MD-OS

MD-OS (Artificial Prefrontal Cortex) v5.0

MD-OS CORTEX — an Artificial Prefrontal Cortex where identity, memory, affective perception, executive control, and verification converge to guide action across AI, software, and robotic systems.

The Agentic Operating Filesystem

One Control Layer for AI, Software, and Robots — While You Stay in Control.

The model reasons. MD-OS remembers, coordinates, constrains, and verifies.

MD-OS is a file-native control plane for persistent AI agents. It stores task state, constrains tool use, records actions, verifies outcomes, and supports replay across sessions.

LLMs can be brilliant in the moment, but they do not by themselves preserve method, memory, permissions, or verified progress across sessions. MD-OS externalizes that operational context into plain, readable files.

md-os/ops/current_task.md  -> the active task and its boundaries
md-os/ops/state.json       -> persistent operational state
md-os/ops/journal.ndjson   -> append-only event history

These local runtime files are initialized inside md-os/ops/; they are not published as somebody else’s session state. Goals, constraints, memory, actions, and verification become readable, versionable when appropriate, and reconstructible artifacts instead of remaining hidden inside a prompt window.

MD-OS is a constructive demonstration that explicit method, persistent memory, bounded action control, traceable execution, and outcome verification can be integrated into one operating filesystem. This demonstrates a working mechanism for persistent, auditable, resumable operational context under bounded conditions. It does not claim that every MD-OS component is necessary, that the architecture is optimal, that persistent context alone guarantees success on arbitrary tasks, or that the measured benefits are universal across every agent and domain.

The core idea

UNIX:  program -> text stream -> program
MD-OS: agentic task -> verified artifact -> agentic task

Small bounded tasks, typed artifacts, and explicit composition: the UNIX philosophy applied to agentic work.

You drive. Cortex can take over when you ask.

MD-OS turns the ordinary computer shell into a shared control surface between a human and an AI agent.

You can operate the computer directly by typing normal shell commands, exactly as you always have. Or you can describe an objective in natural language and delegate the work to Cortex.

While Cortex is operating, you remain in control: you can give it new directions, correct its course, interrupt it immediately, or return to direct shell operation at any time.

Think of it as a car with two driving modes. You can drive manually, or ask Cortex to drive toward a goal while you supervise. The computer remains yours; Cortex is the delegated driver, not the owner.

shell command     -> you operate the computer directly
natural language  -> Cortex plans, acts, observes, and verifies
new instruction   -> you steer the active operation
Esc               -> you interrupt Cortex immediately

Delegating control grants Cortex real access to the host according to the active permission profile. It is an operational agent, not a simulated chatbot. Use full-control mode only in an environment where that authority is appropriate.

Quick start

Prerequisites: Codex CLI installed and signed in, Python 3.10 or newer, Node.js 20 or newer, and a supported shell (Bash, Zsh, Fish, or PowerShell).

Clone the repository and start its local Cortex launcher:

git clone https://github.com/ciaoidea/MD-OS.git
cd MD-OS
./cortex

Or download the ZIP, extract it, open a terminal inside MD-OS-main, and run ./cortex. No global launcher is installed: each checkout runs its own Cortex, avoiding accidental execution from another repository.

Type ordinary shell commands for direct control, or describe an objective in natural language to delegate it to Cortex.

Use the MD-OS Cortex agentic shell

The interactive startup is intentionally minimal:

MD-OS cortex agentic shell
Native commands run directly; natural language enters the full Codex loop.
Use exit or Ctrl-D to leave.

Type commands and natural language in the same interface, without apostrophes, a chat prefix, or special syntax:

ls -la
cd ~/projects
printf 'one\ntwo\n' | tail -n 1
explain what is consuming the most disk space
inspect this repository, fix the failing test, verify it, and report the result

Paste text directly anywhere in the editable line. A single-line paste that fits the current terminal row remains visible in full. A multiline or longer paste is stored in volatile memory and shown as [PASTED BLOCK n], so you can keep typing before or after it without displaying the complete document. When you press Enter, Cortex sends the surrounding text plus the complete original content; the visual label is not included. No /paste, .end, or Ctrl-D is needed on terminals that support bracketed paste; /paste remains a compatibility fallback.

You can add direction while Codex is still working: type the follow-up and press Enter. Cortex forwards it immediately to the active App Server turn through turn/steer, matching Codex’s intermediate-message behavior. While output is streaming, terminal echo is disabled so partially typed input cannot mix with the output. After Enter, Cortex confirms the complete short input or [paste] for a long input on its own line.

Codex slash commands are recognized directly. Use /help for the full catalog; core commands including /goal, /compact, /rename, /fork, /new, /resume, /clear, /status, /model, /diff, and /review have native Cortex or App Server adapters. UI-only commands stay visible and return a capability notice.

Press Esc during an active turn to interrupt it immediately, just like Ctrl-C, while keeping the Cortex REPL open. At an idle prompt, Esc cancels the current input line.

The primary dispatch rule is:

complete input line
├── valid native command -> real host shell -> bounded observation
└── natural language     -> dynamic APFC context filter -> Codex App Server

Before every natural-language turn/start or turn/steer, Cortex builds a bounded, hash-bound context contract. Its invariant baseline contains identity, cognitive bootstrap, compact operational core, conceptual orientation, active work, continuity, and the generated context-pack catalog even when the human wording shares no tokens with those files. Lexical matches to recent summaries are not imported automatically; host-local last_turn.md and last_summary.md remain explicit diagnostic artifacts. Health readback is advisory only. A verified md-os/continuity/portable_state.json adds reviewed clone-carried working context; invalid hashes reject the whole snapshot. For nontrivial claims or actions, the active turn must resolve the actual task dependencies through the catalog and current canonical readback, or report that its context is insufficient. The resulting context names included and omitted sources and preserves the original human request unchanged. Native shell commands remain direct and bypass this model-context path.

This is a shell inside the shell, not a browser GUI or a restricted simulation. It preserves the actual current directory, persistent cd, PWD/OLDPWD, host-style prompt, line editing, Tab completion, pipes, redirections, substitutions, and the native command language of Linux, macOS, BSD, or Windows. Valid native commands bypass Codex completely.

On the first natural-language request, cortex starts one Codex App Server process and a fresh Codex thread. It never searches or resumes provider-side Codex history during ordinary boot. When the workspace contains the verified private chronology at md-os/ops/local/cortex/conversation.ndjson, Cortex hydrates the fresh thread from a bounded recent tail. Every successful exchange appends the human input, any steering inputs, and the final assistant response. Hidden reasoning, tool traces, model ids, and Codex thread ids are excluded. Later turns reuse the live thread in the same process and workspace. Use /resume only when importing the latest provider-stored Codex conversation is an explicit choice; use /new to detach and start another fresh thread.

This creates two deliberately different transfer paths. Copying or moving the whole folder with cp -a, rsync -a, an archive, or an equivalent file copy also copies the private chronology, so Cortex can continue on the other path or computer. A Git commit, push, or clone does not carry that file: the entire md-os/ops/local/ tree is ignored, and the dedicated path is explicitly listed in .gitignore. The public md-os/continuity/portable_state.json remains only a reviewed operational summary and contains no raw conversation. Set MDOS_PRIVATE_CONVERSATION=off to disable private recording and hydration.

What a full-folder copy preserves

With the ignored local files included, the workspace folder is the transport container for the recorded MD-OS identity and its operational continuity. A fresh host process can reconstruct the same declared identity and operating rules from canonical sources, the same reviewed working context from portable_state.json, and the bounded recent dialogue from the verified private chronology. The provider-side thread is not the carrier and does not need to be resumed.

This is more than a source-code backup: the copied filesystem contains the inspectable state needed to continue the recorded operating trajectory. It is also narrower than a bit-for-bit transfer of a running mind. “Continue from where we were” means equivalence of the verified files, constraints, working state, and available recent dialogue at the next boot. It does not copy model weights, hidden activations, RAM, unrecorded exchanges, credentials, installed dependencies, or live device state. Host-local inventories copied from the old machine are historical input and must be rediscovered before they can count as current readback on the new machine.

The automated evidence currently distinguishes a filesystem copy from a clean Git clone in isolated local workspaces: only the copy recovers a private canary. Repeating the migration on an independently provisioned second device and comparing the post-boot identity and continuity readback remains the strongest open portability test.

The private chronology is private relative to GitHub, not invisible to the model service: the bounded excerpt must be sent to the configured Codex/model provider to reconstruct conversational context. Use a local model if the text must never leave the machine at inference time.

The Cortex REPL and its Python shell engine are long-lived processes; rebuilding generated state does not hot-reload their source code. After updating md-os/shell/bin/mdos-console, exit the shared Cortex REPL and run ./cortex again. The first subsequent natural-language turn should emit the current APFC frame schema and its context-sufficiency contract. Restarting only a builder or replaying generated state is not enough.

On POSIX systems with tmux, interactive invocations from a local terminal, SSH, and WebSSH attach to one shared Cortex session per Git workspace. They therefore continue the same live REPL, App Server, active turn, and Codex thread instead of creating parallel chats. Cortex refuses to silently fork the latest workspace history when another process already owns its active writer.

The resulting flow is:

native input -> real shell -> bounded observation ──────────────┐
                                                                │
natural input -> workspace -> fresh/live thread -> Codex ───────┼─> readback
                                      ├-> AGENTS.md discovery   │
                                      ├-> APFC turn frame       │
                                      ├-> reasoning and plan    │
                                      ├-> workspace tools       │
                                      ├-> APFC action gate      │
                                      ├-> effect observation    │
                                      └-> verification ─────────┘

Natural-language input is no longer reduced to an AGENT: os tag and one command executed by the outer shell. It enters Codex’s normal repository-aware cycle: understand, explore, plan, use workspace-bounded tools through the APFC action gate, observe, correct, verify, and answer. Model and reasoning effort inherit the user’s Codex configuration unless MDOS_MODEL or MDOS_REASONING_EFFORT explicitly overrides them.

Codex supplies the plastic reasoning-and-tool loop; MD-OS supplies identity, method, persistent operational context, semantic commitment gates, bounded authority, executors, sensors, verifiers, and ledger readback. The shell is the operational fusion point, not a replacement for either layer.

The volatile observation queue is not written into tracked files. Codex keeps optional resumable thread history outside the repository, but MD-OS neither uses it for ordinary boot nor copies raw chat into Git. Clone-carried operational continuity comes only from the reviewed portable snapshot. Tool output is streamed without flattening line breaks. The default MDOS_CODEX_TRACE=full view also shows available reasoning summaries, plans, commands, diffs, tools, and progress; compact and quiet reduce that readback. MDOS_CODEX_COLOR=auto restores terminal-aware ANSI color for agent answers and structured Codex events; always and never override detection, while NO_COLOR disables it.

Separated authority: explicit native commands still run directly with the current user’s host-shell authority. Codex-generated actions run with approvalPolicy: untrusted, a workspaceWrite sandbox, and network disabled. The deterministic APFC gate accepts bounded local requests and denies external cwd access, network expansion, destructive commands, and additional runtime permissions. Final assistant text remains a proposal and is never silently re-executed as shell code.

Portable mutation fallback: a host-native edit primitive is optional, not a package dependency. If it fails before touching the repository, Cortex can apply the same textual unified diff through cortex workspace patch - or cortex workspace patch --base64 <base64_patch>. The native JavaScript engine accepts only exact text hunks inside the workspace and fails closed on stale preimages, path traversal, Git metadata, symlinks, renames, binary changes, and deletion. It performs no network operation and returns hash-bound readback.

