ACP SDK allows you to wrap an existing agent, regardless of its framework or programming language, into a reusable and interoperable service. By implementing a simple interface, your agent becomes compatible with the ACP protocol. It can communicate over HTTP, interact with other agents in workflows, and exchange structured messages using a shared format. Once wrapped, your agent becomes:
  • Remotely callable over REST
  • Composable with other agents in workflows
  • Discoverable
  • Reusable without modifying its internal logic

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Simple agent

If you haven’t already, install the SDK: uv add acp-sdk
Wrapping an agent with ACP is as simple as annotating a Python function. Use the @server.agent() decorator to define your agent. The name is inferred from the function name, and the description is pulled from the docstring:
echo.py
from collections.abc import AsyncGenerator
from acp_sdk.models import Message
from acp_sdk.server import Context, RunYield, RunYieldResume, Server

# Create a new ACP server instance
server = Server()


@server.agent()
async def echo(input: list[Message], context: Context) -> AsyncGenerator[RunYield, RunYieldResume]:
    """Echoes everything"""
    for message in input:
        yield message

# Start the ACP server
server.run()
This is the minimal structure needed for an ACP-compliant agent. You now have an agent that can receive messages from others, or be called via HTTP, using the ACP protocol.

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Simple LLM agent

Let’s look at an example of how a simple agent can call an LLM model using the beeai-framework and return a response.
llm.py
from collections.abc import AsyncGenerator

from acp_sdk.models import Message, MessagePart
from acp_sdk.server import RunYield, RunYieldResume, Server
from beeai_framework.backend.chat import ChatModel
from beeai_framework.agents.react.agent import ReActAgent
from beeai_framework.backend.message import UserMessage
from beeai_framework.memory.token_memory import TokenMemory

server = Server()

@server.agent()
async def llm(input: list[Message]) -> AsyncGenerator[RunYield, RunYieldResume]:
    """LLM agent that processes input and returns a response"""
    
    # Create a llm instance
    llm = ChatModel.from_name("ollama:llama3.1")
    # Create a memory instance
    memory = TokenMemory(llm)
    # Add messages to memory
    for message in input:
        await memory.add(UserMessage(str(message)))
        
    # Create agent with memory and tools
    agent = ReActAgent(llm=llm, tools=[], memory=memory)

    # Run the agent with the memory
    response = await agent.run()
    
    # Yield the response
    yield MessagePart(content=response.result.content[0].text)


server.run()