🧠 mod-llm-chatter — JainaCore Neural Edition

Every hero has a story. Your companions are ready to tell theirs.

Distro Oficial: Motor de diálogo con inteligencia artificial para el ecosistema JainaCore 3.3.5a y mod-playerbots.
Basado originalmente en el proyecto open-source Hokken/mod-llm-chatter.

⚔️ Parches y Mejoras Exclusivas para JainaCore:

• Auto-Descubrimiento de Motores Locales: Detección automática y enrutamiento dinámico para LM Studio, vLLM, Jan, Ollama y Lemonade sin configuración manual de URLs.
• Caché Semántico de Respuestas: Almacenamiento en memoria volátil de respuestas frecuentes con tiempo de respuesta inferior a 1 ms (0.0125 ms), reduciendo en 100% el consumo innecesario de GPU.
• Sanitización de Razonamiento (Thinking Artifacts): Filtrado automático de bloques <think> en modelos Qwen 2.5 / DeepSeek R1 para garantizar inmersión rolera pura en el chat de juego.

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See my other module: mod-llm-guide — AI-powered in-game assistant

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Chatter Companion Addon

Chatter Companion A lightweight WoW addon that lets you view and edit your bots' personality traits, tone, and background story directly from the game UI. Open it with /chatter or /llmc, pick a bot from your roster, tweak their personality, read their origin story, or regenerate it with a click. Changes are reflected in their dialogue immediately. No server restart required.

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Features

• Roleplay-first characters: Bots speak as distinct inhabitants of Azeroth, shaped by race, class, talents, personality, and lore. Natural pacing, multi-character flow, emotes, and voices keep conversations immersive.
• Persistent personalities and histories: Each companion keeps a stable identity, generated backstory, and memories of shared dungeons, bosses, achievements, and milestones. Backstories can be viewed or regenerated through the Chatter Companion addon.
• Location and world awareness: More than 3,000 zone and subzone descriptions ground dialogue in the surrounding lore. Bots notice nearby creatures, NPCs, objects, points of interest, weather, time, transports, and holidays.
• Interactive parties: Companions banter with one another, ask the player questions, and react to combat, loot, quests, achievements, and travel.
• Open-world PvP and duels: Companions size up enemies of the opposing faction by name, race, class, and level, react to ambushes, kills, deaths, and ganks in character, and cheer or tease during duels. Nearby bots outside the fight and party can react before, during, or after duels, or after a PvP kill. Same-faction onlookers comment in /say; opposite-faction ones emote. Low chances and shared cooldowns keep these reactions restrained, and hidden enemy identities are omitted.
• Living public channels: Ambient General chat, proximity /say, player replies, battleground callouts, and encounter-aware raid dialogue make the wider world feel populated.
• Social guild chat: Guildmates greet returning players, answer messages, hold conversations, and carry shared context forward during a session.
• Non-blocking architecture: LLM work runs in a separate, concurrent bridge service. Worldserver queues events and delivers completed responses without waiting on provider calls.

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Quick Start

1. Clone into modules/ and build AzerothCore
2. Copy conf/mod_llm_chatter.conf.dist to your config directory and name it mod_llm_chatter.conf
3. Set LLMChatter.Provider, LLMChatter.Model, and the matching API key (Ollama does not need a key)
4. Start worldserver once, or run dbimport, so AzerothCore applies the module's character database schema
5. Start the Python bridge
6. Play, bots start chatting when grouped with players

See Setup below for detailed Docker, non-Docker, and SQL preparation steps.

Compatibility

Keep upstream dependencies up to date. Always keep both AzerothCore's Playerbot branch and mod-playerbots up to date when updating mod-llm-chatter. This module is frequently re-aligned with those upstream branches; older versions may no longer compile or work correctly with the latest chatter release.

