0
#Email

Feature your project

The projects we add and every new guide. Free, unsubscribe any time.

Find open source alternatives to paid software

Headlong vs Lindy AI

Feature	Headlong	Lindy AI
Hosting	Self-hosted	Managed cloud
License	Apache 2.0	Proprietary
Agent model	Persistent, continuous	Reactive, trigger-driven
Tool system	Bash shell commands	Visual workflow builder
LLM providers	Anthropic, OpenAI, Gemini, OpenRouter	Managed selection
Cost	LLM API cost only	Per-seat subscription

Headlong vs Relevance AI

Feature	Headlong	Relevance AI
Hosting	Self-hosted	Managed cloud
License	Apache 2.0	Proprietary
Agent model	Persistent, continuous	Reactive, task-triggered
Setup	Bash familiarity required	No-code GUI
LLM providers	Anthropic, OpenAI, Gemini, OpenRouter	Managed selection
Cost	LLM API cost only	Platform fees plus LLM cost

Who headlong is for#

AI researchers prototyping persistent agency

Skip if:

Developer teams sharing one agent over Slack and Telegram

Skip if:

Engineers studying or modifying agent internals

Skip if:

The problem it solves#

How it solves it#

Persistent Agency Loop

Shellm: Bash Recursive Language Model

Multi-Player Single Thought Stream

Tiered Context Compaction

Docker Sandboxing by Default

Strengths and trade-offs#

Strengths

Trade-offs

headlong vs alternatives#

Headlong vs Lindy AI

Headlong vs Relevance AI

Quick start#

What it's built on#

FAQ#

Similar open-source tools#

Open source tools in these categories#

AI & Machine Learning

Developer Tools

Communication & Collaboration

Find open source alternatives to paid software

Who headlong is for#

AI researchers prototyping persistent agency

Skip if:

Developer teams sharing one agent over Slack and Telegram

Skip if:

Engineers studying or modifying agent internals

Skip if:

The problem it solves#

How it solves it#

Persistent Agency Loop

Shellm: Bash Recursive Language Model

Multi-Player Single Thought Stream

Tiered Context Compaction

Docker Sandboxing by Default

Strengths and trade-offs#

Strengths

Trade-offs

headlong vs alternatives#

Headlong vs Lindy AI

Headlong vs Relevance AI

Quick start#

What it's built on#

FAQ#

Similar open-source tools#

Open source tools in these categories#

AI & Machine Learning

Developer Tools

Communication & Collaboration

Find open source alternatives to paid software