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Rhizogrip Spore
A self-hosted runtime for persistent AI agents
A self-hosted runtime for persistent AI agents. Build on reusable programs, files and focused specialists, with Node and HTTP integration and Linux confinement.
Hi Product Hunt, Spore is my new Open Source project.
Spore is built around a few deliberate choices:
Write programs instead of paying the model to repeat a procedure. Agents write, test and keep helpers they can run on later tasks.
Keep the model-facing interface small. Seven built-in tools; everything else happens through ordinary programs, rather than an ever-growing catalogue of specialised tools.
Keep large outputs in files, not automatically in context. The agent searches and reads what it needs instead of carrying every command’s output into subsequent model calls.
Start with a general assistant that creates focused specialists. Delegation has two levels: the assistant and its cells. Cells cannot create more cells.
Enforce boundaries outside the agent. The kernel controls access, brokers hold credentials, and the runtime records spend and activity where the agent cannot rewrite them. Command-word blocklists and promises of good behaviour are not security boundaries.
An agent is a folder and a strictly checked definition file—not an application you have to implement. Its conversation, files and programs persist there. Anything that can write the definition can create an agent, including your application or another agent.
Those choices make Spore both a tool you can use directly and a runtime you can build into your software. There are two ways to start:
Use it yourself: `spore here` opens a coding agent in a Git repository, with a terminal interface, persistent conversation, diff and undo.
Build on it: your application sends work through Node, HTTP or the CLI, follows events and collects results. Your application keeps its own interface and workflow.
The agent is untrusted; the operator is trusted. Linux kernel mechanisms enforce access boundaries, provider credentials stay outside the agent, and each turn has spend, time and model-call limits. Network access is open by default, so additional egress restrictions belong to the host.
To show the kind of work it can support, we published Blender experiments and a playable Godot game, with prompts, results, verification scripts and limitations:
These are individual examples, not benchmarks or guarantees. The Godot work runs inside Spore’s sandbox; the Blender demo uses an application bridge that executes outside it.
Spore is Apache-2.0. Agents execute on Linux; macOS uses a Lima VM and Windows uses WSL2. You’ll need supported model access, and setup offers tested model recommendations.
I’d love to hear what you’d try first: an agent working in your own project, or one embedded in your application. Happy to help with getting started.
Rhizogrip Spore was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #149 on the daily leaderboard. A self-hosted runtime for persistent AI agents. Build on reusable programs, files and focused specialists, with Node and HTTP integration and Linux confinement.
On the analytics side, Rhizogrip Spore competes within Open Source, Artificial Intelligence and Tech — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Rhizogrip Spore performed against the three products that launched closest to it on the same day.
Who hunted Rhizogrip Spore?
Rhizogrip Spore was hunted by Christophe Le Bars. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Rhizogrip Spore including community comment highlights and product details, visit the product overview.
Christophe Le Bars
Hi Product Hunt, Spore is my new Open Source project.
Spore is built around a few deliberate choices:
Write programs instead of paying the model to repeat a procedure. Agents write, test and keep helpers they can run on later tasks.
Keep the model-facing interface small. Seven built-in tools; everything else happens through ordinary programs, rather than an ever-growing catalogue of specialised tools.
Keep large outputs in files, not automatically in context. The agent searches and reads what it needs instead of carrying every command’s output into subsequent model calls.
Start with a general assistant that creates focused specialists. Delegation has two levels: the assistant and its cells. Cells cannot create more cells.
Enforce boundaries outside the agent. The kernel controls access, brokers hold credentials, and the runtime records spend and activity where the agent cannot rewrite them. Command-word blocklists and promises of good behaviour are not security boundaries.
An agent is a folder and a strictly checked definition file—not an application you have to implement. Its conversation, files and programs persist there. Anything that can write the definition can create an agent, including your application or another agent.
Those choices make Spore both a tool you can use directly and a runtime you can build into your software.
There are two ways to start:
Use it yourself: `spore here` opens a coding agent in a Git repository, with a terminal interface, persistent conversation, diff and undo.
Build on it: your application sends work through Node, HTTP or the CLI, follows events and collects results. Your application keeps its own interface and workflow.
The agent is untrusted; the operator is trusted. Linux kernel mechanisms enforce access boundaries, provider credentials stay outside the agent, and each turn has spend, time and model-call limits. Network access is open by default, so additional egress restrictions belong to the host.
To show the kind of work it can support, we published Blender experiments and a playable Godot game, with prompts, results, verification scripts and limitations:
https://rhizogrip.com/spore/demos
These are individual examples, not benchmarks or guarantees. The Godot work runs inside Spore’s sandbox; the Blender demo uses an application bridge that executes outside it.
Spore is Apache-2.0. Agents execute on Linux; macOS uses a Lima VM and Windows uses WSL2. You’ll need supported model access, and setup offers tested model recommendations.
I’d love to hear what you’d try first: an agent working in your own project, or one embedded in your application. Happy to help with getting started.
Source:
https://github.com/RHIZOGRIP/spore