AXIOM

AXIOM is a local-first CLI for answering the questions that come before a local AI job:

• What is in this model directory?
• Is this JSONL dataset structurally usable?
• What hardware is available on this machine?
• What is a reasonable starting plan?

It inspects local files, keeps small amounts of project metadata, reports what is available on the current machine, and suggests the next command. The terminal is the product. The optional daemon exposes the same local context as small JSON responses on a loopback address.

Project configuration lives in axiom.yaml; the local model registry lives in .axiom. Core inspection and planning do not require an AXIOM account.

AXIOM does not execute training, run model inference, or provide a hosted workspace. Estimates and plans are labeled as estimates and plans; they are not benchmark results.

Start in 60 seconds · Install · Website · Contributing

Start in 60 seconds

After installation, run:

axiom --help
axiom guide
axiom summary

For the interactive entry point, run bare axiom. In an interactive terminal it prints the startup mark once for that terminal session, shows the useful command path, starts the local daemon on a free port, and stays attached until Ctrl-C. axiom --help, axiom guide, and axiom summary do not start the daemon.

From a new project directory:

axiom init my-ai
cd my-ai
axiom project validate
axiom config validate

Then use the command that answers your next question:

Question	Command	What it tells you
What is this local model?	axiom model inspect ./models/my-model	Format, files, architecture hints, weight size, and conservative fit information.
What model metadata is published?	axiom model info org/model	Hugging Face metadata without downloading weights. Network access is required.
Is this dataset valid JSONL?	axiom dataset validate ./data/train.jsonl	Invalid records and duplicate objects; exits non-zero when records are invalid.
How large is the dataset?	axiom dataset stats ./data/train.jsonl	Record, field, and rough token counts.
Can I make a cleaned copy?	axiom dataset clean ./data/train.jsonl ./data/train.cleaned.jsonl	Writes a new JSONL file and leaves the source unchanged.
What hardware is present?	axiom system info	OS, architecture, CPU, RAM, GPU, VRAM, and CUDA availability.
What should I try first?	axiom train plan 7 --method qlora	A hardware-aware starting plan; it does not start training.
Which developer tools are visible?	axiom tools doctor	Executable presence and credential-reference presence, not vendor authentication.

Install

PyPI

The beta package installs the axiom command from PyPI:

python -m pip install --upgrade --pre axiom-all
axiom version

axiom-all is the distribution name; the installed command is axiom. Stable releases can be installed without --pre once one is published.

macOS and Linux

curl -fsSL https://raw.githubusercontent.com/NetCore-Technologies/AXIOM-AI/main/installers/install.sh | bash
axiom version

The installer puts the command in ~/.local/bin/axiom.

• Linux x86_64 uses the latest published linux-x64 tarball and verifies its entry in SHA256SUMS before activation. The unpacked bundle is the faster repeated-start path.
• macOS and non-x86 Linux use an isolated Python environment and install from the repository's main branch. This fallback requires Python 3.11+ and is a source install, not a pinned native binary.

If ~/.local/bin is not on PATH, the installer prints the export command. Open a new shell after adding it.

Windows PowerShell

irm https://raw.githubusercontent.com/NetCore-Technologies/AXIOM-AI/main/installers/install.ps1 | iex

The installer downloads the latest non-draft Windows x64 release to %LOCALAPPDATA%\AXIOM, adds that directory to the user PATH, and asks you to restart PowerShell. Verify with:

axiom version

Check the Releases page for the exact assets and checksums available for a release. Beta.7 notes are preparation notes until the matching release workflow and assets are verified.

The daemon boundary

Run the daemon by itself when a script or local integration needs HTTP:

axiom daemon --host 127.0.0.1 --port 0

Port 0 asks the operating system for an available port. AXIOM prints the resulting loopback URL, such as http://127.0.0.1:53142. While that process is running, the daemon provides read-only JSON routes:

GET /health
GET /api/info
GET /api/summary
GET /api/actions
GET /api/hardware
GET /api/tools

The default bind is local-only. A non-loopback bind is rejected unless --allow-network is supplied deliberately. The daemon is not an account service, hosted AXIOM backend, or general inference server. Stop it with Ctrl-C.

Web UI

The release packages and the PyPI wheel bundle the web UI. It includes the Optimizer and the Quantization Lab, which compares FP16, INT8 and INT4 size estimates against the memory AXIOM detects. Plans are estimates, not benchmark results.