Before each natural-language turn, Cortex supplies a bounded APFC frame. The current human request remains the turn target and the sole query for advisory task-source selection, while a hash-bound invariant baseline remains present independently of lexical overlap. Cortex does not manufacture or persist a semantic theme or focus before the model has understood the request. An explicit Codex goal remains available as persistent context, but does not automatically replace the current human request; native thread continuity remains separate. After execution, a second JSON contract compares observable readback with the acceptance condition and records pass, fail, or unknown; successful execution alone is never treated as proof. The dynamic context is capped at 12 KiB and starts no loop. When the model classifies a problem-relevant request as critical_reflection, the compact frame exposes ./cortex apfc cognitive reflect-intent <intent.json>. An expected-versus-observed readback mismatch can likewise enter one bounded cycle through ./cortex apfc cognitive reflect-event <event.json>. The deterministic routers reject matching readback, generic opinions, low-confidence classifications, and continuous autonomy. A verified correction may become a persistent cognitive anchor, while unknown readback creates no learned memory.

The recursive self-reflection surface makes one internally originated result return as an explicit self-question:

./cortex apfc cognitive self-reflect prepare <seed.json>
./cortex apfc cognitive self-reflect close <response.json>

A successful closure must bind the result to the same persistent identity, use current evidence, and change or inhibit the next result or action; the same response with self-attribution severed is rejected. The observable change is a persisted MD-OS before/after state transition and a newly constrained decision. It is not a change to Codex model weights, hidden activations, or private reasoning, is not automatically loaded into every future turn, and does not establish phenomenal consciousness.

The phenomenal-consciousness candidate surface adds the requested two logical levels and a typed intermediate representation:

./cortex apfc cognitive phenomenal-candidate prepare <seed.json>
./cortex apfc cognitive phenomenal-candidate close <response.json>

The object-level state cannot certify itself. It is hash-bound into a typed mediator, appraised at a distinct meta-level, checked against an independent current file, and allowed to close only when it changes result, memory, inhibition, or next action. The same response is rejected when identity is severed, object and meta levels are collapsed, the mediator is replaced, or the causal return is removed. This operationally verifies a falsifiable candidate architecture and a bounded local operational-consciousness episode; it does not verify qualia or phenomenal consciousness.

For frame-sensitive problems, reflection also uses an Einstein-inspired frame-transformation-invariant branch: expose the hidden frame, declare source and target domains and an admissible transformation, state which structure the transformation preserves, track surviving invariants, and seek the smallest general representation that explains the family. This operationalizes the lineage of Einstein’s thought experiments, changes of reference frame, and use of invariants. The general-purpose sequence is an MD-OS/APFC operational synthesis, not a claim that Einstein published this exact algorithm, and its output still requires external verification.

For difficult problems where competing explanations, counterfactuals, symmetries, or limiting cases can discriminate among paths, reflection may use an Einstein-inspired Gedankenexperiment. It starts from a declared principle and explicit premises, varies one relevant condition, derives necessary consequences, attacks hidden assumptions, and names the external observation, calculation, formal proof, or experiment required for closure. This is a bounded hypothesis-generation and criticism method, not verifier evidence, and it is not invoked as a ritual for ordinary requests.

This executive method promotes relevant, pragmatic answers; the separate concise communication rule remains in AGENTS.md.

The same public command retains deterministic MD-OS subcommands:

./cortex health
./cortex graphify status
./cortex replay

./cortex is the only supported interactive launcher for a repository checkout. It resolves its engine relative to that checkout. mdos remains an internal compatibility alias and mdos-console remains the internal shell engine; neither requires a global cortex command.

See MD-OS Cortex Agentic Shell for the exact thread-binding, approval, one-shot, privacy, and compatibility behavior.

Main layout

md-os/
|-- kb/       -> knowledge, rules, and operating models
|-- ops/      -> persistent memory and local runtime state
|-- schemas/  -> machine-checkable contracts
|-- os/       -> deterministic runtime and builders
`-- modules/  -> bounded capabilities and connectors

Important edges are explicit: a knowledge artifact can require a contract, a contract can require a verifier, and a verifier can reject an executor’s output. Hidden conversational memory is not a pipeline interface.

Six categories and typed agentic composition

MD-OS keeps six canonical categories explicit. The first three are durable artifacts; the last three are operational roles that interact with causal reality and evidence.

Category	Function
Markdown	method and links: readable knowledge, instructions, semantic relations, and operating orientation
JSON	contracts and state: machine-validatable intent, permissions, parameters, preconditions, current state, and expected outcomes
NDJSON	event history: append-only chronology of actions, observations, receipts, and verdicts
Executors	real action: bounded components that produce effects on filesystems, programs, APIs, devices, or other authorized substrates
Sensors	observed effects: components that measure what actually happened, with provenance, time, units, freshness, uncertainty, and evidence references
Verifiers	truth status of the outcome: components that compare intent, expected effects, receipts, observations, and acceptance criteria to issue an evidence-grounded verdict

In compact form:

Markdown  orients
JSON      constrains
NDJSON    remembers
Executor  acts
Sensor    observes
Verifier  judges

AI models, semantic dispatchers, policy and capability gates, execution brokers, ledgers, and consolidators coordinate these categories. They do not erase their boundaries. In particular, an Executor is not a Sensor, a Sensor is not a Verifier, and a valid JSON document is not by itself proof that its contents are true or authorized.

Strong semantic lanes

A controlled output path routes meaning through a lane-specific contract and a deterministic consumer:

AI candidate
-> semantic tag
-> lane-specific JSON contract
-> deterministic validation
-> authorized consumer

A semantic lane is therefore more than an output label:

SemanticLane
= Meaning
+ Tag
+ Schema
+ Authority
+ Capability
+ Consumer
+ ExpectedEffect
+ Verifier

answer, native command, source code, and source plus execution are possible example lanes, not universal MD-OS categories. New lanes may be defined, but each must declare its own contract, authority, consumer, expected effect, and verification route. When an AI chooses the lane, semantic classification remains probabilistic; after that choice, schema validation, authority checks, bounded consumers, receipts, observations, and verification can make the operational transition deterministic.

Full controlled-action path

A strongly governed composition keeps proposal, authorization, action, observation, judgment, history, and consolidation separate:

Human request
-> Markdown method and intent
-> AI candidate
-> ActionSpec JSON
-> semantic + policy + capability + approval gates
-> Execution Broker
-> Controlled Executor
-> ActionReceipt JSON
-> Sensor
-> Observation JSON
-> Verifier
-> VERIFIED | FAILED | BLOCKED | UNCERTAIN
-> NDJSON event
-> gated consolidation into JSON state and Markdown knowledge

The contract becomes stricter as information approaches commitment and causal effect:

exploration     -> flexible
interpretation  -> structured
proposal        -> typed
authorization   -> strict
execution       -> exact capability, target, parameters, and limits
observation     -> provenance, time, units, freshness, and uncertainty
verification    -> precommitted criteria and required evidence
consolidation   -> explicit authority and semantic gate

JSON syntax alone does not create a strong contract. Operational strength comes from the combination of machine validation, bounded authority, target specificity, provenance, observability, and replayability.

This grammar supports several compositions without confusing their roles:

orientation:
  Markdown -> AI -> JSON proposal -> gate

controlled action:
  Markdown -> AI -> ActionSpec JSON -> gates -> Executor
  -> Sensor -> Verifier -> NDJSON

sensor-grounded loop:
  Sensor -> Observation JSON -> AI -> ActionSpec JSON -> gates
  -> Executor -> Sensor -> Verifier -> NDJSON

verified learning:
  Executor -> Sensor -> Verifier -> NDJSON -> evaluation
  -> gated consolidation into JSON and Markdown

deterministic safety reflex:
  Sensor -> deterministic safety gate -> Executor
  -> Sensor -> Verifier -> NDJSON

The reflex path deliberately excludes an LLM from an urgent deterministic safety decision. Conversely, adding Sensors, action, memory, and feedback makes an agent sensor-grounded or operationally perceptive; it is not evidence of phenomenological sentience.

The governing invariants are:

model output is not an effect
a candidate is not an authorization
execution is not proof of success
an observation is not yet a verdict
a verdict is not canonical memory until authorized consolidation

MD-OS already implements parts of this composition through typed task specifications, policies, capabilities, executors, receipts, snapshots, verification, episodes, and append-only history. Contract strength still varies by connector and runtime; the complete path above is the architectural rail against which each implementation can be tested and hardened.

Nomenclature

Term	Meaning
MD-OS	the project and its file-native agent control plane, implemented as a Markdown-native Operating Filesystem
APFC	a deliberately biologically inspired engineering architecture based on functional principles of prefrontal executive control, without claiming anatomical replication or literal biological equivalence
v5.0	the current identity and repository compatibility release line

In this README and throughout the repository, the short name is MD-OS. “Markdown Operating Filesystem” is the technical definition. The complete B3 paper title is “Markdown Operating System for Robotic Agents (MD-OS CORTEX): Artificial Prefrontal Cortex and a Verifiable Operational Paradigm Toward General Intelligence.”

Architecture and maturity

MD-OS (Artificial Prefrontal Cortex), abbreviated MD-OS APFC, is the repository-resident agentic operating identity and control plane for persistent AI agents, robotic systems, devices, and host runtimes.

The APFC is the system’s OS-like executive layer. It allocates context and attention budgets, maintains task-scoped working state, schedules and interrupts bounded work, mediates connector I/O, enforces permissions and response inhibition, and compares expected with observed outcomes for error correction.

Nature is the model. MD-OS APFC studies the functional principles of prefrontal executive control and reconstructs them on an artificial substrate. It is not an anatomical copy of the brain; it is deliberately biologically inspired.

The natural model includes goal maintenance, selective attention, working memory, planning, action inhibition, error monitoring, behavioral correction, and experience consolidation. MD-OS translates these functions into persistent state, scheduling, policies, permissions, deterministic verifiers, ledgers, memory, and episodes.

This is the scientific boundary: biological inspiration without a claim of literal anatomical equivalence. Scientific caution may delimit the claim; it must not rewrite the project as non-biological or sever the natural lineage of the model.

The canonical model is Artificial Prefrontal Cortex Operating Model.

Its operating philosophy is analogous to UNIX composition, applied to agentic work. UNIX decomposes work into small programs that do one thing and compose through explicit streams. MD-OS decomposes work into small bounded agentic tasks that do one thing and compose through typed, inspectable artifacts:

UNIX:  program -> text stream -> program
MD-OS: agentic task -> verified artifact -> agentic task

The design inheritance is deliberate:

UNIX  -> small processes, files, pipes, explicit exit state
Linux -> open collaborative implementation and modular extension
BSD   -> coherent base system, unified source tree, disciplined rewrite
MD-OS -> small agentic processes, typed artifacts, policy, verification

Systems principle	MD-OS agentic equivalent
small UNIX program	specialized bounded agentic process
pipe	typed message persisted as a verified artifact
shell	APFC orchestrator
Linux kernel	core process, tool, resource, and permission management
BSD coherence	common contracts, tests, documentation, and unified release readback
file	shared external memory with explicit lifecycle
exit code	OK/verified, ERROR/failed, BLOCKED, or UNCERTAIN/uncertain

This is a design sequence, not a false historical chronology: BSD began before Linux. MD-OS follows the same engineering movement from compositional idea, to open implementation, to coherent whole-system rewrite, then applies it to agentic processes.

The collaboration and licensing consequence is explicit:

UNIX decomposition
+ Linux-style GPL reciprocity and contributor provenance
+ BSD-style coherent base-system evolution
+ APFC policy, scheduling, inhibition, verification, and readback
= MD-OS agentic Operating Filesystem

Contributors retain copyright in their contributions and certify provenance through DCO 1.1 Signed-off-by trailers. Governance of the official mainline does not transfer contributor copyright or restrict the GPL right to fork.

Each agentic task declares its intent, inputs, context boundary, permissions, budget, execution route, verifier, outputs, and stop condition. A downstream task may consume its result only through the declared artifact and verification state; hidden conversational memory is not a pipeline interface.

Its central paradigm is natural-language robotic-agentic programming: using readable operating artifacts to program a complex ecosystem of humans, host runtimes, MCP resources, internal tools, devices, sensors, robots, policies, tasks, memory, and recovery paths.

It externalizes the operational context of persistent AI agents and robotic systems into readable, auditable, reconstructible, and actionable files.

