cuga

CUGA: The Configurable Generalist Agent

Start with a generalist. Customize for your domain. Deploy faster!

Building a domain-specific enterprise agent from scratch is complex and requires significant effort: agent and tool orchestration, planning logic, safety and alignment policies, evaluation for performance/cost tradeoffs and ongoing improvements. CUGA is a state-of-the-art generalist agent designed with enterprise needs in mind, so you can focus on configuring your domain tools, policies and workflow.

Why CUGA?

🏆 Benchmark Performance

CUGA achieves state-of-the-art performance on leading benchmarks:

• 🥇 #1 on AppWorld — a benchmark with 750 real-world tasks across 457 APIs
• 🥈 Top-tier on WebArena (#1 from 02/25 - 09/25) — a complex benchmark for autonomous web agents across application domains

✨ Key Features & Capabilities

• High-performing generalist agent — Benchmarked on complex web and API tasks. Combines best-of-breed agentic patterns (e.g. planner-executor, code-act) with structured planning and smart variable management to prevent hallucination and handle complexity

• Configurable reasoning modes — Balance performance and cost/latency with flexible modes ranging from fast heuristics to deep planning, optimizing for your specific task requirements

• Flexible agent and tool integration — Seamlessly integrate tools via OpenAPI specs, MCP servers, and Langchain, enabling rapid connection to REST APIs, custom protocols, and Python functions

• Integrates with Langflow — Low-code visual build experience for designing and deploying agent workflows without extensive coding

• Open-source and composable — Built with modularity in mind, CUGA itself can be exposed as a tool to other agents, enabling nested reasoning and multi-agent collaboration. Evolving toward enterprise-grade reliability

• Configurable policy and human-in-the-loop instructions (Experimental) — Configure policy-aware instructions and approval gates to improve alignment and ensure safe agent behavior in enterprise contexts

• Save-and-reuse capabilities (Experimental) — Capture and reuse successful execution paths (plans, code, and trajectories) for faster and consistent behavior across repeated tasks

Explore the Roadmap to see what's ahead, or join the 🤝 Call for the Community to get involved.

🎬 CUGA in Action

Hybrid Task Execution

Watch CUGA seamlessly combine web and API operations in a single workflow:

Example Task: get top account by revenue from digital sales, then add it to current page

https://github.com/user-attachments/assets/0cef8264-8d50-46d9-871a-ab3cefe1dde5

Would you like to test this? (Advanced Demo)

Experience CUGA's hybrid capabilities by combining API calls with web interactions:

Setup Steps:

• Switch to hybrid mode:

  # Edit ./src/cuga/settings.toml and change:
  mode = 'hybrid'  # under [advanced_features] section
  

• Install browser API support:

  • Installs playwright browser API and Chromium browser
  • The playwright installer should already be included after installing with Quick Start

  playwright install chromium
  

• Start the demo:

  cuga start demo
  

• Enable the browser extension:

  • Click the extension puzzle icon in your browser
  • Toggle the CUGA extension to activate it
  • This will open the CUGA side panel

• Open the test application:

  • Navigate to: Sales app

• Try the hybrid task:

  get top account by revenue from digital sales then add it to current page
  

🎯 What you'll see: CUGA will fetch data from the Digital Sales API and then interact with the web page to add the account information directly to the current page - demonstrating seamless API-to-web workflow integration!

Human in the Loop Task Execution

Watch CUGA pause for human approval during critical decision points:

Example Task: get best accounts

https://github.com/user-attachments/assets/d103c299-3280-495a-ba66-373e72554e78

Would you like to try this? (HITL Demo)

Experience CUGA's Human-in-the-Loop capabilities where the agent pauses for human approval at key decision points:

Setup Steps:

• Enable HITL mode:

  # Edit ./src/cuga/settings.toml and ensure:
  api_planner_hitl = true  # under [advanced_features] section
  

• Start the demo:

  cuga start demo
  

• Try the HITL task:

  get best accounts
  

🎯 What you'll see: CUGA will pause at critical decision points, showing you the planned actions and waiting for your approval before proceeding.