This module requires a working AzerothCore server with mod-playerbots. If you don't have one yet, start here:

• AzerothCore Docker install guide
• AzerothCore Playerbot branch
• mod-playerbots

Requirement	Version
AzerothCore	Playerbot branch (WotLK 3.3.5a)
mod-playerbots	liyunfan1223/mod-playerbots
Python	3.10+
LLM Provider	Anthropic, OpenAI, Google Gemini, OpenRouter, or Ollama

Install the Python bridge dependencies from tools/requirements.txt. Anthropic deployments use the supported 1.x SDK; installing provider packages individually can bypass the module's compatibility constraints.

Recommended Models

Tested extensively with excellent results:

• Claude Haiku 4.5 (Anthropic), fast, affordable, excellent quality
• GPT-5.6 Luna (OpenAI), fast and inexpensive; use LLMChatter.OpenAI.ReasoningEffort = none for short-form chatter
• GPT-4o-mini (OpenAI), great alternative, similar cost
• Gemini 3.1 Flash-Lite (Google), fast, cheap, tested with structured chatter and pre-cache JSON
• Gemini 2.5 Flash (Google), reliable with LLMChatter.Google.ThinkingBudget = 0
• GPT-4.1-mini (OpenAI), a little more expensive, but tested with fantastic quality and speed
• OpenRouter model slugs such as anthropic/claude-haiku-4.5, openai/gpt-4o-mini, and openai/gpt-4.1-mini, useful when users want OpenRouter routing while keeping OpenAI-compatible calls

Ollama is supported for local/free inference, but the module's structured JSON, system/user messages, emotes, and actions demand strong instruction following. Smaller open-source models may not deliver it consistently. For the best experience, use Claude Haiku, GPT-5.6 Luna, GPT-4o-mini, GPT-4.1-mini, Gemini 3.1 Flash-Lite, or an equivalent fast model through OpenRouter. See the config header for more provider guidance.

Provider and Model Setup

Configuration uses unquoted Key = value lines. Copy model IDs exactly: direct-provider IDs look like gpt-5.6-luna, OpenRouter IDs use vendor/model, and Ollama IDs use the name and tag shown by ollama list. Leave an optional value empty after =. Keep comments on separate lines; the chatter parser treats an inline comment as part of the value.

Provider	Provider value	Model setting	Credential
Anthropic	anthropic	Exact Anthropic model ID	LLMChatter.Anthropic.ApiKey
OpenAI	openai	Exact OpenAI API model ID	LLMChatter.OpenAI.ApiKey
Google	google	Exact Gemini API model ID	LLMChatter.Google.ApiKey
OpenRouter	openrouter	A vendor/model slug	LLMChatter.OpenRouter.ApiKey
Ollama	ollama	A name/tag from ollama list	None

Ready-to-copy examples (replace only the placeholder key):

# Anthropic
LLMChatter.Provider = anthropic
LLMChatter.Model = claude-haiku-4-5-20251001
LLMChatter.Anthropic.ApiKey = sk-ant-xxxxx

# OpenAI Luna
LLMChatter.Provider = openai
LLMChatter.Model = gpt-5.6-luna
LLMChatter.OpenAI.ApiKey = sk-xxxxx
LLMChatter.OpenAI.ReasoningEffort = none
LLMChatter.OpenAI.MaxTokensMultiplier = 4

# Google Gemini
LLMChatter.Provider = google
LLMChatter.Model = gemini-3.1-flash-lite
LLMChatter.Google.ApiKey = AIza-xxxxx

# OpenRouter
LLMChatter.Provider = openrouter
LLMChatter.Model = anthropic/claude-haiku-4.5
LLMChatter.OpenRouter.ApiKey = sk-or-v1-xxxxx

# Local Ollama from a Docker bridge
LLMChatter.Provider = ollama
LLMChatter.Model = qwen3:8b
LLMChatter.Ollama.BaseUrl = http://host.docker.internal:11434

Use only one provider recipe at a time. Existing credentials for inactive providers can remain in the file. Restart ac-llm-chatter-bridge after a provider or model change. The bridge chooses compatible token, temperature, and reasoning parameters automatically, then caches any explicit unsupported-parameter correction for the rest of that process.

Structured Output: Recommended for Reliable Responses

Strongly recommended when all your configured models and endpoints support it. Enabling structured output makes response formatting much more reliable than leaving it off. It remains off by default for compatibility with endpoints that do not support the required format.