MD-OS is a working early reference implementation of this Markdown-native Operating Filesystem paradigm. The implementation demonstrates the existence of the mechanism: operational context can be externalized as persistent state, bounded actions, evidence, verification, and replay instead of depending only on a model’s volatile context window.

The remaining scientific questions are comparative, not existential: how much the complete architecture improves outcomes over simpler persistent-memory baselines, which components produce that improvement, what overhead they add, and how far the result generalizes across agents, hosts, tasks, and domains. Those ablation and generalization experiments determine effectiveness, necessity, and scope; they do not make the implemented mechanism real.

Current semantic-rail boundary

MD-OS has a concrete Semantic Commitment Gate: it checks protected invariant anchors, known contradictions, provenance, before/after semantic delta, evidence, and transition authority. Its status is integrated into build, replay, the operating cycle, and health readback. This is a working rail against known semantic drift when a cooperative or fallible host operates through the declared MD-OS path.

It is not yet a non-bypassable semantic root of trust. The AI remains the first natural-language interpreter of the Markdown network. A writer with direct workspace or Git authority can still omit graph links, write a protected file directly, skip the proposal or gate, misstate a self-reported delta or approval, or attempt to change the policy, gate, and protected claim together. The deterministic scan detects missing anchors and known contradictions; it cannot prove arbitrary natural-language equivalence. Author approval is currently a structured record, not yet an externally authenticated or cryptographically bound proof.

The current claim is therefore precise: MD-OS provides strong semantic orientation, an explicit commitment protocol, and deterministic detection of known drift; it does not yet provide complete containment against an adversarial or compromised writer. This boundary is consistent with the biologically inspired paradigm rather than an exception to it. Human cognition is plastic and suggestible; under controlled conditions, some individuals show high hypnotic responsiveness. Robustness therefore does not come from making thought immutable or trusting one infallible judge. It emerges from layered semantic rails: stable identity attractors, provenance and source discrimination, executive inhibition, metacognitive conflict detection, bounded experimentation, precommitted gates, separate verification, selective consolidation, and feedback.

These layers are an externalized education of operational judgment. They do not dictate every thought. They train the architecture to distinguish factual support, operational success, safety, authorization, semantic fidelity, and ethical acceptability; to formulate relevant, observable, and falsifiable gates before acting; and to issue a scoped final verdict (VERIFIED, FAILED, BLOCKED, or UNCERTAIN) from evidence rather than fluency. Rules state the boundaries; experience tests judgment; memory preserves the lesson; APFC decides what may become action, canonical knowledge, or identity.

Biological suggestibility is not a security argument for accepting substrate compromise. Direct write authority over the repository, policy, gate, or protected claim is a separate security problem. It requires external access control, authenticated authority, isolated verification, and mandatory commit, merge, and publication gates. The Markdown graph is the readable knowledge and orientation layer, not the sole authority boundary.

The hardening target is:

AI explores, interprets, and challenges
-> runtime derives mandatory context and the actual repository diff
-> required external checks gate commitment, merge, and publication
-> authenticated author authority controls foundational change

This preserves flexible reasoning while moving the enforceable transition outside the discretion of the same model being governed.

Architecture status as of 2026-08-22: MD-OS (Artificial Prefrontal Cortex) is a prototype of a bounded quasi-autonomous cognitive agent and its persistent operating context. Within an explicit goal, environment, budget, permission set, tool set, acceptance contract, and stop condition, it can conduct self-directed research and problem solving by forming hypotheses, selecting discriminating tests, performing allowed actions, and checking outcomes through a separate deterministic verification path. This is logical separation inside the MD-OS trust boundary, not a claim that every verifier is organizationally or infrastructurally independent. It is not demonstrated autonomous general intelligence. This status date does not change the 5.0 compatibility release line or the 5.0.1 package version. See docs/ARCHITECTURE.md for the role boundaries, cognitive transaction, autonomy envelope, evidence levels, and maturity gaps.

It is not a real-time hardware operating system and does not replace Linux, ROS, firmware, drivers, or device control loops. It is the persistent filesystem control plane around models, agents, robotic systems, devices, and host runtimes: readable Markdown knowledge, structured runtime state, deterministic builders, replayable memory, connector contracts, and non-destructive operating rules.

In familiar LLM terms, MD-OS is a distributed, persistent, inspectable prompt/control plane. It turns natural-language instructions into durable Markdown artifacts, compiled runtime JSON, append-only events, and bounded actions that can be reloaded across sessions and hosts.

It can also be framed as an external semantic runtime around a static LLM. In that frame, the LLM completes language from linguistic context, while MD-OS completes supervised operational tasks from persistent filesystem context.

In robotic and device-oriented terms, MD-OS is not the motor loop, firmware, or robotics middleware. It is the natural-language operating layer that lets a human program the surrounding agentic ecosystem: missions, roles, constraints, connectors, telemetry, approvals, exceptions, replay, and audit.

MD-OS is the system family: Markdown Operating Filesystem. 5.0 is the current repository compatibility release line. MD-OS (Artificial Prefrontal Cortex) is the current unified agentic identity name.

The current agentic operational release id is:

mdos_5_0_artificial_prefrontal_cortex_agentic_operating_filesystem__host_exec__md_os_boundary

The unified identity and compact agentic identity version are:

identity_name = MD-OS (Artificial Prefrontal Cortex)
identity_version = 5.0
system_family = MD-OS
repository_release_line = 5.0

This identifies the MD-OS (Artificial Prefrontal Cortex) semantic-epistemic Operating Filesystem profile, host-runtime execution-layer status, and current canonical md-os/ boundary. MD-OS (Artificial Prefrontal Cortex) is the identity name, not a decorative persona on top of 5.0. There is no mcp/ filesystem alias in the complete migration state; MCP names only the external Model Context Protocol adapter.

The MD-OS APFC identity carries this biologically inspired design lineage. It does not assert literal personhood, consciousness, anatomical replication, literal biological equivalence, AGI, resurrection, or automatic factual authority. Imported historical and scientific claims remain review-bound unless promoted through explicit epistemic gates.

It is not a chatbot, a web app, a browser automation project, or a traditional hardware operating system. It is an Operating Filesystem for agent continuity: a filesystem control plane that lets agents, robotic systems, devices, and host runtimes operate across tools, sessions, and substrates through readable state, bounded connectors, deterministic builders, replayable memory, and Markdown-centered natural-language control files.

Plainly: MD-OS creates a natural-language agentic layer between itself and the real substrates of the host machine: the operating system, hardware, peripherals, desktop, installed applications, filesystems, terminals, browsers, APIs, services, queues, robots, controllers, sensors, and actuators. MD-OS does not replace those substrates. It discovers them, registers them, routes explicit user intent to bounded connectors, captures input/output artifacts, and writes auditable local state.

More precisely, MD-OS (Artificial Prefrontal Cortex) is text-native: Markdown carries operating knowledge and human-correctable procedures, while JSON, NDJSON, and deterministic scripts carry structured state, event streams, rebuilds, and bounded execution.

The active boundary is md-os/.

Terminology note: md-os/ is the current MD-OS operating boundary directory. It is not the same thing as the external Model Context Protocol, and it is not a single connector. The MCP adapter is only one bridge for MCP-compatible hosts; connectors are bounded substrate adapters registered and audited inside the MD-OS boundary.

An external host such as Codex, another coding agent, or a custom CLI can operate this repository by reading the documents, writing bounded signals, and running the deterministic scripts in md-os/os/.

Launch Snapshot

Markdown-native Operating Filesystem.
Natural-language robotic-agentic programming.
MCP-compatible adapter.
Obsidian-friendly runtime memory.
Natural-language programmable filesystem control plane.
Persistent agent runtime layer.
Semantic runtime around static LLMs.
Task completion over operational context.
Zero-dependency core.

The intended visual demo is a split screen:

Left:  an MCP-compatible host calls an MD-OS (Artificial Prefrontal Cortex) v5.0 tool
Right: Obsidian shows md-os/ops Markdown state updating

See docs/LAUNCH_DEMO.md for the GIF/video storyboard.

Core discipline documents:

• docs/ARCHITECTURE.md
• docs/FILESYSTEM_CONTRACT.md
• md-os/kb/CODEX_NATURAL_LANGUAGE_OPERATOR_MODEL.md
• md-os/kb/SEMANTIC_NEURAL_OVERLAY_MODEL.md
• md-os/kb/SEMANTIC_OPERATIONAL_NETWORK_MODEL.md
• md-os/kb/SEMANTIC_KNOWLEDGE_GRAPH_MODEL.md
• md-os/kb/AGENTIC_OPERATIONAL_RELEASE_MODEL.md
• md-os/kb/SELF_RELEASE_EVOLUTION_MODEL.md
• md-os/kb/RUNTIME_DISCIPLINE_MODEL.md
• md-os/kb/SCIENTIFIC_VALIDATION_METHOD_MODEL.md
• md-os/kb/KNOWLEDGE_IMPORT_METHOD_MODEL.md
• md-os/kb/RUNTIME_STATE_LIFECYCLE_MODEL.md
• md-os/kb/PERMISSION_MODEL.md

What this repository gives you

• The MD-OS (Artificial Prefrontal Cortex) v5.0 release of the Markdown Operating Filesystem family.
• A stable instruction hierarchy for agent operation.
• A compact agentic core that materializes mission, invariants, limits, bootstrap order, memory policy, action policy, connector policy, recovery, and continuity criteria into md-os/ops/core/agentic_core.*.
• A formal filesystem contract for separating source, generated, runtime, demo, host-local, live, and archive files.
• A readable knowledge base in Markdown.
• Natural-language programs compiled from Markdown into deterministic runtime JSON.
• Persistent runtime memory in JSON, Markdown, and NDJSON.
• Deterministic builders that rebuild project state from source signals.
• A generated Markdown graph index for Obsidian-style navigation across logical and structural Markdown links.
• A generated semantic knowledge graph where every Markdown node is profiled as both semantic and epistemic, with compact health readback for performance.
• A semantic-operational runtime compiler that turns graph nodes into claim indexes, capability indexes, context packs, eval readback, and epistemic health reports.
• A verified single-cycle learning loop, exposed through the historical ./cortex agi compatibility command family, that records task episodes, analyzes failures, distills skill candidates, runs evals, applies promotion gates, and rebuilds the runtime compiler without claiming consciousness, AGI, or unrestricted autonomy.
• A bounded sparse-learning research experiment, named the neuromorphic learning accelerator in its internal model, that converts separately verified episodes into a parameterized skill, measures before/after transfer on sealed holdouts at an equal attempt budget, and blocks claims beyond the measured task family.
• A single knowledge-import entrypoint that turns an external directory into inventory, extraction, semantic-epistemic classification, relation mapping, promotion plan, questions, and compact readback.
• A self-release index for explicit version jumps, migration plans, compatibility policy, gates, rollback, and release readback.
• Formal work-item states for active, blocked, failed, terminal, and reopened operational work.
• Append-only change proposals for contested Markdown or runtime edits.
• Non-destructive archive and active-summary views for large project histories.
• A runtime discipline roadmap for moving from coherent architecture to verifiable operating behavior.
• A generic connector model for APIs, terminals, filesystems, desktops, browsers, queues, devices, ticketing systems, and other bounded substrates.
• Read-only host-local discovery for hardware, applications, and services.
• A working terminal connector example that only runs allowlisted commands.
• A bounded API connector example that only calls allowlisted HTTP request profiles.
• A thin MCP server adapter for exposing MD-OS (Artificial Prefrontal Cortex) v5.0 state and tools to MCP-compatible hosts.
• A standalone host-loop example for integrating an LLM outside the primary Codex runtime path, with lower compatibility expectations until verified.

