cuga-agent MCP工具

Quick install

Use Python 3.12 and uv.

git clone https://github.com/cuga-project/cuga-agent.git
cd cuga-agent
uv sync --python 3.12
test -f .env || cp .env.example .env

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Set your provider, model, and credentials in .env. See model configuration for supported providers.

uv run cuga start manager --crm

Open /manage at the local URL printed by the CLI. Try the CRM tools, configure policies, and test a draft before publishing. More source and demo options.

See what you can build

Configure tools and policies beside a draft chat, then publish a version for users.

Current UI, captured from this repository with illustrative Revenue Operations configuration data. Internal endpoints and version labels are examples.

Build agents around your team's expertise

Deploy within your boundaries

Sovereignty covers the entire deployment: configure each model provider, connector, and optional service to stay inside your boundary. The air-gapped image preloads supporting models; deploy the reasoning model and its inference server separately.

Build with the Python SDK

Add your tools and invoke an agent from your application:

import asyncio

from cuga import CugaAgent
from langchain_core.tools import tool


@tool
def get_account_revenue(account_id: str) -> dict:
    """Return account revenue from a sample data source."""
    return {"account_id": account_id, "revenue": 250000}


async def main():
    agent = CugaAgent(tools=[get_account_revenue])
    result = await agent.invoke("What is the revenue for account acc_01?")
    print(result.answer)


asyncio.run(main())

Configure a model before running the example. Replace the sample tool with your own service integration.

CugaAgent SDK · CugaSupervisor · Tool integration examples

Build with us

Share an enterprise use case, report an issue, or contribute an integration.

Open an issue · Contributing · Contributors

settings.toml

[advanced_features] run_receipt = true

Receipts report usage and timing. Calculate cost using your own provider rates or hosting costs. See run receipts, reasoning profiles, and tool shortlisting.

Use your own inference endpoint

Point the OpenAI-compatible configuration at your private inference service:

AGENT_SETTING_CONFIG=settings.openai.toml
OPENAI_BASE_URL=http://your-inference-service:8000/v1
OPENAI_API_KEY=your-endpoint-key
MODEL_NAME=your-served-model-name

Use the model identifier exposed by your server. Hosted providers and private endpoints share the same harness; model capability, task complexity, and your configuration determine accuracy and inference spending.

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From a business request to a completed workflow

Start with the CRM demo, the knowledge walkthrough, or the multi-agent examples.