Agent Framework Integration

opendaw-mcp provides tool wrappers for popular agent frameworks. All wrappers share the same underlying MCP server — pick the one that matches your stack.

LangChain

from opendaw_mcp.langchain_tools import OpendawToolkit

toolkit = OpendawToolkit()
tools = toolkit.get_tools()  # or filter by category
# tools = toolkit.get_tools(categories=["transport", "orchestration"])

# Use with any LangChain agent
from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

executor.invoke({
    "input": "Create a dark techno track at 130 BPM with a driving kick, "
             "hypnotic bass, and reverb on the lead. Render to WAV."
})

Category filtering

Category	Tools	What's included
transport	3	BPM, playback, time signature
tracks	4	Create synth/audio/note tracks, list tracks
effects	4	Add, configure, list, remove effects
notes	3	Create, list, quantize MIDI notes
mixer	4	Volume, pan, mute, mixer state
export	4	Render, export stems, measure LUFS, auto-gain
orchestration	7	Drum patterns, chord progressions, mastering, song structure
stems	1	Stem separation

→ See examples/langchain_integration.py

AutoGen

from opendaw_mcp.autogen_tools import get_autogen_tools, cleanup

tools = get_autogen_tools()

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    "producer",
    llm_config=llm_config,
    tools=tools,
    system_message=(
        "You are a music producer agent. Use the opendaw tools to "
        "create, mix, and render music. Always set BPM first."
    ),
)

user = UserProxyAgent("user", human_input_mode="NEVER")
user.initiate_chat(
    assistant,
    message="Create a house track at 124 BPM with 4-on-floor drums and render it"
)

# Clean up when done
import asyncio
asyncio.run(cleanup())

→ See examples/autogen_integration.py

CrewAI

from opendaw_mcp.crewai_tools import get_crewai_tools, cleanup

tools = get_crewai_tools()

from crewai import Agent, Task, Crew, LLM

llm = LLM(model="gpt-4o-mini")
producer = Agent(
    role="Music Producer",
    goal="Create and mix music tracks",
    backstory="Expert producer with 20 years of experience",
    tools=tools, llm=llm,
)
task = Task(
    description="Create a dark techno track at 130 BPM and render it",
    agent=producer, expected_output="A WAV file with the finished track",
)
crew = Crew(agents=[producer], tasks=[task], verbose=True)
result = crew.kickoff()

import asyncio
asyncio.run(cleanup())

→ See examples/crewai_integration.py

MCP protocol (direct)

The server speaks MCP natively — no wrapper needed. Add to your MCP client config:

{
  "mcpServers": {
    "opendaw": {
      "command": "opendaw-mcp",
      "env": {
        "OPENDAW_URL": "http://localhost:5174"
      }
    }
  }
}

Works with Claude Desktop, Cursor, Hermes Agent, and any MCP-compatible client. All 263 tools are available directly.

Hermes Agent

# ~/.hermes/config.yaml
mcp:
  opendaw:
    command: opendaw-mcp
    env:
      OPENDAW_URL: http://localhost:5174

Which should I use?

Framework	When to use
MCP direct	Claude Desktop, Cursor, or any MCP client — all 263 tools
LangChain	Building agents with LangChain's tool-calling ecosystem
AutoGen	Multi-agent conversations with Microsoft AutoGen
CrewAI	Role-based crews with CrewAI's Agent/Task/Crew pattern
Hermes	Hermes Agent framework with skill-based workflows