Chatter needs more than spoken text from the model: replies can also carry speaker names, emotes, actions and conversation state. With structured output off, prompts ask the model to follow that format and parsers interpret what it returns. With it on, Chatter sends a JSON Schema describing the required fields and types to the provider, then validates the response locally before using it. This reduces malformed replies, missing fields and formatting errors that can otherwise disrupt conversations.

Setting	Behavior
0 (default)	Existing prompt and parser behavior; output formatting depends on the model following instructions.
1 (recommended on supported endpoints)	Native schema output plus local validation; malformed or incomplete responses are rejected.

This improves the reliability of the response format, not the model's factual accuracy or creativity. A valid text field can still contain inappropriate content, so existing speaker, channel and delivery checks remain necessary.

Enable it in mod_llm_chatter.conf:

LLMChatter.StructuredOutput.Enable = 1

The setting applies to JSON generation across dialogue, quick analysis, memory and screenshot vision, including separately configured models for those roles. Farewells and identity tone/backstory generation remain free text. Every configured JSON target must support the required schema format. Support depends on the exact model and serving endpoint; a model name alone is not enough. Chatter does not automatically detect support or silently retry without the schema. Unsupported requests fail and are logged; use 0 if your setup does not support the feature.

Install the new dependency before enabling: local validation requires jsonschema>=4.23.0,<5.0.0, included in tools/requirements.txt.

• Docker bridge: The Docker setup below already installs requirements.txt automatically before starting the bridge. After updating the module and setting the flag, restart only the chatter bridge:

  docker compose restart ac-llm-chatter-bridge
  docker compose logs --tail=50 ac-llm-chatter-bridge

  Check that dependency installation succeeds and the bridge starts. Package downloads require network access. Custom images that install dependencies only at image-build time need to be rebuilt with the updated requirements.

• Manual bridge or host screenshot agent: From the module directory, run this with the Python interpreter or virtual environment used by that process:

  python -m pip install -r tools/requirements.txt

  Do this in each separate Python environment. Docker does not install packages into the host screenshot agent's environment. Keep its screenshot capture dependencies installed as well.

Restart the chatter bridge and, when used, the host screenshot agent after changing the setting. .reload config does not reload these Python processes. No C++ compilation, database migration or worldserver restart is needed for this feature. If jsonschema is missing with the feature enabled, startup stops with an installation error.

To turn the feature off, set the flag back to 0 and restart the same Python processes. See the structured-output guide for provider formats and diagnostics.

Ignoring Visible Protocol Chat

If a server-specific addon or command sends machine-readable data through visible player chat, Chatter can ignore it without blocking the message from the game's normal chat system:

LLMChatter.PlayerChat.IgnoredPrefixes = !addon:,.custom:

The comma-separated list applies to real-player Party, General, Guild, and /say input. Matching ignores leading whitespace and ASCII letter case and runs before Chatter stores history or queues LLM work. Use distinctive punctuation-bearing prefixes: configured entries are trimmed, and matching is literal rather than word-aware. Normal SendAddonMessage traffic already arrives as LANG_ADDON and does not need an entry. After the supporting C++ version is installed, apply list changes with .reload config.

For OpenAI Luna, none gives the lowest-latency behavior and permits the configured temperature. Higher reasoning efforts can consume more of the output budget, so the bridge applies OpenAI.MaxTokensMultiplier whenever hidden reasoning may be active. It omits temperature where the model does not support it. See the official Luna model page.

For Ollama, run ollama pull <model> on the Ollama host first. A host-run bridge normally uses http://localhost:11434; a Docker bridge normally uses http://host.docker.internal:11434. Do not append /v1 to the configured base URL.

Ollama's OpenAI-compatible endpoint does not accept a per-request context size. Set OLLAMA_CONTEXT_LENGTH before starting Ollama, or create a custom model whose Modelfile contains PARAMETER num_ctx 4096. Confirm the loaded value in the CONTEXT column from ollama ps. Ollama.DisableThinking = 1 uses both the supported reasoning_effort = none request and /no_think fallback for compatible local models.