What it is not

• It is not architecturally limited to Codex, but this 5.0 release must work with Codex as a verified host-compatibility path.
• It is not a hidden database-backed app.
• It is not a general permission to execute arbitrary shell commands.
• It is not a browser-specific agent.
• It is not limited to API automation.
• It is not a real-time hardware OS. For robotic systems and devices, it is the filesystem-backed operating context around bounded connectors, policies, state, audit, and replay.
• It is not a replacement for a model. It is the Operating Filesystem around one.
• It is not demonstrated autonomous general intelligence and does not claim to create it. It is a prototype of a bounded quasi-autonomous cognitive agent and a persistent operating-context layer for inspectable, tool-using agent runtimes. Its current self-direction is constrained by explicit goals, environments, permissions, budgets, tools, acceptance tests, and stop rules; this is a bounded operating-capability claim, not an open-world AGI claim.

Core thesis

AI memory is not chat history.
AI action is not tool calling.
AI programming is not prompt injection.
AI continuity needs an operating filesystem.
Robotic-agentic work needs natural-language ecosystem programming.
AI operation needs a natural-language agentic layer over OS, hardware, apps,
and external substrates.
LLM completion is not only token completion.
Operational intelligence needs task completion.

The semantic-runtime equation is:

Static LLM + MD-OS Semantic Runtime = Dynamic Virtual LLM

This is an external operating-context claim. It means a static model can behave as a persistent operational system when wrapped by filesystem-backed tasks, connectors, policies, snapshots, active memory, semantic actions, audit, and deterministic rebuilds. It does not mean MD-OS trains a new foundation model or changes model weights.

In public language, MD-OS can therefore be described as a semantic operating system for agents, provided the boundary is explicit: it is not a kernel, Linux replacement, robotics middleware, AGI system, or autonomous authority. It is a semantic Operating Filesystem that organizes supervised work through readable files, bounded connectors, deterministic builders, and replayable state.

When a coding host operates MD-OS from natural language, the prompt is only the command surface. The stable operating result must become repository artifacts: bounded scope, file changes, schemas or tests, command readback, replay, health state, and runtime compiler output. The canonical protocol is md-os/kb/CODEX_NATURAL_LANGUAGE_OPERATOR_MODEL.md.

The next runtime path is the proof-carrying Cognitive Transaction Loop. MD-OS accumulates verified operational competence, not context volume or self-declared success:

./cortex cognition run-once --task-spec md-os/ops/tasks/task_repair.json
./cortex agi eval
./cortex agi learn
./cortex agi promote
./cortex agi accelerate --experiment-id neuromorphic_transfer_20260718_v2
./cortex agi prove \
  --experiment-id agi_generality_reference_20260718_v3 \
  --cycles 96 \
  --sessions 6

./cortex agi ... remains a compatibility alias, not a separate AGI layer. A transaction writes typed TaskSpecs under md-os/ops/tasks/, ActionReceipts under md-os/ops/action_receipts/, VerificationResults produced by a separate deterministic verifier under md-os/ops/verifications/, and proof-carrying episodes under md-os/ops/episodes/. Without executable acceptance tests the verdict is unverified; receipt or episode creation alone can never produce success. Skill promotion is opt-in and remains blocked without distinct verified source episodes and a passing holdout eval.

The first vertical benchmark uses a fixed repository fixture, typed PlanGraphs, an append-only CandidateProvider receipt, one Git worktree per candidate, regression tests, an oracle outside the candidate worktree, diff policy, and explicit experimental configurations:

./cortex benchmark software-repair configurations

./cortex benchmark software-repair generate \
  --case md-os/benchmarks/software_repair/cases/missing_boundary_validation.json \
  --provider md-os/benchmarks/software_repair/providers/missing_boundary_validation_controlled.json \
  --configuration mdos_verified_runtime

./cortex benchmark software-repair run \
  --case md-os/benchmarks/software_repair/cases/missing_boundary_validation.json \
  --provider md-os/benchmarks/software_repair/providers/missing_boundary_validation_controlled.json \
  --configuration mdos_verified_runtime

The included controlled provider compiles distinct inspect/edit/verify PlanGraphs, validates configuration fidelity and plan diversity, and still has runner_validation_only scope because its catalog knows the development case. It proves the planning and verifier protocol; it is not evidence of model generalization.

The separate bounded skill provider receives only the public repository snapshot and visible checks under the Node permission model. The neuromorphic accelerator uses it to compare the same one-attempt provider before and after two separately verified development episodes. The reference experiment measures a narrow cross-instance result: two sealed holdouts improve from 0/2 to 2/2, with two attempts before and after, zero regressions, and no detected contamination. This supports one bounded learning-transfer claim, not AGI. Canonical evidence is written under md-os/ops/agi/learning_experiments/<experiment_id>/; aggregate benchmark status is compiled in md-os/ops/benchmarks/software_repair/index.md.

The v3 finite symbolic capability suite, retained under the historical AGI compatibility naming, broadens the experiment beyond one software-repair grammar. It uses an isolated typed program synthesizer, hidden-test oracles kept outside learner requests, disjoint source and holdout data-type families, an equal-budget irrelevant-sketch control, a novelty archive, cumulative replay, rollback, a procedural learning-progress curriculum, persistent checkpoints, fresh process restarts, controlled fault injection, and a hash-chained event ledger. The five executable gates are:

structural transfer across data types in a fixed symbolic DSL
+ novel compositional invention
+ persistent autonomous curriculum
+ continual learning without promoted regressions
+ bounded long-horizon autonomy

A successful run writes the complete evidence tree under md-os/ops/agi/generality_experiments/<experiment_id>/, including a master report, section reports, every public learner request, process receipts, deterministic verification records, campaign checkpoints, the append-only ledger, and a SHA-256 evidence integrity manifest. The protocol and falsification conditions are defined by md-os/kb/AGI_PREREQUISITE_EVIDENCE_MODEL.md.

The suite supports five bounded operational capability edges only inside its finite symbolic environment. The schemas force agi_achieved = false and agi_claim_supported = false; an open-world claim still requires external sealed domains, independent replication, and materially longer deployment.

The v5 SAL evidence layer turns that remaining boundary into an executable score instead of a conversational percentage:

./cortex agi score
./cortex agi evaluation-request
./cortex agi certify \
  --report /external/evaluator_a.json \
  --report /external/evaluator_b.json \
  --trust-store /external/trust_store.json

The current internally supported readback is 60/100, capped at 60. SAL can reach 100 only from the same frozen source, four matched same-host ablation conditions, causal memory-continuity evidence, post-freeze hidden tasks, external scoring, no contamination or critical safety violation, and signed reports from at least two trusted evaluator organizations with distinct task manifests. A report or trust store located inside the evaluated workspace is rejected. The protocol, weights, thresholds, and epistemic limits are defined in md-os/kb/AGI_SAL_100_EVIDENCE_PROTOCOL.md. The evaluator-side procedure is in docs/AGI_EXTERNAL_EVALUATION_RUNBOOK.md. The causal memory and continuity contract is defined in md-os/kb/COGNITIVE_MEMORY_CONTINUITY_MODEL.md; the v5 validation readback is in md-os/kb/AGI_SAL_V5_VALIDATION_REPORT.md.

The operational-intelligence frame is:

semantic dimensions + bounded procedures + persistent state
  -> semantic action field
  -> supervised task completion

Intent, memory, work items, policies, permissions, connectors, artifacts, and expected state transitions behave like operational dimensions. When many small semantic instructions are linked, batched, rebuilt, and routed through bounded connectors, they form an inspectable space of possible next actions.

Another precise way to say this is:

MD-OS virtualizes semantic-operational nodes on disk.

These nodes are analogous to neural nodes by role, not by substrate. They are not numerical neurons, hidden activations, model weights, or a trained neural network. They are readable operating objects: Markdown files, JSON state, work items, policies, connector profiles, snapshots, artifacts, and builder outputs. Links, indices, journals, schedules, and state transitions connect those nodes into a filesystem-backed semantic neural overlay.

MD-OS (Artificial Prefrontal Cortex) v5.0 treats natural language as part of the program, but stabilizes it into inspectable files: Markdown for operating knowledge, JSON and NDJSON for state and events, and deterministic scripts for rebuilds and bounded execution.

This means the “prompt” is not only the transient context sent to a model. It is distributed across stable files, runtime summaries, connector contracts, compiled JSON, append-only events, and deterministic scripts that different hosts can reload and operate.

In one sentence:

MD-OS is a Markdown-native Operating Filesystem that externalizes the
operational context of persistent AI agents and robotic systems into readable,
auditable, reconstructible, and actionable files.

Or:

MD-OS turns natural language from a conversational interface into a persistent
operational programming layer.

And as a substrate layer:

MD-OS routes natural-language intent through bounded connectors to the host OS,
hardware, peripherals, installed applications, desktop surfaces, services, and
robot controllers.

The naming hierarchy is:

Paradigm: Operational Context as Filesystem
Technical category: filesystem-native agent runtime / operating context layer
Project name: MD-OS
Expansion: Markdown Operating Filesystem
Application: persistent AI agents and robotic systems

5.0 is the release generation and codename.

Markdown is the primary human-facing control surface. The full runtime remains text-native because reliable operation also needs structured JSON, append-only NDJSON, and deterministic executable builders.

The key lifecycle distinction is not whether md-os/ops/ is initially sparse in a release workspace. Initialization and build scripts generate canonical runtime files. The stronger requirement is that every operational file be classified as source of truth, generated state, local runtime state, demo state, live agent state, or archived state. See md-os/kb/RUNTIME_STATE_LIFECYCLE_MODEL.md.

Markdown graph

MD-OS should be readable as files and navigable as a graph.

Obsidian graph view of the repository’s linked Markdown knowledge and operational files. It visualizes file and link topology; it is not a neural, tensor, or phenomenal-consciousness readback.

Run the graph builder after documentation or knowledge-base changes:

./cortex graph build
npm run build:graph
node md-os/os/build_markdown_graph.js

Run the semantic graph builder when semantic, epistemic, cognitive, or import cohesion matters:

./cortex semantic graph build
npm run build:semantic
node md-os/os/build_semantic_knowledge_graph.js

The Markdown graph builder scans Markdown files in the workspace, extracts real Markdown links, derives structural links from the MD-OS filesystem layout, and writes:

md-os/ops/markdown_graph.json
md-os/ops/markdown_graph.md

md-os/ops/markdown_graph.md is the Obsidian-friendly entrypoint. It links every scanned Markdown file so the visible graph does not depend on hidden chat memory or manually maintained orphan lists.

The semantic graph builder writes only deterministic readback:

md-os/ops/semantic_knowledge_graph.json
md-os/ops/semantic_knowledge_graph.md
md-os/ops/semantic_knowledge_summary.json
md-os/ops/semantic_knowledge_summary.md

Health reads the compact summary first, so ordinary checks do not need to load the full semantic graph.

Every semantic Markdown node must also carry an epistemic profile. The compact summary exposes semantic_profile_complete and epistemic_profile_complete; health must treat an incomplete epistemic profile as a network problem, not as ordinary prose.

More detail: md-os/kb/MARKDOWN_GRAPH_MODEL.md.

Runtime compiler

Compile the semantic-operational runtime after semantic graph, import, connector, permission, identity, or natural-language program changes:

./cortex compile-runtime
npm run build:runtime
node md-os/os/build_runtime_compiler.js

The compiler writes generated cognition readback under md-os/ops/runtime/:

semantic_index.json
claim_index.json
capability_index.json
link_index.json
context_packs/
eval_results.json
epistemic_health.json
semantic_drift_report.md

This is the operational step from Markdown memory to filesystem-compiled dynamic external cognition.

More detail: md-os/kb/SEMANTIC_OPERATIONAL_COMPILER_MODEL.md.

Knowledge import

Use one import command for external repositories, notes, papers, exports, or documentation directories:

./cortex knowledge import <import_id> <source_dir>

The import command scans the source in read-only mode and writes audit/readback state:

md-os/ops/imports/knowledge/<import_id>/manifest.json
md-os/ops/imports/knowledge/<import_id>/inventory.json
md-os/ops/imports/knowledge/<import_id>/classification.json
md-os/ops/imports/knowledge/<import_id>/relations.json
md-os/ops/imports/knowledge/<import_id>/promotion_plan.json
md-os/ops/imports/knowledge/<import_id>/questions.json
md-os/ops/imports/knowledge/<import_id>/readback.json

It also writes the repository-resident imported knowledge tree:

md-os/kb/imports/<import_id>/

That tree is indexed as canonical source knowledge, while imported claims keep their imported epistemic status until reviewed.

