FastAgency: The Adapter Pattern That Makes AutoGen Agents Production-Ready

Hook

Most AutoGen agent prototypes die in Jupyter notebooks. FastAgency aims to fix that by making the jump from notebook to production deployment a matter of changing three lines of code.

Context

The AutoGen framework (now AG2) made building multi-agent workflows accessible to thousands of developers, but it left a critical gap: how do you actually deploy these agents? You could prototype a customer service agent that orchestrates three specialized AI workers in a notebook, watch it work beautifully, then face the reality of productionization. Do you wrap it in Flask? Build a custom UI? How do you handle WebSocket connections for streaming responses? What about distributed deployments where agents run on different machines?

This gap is where most agent projects stall. Teams rewrite their agent logic to fit web frameworks, create bespoke deployment code for each interface, and maintain multiple versions of the same workflow. FastAgency emerged from the AG2 ecosystem to solve this exact problem: provide a standardized deployment layer that treats the execution environment as a swappable adapter, not a hardcoded dependency. The promise is simple—write your agent workflow once, deploy it anywhere.

Technical Insight

FastAgency's architecture centers on a clean separation between workflow definition and execution context. At its core, it uses the adapter pattern to abstract away the UI and networking layers from your agent logic. This means your AG2 workflow code remains pure and focused on agent behavior, while adapters handle the messy details of rendering UIs, managing HTTP connections, or coordinating distributed systems.

Here's what a basic FastAgency deployment looks like. First, you define your AG2 workflow as you normally would:

import os
from autogen.agentchat import AssistantAgent, UserProxyAgent
from fastagency import FastAgency
from fastagency.runtimes.autogen import AutoGenWorkflows

# Configure your AG2 agents
llm_config = {
    "config_list": [{
        "model": "gpt-4",
        "api_key": os.getenv("OPENAI_API_KEY")
    }]
}

wf = AutoGenWorkflows()

@wf.register(name="research_assistant", description="Research assistant workflow")
def research_workflow(ctx, initial_message: str):
    researcher = AssistantAgent(
        name="researcher",
        llm_config=llm_config,
        system_message="You research topics and provide detailed summaries."
    )
    
    user_proxy = UserProxyAgent(
        name="user_proxy",
        human_input_mode="NEVER",
        code_execution_config=False
    )
    
    user_proxy.initiate_chat(
        researcher,
        message=initial_message
    )

Now comes the interesting part. To run this in a console, you swap in the ConsoleUI adapter:

from fastagency.ui.console import ConsoleUI

app = FastAgency(workflows=wf, ui=ConsoleUI())
app.start()

Want to deploy the exact same workflow as a web application? Change exactly one line:

from fastagency.ui.mesop import MesopUI

app = FastAgency(workflows=wf, ui=MesopUI())

This adapter architecture extends beyond just UI. FastAgency includes network adapters for distributed deployments. You can run your agent workflow behind a REST API using the FastAPI adapter, allowing external services to trigger agent workflows via HTTP. The framework automatically handles message serialization, streaming responses, and connection management.

The OpenAPI integration is particularly clever. FastAgency can consume OpenAPI specifications and automatically generate tool functions that your agents can call. Instead of manually writing wrapper functions for every external API, you point FastAgency at an OpenAPI spec and it handles the rest:

from fastagency.api.openapi import OpenAPI

# Automatically generate tools from OpenAPI spec
weather_api = OpenAPI.from_url("https://api.weather.com/openapi.json")

@wf.register(name="weather_agent")
def weather_workflow(ctx, location: str):
    assistant = AssistantAgent(
        name="weather_assistant",
        llm_config=llm_config,
        system_message="You help users check weather conditions."
    )
    
    # Agent can now call weather API functions automatically
    assistant.register_function(
        function_map=weather_api.get_function_map()
    )

The framework maintains a context object (ctx) that flows through your workflow, providing a consistent interface for logging, state management, and inter-agent communication regardless of the underlying UI or network adapter. This context abstraction is what makes the same workflow code portable across execution environments.

FastAgency also includes testing infrastructure designed for CI/CD pipelines. You can write unit tests for your workflows that run without any UI dependencies, then integration tests that validate behavior across different adapters. The CLI tools integrate with standard Python testing frameworks, making it straightforward to add agent workflow tests to existing continuous integration setups.

Gotcha

Despite marketing itself with "multi-framework support," FastAgency currently works exclusively with AG2/AutoGen. The architecture theoretically supports other agent frameworks through its adapter pattern, but in practice, you'll find no implementations for LangChain, CrewAI, or other popular frameworks. If you're not already invested in the AG2 ecosystem, FastAgency won't help you.

The Mesop dependency for web UIs is another consideration. Mesop is Google's relatively new web framework for Python applications, and while it's functional, it lacks the ecosystem maturity of alternatives like Gradio or Streamlit. You'll find fewer examples, fewer community extensions, and potentially more breaking changes. If you need advanced UI customization or specific component libraries, you may end up fighting against Mesop's constraints. The project is also young enough (538 stars) that production deployments should proceed with caution—expect to encounter rough edges, incomplete documentation, and potentially breaking API changes as the project evolves.

Verdict

Use FastAgency if you're already building AG2/AutoGen workflows and need to quickly move from prototype to deployed application. It excels when you want to provide multiple interfaces to the same agent workflow—maybe a CLI for developers, a web UI for business users, and a REST API for external integrations—without maintaining separate codebases. The OpenAPI integration alone can save significant development time if your agents need to interact with external services. Skip it if you're using any agent framework other than AG2, need a battle-tested production deployment system with extensive track record, or require advanced UI customization beyond what Mesop provides. Also skip if you're building simple single-agent applications where the deployment abstraction adds more complexity than value—sometimes a simple Gradio wrapper is all you need.