agentchat

Connect chat platforms to any Python agent

AgentChat handles messages, conversations, acknowledgements, deduplication, and replies. Your application owns the agent

Install

pip install -e ".[openai]"

Slack and OpenAI Agents SDK

import asyncio

from agents import Agent, Runner
from agentchat import AgentChat
from agentchat.channels import Slack
from agentchat.integrations import to_openai_input

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
)

app = AgentChat(channels=[Slack.from_env()])


@app.on_message
async def respond(context):
    history = await context.conversation.history(limit=30)
    result = await Runner.run(agent, input=to_openai_input(history))
    return str(result.final_output)


asyncio.run(app.run())

AgentChat does not depend on an agent framework. The handler can call OpenAI Agents SDK, Pydantic AI, LangGraph, or your own runtime

Slack setup

Create a Slack app with Socket Mode enabled, then add these bot scopes:

• app_mentions:read
• chat:write
• im:history

Subscribe to the app_mention and message.im bot events. Install the app, then export its tokens:

export SLACK_BOT_TOKEN="xoxb-..."
export SLACK_APP_TOKEN="xapp-..."
export OPENAI_API_KEY="sk-..."

Run the example:

python examples/slack_openai_agent.py

Send the bot a direct message or mention it in a channel. Follow-up direct messages share one conversation. Channel mentions continue inside their Slack thread

Core interface

@app.on_message
async def respond(context):
    return await my_agent(context)

The context exposes the normalized message, sender, and conversation:

context.message
context.sender
context.conversation

history = await context.conversation.history(limit=30)
await context.reply("Hello")

Incoming messages are acknowledged, deduplicated, and serialized by conversation before the handler runs. Returning a string posts it to the originating Slack DM or thread

Development

python -m pip install -e ".[dev]"
ruff check .
pytest