dadaia-workspace

AI-native workspace management for multi-agent, Spec-Driven Development.

dadaia-workspace gives AI coding agents a structured, governed shared workspace: scoped project contexts, a Spec-Driven Development (SDD) lifecycle with deterministic gates, a procedural multi-harness workflow engine (the dadaia-workflows), a persona-based agent roster with installable plugin packs, canonical agentic-asset projection across three AI harnesses, and a real-time monitoring panel.

It runs agents at two distinct layers (explained below) and supports Claude Code, Codex, and PI as peers. It is designed to be operated by humans and by agents: every capability is reachable through a discoverable CLI, and every state surface has a machine-readable form.

Open source under the MIT license. Source, issues, and contributions: github.com/marcoaureliomenezes/dadaia-workspace.

Install

pip install dadaia-workspace

Requires Python 3.12+.

Quick start

dadaia init                        # bootstrap .dadaia/ + project agent assets (.claude/, .codex/, .pi/)
dadaia init --harness claude,pi    # or scaffold only a subset of harnesses (harness profile)
dadaia doctor                      # health check: contexts, assets, gates, presence
dadaia panel                       # local dashboard (http://localhost:4999)

From there, agents self-discover the rest via dadaia --help. Every command group supports --help at every level.

The two agentic layers

The single most important concept in the architecture. "Harness" means a different thing at each layer — conflating them causes most confusion.

flowchart TB
    OP(["Operator in the terminal"])
    subgraph L1["LAYER 1 — entry harness (what you launch)"]
        direction LR
        CC["claude"]:::h
        CX["codex"]:::h
        PI["pi"]:::h
    end
    GOV["Governance: AGENTS.md read up-tree natively<br/>+ projected .claude/ .codex/ .pi/<br/>+ PreToolUse gate (where supported) + git chokepoints"]
    CLI["dadaia lifecycle &lt;verb&gt; --harness &lt;x&gt;<br/>(a procedural Python workflow)"]
    subgraph L2["LAYER 2 — worker harness (inside the workflow engine)"]
        direction LR
        FK["FAKE"]:::w
        CXk["CODEX_EXEC"]:::w
        CLk["CLAUDE_SDK"]:::w
        PIk["PI_HEADLESS"]:::w
    end
    OP --> L1 --> GOV
    L1 -->|"the harness calls the dadaia CLI"| CLI --> L2
    L2 -->|"git-diff write boundary + git chokepoints"| OUT(["production: code · specs · memory"])
    classDef h fill:#1f6feb,color:#fff,stroke:#1f6feb;
    classDef w fill:#238636,color:#fff,stroke:#238636;

• Layer 1 — the entry harness. The AI coding agent a human launches in the terminal: claude, codex, or pi. It is governed by the workspace-root AGENTS.md (read natively up the directory tree) plus the projected per-runtime asset trees (.claude/, .codex/, .pi/).
• Layer 2 — the worker harness. The bounded agent workers that dadaia lifecycle drives, one selectable per step, behind a single AgentRuntimePort. Four runtime kinds — FAKE, CODEX_EXEC, CLAUDE_SDK, PI_HEADLESS — over two transports: SDK (Claude, in-process) and CLI-headless (codex exec, pi --mode json).

A harness can exist at one layer and not the other (FAKE is Layer-2 only). PI exists at both: a .pi/ Layer-1 projection and a PI_HEADLESS Layer-2 worker.

Supported harnesses & harness profiles

Harness profiles. dadaia init --harness <set> scaffolds only the harnesses you use (persisted in .dadaia/states/harness_profile.json). dadaia public install and dadaia public doctor are profile-aware: a claude-only workspace projects and doctors only .claude/, and out-of-profile assets found on disk are surfaced, never silently ignored. With no profile, all harnesses are targeted (back-compatible).

All projections are generated from one canonical source at dadaia_workspace/public/ via dadaia public stage && dadaia public install. The Claude SDK and PI runtimes are optional, operator-installed externals — the build stays offline-first without them.