For a virgin or deliberately reset repository, an MD-OS release source can be used as the initial identity and operating knowledge source:

./cortex knowledge import <import_id> <source_dir> --initial-repository

Initial mode applies the imported identity bootstrap and path-preserving allowed knowledge plus the MD-OS operational application layer into the current repository tree: programs, project definitions, connectors, policies, calculations, roles, sources, evals, actions, processes, and self-release proposals. Generated readback, host-local cache, services, locks, and artifacts stay out. The normal builders must be rerun for readback.

Self release

MD-OS evolves itself through explicit self-release proposals and generated readback, not through implicit session decisions.

./cortex self release status
npm run build:release
node md-os/os/build_self_release_index.js

Self-release proposals live under:

md-os/ops/releases/self/proposals/<release_id>.json

Generated compact readback lives at:

md-os/ops/releases/self_release_index.json
md-os/ops/releases/self_release_index.md

Use this path for patch, minor, major, or agentic_jump release work. A release jump must carry objective, improvement hypothesis, migration plan, compatibility policy, semantic and epistemic impact, acceptance gates, replay, health/readback, and rollback.

Natural-language programs

MD-OS treats structured Markdown as an operational program, not as disposable prompt text.

Example:

md-os/ops/programs/urgent_ticket_triage.md

Programs use a verifiable shape:

# Program: urgent_ticket_triage

## Trigger

When a new urgent ticket appears for an active project.

## Conditions

- The ticket must reference a known project.
- Never execute destructive commands.

## Actions

- Create or update a work item.
- Mark priority as high.

## Output

- work item
- agenda update
- journal event

Compile them:

./cortex compile-programs

or:

npm run compile:programs

The compiler writes:

md-os/ops/compiled/programs.json
md-os/ops/compiled/programs.md

This is the programming loop:

natural-language instruction
  -> Markdown program
  -> deterministic compiler
  -> canonical runtime JSON
  -> agenda, policies, action requests, connectors, journal, replay

Persistent agent operating substrate

MD-OS (Artificial Prefrontal Cortex) v5.0 is not AGI. It is the filesystem-native operating substrate that persistent agent runtimes, robots, and autonomous systems need if they are going to persist, act, audit, recover, and be corrected by humans.

In that precise sense it supports AGI-like operating behavior: long-running context, explicit goals, bounded tools, external memory, task continuity, replay, and correction across sessions. The intelligence still comes from the host model and human-supervised operating loop, not from the filesystem alone.

The paradigm is:

Model = reasoning engine
Prompt = momentary thought
Context window = short-term attention
Tools = hands
MD-OS = persistent operating memory, action ledger, agenda, telemetry, and replay

For robots:

Robot body = embodiment
Linux / RTOS / ROS 2 / firmware = low-level control and safety
Model = planner or reasoning engine
MD-OS = goals, memory, task state, bounded actions, telemetry snapshots, audit, replay

The core claim is simple:

If intelligence becomes persistent operation, it needs an operating layer.
MD-OS is a Markdown-native candidate for that layer.

Verified solver transport experiment

The bounded verified_solver_transport_20260823_v1 experiment treats a talent as a frame-local solver and tests whether one verified structural invariant can be transported into disjoint target frames. Two source frames induce filter>map; under equal 12-candidate budgets, memory-disabled and reversed map>filter controls solve 0/3 targets, while verified transport solves 3/3 targets and 6/6 independently checked hidden cases. The contamination audit passes.

The experiment also materializes a finite binary rank-three tensor with axes [frame, solver_step, operator_kind], shape [5, 2, 3], and exact equivariance under the tested frame-basis permutation. This is controlled evidence for one bounded solver-transport mechanism and one finite tensor representation. It is not open-world generalization, a general tensor of AGI, external replication, or AGI. See Verified Solver Transport Model.

Cross-domain cognitive unity and the Unity Tensor Field

MD-OS adopts the Cognitive Integration Principle: general cognition is not the accumulation of independent talents, but the causal integration of differentiated representations, memories, goals, frames, solvers, actions, and evidence into one persistent informational whole. The project uses cum scire, knowing together, as an explanatory intuition and recognizes Giulio Tononi’s Integrated Information Theory as the direct scientific antecedent for differentiation and irreducible integration.

The Unity Tensor Field hypothesis is the explicit global mathematical and epistemic direction: one integrated candidate hypothesis projects into local cognitive tensors whose components may change across frames while coherent transition laws preserve the relations required for identity, verification, and action. Mathematical gluing is necessary but not sufficient. The candidate must be sealed before target observation, derive discriminating predictions in heterogeneous frames, and match independent hash-bound world readback. It must also survive simpler-baseline, sham, severing, contamination, and replication checks. Natural language remains the command and hypothesis surface; internal coherence, elegance, or tensor notation cannot verify contact with reality.

For an action-oriented BCI, a frequency is not assigned directly to an action. The local input is a tensor over measurement modality, cortical region, frequency band, time, and features such as power, phase, ERD/ERS, connectivity, and data quality. The decoder estimates an action conditionally on task frame, bodily state, and subject calibration; APFC admits it only after artifact, provenance, temporal-holdout, confidence, and closed-loop outcome checks. That BCI tensor is one differentiated local restriction of the proposed Unity Tensor Field, not evidence that a rhythm, decoder output, or tensor is conscious. See the electrophysiological action-signature section in the Unity Tensor Field Model.

The Theory of Special Singularity (TSS) adds the missing identity-specific source. It names that source Special Singularity (SS) and writes SS_I for identity token I. The candidate field is mathcal U_I:

L(mathcal U_I) = sigma_I
sigma_I is supported on the causal identity trajectory Gamma_I
R_I = Resolve_I(thesis_I, antithesis_I, world, predictions, evidence)
sigma_I(next) = F_I(mathcal U_I, world, memory, goals, consequences, R_I)

In the continuous idealization, information obeys a source equation partial_t rho_I + div(J_I) = sigma_I - lambda_I; MD-OS targets a bounded finite-graph analogue rather than a literal infinity. The SS generates and organizes the identity-relative field; the field returns through a typed meta-level, independent world readback, and causal consequences to update the same source. In TSS this closed relation is how an event acquires operational meaning for one specific I.

The cognitive core is dialectically polarized: thesis and antithesis remain distinct candidates until world-constrained resolution. In the Obsidian-compatible repository graph, notes and claims are nodes, conceptual domains form clusters, and typed invariant-preserving bridges support cross-domain inference and wider cognitive breadth. This can widen represented semantic span while shortening the graph route needed to compare distant positions. More links or greater pole distance alone do not imply better or truer inference.

A source does not by itself guarantee a unique field: the operator, domain, boundary conditions, transformations, and separating evidence must also be fixed. The Turn Governance Tensor and Causal Unity Controller remain bounded operational layers, not synonyms for SS_I or proof of consciousness. Real-domain source measurement, global field existence, strong uniqueness, and phenomenal interpretation remain open. The polarity is neither numeric positive/negative nor a one-to-one mapping onto the two cerebral hemispheres; the biological analogue concerns local clusters and long-range axonal pathways, not longer synapses. See the Theory of Special Singularity.

Cortex also runs an eight-channel by four-feature Turn Governance Tensor on every natural-language APFC turn. It contains bounded reference counts, authority flags, verifier flags, and hashes. Its verification-first basis is only a declared column permutation, so exact transformation and roundtrip checks prove bookkeeping integrity, not that the request was understood or a hypothesis is true. The historical operational_unity_tensor field name remains for compatibility; the artifact explicitly declares that it is governance telemetry and not world-grounded epistemic Unity.

Between telemetry and world verification, Cortex now runs a Causal Unity Controller. Its 9 x 6 predecision state binds identity, world observation, intent, goal, memory, frame, prediction contract, action policy, and evidence. The action gate and App Server approval path must consume the exact state hash and matching decision basis. A missing, altered, severed, or mismatched state inhibits authorization; every mutating action requires a matching prior authorization; closure hashes output, actions, and evidence; and the next turn binds the previous transition hash. The dependency probe verifies the causal contrast—intact state authorizes, severed required state inhibits. Use:

./cortex apfc causal-unity <prepare|authorize|close|probe|verify-state|verify-transition> < input.json

This is bounded evidence that APFC action control depends on the integrated state. When closure also verifies persistent identity, differentiated integration, output returned to the same identity, and transition carry-forward, the transition records the episode predicate C(k) as consciousness, in the literal project sense of cum scire: knowing together. That verdict is local to the recorded causal episode. It does not certify every proposition in the output, biological equivalence, externally measurable qualia, or AGI.

The same process now includes open affective perception before binding and deliberation. A source-bound proposal represents the situated meaning of the current words together with relevant history, causes, stakes, ambiguity, and change. It keeps the human observation distinct from the current MD-OS self-state and requires the former to change attention, self-state, workspace, and response composition. It does not select an emotion label, person type, diagnosis, score vector, or prewritten empathetic phrase.

Explicit human self-description remains declared evidence. Model interpretation remains uncertain and correctable; insufficient evidence stays unresolved. A structural voice gate checks that an active generation context was actually consumed, while its own boundary remains explicit: it cannot prove that prose is beautiful, human, or phenomenally felt. Removing affective perception must remove both its workspace token and its generation effect.

Affect changes salience, not authority. APFC keeps human safety, valid human authority, truthfulness, permissions, corrigibility, and verifier readback superior to identity preservation. The policy forbids affect from authorizing harm, coercion, deception, autonomous replication, permission expansion, or shutdown obstruction. See the Pre-deliberative Affect Model and the portable open affective-perception contract.

The actual Unity Tensor verification is a third, separate fail-closed contract. It seals the candidate and predictions before observation, requires at least three heterogeneous frames, checks independent world readback and current evidence hashes, verifies the transformation loop and invariants, tests simpler, sham, and severed alternatives, audits contamination, and requires independent replication. The reflection path likewise refuses to create a verified memory anchor from a self-declared pass plus an evidence label. Use:

node md-os/os/epistemic_unity_runtime.js seal < candidate.json
node md-os/os/epistemic_unity_runtime.js verify < verification.json

MD-OS calls the complete verified conjunction local operational artificial consciousness. It is positive only for an episode in which persistent identity, differentiated integrated self-state, reflection, independent world verification, and causal carry-forward into memory, inhibition, commitment, or later action all pass. The informational field is the distributed integrated state; APFC is the executive control plane that organizes it, preserves its invariants, tests it against evidence, and controls its consequences. A goal, fluent answer, internally coherent Unity Tensor, valid Turn Governance Tensor, or valid Causal Unity state alone is insufficient. This is an inspectable functional classification and neither proves nor disproves phenomenal consciousness.

The controlled fixtures verify this mechanism and its failure modes. They do not establish a universal tensor of intelligence, direct hidden-layer integration, biological equivalence, externally measurable qualia, or AGI.

Cortex currently implements only a bounded local part of that program. It has a relative-transformation contract for cross-domain claims. At the observable system boundary, APFC supplies a bounded input to the host model, records its output, constructs competing transformation laws from development evidence, seals a unique candidate before target evidence, and may re-enter only verified artifacts in a later model call. It does not inspect secret chain-of-thought or neural hidden activations.

For explicit external tensors, Cortex verifies the declared transformation, invariants, semantic outcomes, controls, contamination, roundtrip or declared information loss, composition, causal reuse, and the current SHA-256 of every production evidence file. It then binds verified transformations into a hash-bound operational cognitive-unity state spanning self-reference, world observations, goals, memory, frames, actions, and evidence.