Tuning the Chattiness

The default config ships on the chatty side so you can experience all the features out of the box. If you prefer a quieter, more immersive atmosphere, the key knobs are below.

An optional lower-volume template is available at conf/presets/mod_ll_chatter_quieter.conf.dist. It is not automatically installed or loaded. To use it, back up your active config, then copy the preset to your server's module-config directory as mod_llm_chatter.conf and configure its database and provider credentials. Keep alternate presets in conf/presets/: files directly under conf/*.conf.dist are registered as required config filenames at build time.

Reducing General channel chatter (ambient bot conversations in zone-wide chat):

# How often each zone is checked for ambient chatter
LLMChatter.TriggerIntervalSeconds = 60  # default 30, try 60-90

# Chance per check that bots start talking unprompted
LLMChatter.TriggerChance = 10            # default 15, try 5-10

# Chance that ambient chatter becomes a multi-bot conversation
LLMChatter.ConversationChance = 30      # default 40, try 15-20

# World event reactions (weather, transports, holidays)
LLMChatter.EventReactionChance = 10     # default 25, try 10-15

Reducing party chatter (group chat while questing):

# Idle chatter frequency and cooldown
LLMChatter.GroupChatter.IdleCheckInterval = 60  # default 30
LLMChatter.GroupChatter.IdleChance = 10          # default 15
LLMChatter.GroupChatter.IdleCooldown = 90       # default 40

# Quest reactions (accept, objectives, turn-in)
LLMChatter.GroupChatter.QuestAcceptChance = 30    # default 50
LLMChatter.GroupChatter.QuestObjectiveChance = 30 # default 50
LLMChatter.GroupChatter.QuestCompleteChance = 30  # default 50

# Combat reactions
LLMChatter.GroupChatter.KillChanceNormal = 5    # default 20
LLMChatter.GroupChatter.SpellCastChance = 10    # default 30

# Open-world PvP and duel reactions
LLMChatter.GroupChatter.PvP.KillChance = 40     # default 80
LLMChatter.GroupChatter.Duel.StartChance = 30   # default 60

# Nearby object/creature comments
LLMChatter.GroupChatter.NearbyObjectChance = 5  # default 20

All values are percentages (0-100) unless noted. Setting any chance to 0 disables that trigger entirely. See the config file comments for the full list of tunable keys.

Tuning recent conversation context:

# Recent Party transcript lines included in prompts (1-50)
LLMChatter.ChatHistoryLimit = 10

# Recent General lines per zone, both factions (1-50)
LLMChatter.GeneralChat.HistoryLimit = 15

Increasing these limits can improve short-term continuity, but it also increases prompt size and token use. The General-specific value falls back to ChatHistoryLimit when omitted. These windows contain recent verbatim lines; they are not long-term memory or rolling summaries.

Known Limitations

• Ollama / open-source models: Local inference needs fast hardware and strong instruction following. Small or reasoning-heavy models can be slow, return malformed JSON, or spend the output budget before producing visible chat. Prefer an instruct/tool-capable 8B-or-larger model and enable LLMChatter.Ollama.DisableThinking for compatible thinking models.
• Ollama cloud models add routing overhead compared to direct Anthropic/OpenAI APIs

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Setup

Important: Disable Default Bot Chat

This module replaces built-in playerbot chat. Add to playerbots.conf:

AiPlayerbot.EnableBroadcasts = 0
AiPlayerbot.RandomBotTalk = 0
AiPlayerbot.RandomBotEmote = 0
AiPlayerbot.RandomBotSuggestDungeons = 0
AiPlayerbot.EnableGreet = 0
AiPlayerbot.GuildFeedback = 0
AiPlayerbot.RandomBotSayWithoutMaster = 0

Docker

1. Configure

Copy modules/mod-llm-chatter/conf/mod_llm_chatter.conf.dist to env/dist/etc/modules/ and rename it to mod_llm_chatter.conf. Open it in a text editor and set at minimum:

• LLMChatter.Provider, choose anthropic, openai, google, openrouter, or ollama
• LLMChatter.Model, using the exact ID format shown in Provider and Model Setup
• the matching provider API key, for example LLMChatter.OpenRouter.ApiKey when using OpenRouter (not needed for Ollama)

2. Add bridge to docker-compose.override.yml

services:
  ac-llm-chatter-bridge:
    container_name: ac-llm-chatter-bridge
    image: python:3.11-slim
    networks:
      - ac-network
    working_dir: /app
    environment:
      - PYTHONUNBUFFERED=1
    command: >
      bash -c "
        pip install --quiet -r /app/requirements.txt &&
        python llm_chatter_bridge.py --config /config/mod_llm_chatter.conf
      "
    volumes:
      - ./modules/mod-llm-chatter/tools:/app:ro
      - ./env/dist/etc/modules:/config:ro
    restart: unless-stopped
    depends_on:
      ac-database:
        condition: service_healthy
    profiles: [dev]

3. Initialize character tables

The chatter bridge does not create database tables. AzerothCore imports the module SQL automatically when worldserver or dbimport runs, but the bridge can fail on a fresh database if it starts first.

On a fresh install, either start worldserver once before starting the bridge, or import the base character schema manually after the database container is running:

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/base/00000000_llm_chatter_tables.sql

4. Load talent data (optional)

Populates talent and spell lookup tables that give the LLM richer context about each bot's specialization. Worldserver treats talenttab_dbc rows as runtime DBC overrides, so the included masks and ordering match the WotLK 3.3.5a client DBC.

docker exec -i ac-database mysql -uroot -ppassword acore_world < \
  modules/mod-llm-chatter/data/sql/world/base/llm_chatter_talent_dbc.sql

5. Start

docker compose --profile dev up -d

Non-Docker

1. Build, place this repo under modules/ and rebuild AzerothCore.

2. Configure

Copy conf/mod_llm_chatter.conf.dist to your server's config directory (typically etc/modules/) and rename it to mod_llm_chatter.conf. Open it in a text editor and set at minimum:

• LLMChatter.Provider, choose anthropic, openai, google, openrouter, or ollama
• LLMChatter.Model, using the exact ID format shown in Provider and Model Setup
• the matching provider API key, for example LLMChatter.OpenRouter.ApiKey when using OpenRouter (not needed for Ollama)

3. Initialize character tables

The chatter bridge does not create database tables. AzerothCore imports the module SQL automatically when worldserver or dbimport runs, but the bridge can fail on a fresh database if it starts first.

On a fresh install, either start worldserver once before starting the bridge, or import the base character schema manually:

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/base/00000000_llm_chatter_tables.sql

4. Start the bridge

cd tools/
pip install -r requirements.txt
python llm_chatter_bridge.py --config /path/to/mod_llm_chatter.conf

5. Load talent data (optional)

Populates talent and spell lookup tables that give the LLM richer context about each bot's specialization. Worldserver treats talenttab_dbc rows as runtime DBC overrides, so the included masks and ordering match the WotLK 3.3.5a client DBC.

mysql -uroot -ppassword acore_world < \
  data/sql/world/base/llm_chatter_talent_dbc.sql

6. Start or keep worldserver running.

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Screenshot Vision

This feature is experimental and optional. Everything else works without it.

Screenshot Vision lets your bots react to what's actually on your screen. A small helper program runs alongside your game, takes a screenshot every now and then, and asks a cheap AI model to describe what it sees. The description is then fed to your bots so they can comment on the scenery in party chat.

What you need

• Windows (the helper runs on the same machine as your WoW client)
• Python 3.10+ installed on your machine (not inside Docker)
• An OpenAI API key (GPT-4o-mini is recommended — extremely cheap) or an Anthropic key

Step-by-step setup

1. Install the required Python packages

Open a terminal (PowerShell or Command Prompt) and run:

pip install mss Pillow openai mysql-connector-python pywin32

If you want to use Claude instead of GPT-4o-mini, also install anthropic:

pip install anthropic

If you enable structured output, also install the module's tools/requirements.txt in this host Python environment. It includes the JSON Schema validator; installing dependencies inside the Docker bridge does not install them on your Windows host.

