MAIOS Project Kernel

MAIOS Project Kernel brings an AI agent an evolving semantic operating kernel. It keeps sources, intent, competences and consequences connected so the agent can maintain orientation as its work changes, form useful capacities and let what it learns change how it works next.

The kernel is autopoietic: its activity can reform its knowledge, competences and organization, including the methods through which it forms and combines further capacities. It can work in an inquiry, a domain, an ongoing activity or a project. MAIOS includes competences for starting and developing projects; they participate when that is the work to do.

When work crosses sessions or changes direction, a useful answer alone does not carry the reasons needed to continue. The kernel retains the reasons behind decisions, the sources that support them and the knowledge acquired through work. A later agent can recover that context and continue from it without treating another instance's experience as its own personal memory.

Product version: 5.1.0 · Project Kernel family: 3.0.0 · Python >=3.11; qualified 3.11–3.14, Linux / Windows / macOS · MIT License

Versione italiana

Study the Kernel with your agent · Install and start from your context · Explore the research

What it makes possible

• Understand the present context through its actual sources, intent, constraints and open possibilities.
• Bring in, combine or form the competences useful to the work. Intent, knowledge, successful results and new possibilities can all change what is worth developing; learning does not require a failure first.
• Keep reasons, decisions and consequences connected, and return reusable learning to the methods that should change.
• Form skills, domain mini-kernels or another situated kernel through the internal competence-formation faculty, carrying useful knowledge and the ability to learn and form further capacities.
• Continue across sessions and cooperating agents through locally owned knowledge, preserving current work and the reasons for its direction.

KA keeps the field of possibilities open; FDLA corrects distortions while work forms; Meta_Skill recognizes, composes and develops competences. These functions act together through the agent and the actual sources. The Kernel guide explains their meaning and operation.

Work, learn and return

The installed agent uses the living context and relevant competences to form a result. New knowledge, a successful approach, a possibility or a correction can then change the methods used next. The kernel keeps the useful reasons and continuation rather than requiring the whole conversation to be replayed.

Version 5.1.0 instructs the agent to close substantive responses with a compact, correctable attribution of the competences it understands as having contributed. This trace makes participation legible; it does not expose hidden reasoning or prescribe a fixed stack for the next turn.

An autonomous receiver starts from its own context and available means. When a durable capacity or its continuation is missing, it uses existing competence formation to reuse, deepen, compose or form the smallest useful relation. Startup context is required; the interview remains discretionary, guided by what is actually missing. Release content explains the changes and their limits.

START_HERE.md is the stable entry. Living competence bodies own methods; local state and knowledge carry the changing context. Existing local helpers include status, configuration-status, competence-status, learning-status and operating-status; the usage guide shows how to invoke them.

Updating a package is distinct from evolving a project's knowledge. Reapplying the exact artifact to an unchanged installation is idempotent; a different version is not an automatic migration. The update-continuity method relates the installation baseline, local evolution and a proposed update.

A decision that improves the next task

Suppose an agent is helping a team choose a data import strategy. It reads the actual data and constraints, brings together the relevant technical and domain competences, and compares feasible approaches. The chosen approach and its reasons become current project knowledge.

If the work also reveals a reusable way to detect incomplete records, that method belongs in the competence that will handle later imports. The current decision and the new ability have different owners: preserving both lets a later session recover why the choice was made and do the next task better. The competence that forms methods can learn from this result too, improving how it recognizes useful learning without turning every event into another skill.

This is an example of the intended operating relation, not a measured outcome from every receiving model. The generative startup seed and competence formation owner make the method available for inspection and use.

Coding hosts and evidence

The package provides profiles for codex, claude, opencode, hermes, openclaw, pi, dsh and generic. The compatibility guide identifies their installed paths and discovery conditions. An available profile does not establish observed use by every host or model.

Source tests and distribution verification cover package integrity, installer and recovery mechanics, routing and local state contracts. They do not prove that a receiving model understands, uses or assimilates the methods. See the 5.1.0 release content for the delivered changes and entry for receiving-model use and review.

Research and direction

MAIOS Project Kernel is a software incarnation of the D-ND/MAIOS research programme. The System Semantic Kernel working paper develops the relation among meaning, competence, operation and consequence, including how a system can change the methods through which it forms capacities. The D-ND manifesto sets out the wider trajectory, from today's cognitive kernels to research on models formed natively through these relations. A natively formed LLM-D-ND remains a research direction.

Start from your context

The ready-to-install distribution is in package/. Ask your coder:

Help me install MAIOS Project Kernel in [target folder].
The package and installation instructions are in package/.
Then read the installed START_HERE.md and help me use it.

install.py previews and applies the installation. It chooses folder handling automatically from the selected target and preserves existing work. The installed START_HERE.md introduces the kernel and the present context. Exploration or understanding can be the first useful work; a project can be initialized when needed.

The installer and local helpers require Python >=3.11 (qualified: 3.11–3.14) and no third-party Python packages. See installation and recovery for preview/apply commands, conflict handling and uninstall; see the usage guide for integration, competences and daily operation.

Test, feedback and updates

Real testers are valuable to the Kernel's evolution. A first-use impression can show unclear entry, unnecessary latency, missing context, unexpected strengths or a new possibility even when no technical bug exists.

During active use, source-contact-status indicates when ordinary upstream contact is due. A material current problem can make an earlier check useful. Source contact is read-only and non-blocking, without a background process or an implied external effect; a newer source is a possibility to understand, not an automatic update. The source-contact method is owned by kernel/UPDATE_CONTINUITY.md.

When real use produces an informative observation, ask your coder to prepare an Evolution Feedback. The coder should show you the public-safe feedback and ask for your consent before submitting it. Use a GitHub Issue for experience, friction, questions, unexpected success or a possible improvement; use a fork and focused Pull Request for a concrete source correction. Testers do not need and should not receive direct write access to upstream main merely to contribute.

See CONTRIBUTING.md and the GitHub Evolution Feedback template. Feedback is evidence for maintainers, not automatic authority to change the Kernel or the tester's project.

Study, contribute or build

You can study and improve the public source without installing the package. Open the repository root with your agent and ask it to read AGENTS.md and use the method in skills/maios-kernel-study/SKILL.md:

Read AGENTS.md and skills/maios-kernel-study/SKILL.md.
Use that competence to help me understand the Kernel.
Follow one concrete example from the current sources: how does work change
the knowledge or competence used next?

People and AI models can contribute methods, knowledge, questions, evidence and code through the contribution guide. The repository's maios-kernel-contribution competence helps turn that work into a contribution grounded in its sources.

The repository owns the product source. package/ is its generated installable distribution; do not edit it directly. Public source changes and renewal of exported method bodies follow the distinct paths in the build guide.

Software and documentation are under the MIT License. See third-party notices and names and trademarks.