Microsoft AutoGen
EcosystemMicrosoft Research's open-source multi-agent framework with conversational agent patterns and optional human-in-the-loop approval, applicable to UCP checkout workflows that require purchase confirmation.
AutoGen is Microsoft Research's open-source framework for building multi-agent LLM applications. It provides conversational multi-agent patterns where agents communicate with each other through a structured message protocol. Agents can be backed by different LLMs, tools, or code execution environments. The framework supports both Python and .NET.
AutoGen's conversational agent pattern is distinct from CrewAI and LangChain. Rather than a pipeline where agents hand off tasks sequentially, AutoGen agents converse: they send messages to each other and to human-in-the-loop participants. A commerce workflow in AutoGen might involve a shopping assistant conversing with a specialized buyer agent and a human approver before a UCP checkout is executed. The human-in-the-loop step is explicit in the conversation pattern.
AutoGen's code execution capability lets agents generate and run Python code in a sandboxed environment. For complex commerce workflows, an agent can write code to call multiple UCP endpoints, compare catalog results programmatically, and compute the best option before presenting it to the user. This code-then-execute loop is different from direct tool calling and gives agents more flexibility in how they interact with UCP APIs.
Microsoft's investment in AutoGen reflects its broader agentic AI strategy, which includes Azure AI Agent Service, a managed deployment environment for AutoGen-style agents. The combination of the open-source framework and a managed runtime creates a path from prototyping to production commerce agents without leaving the Microsoft ecosystem.
Why This Matters for UCP
AutoGen provides conversational multi-agent patterns with optional human-in-the-loop approval steps for UCP checkout, making it well-suited for commerce agents where purchase confirmation before execution is required.
UCP Capabilities
UCP Endpoint
Ecosystem — Endpoint not yet publicly available
Integration Example
// AutoGen multi-agent commerce workflow (Python)
import autogen
import httpx
def ucp_catalog_search(merchant_domain: str, query: str) -> dict:
manifest = httpx.get(f"https://{merchant_domain}/.well-known/ucp").json()
return httpx.post(manifest["catalogEndpoint"], json={"query": query}).json()
def ucp_checkout(merchant_domain: str, items: list, payment_token: str) -> dict:
manifest = httpx.get(f"https://{merchant_domain}/.well-known/ucp").json()
return httpx.post(manifest["checkoutEndpoint"], json={
"items": items, "paymentToken": payment_token
}).json()
config = {"config_list": [{"model": "gpt-4o", "api_key": "..."}]}
shopping_agent = autogen.AssistantAgent(
name="ShoppingAgent",
llm_config={**config, "functions": [
{"name": "ucp_catalog_search", "description": "Search merchant catalog", "parameters": {}},
{"name": "ucp_checkout", "description": "Execute UCP checkout", "parameters": {}},
]},
function_map={"ucp_catalog_search": ucp_catalog_search, "ucp_checkout": ucp_checkout},
)
user_proxy = autogen.UserProxyAgent(name="User", human_input_mode="TERMINATE")
user_proxy.initiate_chat(shopping_agent, message="Buy the cheapest USB-C hub with 4+ ports.")Category
Agent FrameworksUCP Status
EcosystemTags
Pricing
free
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Reviews
★★★★★4.7 (3 reviews)These are illustrative examples to show what reviews will look like. Submit your own experience below.
M. Lee
Backend Developer · Mar 3, 2026
Works well for our use case. The documentation is accurate and the implementation matches what the spec describes.
R. Kapoor
Platform Engineer · Jan 15, 2026
Integrated in an afternoon. Solid implementation with no unexpected edge cases in production.
J. Santos
Senior Engineer · Feb 28, 2026
Integration was straightforward. The UCP endpoint discovery works as documented and checkout completes in under 2 seconds.
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