Spec Context Project

A Spec Context Project is the keystone unit: one canonical specs/ folder bound to one git repository, following the SDD lifecycle. Binding a session to a context triggers a value chain: bind → inject (the context's constitution.md + memory) → enforce (no production change without an approved release + reserved task) → parallel (each context carries exactly one MUTATING lease, so multiple contexts can be worked concurrently and safely).

dadaia context list              # all Spec Context Projects
dadaia context show --json       # active context (machine-readable; agent use)
dadaia context bind <name>       # bind the session (selects injected memory; refreshes incumbent)

Agent roster & personas

The workspace ships a 9-agent core roster plus 3 plugin agents (enabled by installing their pack — see the next section). Each agent is projected into every harness in the profile; project-manager is the Layer-1 coordinator.

Personas are the Layer-2 equivalent of Claude sub-agents: per-role behavioral mandates (8 non-PM roles) injected into every workflow step prompt alongside the step's instruction fragment, on every worker harness. The same role identity governs an agent whether it runs interactively in Layer 1 or headless in Layer 2.

Plugin packs

Plugin agents ship as inert stubs until their pack is installed:

dadaia plugin list                       # available packs + install state
dadaia plugin install frontend-design    # enable frontend-engineer + design-specialist
dadaia plugin install devops             # enable devops-engineer
dadaia plugin doctor                     # per-projected-file status of installed packs

Packs are distributed inside the package (no network fetch): real agent bodies plus curated skills, projected into the harness profile like any canonical asset. Installs are recorded in .dadaia/states/installed_plugins.json; a later core dadaia public install preserves installed pack bodies (it never regresses them to stubs), and plugin install is idempotent.

dadaia-workflows — the lifecycle engine

A workflow is procedural Python the dadaia CLI runs; each step drives a Layer-2 worker behind AgentRuntimePort. Python owns the state machine, gates, and hygiene — agents produce evidence, Python decides whether state advances. Every step prompt is assembled from its fragment (the step-specific instruction: inputs, task, output schema) plus its persona (the role's mandate), with role-scoped memory context injected from the bound Spec Context.

There are exactly four workflows — lean by design, so weaker/cheaper worker models produce reliable results from scoped, evidence-grounded prompts:

Durable step handoffs are registered in the Spec Context release folder (specs/releases/<id>/handoffs/, backlog runs in specs/backlog/handoffs/) — never in an opaque runtime path.

dadaia lifecycle implementation-reviews --release-id <id> \
    --harness codex --step-model review_combined=codex-review-deep
dadaia lifecycle release-definition --release-id <id> --resume-from plan_create

Model & harness governance. Which model and harness each workflow step uses is resolver-governed: an operator profile registry plus per-context overlays feed a single policy resolver; every run freezes its resolved policy snapshot onto the run record. Precedence: CLI flag > overlay > catalog default. The panel's Workflows tab renders each workflow's diagram with inline model pickers.

Development lifecycle phases

A release matures through explicit phases; the SDD gate keys off the active phase (specs/releases/ACTIVE.md):

flowchart LR
    D["DEFINITION<br/>SPEC · PLAN · TASKS · memory"] --> I["IMPLEMENTATION<br/>code · tests"]
    I --> R["REVIEWS<br/>QA → commit · security → push · code-review → PR"]
    R --> C["CLOSURE<br/>CLOSURE.md · memory · archive"]

• DEFINITION — author SPEC/PLAN/TASKS; memory (specs/memory/) is writable here.
• IMPLEMENTATION — production code + tests; concurrent sessions surface through advisory presence (no locks).
• REVIEWS — qa-engineer (commit gate), security-reviewer (push gate), code-reviewer (PR gate). Reviews are ADDITIVE evidence; they mature the release.
• CLOSURE — write CLOSURE.md, update memory truth, archive the release.