2. Run the database migration

If you're upgrading from a previous version (fresh installs can skip this):

# Docker
docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260329_screenshot_event_type.sql

3. Add the screenshot settings to your config

Open your mod_llm_chatter.conf and add these lines at the bottom (or copy them from mod_llm_chatter.conf.dist):

# Enable the feature
LLMChatter.Screenshot.Enable = 1

# How often to capture (seconds). Default: every 45-120 seconds
LLMChatter.Screenshot.IntervalMinSeconds = 45
LLMChatter.Screenshot.IntervalMaxSeconds = 120

# Chance (1-100) to actually process each capture. Default: 90
LLMChatter.Screenshot.Chance = 90

# Which AI to use for analyzing screenshots
# Options: "openai" (recommended), "anthropic", "google", or "openrouter"
LLMChatter.Screenshot.VisionProvider = openai

# Which model to use. GPT-6 Luna is fast, cheap, and good at vision
LLMChatter.Screenshot.VisionModel = gpt-6-luna

# Chance (1-100) that a screenshot triggers a multi-bot
# conversation instead of a single comment. Default: 40
LLMChatter.Screenshot.ConversationChance = 40

# Database host override for the host-side agent.
# Your bridge uses a Docker hostname (like ac-database) that
# your Windows machine can't reach. Set this to 127.0.0.1
LLMChatter.Screenshot.DBHost = 127.0.0.1

Make sure your config also has the matching API key set (LLMChatter.OpenAI.ApiKey, LLMChatter.Anthropic.ApiKey, LLMChatter.Google.ApiKey, or LLMChatter.OpenRouter.ApiKey). The screenshot agent uses the same model-aware token-field negotiation as the bridge, so direct OpenAI reasoning/vision model IDs do not require a separate max_tokens workaround.

4. Restart the chatter bridge

docker restart ac-llm-chatter-bridge

5. Start the screenshot agent

Open a new terminal window and run:

python modules/mod-llm-chatter/tools/screenshot_agent.py --config env/dist/etc/modules/mod_llm_chatter.conf

Keep this window open while you play. The agent will quietly capture screenshots in the background and your bots will start making observations about the scenery.

6. Play the game!

Make sure WoW is in the foreground (the agent only captures when WoW is the active window). Group up with some bots, and within a couple of minutes you should see them commenting on what they see around them.

Tips

• The agent saves screenshots to modules/mod-llm-chatter/logs/screenshots/ so you can see exactly what the AI is analyzing
• If bots aren't saying anything, check that the agent terminal shows Queued observation: messages
• Vision cost varies with the provider, model, image size, and current pricing
• You can stop the agent at any time (Ctrl+C) — the rest of the module continues working normally

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Upgrading

First-time installing the module? Skip this section. The base schema in data/sql/characters/base/00000000_llm_chatter_tables.sql already contains everything every migration adds. Fresh installs do not need the dated migration files below after the base schema has been applied. That can happen automatically through worldserver or dbimport, or manually with the setup command above if the bridge is started before worldserver.

Existing installs must apply migration scripts manually when updating to a newer version. Migrations live under data/sql/*/updates/ and are named by date.

Required spell override repair

Installations that loaded the optional talent data before April 9, 2026 must apply the following world-database migration. Older versions inserted incomplete spell_dbc overrides that could cause spell-script validation warnings and hide real client spell effects. The migration only removes rows that still match that legacy placeholder shape and is safe to rerun.

# Docker
docker exec -i ac-database mysql -uroot -ppassword acore_world < \
  modules/mod-llm-chatter/data/sql/world/updates/20260913_remove_legacy_spell_dbc_placeholders.sql

# Non-Docker
mysql -uroot -ppassword acore_world < \
  data/sql/world/updates/20260913_remove_legacy_spell_dbc_placeholders.sql

Restart worldserver after applying this repair so it reloads the restored client DBC records. Fresh installations using the current talent-data SQL do not create the incomplete rows and do not need this repair.

