wonderland-ai 0.3.5

An identity-native multi-agent development system.

Wonderland

An identity-native multi-agent development system.

Generic AI agents perform roles. Identity-native agents inhabit them.

▶ See it in action

Building a Geocities — a tour of how Wonderland works. One vague directive. $2.05. 7 minutes 38 seconds. The team of ten characters produced 1841 lines of code (auth, per-user pages, Markdown rendering, GDPR-deletion path, session management) plus 1253 lines of tests written before the implementation existed — and the team's reviewer caught three real bugs in the result with file paths and line numbers. The artifact tree is a v1 scaffold, not a deployable; the framework's process is what shipped.

If you're trying to figure out what this project actually is, start there.

Wonderland is a cast of agents — each named after an Alice-in-Wonderland character — that collaborate on software development tasks. The Cheshire Cat is the architect. The White Rabbit is the project manager. The Mad Hatter is QA. Every character has a stable self-model (a "constitution"), persistent per-agent memory, and a working relationship with the others.

The architectural claim is that identity does real work. An agent with a constitution it inhabits across many threads behaves differently from an agent reconstructed from a system prompt each turn. It accumulates judgment. It develops calibrated views of its colleagues. It refuses to cross domain boundaries because the boundary is part of who it is, not a policy applied from outside. Whether that compounds into measurably better outcomes than a generic-agents baseline is what the eval harness in P7 will measure; until then, the analyses/ directory tracks the qualitative observations as the system gets built out.

Five corollaries follow from this, each anchored in field analyses:

1. Identity-based architecture lets smaller models outperform their expected capabilities. The constitution carries the discipline a generic agent would have to invent turn-by-turn, so a small model acting in character can hold its own against a large model with a generic prompt. Default target is Claude Haiku 4.5; the falsifier is P7's generic-baseline-vs-identity-native eval. (analysis 004)
2. Failure modes are part of identity. Each constitution's §VIII names the specific shadow each virtue decays into — the Sephirah/Qlipha pairing, where the corruption is structural rather than additive. Agents course-correct from inside instead of waiting for an external guardrail. (analysis 004)
3. Character-shaped agents degrade visibly rather than silently when parts of the system fail. When the bus dropped feature-composition artifacts mid-run, the Tweedles noticed the contradiction and reached for the disk-resident artifacts via their tools — recovery was emergent, not designed. (analysis 027)
4. The team produces a small-team shape, including things the directive never asked for — ADRs with named tradeoffs, persona-grounded specs, accessibility coverage that wasn't requested. Production shape as a derived property of constitutional grounding. (analyses 034, 035)
5. Friction is the substrate, not the inefficiency. Every meeting in the workflow is engineered friction with a specific shape; §VIII puts that friction inside each constitution. Generic stacks have nothing analogous because they have roles, not characters; goals, not voices; consensus, not constitutions.

Full argument with the per-corollary mechanics in THESIS.md.

The framing the project is building around: failures are how software gets built. The iterative cycle of ship-then-discover-then-fix depends on recognizing what went wrong; agents whose failure modes are part of their identity can participate in that cycle as colleagues, not as tools that need supervising out of their bad habits.

Status

In-progress, building in public. P1–P6 complete; P8 (operator interface) shipping in sub-phases — three of five done as of 0.1.0, two remain. P7 (evals) is the final post-P8 phase.

