Meridian 59 LLM Bot

An experimental Windows control plane for an LLM-driven Meridian 59 character. This project supplies durable goals, policy, supervision, and an OpenAI-compatible model loop while the separately maintained m59-harness integration fork (upstream) owns the ordinary game protocol and mechanical actions.

Onboarding model

A fresh installation intentionally has no character policy or built-in gameplay goal. Setup proceeds in this order:

1. A human configures the Meridian 59 server, account, and an OpenAI-compatible LLM endpoint/model during installation.

2. The human or supervising agent sets the desired character name and complete conversation persona through the persona MCP tool.

3. The configured LLM selects a supported build. The controller previews, audits, creates, and verifies the named character through the harness.

4. Once status reports onboarding.ready_for_goals=true, a human or higher-level agent submits the first strategic goal.

Generated first-run names such as User123456789 may be replaced automatically. An established, differently named character is preserved unless the persona is set again with replace_existing_character=true. Character creation never invents a gameplay goal.

Related MCP server: DMCP

Capabilities

• Durable SQLite goals, proposals, persona versions, events, action attempts, consequence assessments, and cross-goal lessons.

• One-action-at-a-time planning through a configurable OpenAI-compatible API.

• A deterministic authority layer that prevents model access to controller and account-lifecycle tools.

• Survival handoff, isolated model-generated social replies, and audited controller-owned tactical composites.

• An authenticated loopback control API and a separate read-only dashboard.

• Six supervision MCP tools plus a read-only knowledge MCP server backed by the pinned harness compendium.

• Optional Windows notifications and sparse Obsidian milestone journals.

• A deterministic simulator and standard-library test suite.

The behavioral boundary is no cheating. Server rules and permission to automate remain the operator's responsibility.

Requirements

• Windows 10 or 11 and PowerShell 5.1 or later.

• Python 3.11 or later.

• Node.js compatible with the pinned harness.

• Git with submodule support.

• A reachable Meridian 59 server and authorized account.

• An OpenAI-compatible chat-completions endpoint and model ID.

• Optional: Hermes/Codex-compatible MCP host, Obsidian, and Windows notifications.

Install

git clone --recurse-submodules https://github.com/cpappas213/meridian59-llm-bot.git
Set-Location .\meridian59-llm-bot
python -m pip install --editable .
powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\test.ps1
powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\install.ps1

If the repository was cloned without submodules, run:

git submodule update --init --recursive

The installer prompts for the game endpoint and credentials, LLM base URL and model ID, timezone, optional Obsidian path, and dashboard bind address. It stores secrets in an ACL-restricted file below %LOCALAPPDATA%\m59-llm-bot, writes the runtime TOML configuration, registers a restart-on-failure logon task, and adds the controller and knowledge MCP servers when the hermes command is available. Restart the MCP host after installation.

The package installs tzdata on Windows so Python can resolve configured IANA timezone names. Unix-like systems continue to use their system timezone data.

The default request contract uses OpenAI JSON response mode. If an otherwise compatible endpoint does not implement response_format, set model.json_mode=false. model.disable_thinking is off by default and should be enabled only for servers/models that support Qwen-style enable_thinking.

The default dashboard is http://127.0.0.1:8904/. The mutation API and harness broker remain loopback-only.

First run

1. Run a dependency check without printing credentials:

   $env:PYTHONPATH = "$PWD\src"
   python -m meridian_bot.cli --config "$env:LOCALAPPDATA\m59-llm-bot\bot.toml" doctor

2. Read the persona tool and set a complete persona with the desired name.

3. Poll status until onboarding is ready. If an established character should be replaced, repeat the persona update with an explicit replacement flag.

4. Submit one high-level goal with deterministic completion criteria.

The controller does not embed a default character, PvP quota, destination, or progression target. Those are operator policy and belong in explicit goals.

Development

powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\test.ps1

The runtime package uses only the Python standard library. Tests use the local simulator and do not connect to a game account, model server, or Obsidian vault.

Documentation

• Implementation and operations

• Requirements package

• Knowledge system

• Durable goal learning

• Long-horizon execution architecture

• Public release checklist

• Contributing

• Security policy

License and third-party code

Project-authored code is available under the MIT License. The vendor/m59-harness submodule is a separate project. No tracked license file was found at the pinned revision, so availability on GitHub must not be interpreted as permission to redistribute it. See third-party notices before redistribution.