Discord Bots with Honcho

Use Honcho to build a Discord bot with conversational memory and context management.

Example code is available on GitHub

Any application interface that defines logic based on events and supports special commands can work easily with Honcho. Here’s how to use Honcho with Discord as an interface. If you’re not familiar with Discord bot application logic, the py-cord docs would be a good place to start.

Events

Most Discord bots have async functions that listen for specific events, the most common one being messages. We can use Honcho to store messages by user and session based on an interface’s event logic. Take the following function definition for example:

@bot.event
async def on_message(message):
    """
    Receive a message from Discord and respond with a message from our LLM assistant.
    """
    if not validate_message(message):
        return

    input = sanitize_message(message)

    # If the message is empty after sanitizing, ignore it
    if not input:
        return

    peer = honcho_client.peer(id=get_peer_id_from_discord(message))
    session = honcho_client.session(id=str(message.channel.id))

    async with message.channel.typing():
        response = llm(session, input)

    await send_discord_message(message, response)

    # Save both the user's message and the bot's response to the session
    session.add_messages(
        [
            peer.message(input),
            assistant.message(response),
        ]
    )

Let’s break down what this code is doing…

@bot.event
async def on_message(message):
    if not validate_message(message):
        return

This is how you define an event function in py-cord that listens for messages. We use a helper function validate_message() to check if the message should be processed.

Helper Functions

The code uses several helper functions to keep the main logic clean and readable. Let’s examine each one:

Message Validation

def validate_message(message) -> bool:
    """
    Determine if the message is valid for the bot to respond to.
    Return True if it is, False otherwise. Currently, the bot will
    only respond to messages that tag it with an @mention in a
    public channel and are not from the bot itself.
    """
    if message.author == bot.user:
        # ensure the bot does not reply to itself
        return False

    if isinstance(message.channel, discord.DMChannel):
        return False

    if not bot.user.mentioned_in(message):
        return False

    return True

This function centralizes all the logic for determining whether the bot should respond to a message. It checks that:

• The message isn’t from the bot itself
• The message isn’t in a DM channel
• The bot is mentioned in the message

Message Sanitization

def sanitize_message(message) -> str | None:
    """Remove the bot's mention from the message content if present"""
    content = message.content.replace(f"<@{bot.user.id}>", "").strip()
    if not content:
        return None
    return content

This helper removes the bot’s mention from the message content, leaving just the actual user input.

Peer ID Generation

def get_peer_id_from_discord(message):
    """Get a Honcho peer ID for the message author"""
    return f"discord_{str(message.author.id)}"

This creates a unique peer identifier for each Discord user by prefixing their Discord ID.

LLM Integration

def llm(session, prompt) -> str:
    """
    Call the LLM with the given prompt and chat history.

    You should expand this function with custom logic, prompts, etc.
    """
    messages: list[dict[str, object]] = session.get_context().to_openai(
        assistant=assistant
    )
    messages.append({"role": "user", "content": prompt})

    try:
        completion = openai.chat.completions.create(
            model=MODEL_NAME,
            messages=messages,
        )
        return completion.choices[0].message.content
    except Exception as e:
        print(e)
        return f"Error: {e}"

This function handles the LLM interaction. It uses Honcho’s built-in to_openai() method to automatically convert the session context into the format expected by OpenAI’s chat completions API.

Message Sending

async def send_discord_message(message, response_content: str):
    """Send a message to the Discord channel"""
    if len(response_content) > 1500:
        # Split response into chunks at newlines, keeping under 1500 chars
        chunks = []
        current_chunk = ""
        for line in response_content.splitlines(keepends=True):
            if len(current_chunk) + len(line) > 1500:
                chunks.append(current_chunk)
                current_chunk = line
            else:
                current_chunk += line
        if current_chunk:
            chunks.append(current_chunk)

        for chunk in chunks:
            await message.channel.send(chunk)
    else:
        await message.channel.send(response_content)

This function handles sending messages to Discord, automatically splitting long responses into multiple messages to stay within Discord’s character limits.

Honcho Integration

The new Honcho peer/session API makes integration much simpler:

peer = honcho_client.peer(id=get_peer_id_from_discord(message))
session = honcho_client.session(id=str(message.channel.id))

Here we create a peer object for the user and a session object using the Discord channel ID. This automatically handles user and session management.

# Save both the user's message and the bot's response to the session
session.add_messages(
    [
        peer.message(input),
        assistant.message(response),
    ]
)

After generating the response, we save both the user’s input and the bot’s response to the session using the add_messages() method. The peer.message() creates a message from the user, while assistant.message() creates a message from the assistant.

Slash Commands

Discord bots also offer slash command functionality. Here’s an example using Honcho’s dialectic feature:

@bot.slash_command(
    name="dialectic",
    description="Query the Honcho Dialectic endpoint.",
)
async def dialectic(ctx, query: str):
    await ctx.defer()

    try:
        peer = honcho_client.peer(id=get_peer_id_from_discord(ctx))
        session = honcho_client.session(id=str(ctx.channel.id))

        response = peer.chat(
            queries=query,
            session_id=session.id,
        )

        if response:
            await ctx.followup.send(response)
        else:
            await ctx.followup.send(
                f"I don't know anything about {ctx.author.name} because we haven't talked yet!"
            )
    except Exception as e:
        logger.error(f"Error calling Dialectic API: {e}")
        await ctx.followup.send(
            f"Sorry, there was an error processing your request: {str(e)}"
        )

This slash command uses Honcho’s dialectic functionality to answer questions about the user based on their conversation history.

Setup and Configuration

The bot requires several environment variables and setup:

honcho_client = Honcho()
assistant = honcho_client.peer(id="assistant", config={"observe_me": False})
openai = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=MODEL_API_KEY)

• honcho_client: The main Honcho client
• assistant: A peer representing the bot/assistant
• openai: OpenAI client configured to use OpenRouter

Recap

The new Honcho peer/session API makes Discord bot integration much simpler and more intuitive. Key patterns we learned:

• Peer/Session Model: Users are represented as peers, conversations as sessions
• Automatic Context Management: session.get_context().to_openai() automatically formats chat history
• Message Storage: session.add_messages() stores both user and assistant messages
• Dialectic Queries: peer.chat() enables querying conversation history
• Helper Functions: Clean code organization with focused helper functions

This approach provides a clean, maintainable structure for building Discord bots with conversational memory and context management.

Assistant

Responses are generated using AI and may contain mistakes.