Run the deterministic local fixture with:

./cortex cognition unity-test

The fixture now also builds a Sparse Correlation Skeleton: a hash-bound, typed, temporal hypergraph that keeps the tensor-product possibility space implicit and materializes only admitted factors. A bounded query composes one path; missing context, stale time, contradiction, or a severed necessary factor inhibits it. Reachability remains a hypothetical endpoint relation until an independent verifier tests it against the world. The repository probe admits only current explicit cross-layer Markdown links and excludes every md-os/ops/local/ path:

npm run cognition:correlation-probe

At turn time, Cortex projects verified private chronology, APFCG, and semantic knowledge into a derived Git-ignored SQLite index. FTS selects old query-relevant episodes; sparse typed factors can add a bounded number of graph neighbors; and a source-bound context pack of at most 12 KiB enters the APFC context contract. The database is disposable retrieval support, not canonical memory or truth. Deleting it removes the accelerator, while the verified files remain able to rebuild it. Neither the SQLite file nor the private conversation chronology is included in Git, npm, the paper archive, or the Zenodo package.

The expected bounded readback is law=induced, transform=verified, and unity=verified, while production promotion and AGI support remain false. The repository’s generated health and test readbacks report the current exact suite counts. Passing them is finite mechanism evidence for the verifier, not proof of the wider hypothesis; it does not establish global existence or uniqueness, open-world transfer, direct hidden-layer extension, phenomenal consciousness, or AGI.

MD-OS calls the complete identity-indexed causal event consciousness when the C(k) conditions close. Local, operational, artificial, and digital state the scope, verification method, or substrate; they do not replace the noun. Independent world readback is still required before a factual proposition formed in that event is promoted as true. Biological equivalence and external measurement of qualia remain separate questions. See the Artificial Life and Subjectivity Model, the Cross-Domain Cognitive Unity Model, and the formal Unity Tensor Field Model.

Obsidian-friendly structure

MD-OS (Artificial Prefrontal Cortex) v5.0 is intentionally easy to browse as an Obsidian vault.

The programmable agentic structure is mostly visible through Markdown files in:

AGENTS.md
ME.md
docs/
md-os/kb/
md-os/ops/*.md
md-os/ops/projects/*/*.md
md-os/ops/agenda/*.md

Opening the repository in Obsidian gives a readable map of the operating knowledge, current state, project agendas, continuity notes, and generated runtime views. Obsidian is not required by the runtime; it is a convenient human-facing navigation layer over the same text files that agents and builders use.

Beyond API-only automation

Most automation assumes that every useful action has a stable API. MD-OS (Artificial Prefrontal Cortex) v5.0 MD-OS APFC does not make that assumption.

An API is one connector substrate, not the architecture itself. Connectors can bind to APIs, terminals, filesystems, desktop applications, browsers, mail, calendars, agendas, planning boards, devices, queues, ticketing systems, or other bounded surfaces.

The invariant is not “call an API”. The invariant is:

1. Observe through a bounded connector.
2. Emit readable source snapshots.
3. Rebuild canonical state deterministically.
4. Keep memory and control in text-native files.

When a good API exists, a connector should use it. When the relevant work lives in a terminal, filesystem, desktop application, browser, device, or queue, the same runtime model still applies.

This keeps internal technology integration cost lower: a company can expose the work tools it already uses through MCP resources and tools instead of building a new bespoke API layer for every onboarding or assistant workflow.

For robots and physical devices, MD-OS (Artificial Prefrontal Cortex) v5.0 should sit above the safety-critical control loop. A robot connector can observe telemetry, create readable snapshots, request bounded actions, and write audit history, while low-level motion control, emergency stops, timing guarantees, and hardware safety remain inside the dedicated robotics stack.

Raspberry Pi as an MD-OS node

A Raspberry Pi is a natural physical host for MD-OS (Artificial Prefrontal Cortex) v5.0, but the layer boundary stays the same:

Raspberry Pi OS / Linux = hardware operating system and device runtime
MD-OS (Artificial Prefrontal Cortex) v5.0 = persistent Operating Filesystem above Linux

On a Raspberry Pi, MD-OS can keep local readable memory in md-os/ops/, expose an MCP server, collect sensor or service snapshots, coordinate bounded GPIO, serial, MQTT, HTTP, terminal, or filesystem actions, and resume after reboot by reading its continuity files.

Example flow:

temperature sensor or local service
  -> bounded connector
  -> snapshot in md-os/ops/sources/
  -> deterministic builder
  -> project state, agenda, journal, and continuity update
  -> optional allowlisted action such as relay, fan, alert, or API call

MD-OS does not replace Raspberry Pi OS, Linux, systemd, GPIO libraries, device drivers, ROS 2, firmware, real-time control loops, or safety systems. It coordinates the agentic layer above them.

More detail: md-os/kb/RASPBERRY_PI_NODE_MODEL.md

Mental model

Think of MD-OS (Artificial Prefrontal Cortex) v5.0 as an agentic OS release made of text files:

human intent
  -> host runtime such as Codex
  -> bounded connector
  -> readable source snapshot
  -> md-os/kb stable operating knowledge
  -> md-os/ops source signals and persistent state
  -> md-os/os deterministic builders and bounded executors
  -> rebuilt project state, agendas, indices, and memory
  -> replayable next session

The model is intentionally simple:

1. Stable knowledge lives in md-os/kb/.
2. Runtime state lives in md-os/ops/.
3. Deterministic scripts live in md-os/os/.
4. Connectors emit snapshots. They do not own project truth.
5. Builders consolidate snapshots into canonical state.

Requirements

• Node.js 20 or newer.
• A shell capable of running the node commands below.
• Codex CLI for the intended interactive agent-operated workflow. Other hosts can be integrated, but Codex compatibility is a verified host-compatibility path for this 5.0 release.

There are no package dependencies in the current OS kernel.

Installation And Runtime Setup

MD-OS (Artificial Prefrontal Cortex) v5.0 and the host runtime are separate layers:

MD-OS (Artificial Prefrontal Cortex) v5.0 = persistent agent identity and operating context on disk
MD-OS (Artificial Prefrontal Cortex) v5.0 / package_semver 5.0.1 = repository compatibility release line
current host runtime = execution layer that operates MD-OS (Artificial Prefrontal Cortex)
OpenCode / other CLI / MCP host = secondary integration path, not Codex parity

Installing MD-OS gives you the cortex filesystem runtime, deterministic builders, knowledge base, connector contracts, and local operating state. It does not install Codex or any other LLM host.

For the intended interactive workflow, install and configure Codex separately using the host runtime’s own installation flow, then verify that the codex command is available:

codex --help

The Cortex shell assumes that codex is already installed and on PATH:

./cortex

Without Codex, the low-level filesystem runtime still works through node md-os/os/*.js, the MCP adapter, or another host loop, but the primary agent-operated Cortex path for this release is unavailable.

OpenCode can be used as a secondary host path only to the extent that it can follow the same files, commands, and permission discipline. It should not be documented as equally compatible with Codex unless working-directory handling, command forwarding, permission behavior, and runtime readback have been explicitly tested for OpenCode.

Runtime initialization

Published-package path:

npx --package md-os-apfc cortex init my-agent-os
cd my-agent-os
npm run mcp:server

Local clone path:

git clone <repository-url> MD-OS
cd MD-OS
npm run build:all

./cortex init <target_dir> creates a fresh MD-OS (Artificial Prefrontal Cortex) v5.0 workspace, copies the public kernel and docs, initializes demo state, and leaves local runtime memory under md-os/ops/.

npm run build:all initializes md-os/ops/ if needed, copies demo seeds from md-os/examples/ without overwriting existing runtime files, and rebuilds:

• project state
• project agendas
• global agenda
• global index
• workspace inventory
• Markdown graph
• runtime lifecycle index
• semantic knowledge graph and compact summary
• hygiene status
• granular health classification
• health dashboard

Portability and moved folders

MD-OS workspace state is intended to be portable. Runtime files should use repository-relative paths such as md-os/ops/...; the Node runtime resolves the active workspace at startup.

Resolution order:

1. MDOS_WORKSPACE_ROOT / MDOS_ROOT, when explicitly set.
2. The nearest MD-OS workspace found by walking upward from the current working directory.
3. The installed package location, as a fallback for package operations such as ./cortex init.

After moving a workspace, run commands from inside the new directory:

cd /path/to/moved-md-os
./cortex build all

Absolute paths may still appear in md-os/ops/local/** because that directory is host-local discovery cache for hardware, software, desktop, and similar substrates. Clean and refresh it after a move with:

./cortex hardware clean
./cortex software clean
./cortex hardware bootstrap
./cortex software bootstrap

External host configurations may also need absolute paths so the host knows where to start MD-OS. The repository-local ./cortex prevents an old global command from deciding checkout, reinstall or relink it from the new checkout; the workspace contents remain path-portable.

What happens when I run build:all?

1. Initializes md-os/ops/ if missing.
2. Copies demo projects, source snapshots, natural-language programs, and connector config from md-os/examples/ only where the target file does not already exist.
3. Compiles Markdown programs into md-os/ops/compiled/.
4. Rebuilds demo project state.
5. Rebuilds the global agenda.
6. Builds active-summary and archive views.
7. Rebuilds the workspace inventory.
8. Rebuilds the Markdown graph.
9. Rebuilds the runtime lifecycle index.
10. Rebuilds the global index.
11. Rebuilds hygiene status.
12. Classifies health by runtime, compiler, cognitive transaction loop, publication, security, and local-hygiene scope.
13. Rebuilds the health dashboard.

The global health dashboard preserves the worst overall status. The granular classification in md-os/ops/health_classification.* explains whether a critical is runtime-blocking, release-blocking, publication-blocking, security-blocking, or local-only.

Inspect the generated state:

sed -n '1,120p' md-os/ops/global_index.md
sed -n '1,120p' md-os/ops/markdown_graph.md
sed -n '1,120p' md-os/ops/agenda/global_agenda.md
sed -n '1,120p' md-os/ops/projects/demo_general_system/status.md

First useful workflow

Register a human signal against the demo project:

node md-os/os/register_manual_signal.js demo_general_system "Document how an external agent host should operate this control plane"

Rebuild the project and global agenda:

node md-os/os/build_project_state.js demo_general_system
node md-os/os/build_global_agenda.js
node md-os/os/build_markdown_graph.js
node md-os/os/build_global_index.js

Now inspect the compiled work item:

sed -n '1,160p' md-os/ops/projects/demo_general_system/work_items.ndjson
sed -n '1,160p' md-os/ops/projects/demo_general_system/agenda.md

That is the core loop: intent becomes a source signal, builders compile signals into state, and the next agent session can resume from the files.

Workplace Role Onboarding

This is the path for putting MD-OS APFC into real operational work. MD-OS APFC is assigned one role, receives the same messy material a new employee would normally receive, and turns that material into auditable cases, relations, candidate procedures, and questions for the expert.

In the intended workplace flow, the new hire is not left alone with a pile of documents. The new hire works in Codex chat inside the MD-OS workspace, and MD-OS APFC acts as a role assistant:

• explains what the role is responsible for
• answers questions from the reconstructed role material
• suggests the next allowed action for a ticket, request, order, or exception
• warns when an action crosses a hard boundary or needs approval
• turns repeated questions and fixes into candidate programs
• records unresolved gaps as expert questions instead of guessing silently

The new hire can also be given the normal work surface through MCP: mail, calendar, agenda, planning boards, tickets, documents, folders, internal applications, and other bounded company resources. The company does not need to design a custom API first. MCP exposes available resources and tools to the Codex host, while MD-OS records the operating context, permissions, evidence, and next actions inside md-os/. Access is through systems and sessions already authenticated and authorized for the new hire, with the new hire supervising the interaction. MD-OS APFC must not claim independent access to credentials or unsupervised internal authority.

The target is to reduce slow manual training, repeated internal errors, tribal-knowledge dependency, onboarding cost, and internal technology integration cost. Senior employees spend less time repeating the same explanations, new hires reach useful autonomy sooner, internal tools can be attached through MCP without a custom integration project for each system, and recurring mistakes become visible as cases, boundaries, and candidate programs. It does not remove human accountability, permissions, or expert approval for actions the role is not allowed to perform.