🚀 Quick Start

📋 Prerequisites (click to expand)

• Python 3.12+ - Download here
• uv package manager - Installation guide

🔧 Optional: Local Digital Sales API Setup (only if remote endpoint fails)

The demo comes pre-configured with the Digital Sales API → 📖 API Docs

Only follow these steps if you encounter issues with the remote Digital Sales endpoint:

# Start the Digital Sales API locally on port 8000
uv run digital_sales_openapi

# Then update ./src/cuga/backend/tools_env/registry/config/mcp_servers.yaml to use localhost:
# Change the digital_sales URL from the remote endpoint to:
# http://localhost:8000

# In terminal, clone the repository and navigate into it
git clone https://github.com/cuga-project/cuga-agent.git
cd cuga-agent

# 1. Create and activate virtual environment
uv venv --python=3.12 && source .venv/bin/activate

# 2. Install dependencies
uv sync

# 3. Set up environment variables
# Create .env file with your API keys
echo "OPENAI_API_KEY=your-openai-api-key-here" > .env

# 4. Start the demo
cuga start demo

# Chrome will open automatically at https://localhost:7860
# then try sending your task to CUGA: 'get top account by revenue from digital sales'

# 5. View agent trajectories (optional)
cuga viz

# This launches a web-based dashboard for visualizing and analyzing
# agent execution trajectories, decision-making, and tool usage

🤖 LLM Configuration - Advanced Options

Refer to: .env.example for detailed examples.

CUGA supports multiple LLM providers with flexible configuration options. You can configure models through TOML files or override specific settings using environment variables.

Supported Platforms

• OpenAI - GPT models via OpenAI API (also supports LiteLLM via base URL override)
• IBM WatsonX - IBM's enterprise LLM platform
• Azure OpenAI - Microsoft's Azure OpenAI service
• RITS - Internal IBM research platform
• OpenRouter - LLM API gateway provider

Configuration Priority

• Environment Variables (highest priority)
• TOML Configuration (medium priority)
• Default Values (lowest priority)

Option 1: OpenAI 🌐

Setup Instructions:

• Create an account at platform.openai.com
• Generate an API key from your API keys page
• Add to your .env file:

  # OpenAI Configuration
  OPENAI_API_KEY=sk-...your-key-here...
  AGENT_SETTING_CONFIG="settings.openai.toml"
  
  # Optional overrides
  MODEL_NAME=gpt-4o                    # Override model name
  OPENAI_BASE_URL=https://api.openai.com/v1  # Override base URL
  OPENAI_API_VERSION=2024-08-06        # Override API version
  

Default Values:

• Model: gpt-4o
• API Version: OpenAI's default API Version
• Base URL: OpenAI's default endpoint

Option 2: IBM WatsonX 🔵

Setup Instructions:

• Access IBM WatsonX

• Create a project and get your credentials:

  • Project ID
  • API Key
  • Region/URL

• Add to your .env file:

  # WatsonX Configuration
  WATSONX_API_KEY=your-watsonx-api-key
  WATSONX_PROJECT_ID=your-project-id
  WATSONX_URL=https://us-south.ml.cloud.ibm.com  # or your region
  AGENT_SETTING_CONFIG="settings.watsonx.toml"
  
  # Optional override
  MODEL_NAME=meta-llama/llama-4-maverick-17b-128e-instruct-fp8  # Override model for all agents
  

Default Values:

• Model: meta-llama/llama-4-maverick-17b-128e-instruct-fp8

Option 3: Azure OpenAI

Setup Instructions:

• Add to your .env file:

   AGENT_SETTING_CONFIG="settings.azure.toml"  # Default config uses ETE
   AZURE_OPENAI_API_KEY="<your azure apikey>"
   AZURE_OPENAI_ENDPOINT="<your azure endpoint>"
   OPENAI_API_VERSION="2024-08-01-preview"
  

Option 4: LiteLLM Support

CUGA supports LiteLLM through the OpenAI configuration by overriding the base URL:

• Add to your .env file:

  # LiteLLM Configuration (using OpenAI settings)
  OPENAI_API_KEY=your-api-key
  AGENT_SETTING_CONFIG="settings.openai.toml"
  
  # Override for LiteLLM
  MODEL_NAME=Azure/gpt-4o              # Override model name
  OPENAI_BASE_URL=https://your-litellm-endpoint.com  # Override base URL
  OPENAI_API_VERSION=2024-08-06        # Override API version
  

Option 5: OpenRouter Support

Setup Instructions:

• Create an account at openrouter.ai
• Generate an API key from your account settings
• Add to your .env file:

  # OpenRouter Configuration
  OPENROUTER_API_KEY=your-openrouter-api-key
  AGENT_SETTING_CONFIG="settings.openrouter.toml"
  OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"
   # Optional override
  MODEL_NAME=openai/gpt-4o                    # Override model name
  

Configuration Files

CUGA uses TOML configuration files located in src/cuga/configurations/models/:

• settings.openai.toml - OpenAI configuration (also supports LiteLLM via base URL override)
• settings.watsonx.toml - WatsonX configuration
• settings.azure.toml - Azure OpenAI configuration
• settings.openrouter.toml - OpenRouter configuration

Each file contains agent-specific model settings that can be overridden by environment variables.

