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llmcord.py

Talk to LLMs with your friends!

llmcord.py lets you (and your friends) chat with LLMs directly in Discord. It works with practically any LLM, remote or locally hosted.

Features

Reply-based chat system

Just @ the bot to start a conversation and reply to continue. Build conversations with reply chains!

You can do things like:

  • Continue your own conversation or someone else's
  • "Rewind" a conversation by simply replying to an older message
  • @ the bot while replying to any message in your server to ask a question about it

Additionally:

  • Back-to-back messages from the same user are automatically chained together. Just reply to the latest one and the bot will see all of them.
  • You can seamlessly move any conversation into a thread. Just create a thread from any message and @ the bot inside to continue.

Choose any LLM

llmcord.py supports remote models from OpenAI API, Mistral API, Anthropic API and many more thanks to LiteLLM.

Or run a local model with ollama, oobabooga, Jan, LM Studio or any other OpenAI compatible API server.

And more:

  • Supports image attachments when using a vision model (like gpt-4o, claude-3, llava, etc.)
  • Supports text file attachments (.txt, .py, .c, etc.)
  • Customizable system prompt
  • DM for private access (no @ required)
  • User identity aware (OpenAI API only)
  • Streamed responses (turns green when complete, automatically splits into separate messages when too long)
  • Displays helpful user warnings when appropriate (like "Only using last 20 messages" when the customizable message limit is exceeded)
  • Caches message data in a size-managed (no memory leaks) and mutex-protected (no race conditions) global dictionary to maximize efficiency and minimize Discord API calls
  • Fully asynchronous
  • 1 Python file, ~200 lines of code

Instructions

Before you start, install Python and clone this git repo.

  1. Install Python requirements: pip install -U -r requirements.txt

  2. Create a copy of "config-example.json" named "config.json" and set it up (see below)

  3. Create a copy of "example.env" named ".env" and add API keys as needed for your LiteLLM supported providers

  4. Run the bot: python llmcord.py (the invite URL will print to the console)

LLM settings:

Setting Description
llm For LiteLLM supported providers (OpenAI API, Mistral API, ollama, etc.), follow the LiteLLM instructions for its model name formatting.

For local, OpenAI compatible API servers (oobabooga, Jan, LM Studio, etc.), set to local/openai/model (or local/openai/vision-model if using a vision model). Some setups will instead require local/openai/<MODEL_NAME> where <MODEL_NAME> is the exact name of the model you're using.
local_server_url The URL of your local API server. Only applicable when "llm" starts with local/.
(Default: http:https://localhost:5000/v1)
extra_api_parameters Extra API parameters for your LLM. Add more entries as needed.
(Default: max_tokens=1024, temperature=1.0)
system_prompt Write anything you want to customize the bot's behavior!

Discord settings:

Setting Description
bot_token Create a new Discord bot at discord.com/developers/applications and generate a token under the "Bot" tab. Also enable "MESSAGE CONTENT INTENT".
client_id Found under the "OAuth2" tab of the Discord bot you just made.
status_message Set a custom message that displays on the bot's Discord profile. Max 128 characters.
allowed_channel_ids A list of Discord channel IDs where the bot can send messages. Leave empty to allow all channels.
allowed_role_ids A list of Discord role IDs that can use the bot. Leave empty to allow everyone. Specifying at least one role also disables DMs.
max_text The maximum amount of text allowed in a single message, including text from file attachments.
(Default: 100,000)
max_images The maximum number of image attachments allowed in a single message. Only applicable when using a vision model.
(Default: 5)
max_messages The maximum number of messages allowed in a reply chain.
(Default: 20)

Notes

  • If you're having issues, try my suggestions here

  • Only models from OpenAI are "user identity aware" because only OpenAI API supports the message "name" property. Hopefully others support this in the future.

  • PRs are welcome :)

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