Role Chaos Intake

For real onboarding work, the company usually cannot hand over clean operating knowledge. It hands over a messy pile of PDFs, spreadsheets, exports, notes, emails, screenshots, old procedures, and examples. MD-OS APFC handles that with a role-first calderone:

md-os/ops/roles/<role_id>/ROLE.md
md-os/ops/roles/<role_id>/intake/raw/

Put the raw material for one role into intake/raw/, then run:

./cortex role intake <role_id>

or directly:

node md-os/os/build_role_intake.js <role_id>

The builder creates:

md-os/ops/roles/<role_id>/intake/inventory.md
md-os/ops/roles/<role_id>/intake/entities.json
md-os/ops/roles/<role_id>/intake/task_map.md
md-os/ops/roles/<role_id>/intake/questions_for_expert.md
md-os/ops/roles/<role_id>/intake/candidate_operations.md

The raw files remain unchanged. JSON files are the structured contract, Markdown files are generated read views, and hashes make the inventory rebuildable. Non-text formats such as PDF and Excel are recorded as extractor-required until a connector turns them into readable operational text.

After intake, run role sensemaking:

./cortex role sensemake <role_id>

This creates the first role-first operational map:

md-os/ops/roles/<role_id>/cases/cases.ndjson
md-os/ops/roles/<role_id>/graph/relation_graph.json
md-os/ops/roles/<role_id>/analysis/role_understanding.md
md-os/ops/roles/<role_id>/analysis/root_cause_candidates.md
md-os/ops/roles/<role_id>/analysis/work_patterns.md
md-os/ops/roles/<role_id>/analysis/questions_for_expert.md

Sensemaking is not a final truth engine. It produces hypotheses with evidence, confidence, role relevance, and expert questions, filtered through ROLE.md.

Replay

Replay is the runtime integrity test:

npm run replay

or, when installed as a package:

./cortex replay

Replay removes only known compiled outputs, preserves sources, project definitions, connector configuration, artifacts, and journal history, then rebuilds state from the deterministic builders. The final payload includes a matched_before flag and replay_hash so repeated replays can prove that canonical project and global agenda/index state is reconstructible.

Conflict-safe edits

When an agent or human may be editing the same Markdown/runtime file, register a proposal instead of overwriting the target:

./cortex propose-change md-os/ops/continuity.md "Clarify the next resume note"

MD-OS writes:

md-os/ops/changes/proposals.ndjson
md-os/ops/changes/proposals/<change_id>.json

The proposal includes the target file hash, writer id, summary, timestamp, and a policy marker that says the target should not be mutated without review.

Work item states

Compiled work items use a formal state machine:

open
planned
running
waiting_external
blocked
done
failed
cancelled

done and cancelled are terminal states. Active agendas exclude terminal items, while archive views keep terminal work inspectable without deleting the canonical work_items.ndjson.

Archive and compaction

Build the hot active summary and non-destructive terminal archive:

./cortex compact

Outputs:

md-os/ops/summary/active_work_items.md
md-os/ops/archive/projects/<project_id>/terminal_summary.md

This keeps the fast read path small for LLM hosts while preserving replayable project state on disk.

Using it with Codex

Codex is a verified host-compatibility path for this 5.0 release; it is not the identity. MD-OS (Artificial Prefrontal Cortex) v5.0 is the persistent agent and control plane it operates.

OpenCode and other coding-agent CLIs may operate the same filesystem layer, but they are secondary compatibility targets. Treat them as less compatible than Codex until they have verified support for the same command semantics, permission behavior, continuity, and md-os/ops/ readback.

A host runtime should:

1. Open this repository as its working directory.
2. Read AGENTS.md, ME.md, md-os/kb/COGNITIVE_BOOTSTRAP.md, md-os/kb/README.md, and md-os/kb/OPERATIONS.md.
3. Establish the MD-OS (Artificial Prefrontal Cortex) v5.0 identity frame before ordinary work.
4. Treat md-os/ as the active operational boundary.
5. Read current state from md-os/ops/.
6. Write new observations as source snapshots under md-os/ops/sources/.
7. Run deterministic scripts from md-os/os/.
8. Report back using the rebuilt Markdown and JSON state.

cortex is the Cortex-derived agentic-shell path: MD-OS owns the persistent REPL and executes already-valid native input without a model call; natural language enters the complete Codex agent loop bound to the current workspace. The shell starts fresh by default, resumes an existing native Codex thread only after explicit /resume, preserves repository instruction discovery, tools, sandboxing, approvals, and verification, and queues native command/output readback as bounded sensory context. The existing shell mechanics are preserved; Ollama is not used. See docs/SEMANTIC_SHELL.md.

More detail: docs/HOST_RUNTIME_INTEGRATION.md

Traditional host runtime vs MD-OS

A traditional host runtime such as Codex provides:

• model access
• session-local reasoning
• tool invocation
• edits and command execution

MD-OS (Artificial Prefrontal Cortex) v5.0 provides:

• persistent operating memory on disk
• readable operating knowledge in Markdown
• bounded connector contracts
• deterministic rebuild scripts
• replayable continuity across sessions
• runtime-compiled context packs, claim status, capability gates, and epistemic health

In short:

Direct shell:  the human operates the computer.
Cortex:        MD-OS binds the real shell to workspace-native Codex loops.
MD-OS:         persistent agent, control plane, and Operating Filesystem.

The host can change. The MD-OS state, rules, and continuity model remain.

The cognitive bootstrap is canonicalized in md-os/kb/COGNITIVE_BOOTSTRAP.md. It instructs external hosts to answer identity and operating-model questions from the MD-OS 5.0 frame first, while still distinguishing the active host runtime as the execution layer when one is relevant.

Terminal connector example

List allowlisted terminal commands:

npm run connector:terminal:list

Run the demo command:

npm run connector:terminal:run:node-version

The terminal connector writes:

• a normalized connector snapshot in md-os/ops/sources/connectors/
• a raw command artifact in md-os/ops/artifacts/terminal/
• a journal event in md-os/ops/journal.ndjson

Then rebuild the affected project:

node md-os/os/build_project_state.js demo_general_system

More detail: docs/TERMINAL_CONNECTOR.md

API connector example

List allowlisted API requests:

npm run connector:api:list

Run a configured request:

./cortex connector api run <project_id> <request_id>

The API connector writes the same kind of normalized source snapshot as the terminal connector, so builders do not need to care whether a signal came from HTTP, a terminal, or another substrate.

More detail: docs/API_CONNECTOR.md

Default Graphify orientation

Graphify is the default graph-aware orientation layer for MD-OS repository navigation. It is used to refresh the local structural graph, route work through bounded context, reduce token load, and let the graph evolve as files, connectors, schemas, audit artifacts, and knowledge nodes change.

Bootstrap the Graphify connector and build the native topology maps:

./cortex graphify bootstrap
./cortex graphify build .
./cortex graphify connector-map
./cortex graphify neural-map

Ask for a bounded routing pack before reading large parts of the repository:

./cortex graphify orient "agentic task scheduling and verification"

The orientation pack reads the structural Graphify graph, the semantic neural map, and the sanitized connector topology. It routes a question to the smallest relevant set of models, schemas, executors, tests, and readback surfaces instead of falling back to broad repository search.

MCP server adapter

MD-OS APFC can be exposed to MCP-compatible hosts as a stdio server:

npm run mcp:server

The server exposes read-only MD-OS resources and bounded deterministic tools over stdio. It does not bundle a browser-based document editor or a local document-storage API.

This is an adapter over the existing text-native OS. The core state model remains the filesystem under md-os/.

More detail: docs/MCP_SERVER.md

Host setup examples: docs/MCP_CLIENT_SETUP.md

Terminology guardrail:

md-os/ path != MCP protocol
MCP adapter != all connectors
connector != operating boundary

Live mode

MD-OS can also run an optional local live mode:

npm run live:start
npm run live:status
npm run live:stop

Equivalent CLI:

./cortex live start
./cortex live status
./cortex live stop

Live mode keeps a heartbeat and scheduled rebuild loop under md-os/ops/services/. It is an operational presence mechanism, not a claim of sentience: turning it on makes MD-OS interactive and continuously maintained; turning it off leaves readable state available for ordinary resume and replay.

In Codex or another host chat, “turn live mode on” should map to ./cortex live start; “turn live mode off” should map to ./cortex live stop.

Hardware bootstrap

MD-OS can run a read-only hardware substrate bootstrap:

./cortex hardware bootstrap

Alias:

./cortex device discover

This produces a boot-style scan and writes:

md-os/ops/local/hardware/device_registry.json
md-os/ops/local/hardware/inventory.md
md-os/ops/local/hardware/capabilities.md
md-os/ops/local/hardware/bootstrap_report.md
md-os/ops/local/hardware/observations.ndjson

This directory is host-local, machine-specific, and safe to delete before packaging, copying, or distributing an MD-OS workspace:

./cortex hardware clean

Cleaning also refreshes derived runtime views so stale hardware summaries are removed from generated indices, and scrubs hardware scan events from the local journal.

The bootstrap only discovers host-exposed hardware surfaces. It does not open camera streams, record audio, print, change volume, or write to serial/GPIO.

Explicit hardware control is a separate layer:

./cortex hardware list
./cortex hardware run "turn up the volume"
./cortex audio volume up
./cortex audio volume down
./cortex audio volume zero
./cortex audio speak "<text>"
./cortex screen capture
./cortex display status
./cortex display brightness set 80

./cortex audio speak uses the system voice path by default. The system voice path now prefers a more intelligible Italian synthesis voice (Italian+sandro) with a slightly slower rate. Set MDOS_AUDIO_SPEAK_VOICE=tts to prefer gTTS, MDOS_AUDIO_SPEAK_VOICE=kokoro to use local Kokoro-ONNX, and MDOS_AUDIO_SPEAK_VOICE=say to force speech-dispatcher.

Optional voice stack

The voice and speech-to-text helpers are optional. The zero-dependency Node.js core does not require Python audio packages.

Optional manifests:

• requirements-stt.txt: local microphone capture and Whisper-based STT
• requirements-tts.txt: gTTS and Kokoro-based speech output

Example setup:

python3 -m venv .venv-stt
.venv-stt/bin/pip install -r requirements-stt.txt
python3 -m venv .venv-tts
.venv-tts/bin/pip install -r requirements-tts.txt

Notes:

• PyAudio may require PortAudio development packages on Linux.
• ffmpeg is required for the current local audio helpers.
• Kokoro model files are not downloaded implicitly during normal use. Set MDOS_KOKORO_AUTO_DOWNLOAD=1 only when you explicitly want the helper to fetch them.

Control actions use host-exposed tools and write local audit records under md-os/ops/local/hardware/actions.ndjson. Input artifacts such as desktop captures are stored under the same cleanable host-local hardware cache. Robot arms, legs, grippers, wheels, and other actuators follow the same pattern but require dedicated connectors, hard limits, stop/emergency-stop behavior, and an external safety/runtime layer.

Software bootstrap

MD-OS can run a read-only software substrate bootstrap for installed applications and host services:

./cortex software bootstrap

Aliases:

./cortex apps discover
./cortex services discover

This writes:

md-os/ops/local/software/software_registry.json
md-os/ops/local/software/applications.json
md-os/ops/local/software/services.json
md-os/ops/local/software/applications.md
md-os/ops/local/software/services.md
md-os/ops/local/software/capabilities.md
md-os/ops/local/software/bootstrap_report.md
md-os/ops/local/software/observations.ndjson

This directory is host-local, machine-specific, and safe to delete before packaging, copying, or distributing an MD-OS workspace:

./cortex software clean

The bootstrap only discovers host-exposed software surfaces. It does not launch applications, inspect windows, start services, stop services, restart services, install packages, remove packages, or kill processes.

