Build an agent using Microsoft Agent Framework

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This guide walks through building a Teams agent with Microsoft Agent Framework (MAF) — Microsoft's open-source SDK for AI agents. MAF gives you typed primitives — Agent, Tool, AgentSession, FunctionMiddleware — that wrap the underlying model API, the tool-dispatch loop, and conversation history into composable pieces, so you don't hand-roll chat completions or thread tool calls yourself. It works against multiple model backends (OpenAI, Azure OpenAI, and others) and scales from a single chat agent up to coordinated multi-agent workflows. In a Teams app, MAF runs the agent loop (model calls, tool invocations, and per-conversation memory) while the Teams SDK handles activity routing, streaming, and Teams-native affordances like Adaptive Cards and feedback controls.

Defining the agent

An agent is composed of three core elements: a client (model backend), instructions (system prompt), and tools (capabilities beyond text generation). A minimal setup starts with just a chat-enabled agent:

from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient

client = OpenAIChatClient(
    model=getenv("AZURE_OPENAI_MODEL"),
    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
    api_key=getenv("AZURE_OPENAI_API_KEY"),
)

agent = Agent(
    client=client,
    instructions="You are a helpful Teams assistant.",
)

Adding a local tool

Tools extend the agent with executable capabilities. They are regular functions annotated so the model can decide when to invoke them. Anything that runs in your process — such as database lookups, business logic, or Teams-specific actions like attaching an Adaptive Card to the reply — belongs here.

Tools are declared using the @tool decorator from the Agent Framework. The function name, docstring, and type hints (or annotations) are used by the model to understand when and how to call the tool.

from agent_framework import tool

@tool
async def send_welcome_card(
    greeting: Annotated[str, Field(description="Greeting message, e.g. 'Hello, Alex!'")],
) -> str:
    """Attach a welcome card to the reply."""
    cards = pending_cards.get()
    if cards is None:
        return "No active turn context."
    cards.append(AdaptiveCard(version="1.5").with_body([
        TextBlock(text=greeting, size="Large", weight="Bolder", wrap=True),
    ]))
    return "Card attached."

Tools run inside the agent loop, and their outputs can influence both the final response and side effects in the Teams message (such as cards or metadata).

Adding remote MCP tools

Remote tools are exposed via MCP servers and live behind a network boundary. The agent discovers their schemas at runtime and invokes them over HTTP. From the model’s perspective, they behave like any other tool.

Remote tools are declared using MCP tool wrappers from Agent Framework and passed to the agent just like local tools:

from agent_framework import MCPStreamableHTTPTool

mcp_tools = [
    MCPStreamableHTTPTool(name="MSLearn", url="https://learn.microsoft.com/api/mcp"),
]

agent = Agent(
    client=client,
    instructions="You are a helpful Teams assistant with access to local tools and remote MCP servers..",
    tools=[send_welcome_card, *mcp_tools],
)

Running the agent in Teams

Integrate with Teams by forwarding incoming messages to the agent runtime and streaming the response back to the chat interface chunk by chunk.

@app.on_message
async def handle_message(ctx: ActivityContext[MessageActivity]):
    async for chunk in agent.run(ctx.activity.text or "", stream=True):
        if chunk.text:
            ctx.stream.emit(chunk.text)

Per-conversation memory

By default, the agent is stateless. Each agent.run(...) call starts with no history — the model only sees the current message. This works well for one-shot interactions, but is insufficient for multi-turn conversations where users refer back to earlier context. A session provides a conversation buffer that maintains state across turns. Create one per Teams conversation and reuse it for subsequent messages:

from agent_framework import AgentSession

_sessions: dict[str, AgentSession] = {}

@app.on_message
async def handle_message(ctx: ActivityContext[MessageActivity]):
    conversation_id = ctx.activity.conversation.id
    session = _sessions.setdefault(conversation_id, agent.create_session())

    async for chunk in agent.run(ctx.activity.text or "", session=session, stream=True):
        ...

In production push session history into Redis, Cosmos DB, or whatever you already use for state.

Middleware

Middleware sits between tool execution and the model response, enabling you to inspect, transform, or annotate results without coupling this logic to the agent itself. It is implemented by subclassing FunctionMiddleware from Agent Framework and overriding the process method, which receives the tool context and allows you to modify the result before it is passed back to the model.

A common use case is grounding responses with citations — extracting references from tool outputs and assigning stable indices so the model can cite them inline.

from agent_framework import FunctionMiddleware

class CitationMiddleware(FunctionMiddleware):
    citations: dict[str, Any]

    async def process(self, context, call_next):
        # Run the wrapped tool first, then post-process its result.
        await call_next()

        data = parse_json(context.result)

        for item in data["results"]:
            pos = len(self.citations) + 1
            url = item.get("contentUrl") or item.get("link")
            snippet = item.get("content") or item.get("description")
            # setdefault dedupes by URL — the same source returned by multiple
            # tool calls keeps a single, stable position.
            entry = self.citations.setdefault(url,{ "position": pos,
                                "url": url,
                                "title": item.get("title", ""),
                                "snippet": snippet[:160],
                                })
            # Attach the citation back onto the result so the model sees it
            # alongside the content and can reference it inline.
            item["citation"] = entry

        context.result = serialize_json(data)


tool_logger = AgentMiddleware()
agent = Agent(
    client=client,
    instructions="""You are a helpful Teams assistant with access to local tools and remote MCP servers.
    When you use information from a search tool, cite your sources inline using the "citation" value """,
    tools=[send_welcome_card, *mcp_tools],
    middleware=[tool_logger],
)

For Teams-specific enhancements — continue to Enhancing the Teams Experience using Teams SDK.