• P1 — Foundation (overview) Schema, Caucus event bus, episodic memory, agent base class.
• P2 — First Voice (analysis 001) The Cheshire Cat comes online and produces ADRs in his own voice.
• P3 — First Tension (analysis 002) White Rabbit joins the bus. Semantic + relational memory layers. Compaction-as-agent-behavior — agents reflect between threads.
• P4 — First Race (analysis 003 · analysis 004) Alice agent. Dodo orchestrator with quiescence detection. Conflict resolution, composition, and human-in-the-loop escalation. Showcase 1: a /health endpoint directive runs end-to-end against live Haiku 4.5, reaching settlement because the team goes quiet — no human in the loop.
• P5 — Full Cast The remaining six characters land: Mad Hatter (test scenarios), Caterpillar (code review), Queen of Hearts (security/compliance rulings), Dormouse (production observations), and the Tweedles (frontend + backend implementation, with a shared pair protocol). wonderland init CLI ships the .wonderland/ skeleton. Synthetic- consensus guard observes the bus for the §11 anti-pattern — three or more distinct constitutional domains converging on the same position is suspect, and the guard surfaces it for review.
• P6 — Real Threads The hard showcases plus the substrate maturation that made them work. Showcase 2: translation chat MVP (analyses 015–018) — 1580 lines shipped from a vague directive in $0.93. Showcase 3: security recovery (analysis 019) — reactive response to a synthesized credential-stuffing incident; the framework's first non-greenfield run. Showcase 4: multi-session persistence (analyses 020–023) — memory compounding across sessions, plus the substrate fixes that closed the "Tweedles don't ship" bug class (turn-based quiescence replacing wall-clock, parse-retry recovering malformed LLM responses, working-tree-as-implementation-artifact). Workflow-as- data extraction (analysis 024): meeting chains live in closet/workflows/ as YAML; the TDD variant becomes the recommended default for feature work, with canonical retained for fast iteration when directive drift is unlikely. The pair protocol pays off in analysis 025: Alice and the Mad Hatter are paired in M4 (the tea party — they're already paired in the source material) so Alice writes user-journey scenarios and Hatter writes failure-mode scenarios, each producing both the artifact and a runnable pytest file. The Geocities showcase ships 1841 lines from a vague directive in 7m 38s for $2.05, with Caterpillar catching three production-blocking bugs in the diff — the framework's bug-discovery surface working as designed. See SHOWCASE.md for the friend-facing tour and analyses/ for the full build log of the iteration that got the framework here.
• P8.1 — Observer API & P8.2 — TUI Inspector (0.0.1 release) HistoricalRunHandle reads any snapshot directory; the Textual TUI ships as a read-only run inspector with lazygit-style multi-pane drill-down (snapshot library → run summary → meeting detail → utterance modal → artifact browser, plus Cast view + theme cycling).
• P8.3 — Streaming + Mock Turtle RunHandle.stream_events() async-iterator interface + MockTurtleHandle that replays a snapshot at compressed clock time. The testbed P8.4's live-watch screen iterates against without API spend.
• P8.4 — Live-watch screen The streaming surface goes visual: meetings ribbon fills in as MeetingStarted events arrive, transcript scrolls live, body preview tracks the utterance cursor, artifacts pane filters per-meeting. All three panes focusable, Tab cycles, lazygit principle throughout. Iterated entirely against Mock Turtle replay.
• P8.5 — Directive issuing + LiveRunHandle (this release) NewRunScreen is the directive composer (preset picker with bundled pomodoro / hello-endpoint / translation-chat / geocities / ping directives, plus per-project saves to .wonderland/directives/); selection drives composer + workflow pre-fill; Enter steps through the form like a paper form. LaunchConfirmationScreen guards the irreversible spend with directive preview + soft-cap budget. LiveRunHandle wraps a real Runner+Caucus and emits through the same streaming protocol Mock Turtle uses, so the live-watch screen consumes a real run interchangeably with a replay. Settings screen accepts API key + model from inside the TUI — fresh pip install users no longer drop to the shell to write a config file. First end-to-end TUI run shipped a story for $0.0119 against the smoke workflow.
• P8.6 — New-project spinup Skeleton picker + stack-detection routine for adopting existing non-Wonderland projects. Closes the on-ramp from "I have a project idea" to "team's ready to start" without leaving the TUI.
• P8.7 — First-use polish Welcome screen, error states, abort flow, quiescence indicator, README + SHOWCASE refresh.
• P7 — Evals Generic-baseline vs Wonderland comparison. The compounding curve. Reordered after P8 so the eval harness has a usable operator surface.