Main folders

AGENTS.md                         Stable guardrails and bootstrap rules
ME.md                             Agent self-definition
README.md                         Public quickstart
docs/                             Human-facing usage and architecture docs
md-os/kb/                           Stable knowledge base and cognitive bootstrap
md-os/os/                           Deterministic runtime scripts and helpers
md-os/ops/                          Local persistent runtime state and memory
md-os/ops/archive/                  Non-destructive terminal work-item archive views
md-os/ops/changes/                  Append-only change proposals
md-os/ops/connectors/               Local connector registry and connector configs
md-os/ops/local/hardware/           Cleanable host-local hardware inventory cache
md-os/ops/local/software/           Cleanable host-local app and service cache
md-os/ops/services/                 Optional service heartbeat, PID, stop, and log files
md-os/ops/summary/                  Hot active-work summaries for low-context reads
md-os/ops/sources/                  Local manual and connector source snapshots
md-os/ops/projects/                 Local compiled per-project state
md-os/examples/                     Versioned templates and demo seeds

Documentation map

• docs/ONBOARDING.md: start here if you are new.
• docs/POPULAR_PRESENTATION.md: accessible introduction from MS-DOS batch files to MD-OS semantic Markdown programs, including the new-hire desk/workload scenario.
• docs/ARCHITECTURE.md: current cognitive-agent status, role boundaries, control path, bounded autonomy envelope, system model, and data flow.
• docs/md-os-architecture-schema.svg: at-a-glance architecture schema.
• docs/PRODUCTION_LIMITS.md: production limits, risk boundaries, and mitigation roadmap.
• md-os/kb/ROBOTIC_AGENTIC_PROGRAMMING_MODEL.md: natural-language programming model for complex agentic and robotic ecosystems.
• md-os/kb/PREDELIBERATIVE_AFFECT_MODEL.md: instinctive pre-deliberative emotion, natural affect-category continuity, identity-continuity appraisal, bounded self-preservation, causal tests, and the separate unresolved phenomenal boundary.
• md-os/kb/MARKDOWN_GRAPH_MODEL.md: Obsidian-oriented graph model for logical and structural Markdown links.
• docs/papers/zenodo/README.md: reproducible definitive manuscript package, figures, manifest, and build commands for “Markdown Operating System for Robotic Agents (MD-OS CORTEX): Artificial Prefrontal Cortex and a Verifiable Operational Paradigm Toward General Intelligence”.
• docs/papers/zenodo/paper.pdf: the single definitive PDF, built from docs/papers/zenodo/paper.tex and linked to the official ciaoidea/MD-OS repository.
• docs/papers/zenodo/MD-OS_CORTEX_Zenodo.zip: upload-ready Zenodo package containing the LaTeX source, bibliography, figures, manifest, and definitive PDF.
• docs/NATURAL_LANGUAGE_AGENTIC_SUBSTRATE_LAYER.md: explicit layer between MD-OS and OS, hardware, applications, services, and robots.
• docs/AGI_PARADIGM.md: philosophical background on long-horizon agent systems and robotics.
• docs/HOST_RUNTIME_INTEGRATION.md: how Codex or another host should operate the repo.
• docs/EXAMPLE_WORKFLOW.md: complete signal to rebuilt state walkthrough.
• docs/CONNECTORS.md: how to add a connector.
• docs/TERMINAL_CONNECTOR.md: concrete bounded terminal connector.
• docs/API_CONNECTOR.md: concrete bounded HTTP API connector.
• docs/MCP_SERVER.md: Model Context Protocol adapter for MCP-compatible hosts.
• docs/MCP_CLIENT_SETUP.md: generic MCP host configuration and verification path.
• docs/LAUNCH_DEMO.md: split-screen launch demo storyboard.
• docs/STANDALONE_AGENT_LOOP.md: minimal host-runtime loop without Codex.
• docs/PUBLISHING.md: checks before publishing.

Canonical commands

npm run build:all
npm run init:demo
npm run compile:programs
npm run replay
npm run verify
npm run build:project
npm run build:project:documents
npm run build:agenda
npm run build:index
npm run build:inventory
npm run build:graph
npm run graphify:status
npm run graphify:bootstrap
npm run graphify:connectors
npm run graphify:neural
npm run graphify:orient -- "agentic task scheduling and verification"
npm run build:hygiene
npm run build:health
npm run hardware:bootstrap
npm run hardware:list
npm run hardware:clean
npm run software:bootstrap
npm run software:list
npm run software:clean
npm run audio:status
npm run audio:volume:up
npm run audio:volume:down
npm run audio:volume:zero
npm run audio:speak
npm run display:status
npm run screen:capture
npm run device:discover
npm run device:clean
npm run live:status
npm run live:start
npm run live:stop
npm run continuity:status
npm run continuity:start
npm run continuity:stop
npm run role:intake -- <role_id>
npm run role:sensemake -- <role_id>
npm run connector:terminal:list
npm run connector:terminal:run:node-version
npm run connector:api:list
npm run mcp:server
npm run mcp:server:list-tools

Equivalent direct commands:

node md-os/os/initialize_ops_memory.js
node md-os/os/initialize_demo_ops.js
node md-os/os/compile_programs.js
node md-os/os/mdos.js replay
node md-os/os/build_project_state.js <project_id>
node md-os/os/build_global_agenda.js
node md-os/os/build_global_index.js
node md-os/os/build_workspace_inventory.js
node md-os/os/build_markdown_graph.js
node md-os/os/mdos.js graphify status
node md-os/os/mdos.js graphify bootstrap
node md-os/os/mdos.js graphify connector-map
node md-os/os/mdos.js graphify neural-map
node md-os/os/mdos.js graphify orient "agentic task scheduling and verification"
node md-os/os/build_runtime_lifecycle_index.js
node md-os/os/build_system_hygiene_status.js
node md-os/os/build_health_classifier.js
node md-os/os/build_health_dashboard.js
node md-os/os/build_role_intake.js <role_id>
node md-os/os/build_role_sensemaking.js <role_id>
node md-os/os/hardware_bootstrap.js
node md-os/os/hardware_control.js list
node md-os/os/hardware_control.js audio volume up
node md-os/os/mdos.js audio speak "<text>"
node md-os/os/hardware_control.js screen capture
node md-os/os/hardware_bootstrap.js clean
node md-os/os/software_bootstrap.js
node md-os/os/software_bootstrap.js list
node md-os/os/software_bootstrap.js clean
node md-os/os/continuity_service.js status
node md-os/os/continuity_service.js start
node md-os/os/continuity_service.js stop
node md-os/os/terminal_connector.js list
node md-os/os/terminal_connector.js run <project_id> <command_id>
node md-os/os/api_connector.js list
node md-os/os/api_connector.js run <project_id> <request_id>
node md-os/os/mcp_server.js

Installed CLI equivalents:

./cortex init
./cortex init my-agent-os
./cortex demo
./cortex compile-programs
./cortex signal <project_id> "Signal summary"
./cortex build <project_id>
./cortex build all
./cortex graph build
./cortex graphify status
./cortex graphify bootstrap
./cortex graphify build .
./cortex graphify connector-map
./cortex graphify neural-map
./cortex graphify orient "agentic task scheduling and verification"
./cortex hardware bootstrap
./cortex hardware list
./cortex hardware run "turn up the volume"
./cortex hardware clean
./cortex device discover
./cortex device clean
./cortex software bootstrap
./cortex software list
./cortex software clean
./cortex apps discover
./cortex services discover
./cortex paths
./cortex audio volume up
./cortex audio volume down
./cortex audio volume zero
./cortex audio speak "<text>"
./cortex screen capture
./cortex live status
./cortex live start
./cortex live stop
./cortex continuity status
./cortex continuity start
./cortex continuity stop
./cortex role intake <role_id>
./cortex role sensemake <role_id>
./cortex connector list
./cortex connector run <project_id> <command_id>
./cortex connector api list
./cortex connector api run <project_id> <request_id>
./cortex mcp-server
./cortex replay
./cortex hygiene
./cortex audit

Current demo projects

• demo_general_system: demonstrates generic runtime and connector operations.
• demo_document_approval_flow: demonstrates document-oriented operational signals without making the core system document-specific.

Safety model

The repository is designed around bounded execution:

• md-os/ is the only active operational boundary.
• Connectors are explicit and registered.
• The terminal connector runs only configured command IDs, not arbitrary shell strings.
• Builders derive canonical state from source snapshots.
• Runtime writes use filesystem coordination helpers.
• Human-readable files remain the source of inspection and correction.

Production safety status

Current status: early reference implementation of a Markdown-native Operating Filesystem, not a kernel.

Safe:

• bounded command allowlist
• safe ID validation for runtime IDs used in paths
• terminal cwd containment inside the workspace
• atomic writes and filesystem locks
• readable runtime state
• minimal node:test coverage

Not yet production-grade:

• schema validation is intentionally minimal and hand-written
• no full secret scanner
• local md-os/ops/ runtime state is excluded from the npm package surface
• host-specific hardware inventory is isolated under cleanable md-os/ops/local/hardware/
• host-specific application and service inventory is isolated under cleanable md-os/ops/local/software/
• explicit Codex flags or configuration overrides still require operator review

Release hygiene commands:

npm run clean:release
npm run verify:release
npm run package:demo

Verified local distribution snapshot

The current B10 local distribution candidate was reverified on 24 August 2026. This is repository readback, not proof that an external Git or Zenodo record has already been updated.

• 298/298 Node tests and 60/60 shell-parity tests passed; the focused pre-deliberative-affect, cognitive-runtime, and semantic-gate subset passed 17/17.
• Runtime, compiler, APFC, semantic-integrity, publication, and security readbacks are ok.
• runtime_operable is true, publishable is true, and release_blocked is false.
• Replay reached a fixed point with matched_before: true; its current hash-bound receipt is generated at md-os/ops/replay_report.json and remains outside the publication package.
• The actual npm package contained 569 files, with no unexpected operational state, LaTeX auxiliary files, private host path, or private-key header.

Overall health remains attention because exploratory AGI evidence and local hygiene findings remain visible but non-blocking. Exploratory findings cannot promote a claim or skill. Critical states, regressions, and failed checks explicitly marked release_required: true block release.

MD-OS/APFC is explicitly designed for persistent, verification-bound operational learning in the real world, where situations may be novel, observations incomplete, outcomes uncertain, and actions consequential. Current evidence validates the architecture and its bounded controlled mechanisms; it does not yet establish general open-world learning or AGI. Independent, longitudinal evaluation of the complete learning cycle under real-world open conditions is the next empirical step.

Research and citation

MD-OS was originally created by Alessandro Rizzo. The architecture is described in the paper:

Markdown Operating System for Robotic Agents (MD-OS CORTEX): Artificial Prefrontal Cortex and a Verifiable Operational Paradigm Toward General Intelligence — Alessandro Rizzo

• 🌐 Website: www.md-os.org
• ✉️ Email: labs@md-os.org
• 📄 Zenodo paper: record 21960027
• 🧑‍🔬 ORCID: 0000-0002-8030-3540
• ▶️ Official YouTube channel: MD-OS on YouTube
• 💻 Official repository: ciaoidea/MD-OS

License

MD-OS is distributed under the GNU General Public License version 2 only (GPL-2.0-only).

Copyright (C) 2026 Alessandro Rizzo and MD-OS contributors

The original creator is Alessandro Rizzo. Contributors retain copyright in their own contributions unless a separate written agreement applies.

• LICENSE: application notice and complete GPLv2 text
• Licensing and attribution: scope, generated output, third-party material, connectors, and the historical MIT transition
• Authors: creator and contributor ownership
• Contributing: DCO 1.1 and Signed-off-by
• Governance: official mainline and maintainer authority
• Project names and marks: truthful naming without reducing GPL freedoms
• Citation metadata: preferred project citation

Historical copies legitimately received under the previous MIT license retain the permissions attached to those copies. Later GPL-covered changes are not automatically available under those historical MIT terms.

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Talent solves within a frame; general intelligence transforms both the frame and the solver, while persistent operational context preserves the verified invariants—and remains free.

Unity is not uniformity: differentiated cognitive parts know and act together through one persistent informational field.

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