Anyworld

A multiplayer text adventure where you never have to roll dice or keep score — just write what your character does. One player (the host) describes the scenario, then everyone takes turns acting in their own words while an AI weaves every choice into a story that keeps unfolding.

Get started

One person runs the game server and connects it to an AI model. Everyone plays in a browser. You need Python 3.11 or newer on the server. Anyworld supports a local llama.cpp backend and direct OpenAI API access; direct OpenAI operation has been tested live with gpt-5.6-luna. With the OpenAI backend, narration follows the language of the host's scenario, allowing non-English play. The local compatible backend currently requests English narration.

config.example.yaml is an optional template to copy to your local config.yaml; it is not loaded automatically. Set distinct host and player passwords in the local configuration or through AD_SERVER__HOST_PASSWORD and AD_SERVER__PLAYER_PASSWORD. Environment values take precedence over YAML, and the project's .env file can supply these variables.

For the direct OpenAI backend, put AD_OPENAI_API_KEY in the project's .env file or process environment. An existing process value takes precedence over .env. The game ignores inherited OPENAI_ORG_ID and OPENAI_PROJECT_ID settings on its SDK clients to avoid conflicts with that key. Restart the server after changing credentials or configuration.

See INSTALL.md for installation, passwords, model settings, backend switching, network access, and troubleshooting. Once installed and configured, run anyworld or python app.py from the repository directory, then open the local game page at https://127.0.0.1:4141/.

How to play

1. The host signs in first, using the host password, and writes a scenario. Include the setting and the party's goal. The host may also add one optional percentage-based event and separate freeform private DM guidance.
2. The AI generates the scenario title. Players can then join using the player password. Everyone sees the banner and the host-typed scenario prompt.
3. When everyone is ready, the host clicks Start Game. The AI writes the Opening scenario, introducing the joined characters and their roles while explaining the setting and goal.
4. Players submit actions in join order. Once all actions are collected, the AI resolves them together as one round. The shared result describes the consequences of player actions within the world, and to them personally. Often new avenues to lead the plot to may appear.
5. Use party chat at any time, including while the AI is responding. Chat is not sent to the AI. If a round fails, the host can retry it with the same actions and dice, or end the game.

The player limit includes the host. The server assigns idle actions to disconnected players so the game can continue. Story quality and consistency depend on the model; the game cannot guarantee that it follows every instruction perfectly.

One private percentage event

Use the One private chance rule (optional) controls for one random event per game:

For example, set Chance to 20, choose conditional cadence, use "A player enters a building" as the trigger, "The building is unstable" as eligibility, and "The building collapses" as the effect. Shared scope makes one check if the trigger and eligibility match; per-player scope makes separate checks for matching players.

For every-round cadence without eligibility, Python schedules the checks directly. The AI identifies conditional triggers and evaluates optional eligibility against current actions and world state. Remaining inside a building does not count as entering again, but can satisfy an every-round eligibility condition. Each matching player receives at most one check per round with per-player scope; shared scope receives at most one check per round. Keep conditions concrete and tied to the current actions or established situation; avoid chains where one random event must trigger another check in the same round.

Alternatively, expand Legacy chance text and enter one single-line rule with exactly one percentage and explicit timing, such as "Add a 40% chance every round that a bell rings." Use the controls or legacy text, not both. Missing or mixed timing is rejected in legacy input.

Python makes the percentage rolls separately from action dice: 0% never triggers and 100% always triggers when the condition occurs. Each new check is independent; a 2% chance does not guarantee an event within 50 rounds. Checks begin with action rounds, not the opening scenario. Rules, rolls, and failed checks stay private; players see only observable story consequences. Server logs and private HTML transcripts record the checks.

The AI identifies whether a conditional trigger occurred and narrates the result, so those steps still depend on model accuracy. A conditional result is used only if its trigger actually happens; it cannot force a blocked action to succeed. A setting-conflicting attempt may still receive a public difficulty roll when its outcome is uncertain, with the low plausibility reflected in the result. Failed LLM rounds receive up to two automatic retries, retaining any dice already rolled. If recovery fails or the overall deadline expires, the host can retry the paused round or end. Leave Chance and legacy text blank when no percentage event is wanted.

Freeform private guidance

Use Additional freeform DM guidance for non-probabilistic secret steering about the world, story direction, pacing, or other compatible presentation choices. The AI should apply compatible steering consistently without quoting the guidance. Do not put percentage-based rules in this field; the server rejects them so the game can never accept more than one percentage event.

Rejoining a game

A disconnected tab tries to reconnect automatically. If you close it, open the same game address in the same browser profile and enter the same player name and password. Your saved browser identity allows you to reclaim that character; the password and name alone are not enough.

Clearing site data, changing browsers, or using a different address can prevent recovery. Rejoining restores the opening and current state, then replays available missed public events. Action drafts and pending submissions are retained through connection interruptions. The on-screen game log holds up to 500 entries and chat holds up to 300.

History and longer games

The History panel offers paginated public events, search, and a JSONL export. This public journal excludes private DM guidance and hidden checks and is separate from the server's HTML transcripts. Archive failures can limit available history; the client reports missing history.

The game writes HTML transcripts to .logged_games/ on the server. They include the original scenario prompt, generated opening, player actions, results, and dice rolls. Transcripts also include private DM guidance and hidden checks, which remain out of the players' live game log.

For longer games, the AI summarizes older rounds into memory and checks the summary for lost facts. If a summary fails those checks, the original history is kept. The context indicator shows how much conversation is retained; its details explain the counting method, context limit, and total AI usage. The total usage across calls is different from the space occupied by the current conversation.

One server runs one game at a time. Restarting loses the live session, and a transcript cannot be loaded as a saved game. Restart the server to begin another game after ending one.

Development

See quality checks, the task list, and the maintenance guide. Offline tests clean up their temporary files and may be run during read-only reviews when temporary files are acceptable.

Credits

Inspired by AI Dungeon, especially its earlier free web version, AI Dungeon 2.

AI Credits

Alibaba Cloud's Qwen 3.8 27b and OpenAI's GPT-5.6 Luna and GPT-6 Astra models assisted in the development of this app.

Model benchmarking

The new benchmarks/benchmark_chance_events.py script can be used to benchmark your local model's ability to follow instructions, and to test out different model settings. It creates a set of trigger-events and runs a benchmark on whether the AI properly responded to the caused trigger-event or not. This is not a direct test for whether the model is fit to be a DM for this game, but a lot of failures means the model is very unlikely to be suitable. Also, the json logged responses can give an indication of the model's general intelligence and creativity.

Development showed that the proper configurations (temp, top-p, top-k, presence-penalty and repeat-penalty and others) are a massive influence on how well the model passes the benchmark. With good model settings, the benchmark pass rate for a model climbed from 62% to a consistent 100% over several runs.

Make sure to find out what are the proper settings for the model you plan to use.

Read a more comprehensive description and a couple of model recommendations in INSTALL.md

Running benchmarks

# Activate venv first if not already done.
python -B benchmarks/benchmark_chance_events.py --output benchmarks/model-bench-<model-name>.json