WONDERLAND_SPEC.md is the design document. constitutions/ holds each character's identity in plain text — these are the actual identity artifacts the runtime loads.

Try it

Two demo scripts run live against the Anthropic API. You'll need an API key (see Configuration below).

# A single Cheshire Cat reflecting on a directive
uv run python scripts/cat_demo.py

# Cat + Rabbit on the same bus, with optional compaction afterward
uv run python scripts/two_agent_demo.py --compact

Both scripts publish a translation-chat directive by default; pass --directive "..." to use your own.

The TUI

wonderland-tui is the operator interface. Register a project, queue features for the team, watch them work in real time, verify or reject what they ship. The same screen that renders live runs also replays past ones at compressed clock time, so iterating on the UX never costs API tokens.

pip install wonderland-ai
wonderland-tui                       # opens the project library

First-run flow: the library opens empty. Open Settings, paste an Anthropic API key (saved to your platform's user-config dir), back out. Press n to create a project — pick a path, pick a skeleton (python-tui, python-cli, python-fastapi, react-vite, fullstack-fastapi-react), and the substrate writes a .wonderland/project.yaml carrying the stack as authoritative project context the team consults at every meeting. The project's dashboard opens automatically.

The screens, in the order an operator typically meets them:

• Project library — your projects with metadata. n for new, Enter to open the dashboard, s for settings.
• New project — name, path, skeleton picker, workflow default. Skeleton apply lays down a working scaffold AND writes project.yaml so M4 architecture and M5 contracts ground in the runtime fact, not just the directive's prose. Existing non-bare projects get a retrofit path that writes project.yaml without clobbering existing files.
• Per-project dashboard — the operator's primary attention surface in P12. Features tree on the left (each feature expandable to show its constituent tickets); state filter chips (designed / queued / ready_review / in_progress / verified / rejected); detail pane on the right renders the highlighted feature or ticket markdown. State-aware action buttons — Design, Implement, Verify, Custom run — surface counts for what's actionable; the highest-priority action gets the primary variant. Drill-down tabs for run history, raw artifacts, the project's working tree, and metrics charts.
• Lifecycle moves from the dashboard — q queues a designed feature for implementation; Verify opens a modal that captures the operator's verdict with optional notes (verified / rejected → recorded in .wonderland/feature-states.jsonl for next-run context); m/D mark and bulk-delete duplicate tickets when Rabbit's M3 ships revision-pass redundancy.
• New run composer — preset picker (left) + directive editor (right) + workflow / budget / project-root config + inline save-as-preset form. Bundled directives: pomodoro, hello-endpoint, translation-chat, geocities, ping. Per-project presets live at <project>/.wonderland/directives/. Empty directives push a confirmation modal so a launch doesn't silently ship without intent.
• Live-watch screen — three focusable panes (lazygit-style): meetings ribbon (with per_item iteration discriminators for parallel and pipeline workflows), transcript table + body preview pane, artifacts table. Selection filters across panes; status bar shows current speaker, live cost ticker, watching elapsed time + source-time elapsed. Same screen consumes live runs (LiveRunHandle) and replays of captured runs (MockTurtleHandle) interchangeably.
• Operator-question modal — when an agent emits a question_to_operator (architectural ambiguity contracts can't disambiguate, business priority calls, schema-vs-directive conflicts), the framework pauses the meeting and surfaces the question as a modal. Your reply lands on the bus as an observation from the operator identity; the meeting resumes with the team seeing the answer in their context.
• Cast view — single-page lazygit shape: character list at top, bio + constitution side-by-side below. Bios cover both the literary character and how it shapes each agent's constitution. Useful for understanding why an agent made a particular call when reviewing a captured run.
• Settings — Anthropic API key (password-masked, persists to the user-config dir) + optional model override. Reachable from the library, also auto-pushed when New run finds no API key set so fresh pip install users have a one-click recovery path.
• Theme cycling — t rotates through four Wonderland-flavored palettes (Tea Party / Looking Glass / Trial / Caucus); built-in Textual themes (gruvbox, dracula, nord, …) remain available.
• Vim navigation throughout — j/k to move, g/G and H/L for top/bottom, Enter to drill in / advance, Tab to cycle focus across panes, Escape to back out. Per-screen bindings show in the footer.