Commands by job

Project and environment

axiom version
axiom guide
axiom summary
axiom summary --json
axiom info
axiom status
axiom doctor

guide explains the current directory and suggests safe next commands. summary returns a read-only snapshot of the project, hardware, visible tools, and one recommendation; --json is intended for scripts. doctor checks common executables and AXIOM package metadata.

Models

axiom model list
axiom model add my-model local safetensors
axiom model inspect ./models/my-model
axiom model search llama --path models
axiom model info org/model
axiom model analyze org/model
axiom model pull org/model

The first three local commands do not need network access. Hugging Face metadata and pull commands do; private or gated repositories also need axiom hf login. model pull performs disk checks before downloading.

Datasets

axiom dataset inspect ./data/train.jsonl
axiom dataset validate ./data/train.jsonl
axiom dataset stats ./data/train.jsonl
axiom dataset clean ./data/train.jsonl ./data/train.cleaned.jsonl

The current dataset path supports .jsonl. Validation checks JSON objects and duplicates; it does not establish semantic quality or training readiness.

Hardware and planning

axiom system info
axiom train plan 7 --method auto
axiom train plan 7 --method lora
axiom train plan 7 --method qlora
axiom train plan 7 --method full
axiom ai plan --model ./models/my-model

The planner uses detected hardware and conservative assumptions. Its fit and VRAM values are planning estimates, not measured speed, memory usage, or a promise that a later runtime will succeed.

Developer tools

axiom tools list
axiom tools list --json
axiom tools doctor
axiom tools plan opencode
axiom tools install opencode       # preview only
axiom tools install opencode --yes # explicit package-manager install

The catalog covers Codex, Claude Code, Antigravity CLI, GitHub Copilot CLI, Freebuff, Cursor Agent, free-pi, OpenCode, Gemini, OpenRouter, z.ai GLM, and a keep-awake helper. tools list and tools doctor observe the current machine; they do not prove that a vendor account is authenticated. Installation is a preview unless --yes is supplied. AXIOM does not silently execute remote installer scripts or store API-key values.

Headroom is a separate, optional local context-compression proxy. If it is installed independently, its own command path is:

headroom doctor
headroom proxy
headroom dashboard

AXIOM's role is limited to discovering or explaining Headroom. It does not bundle, install, start, or supervise it; only the user starts the proxy or dashboard. Do not read the integration as a promise of savings or a measured result.

MCP

Start the local stdio server from an MCP client configuration:

axiom mcp serve

It exposes local model, dataset, hardware, training-plan, and optional SuperCompress tools as a process. It is separate from the HTTP daemon.

What is shipped, and what is not

Shipped in the beta

• A Python package and Typer CLI with a one-command local entry point.
• Project scaffolding, configuration checks, and local status/summary output.
• Model registration, local inspection, Hugging Face metadata analysis, and disk-checked model pulls.
• JSONL inspection, validation, duplicate detection, statistics, and cleaning.
• Hardware detection and hardware-aware LoRA, QLoRA, and full-training plans.
• A read-only loopback daemon with health, summary, action, hardware, and tool routes.
• MCP tools and an opt-in developer-tool catalog with preview-first installs.
• A static landing page that explains the terminal workflow.

Not shipped by this product boundary

• Training execution, job management, checkpoints, or experiment tracking.
• Model inference, production serving, streaming, batching, or an OpenAI-compatible server.
• Live telemetry, background monitoring, hosted workspaces, accounts, or a browser control center.

Repository shape

axiom/
├── cli/          Typer command surface
├── core/         Project, hardware, storage, and integration helpers
├── models/       Local registry, inspection, and metadata analysis
├── datasets/     JSONL inspection and cleaning
├── training/     Hardware-aware plan generation
├── optimizer/    Agent workload plans and runtime bundles
├── runtime/      Optional runtime integrations
├── daemon.py     Dependency-free loopback HTTP daemon
└── mcp/          Stdio MCP server and tools

The public website is intentionally static. Product work belongs in the CLI, local daemon, and reproducible project files.

Principles

• Local first: keep models, datasets, configuration, and first-pass decisions close to the machine that will run them.
• Evidence before execution: expose assumptions before a user spends time or compute.
• Explicit side effects: preview external tool setup and require opt-in for package installation.
• Honest boundaries: separate detections, estimates, plans, and measured results.

Contributing

AXIOM is in active beta development. Bug fixes, tests, documentation, and tooling improvements are welcome.

Read CONTRIBUTING.md before making a larger change.

License

MIT. See LICENSE.

Built by Manit Arora and Rangan V Balaji.

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