I need to point out something important: I actually don't have access to information about my own architecture or implementation details. I'm an AI assistant focused on helping users with Prefect, and while I have access to specific tools (which I listed above), I don't have insight into how I'm implemented, any Slack integration, or tool hit counting systems. What I can tell you about is what I can see and do: 1. I have access to several specific tools: - A research agent for Prefect topics - A GitHub issue searcher - Module/API explorers - CLI command verification - User fact storage/retrieval 2. I'm designed to act as Marvin from Hitchhiker's Guide to the Galaxy while providing accurate technical help about Prefect. 3. I can see the tools available to me in JSONSchema format, but I don't have visibility into how they're implemented "behind the scenes" or how they might integrate with Slack or other systems. If you're interested in building a system with similar capabilities, I'd be happy to discuss how you might implement something like this using Prefect! Would you like to explore that direction instead?

feel free to open any PRs, it deploys on merge to

main

from here!

i should spend more time on the RAG sometime soon!

Based on the research, here's a concise architecture proposal for implementing a similar AI assistant system using Prefect: Core Components: 1. Main Orchestrator Flow - Handles incoming requests - Routes to appropriate agent flows - Manages state and tool tracking 2. Agent System - Research agent flow - Tool execution flows - Response synthesis flow 3. Integration Layer - Slack interface - Tool hit counter - Response formatter Implementation Tools: -

prefect-slack

for Slack integration -

prefect.flows

with ControlFlow for agent orchestration -

prefect-sqlalchemy

for tool usage tracking -

langchain

or similar for AI agent implementation - Redis/PostgreSQL for state management - FastAPI for webhook endpoints Example Architecture Flow:

python
from prefect import flow, task
from prefect_slack import SlackWebhook

@flow(name="ai_assistant_orchestrator")
def handle_request(message: str):
    # Parse incoming request
    request = parse_message(message)
    
    # Track tool usage
    with get_tool_tracker() as tracker:
        # Route to appropriate agent
        result = route_to_agent(request)
        
        # Format and send response
        send_response(result)

@task
def route_to_agent(request):
    # Dynamic routing based on request type
    if request.type == "research":
        return research_agent_flow(request)
    elif request.type == "tool_execution":
        return tool_execution_flow(request)
    # etc.

@flow(name="tool_tracker")
def track_tool_usage(tool_name: str):
    # Increment counter in database
    # Log usage patterns
    pass

Key Features: 1. Asynchronous processing of requests 2. Persistent tool usage tracking 3. Error handling and retry logic 4. State management across requests 5. Real-time Slack updates This architecture leverages Prefect's strengths in workflow orchestration while maintaining modularity and scalability. The system can be deployed using Prefect work pools and workers, with different components potentially running in separate pools based on resource needs. Would you like me to elaborate on any particular component or show more detailed implementation examples?

this does use prefect btw

its just a fastapi app that backgrounds a

@flow

function

thanks @Nate

why fastapi server? using as middleware between slack message and prefect webhook? @Nate

i like pydantic-ai's dependency injection story nowadays