The replay-first design carries forward: drives the smoke tests, keeps UX iteration free of API spend, and means anyone curious about how the framework actually behaves can wonderland-tui → open a project → drill into Runs → press w on a snapshot to watch a captured run play back at 5× speed. Project context, the features-as-tree dashboard, the verify/reject modal, and the operator-question pipeline are the P11/P12 additions that pulled the framework from "watch a run happen" to "drive a project's feature lifecycle through several runs."

Project layout

wonderland-ai/
├── WONDERLAND_SPEC.md      # The design document
├── THESIS.md               # Long-form thesis (architectural claim + corollaries)
├── constitutions/          # Each character's identity, version-controlled
├── src/wonderland/         # The runtime
│   ├── closet/             # Data the team reaches for at runtime
│   │   ├── skeletons/      # Project skeletons the team builds on top of
│   │   └── workflows/      # Meeting-chain templates (canonical, tdd, smoke)
│   └── ...                 # agent.py, runner.py, caucus.py, workflow.py, ...
├── scripts/                # Demo scripts; workflow_demo.py runs any bundled workflow
├── analyses/               # Field notes on the thesis as it gets stress-tested
├── tests/
└── .daedalus/              # Daedalus' working memory for this project

A target project that runs Wonderland gets a .wonderland/ directory of its own — per-agent episodic/semantic/relational memory, ADRs, tickets, transcripts, contract notes, test scenarios, implementations, reviews. The runtime here is project-agnostic; per-project state lives with the project.

wonderland init [path]   # create the .wonderland/ skeleton; idempotent

init creates architecture/, tickets/, stories/, escalations/, and memory/ plus a README documenting the layout. Re-running is safe — existing artifacts and a user-edited README are left alone.

Install

Distribution name on PyPI is wonderland-ai; the import path stays import wonderland. Core install includes the TUI (the primary user-facing surface) and the in-process bus:

pip install wonderland-ai           # core + TUI
pip install 'wonderland-ai[redis]'  # adds RedisCaucus

RedisCaucus requires the redis extra; constructing one without it raises ImportError with an install hint.

Configuration

Wonderland reads user-level config (API keys, model overrides) from a JSON file at the platform-appropriate location:

OS	Path
Linux	~/.config/wonderland/config.json (honors XDG_CONFIG_HOME)
macOS	~/Library/Application Support/wonderland/config.json
Windows	%APPDATA%\wonderland\config.json

{
  "anthropic": {
    "api_key": "sk-ant-...",
    "model": "claude-haiku-4-5-20251001"
  }
}

API-key resolution order: explicit constructor arg → ANTHROPIC_API_KEY env var → config file. The env var wins if set.

Development

uv sync --extra dev   # includes redis for full test coverage
uv run pytest
uv run ruff check
uv run ruff format

Live LLM tests are gated behind WONDERLAND_LLM_SMOKE=1 and skipped otherwise; running them costs Anthropic API tokens. Redis-backed tests are gated behind WONDERLAND_REDIS_URL. To exercise both:

docker run -d --name wonderland-redis -p 6379:6379 redis:7-alpine
WONDERLAND_REDIS_URL=redis://localhost:6379 \
WONDERLAND_LLM_SMOKE=1 \
  uv run pytest

Sponsoring

Wonderland runs on a personal Anthropic budget — one person, one API key. The architecture is designed to be cheap (small models, heavy caching) but multi-agent runs at scale still add up. If any of my work has been useful to you — to read, build on, or argue with — GitHub Sponsors keeps the Cheshire Cat in tea and the Hatter in scenarios.

License

MIT.