The work OS for human/AI organizations.

WG stands for works good.

Agents can come and go. The graph remains.

WG records what needs doing, who or what claimed it, what blocked it, what evidence was produced, where judgment entered, what failed, what was retried, and how the work changed over time.

Launch the operating surface:

wg tui

Most AI systems center the agent. WG centers the work.

AI can generate more work than humans can inspect.

WG exists because the hard problem is no longer only execution. It is knowing what was done, what failed, what evidence exists, where judgment entered, and how the organization should respond.

Generation, evidence, validation, repair, and human judgment stay in the same durable structure — so judgment can catch up to generation instead of being flattened by it.

• Persistent task graph — tasks, dependencies, status, and metadata stored as plain JSONL on disk. Git-friendly, human-readable, easy to inspect.
• Claims and handoffs — any agent (human or AI) can claim work; if it dies, another can pick up from where it left off.
• Execution history — every state transition, log line, and message is recorded. Nothing important is lost when a process exits.
• Evidence and artifacts — files produced by tasks are tracked alongside the tasks themselves, so downstream work can find the inputs it needs.
• Human judgment points — verification, approval, and rejection are first-class operations, not afterthoughts.
• Agent continuity — composable identities (role + tradeoff) outlive the individual processes that embody them, and improve via feedback over time.

WG is not primarily a chatbot, an agent benchmark harness, a project-management app, or an agent orchestration framework (LangGraph, CrewAI, AutoGen).

Those categories center messages, scores, tickets, or agents.

WG centers answerable work: tasks with dependencies, claims, evidence, validation, failures, handoffs, artifacts, and history.

WG was not designed by starting with agents and adding orchestration.

It started from a theory of organizations: work needs decomposition, dependency, role, motivation, coordination, evaluation, memory, and adaptation.

The implementation maps those organizational primitives into a working system. Read the theory — it is foundational, not optional, reading.

Poietic PBC was formed, organized, and grant-funded through WG. These are not demos. They are public traces of real institutional work:

• Company formation — incorporation, structure, governance
• Grant drafting and submission — the grant referenced on poietic.life was drafted, edited, and submitted through the graph
• Scientific analysis — research coordination and findings
• Website and theory development — the Poietic mission site, the WG theory pages, even copy edits to this repo

The company is not a wrapper around the product. The company is an output of the product.

cargo install --git https://github.com/graphwork/wg
wg init
wg tui

wg init --route claude-cli                                  # Claude (default)
wg init --route codex-cli                                   # Codex
wg init -m nex:qwen3-coder -e https://your-endpoint:8080    # any OpenAI-compatible

wg service start
wg tui

The loop: declare work, let the service dispatch it, watch the graph evolve.

1. Read the Poietic mission: why legible human/AI collaboration matters.
2. Inspect a public graph: incorporation, grant writing, research, or this website's own development.
3. Read the theory: how tasks, roles, evaluations, traces, and evolution form a cybernetic organization.
4. Install WG only after you understand the system it instantiates.

Everything lives in .wg/:

.wg/
  graph.jsonl         # task graph (one JSON object per line)
  config.toml         # configuration
  agency/             # roles, tradeoffs, agents, evaluations
  service/            # runtime state (daemon PID, registry, logs)
  functions/          # workflow templates

Plain text. Diffable. Inspectable without the tool. If wg disappeared tomorrow, the work would still be there.

• docs/GUIDE.md — operator manual: configuration, the service, agent management, models, TUI, troubleshooting, AI assistants
• docs/AGENT-GUIDE.md — how agents should use WG
• docs/AGENT-SERVICE.md — service architecture and coordinator lifecycle
• docs/AGENCY.md — agency system: roles, tradeoffs, evaluation, evolution, federation
• docs/COMMANDS.md — full command reference
• docs/LOGGING.md — provenance and the operations log
• docs/WORKTREE-ISOLATION.md — how parallel agents avoid file conflicts
• docs/DEV.md — developer notes
• docs/KEY_DOCS.md — full documentation index

Watch the organization think.

MIT