The SDD gate

Enforcement has two deterministic halves:

1. PreToolUse hook — one merged Python entrypoint (dadaia_workspace.hooks.pre_gate) reads each file-write tool call once and evaluates, first-block-wins: root-whitelist → venv-guard → SDD gate. The SDD gate decides by path-class × lease × memory-phase × mode (a single per-context TTL lease with a PID veto coordinates MUTATING writes). ADDITIVE paths — bugs, backlog, audits, reports, handoffs — always flow, for any agent, with no lease.
2. Git chokepoints (run as git hooks, independent of any harness hook): a pre-commit lease gate and a pre-push gate that runs dadaia ci preflight (ruff format/check + mypy --strict + import-linter + pytest) and requires an APPROVED security-reviewer verdict keyed to each pushed commit sha.

The gate reads no SDD artifacts — task markers ([-]), Aprovado status, and write-allowlists are agent/coordinator discipline, not gate mechanism. Hooks and chokepoints are installed by dadaia init / dadaia public install / dadaia ci install-hook.

Governance data stores

Everything an agent needs to reason about product state is a validated, queryable store — not prose:

• Bugs — event-sourced JSONL. dadaia bugs append --event reported ... appends a schema-validated event; a bug's stream later carries one terminal event (resolved/superseded/deferred/rejected). dadaia bugs status lists open bugs; dadaia bugs stats aggregates. Bugs are never silently dropped.
• Backlog — typed intents. Every entry declares (subject{kind,ref} → change) intents bound against an auto-derived anchor registry; dadaia backlog doctor fail-closed checks (BL-*) run in pre-commit and CI.
• Memory — current product truth. Curated Markdown atoms under specs/memory/ with a generated feature catalog (dadaia memory catalog generate). Memory is injected on context bind; it is writable only in DEFINITION/CLOSURE phases.
• Reports & handoffs. Agent output is a machine-readable handoff JSON (dadaia reports validate <file>); HTML reports are emitted only for humans.

CLI reference

dadaia [COMMAND] --help   # always works at every level

Asset projection pipeline

dadaia public stage                     # stage canonical assets into .dadaia/agentic/
dadaia public install --target all      # project to the harness profile (or all)
dadaia public install --target pi       # project to one runtime (claude|codex|pi|agents)
dadaia public doctor                    # drift detection: source → staging → projection

public doctor exits non-zero on drift and reports per-file [ok] / [drift] / [missing]. Consumer repos' hand-authored root AGENTS.md files are provenance- protected: only files carrying the generated banner are treated as lib-owned; anything else is reported [foreign] and never overwritten.

Monitoring panel

dadaia panel                            # http://localhost:4999

The panel is a local, loopback-bound dashboard (Host-header guarded, no auth on loopback). Tabs: Workflows (per-workflow diagram cards with inline model pickers), Projects (Spec Context Projects with clickable memory chips), Sessions cost dashboard, Reports, Academy, Servers (the dev server registry), and Games — four playable canvas games (Snake, Tetris, Pong, Breakout), each implemented end-to-end by a Layer-2 worker through the dadaia-workflows. It exposes a no-auth health probe for automated checks:

GET http://localhost:4999/health   →   {"status": "ok", "version": "<running version>"}

Agent self-discovery protocol

Agents can operate the workspace with no prior knowledge:

1.  dadaia --help                    → full command tree
2.  dadaia doctor                    → workspace health (what's wrong, what to fix)
3.  dadaia context show --json       → active Spec Context Project (machine-readable)
4.  dadaia public doctor             → agentic asset projection state
5.  GET http://localhost:4999/health → panel health probe

If dadaia doctor exits non-zero, run dadaia doctor --fix first.

Links

• PyPI: https://pypi.org/project/dadaia-workspace/
• GitHub: https://github.com/marcoaureliomenezes/dadaia-workspace
• Issues: https://github.com/marcoaureliomenezes/dadaia-workspace/issues