This directory contains an Agent Client Protocol (ACP) connector that allows you to run a Python Deep Agent within a text editor that supports ACP such as Zed.

It includes an example coding agent that uses Anthropic's Claude models to write code with its built-in filesystem tools and shell, but you can also connect any Deep Agent with additional tools or different agent architectures!
First, make sure you have Zed and uv installed.
Next, clone this repo:
git clone git@github.com:langchain-ai/deepagents.git
Then, navigate into the newly created folder and run uv sync:
cd deepagents/libs/acp
uv sync
Rename the .env.example file to .env and add your Anthropic API key. You may also optionally set up tracing for your Deep Agent using LangSmith by populating the other env vars in the example file:
ANTHROPIC_API_KEY=""
# Set up LangSmith tracing for your Deep Agent (optional)
# LANGSMITH_TRACING=true
# LANGSMITH_API_KEY=""
# LANGSMITH_PROJECT="deepagents-acp"
Finally, add this to your Zed settings.json:
{
"agent_servers": {
"DeepAgents": {
"type": "custom",
"command": "/your/absolute/path/to/deepagents-acp/run_demo_agent.sh"
}
}
}
You must also make sure that the run_demo_agent.sh entrypoint file is executable - this should be the case by default, but if you see permissions issues, run:
chmod +x run_demo_agent.sh
Now, open Zed's Agents Panel (e.g. with CMD + Shift + ?). You should see an option to create a new Deep Agent thread:

And that's it! You can now use the Deep Agent in Zed to interact with your project.
If you need to upgrade your version of Deep Agents, run:
uv upgrade deepagents-acp
uv add deepagents-acp
import asyncio
from acp import run_agent
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
from deepagents_acp.server import AgentServerACP
async def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
async def main() -> None:
agent = create_deep_agent(
tools=[get_weather],
system_prompt="You are a helpful assistant",
checkpointer=MemorySaver(),
)
server = AgentServerACP(agent)
await run_agent(server)
if __name__ == "__main__":
asyncio.run(main())
uv tool install -U batrachian-toad --python 3.14
toad acp "python path/to/your_server.py" .
# or
toad acp "uv run python path/to/your_server.py" .Convert an ACP text block to LangChain content blocks.
Convert an ACP image block to LangChain content blocks.
Convert an ACP audio block to LangChain content blocks.
Convert an ACP resource block to LangChain content blocks.
Convert an ACP embedded resource block to LangChain content blocks.
Extract all command types from a shell command, handling && separators.
Truncate a command string to a maximum length for display.
Format execute tool result for better display.
Run the test ACP agent server.