Character-database migrations

Apply the relevant character migrations when upgrading:

# Docker
docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260320_bot_memory_system.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260328_emote_event_types.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260329_screenshot_event_type.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260403_proximity_chatter.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260405_proximity_player_say.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260406_chatter_addon_identity_tone.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260416_bot_backstory.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260508_group_travel_state.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260508_party_chat_pacing.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260511_general_to_party_reaction.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260601_guild_chat.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260619_owner_subsystem.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260621_guild_chatter.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260724_guild_player_sessions.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260725_guild_login_greeting.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260827_widen_group_bot_traits.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260830_message_action_emote.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260908_instance_proximity_boss_events.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260914_npc_multidirectional_interactions.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260919_real_general_items.sql

docker exec -i ac-database mysql -uroot -ppassword acore_characters < \
  modules/mod-llm-chatter/data/sql/characters/updates/20260926_duel_events.sql

# Non-Docker
mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260320_bot_memory_system.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260328_emote_event_types.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260329_screenshot_event_type.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260403_proximity_chatter.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260405_proximity_player_say.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260406_chatter_addon_identity_tone.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260416_bot_backstory.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260508_group_travel_state.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260508_party_chat_pacing.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260511_general_to_party_reaction.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260601_guild_chat.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260619_owner_subsystem.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260621_guild_chatter.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260724_guild_player_sessions.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260725_guild_login_greeting.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260827_widen_group_bot_traits.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260830_message_action_emote.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260908_instance_proximity_boss_events.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260914_npc_multidirectional_interactions.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260919_real_general_items.sql

mysql -uroot -ppassword acore_characters < \
  data/sql/characters/updates/20260926_duel_events.sql

Migrations are idempotent — safe to run on an already up-to-date database. Run them in date order after each git pull that includes new migration files.

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Troubleshooting

Issue	Solution
No chatter appearing	Check Enable = 1, API key set, bots in zone with player
Group chat not working	Set GroupChatter.Enable = 1, must have bots in party
BG chatter not working	Set BGChatter.Enable = 1, join WSG/AB/EY with bots
Raid chatter not working	Set RaidChatter.Enable = 1, raid group in supported instance
Too much / too little chatter	Tune chance and cooldown settings in config
Ollama slow responses	Try a smaller model or use a cloud provider

Bots won't chat? Check the health report

You don't need to run anything. Every time the bridge starts, it runs a built-in health check and prints a simple PASS / FAIL report. Just look at the bridge's startup output:

• Docker: the bridge window, or run docker logs ac-llm-chatter-bridge
• Non-Docker: the bridge's console output

A copy of the report is also saved to modules/mod-llm-chatter/logs/healthcheck.log, so you can open it like a normal text file.

If something is misconfigured, the report names the problem in plain language and tells you how to fix it. It checks:

• the config file loads and the module is enabled
• the database connection — wrong username/password, unreachable host, or wrong database name
• the required tables exist
• the LLM provider and API key — missing key, a leftover example placeholder, an invalid key, or an unreachable local model

A failing check looks like this:

[FAIL] LLM provider config
      The anthropic API key is still the example placeholder.
      -> Replace the placeholder in LLMChatter.Anthropic.ApiKey with your real key.

Fix the items marked [FAIL], restart the bridge, and check that every line now shows [PASS]. If they all pass and bots still don't talk, see the table above.

The check runs automatically by default. It can be turned off with LLMChatter.HealthCheck.Enable = 0, and the live LLM test call can be disabled with LLMChatter.HealthCheck.LLMProbe = 0.

Check logs: docker logs ac-llm-chatter-bridge --since 5m

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On the Horizon

• More battlegrounds and deeper raid integration
• New features that deepen the fantasy roleplay experience and bring more of Azeroth's lore to life

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License

GNU AGPL v3, same as AzerothCore.

Credits

• Uses mod-playerbots for bot characters
• Powered by Anthropic Claude, OpenAI GPT, Google Gemini, OpenRouter, or Ollama