💡 Tip: Want to use your own tools or add your MCP tools? Check out src/cuga/backend/tools_env/registry/config/mcp_servers.yaml for examples of how to configure custom tools and APIs, including those for digital sales.

Configurations

🔒 Running with a secure code sandbox

Cuga supports isolated code execution using Docker/Podman containers for enhanced security.

• Install container runtime: Download and install Rancher Desktop or Docker.

• Install sandbox dependencies:

  uv sync --group sandbox
  

• Start with remote sandbox enabled:

  cuga start demo --sandbox
  

  This automatically configures Cuga to use Docker/Podman for code execution instead of local execution.

• Test your sandbox setup (optional):

  # Test local sandbox (default)
  cuga test-sandbox
  
  # Test remote sandbox with Docker/Podman
  cuga test-sandbox --remote
  

  You should see the output: ('test succeeded\n', {})

Note: Without the --sandbox flag, Cuga uses local Python execution (default), which is faster but provides less isolation.

⚙️ Reasoning modes - Switch between Fast/Balanced/Accurate modes

Available Modes under ./src/cuga

Mode	File	Description
fast	./configurations/modes/fast.toml	Optimized for speed
balanced	./configurations/modes/balanced.toml	Balance between speed and precision (default)
accurate	./configurations/modes/accurate.toml	Optimized for precision
custom	./configurations/modes/custom.toml	User-defined settings

Configuration

configurations/
├── modes/fast.toml
├── modes/balanced.toml
├── modes/accurate.toml
└── modes/custom.toml

Edit settings.toml:

[features]
cuga_mode = "fast"  # or "balanced" or "accurate" or "custom"

Documentation: ./docs/flags.html

🎯 Task Mode Configuration - Switch between API/Web/Hybrid modes

Available Task Modes

Mode	Description
api	API-only mode - executes API tasks (default)
web	Web-only mode - executes web tasks using browser extension
hybrid	Hybrid mode - executes both API tasks and web tasks using browser extension

How Task Modes Work

API Mode (mode = 'api')

• Opens tasks in a regular web browser
• Best for API/Tools-focused workflows and testing

Web Mode (mode = 'web')

• Interface inside a browser extension (available next to browser)
• Optimized for web-specific tasks and interactions
• Direct access to web page content and controls

Hybrid Mode (mode = 'hybrid')

• Opens inside browser extension like web mode
• Can execute both API/Tools tasks and web page tasks simultaneously
• Starts from configurable URL defined in demo_mode.start_url
• Most versatile mode for complex workflows combining web and API operations

Configuration

Edit ./src/cuga/settings.toml:

[demo_mode]
start_url = "https://opensource-demo.orangehrmlive.com/web/index.php/auth/login"  # Starting URL for hybrid mode


[advanced_features]
mode = 'api'  # 'api', 'web', or 'hybrid'

📝 Special Instructions Configuration

How It Works

Each .md file contains specialized instructions that are automatically integrated into the CUGA's internal prompts when that component is active. Simply edit the markdown files to customize behavior for each node type.

Available instruction sets: answer, api_planner, code_agent, plan_controller, reflection, shortlister, task_decomposition

Configuration

configurations/
└── instructions/
    ├── instructions.toml
    ├── default/
    │   ├── answer.md
    │   ├── api_planner.md
    │   ├── code_agent.md
    │   ├── plan_controller.md
    │   ├── reflection.md
    │   ├── shortlister.md
    │   └── task_decomposition.md
    └── [other instruction sets]/

Edit configurations/instructions/instructions.toml:

[instructions]
instruction_set = "default"  # or any instruction set above

📹 Optional: Run with memory

• Install memory dependencies uv sync --group memory
• Change enable_memory = true in setting.toml
• Run cuga start memory

Watch CUGA with Memory enabled

[LINK]

Would you like to test this? (Advanced Demo)

Setup Steps:

• set enable_memory flag to true
• Run cuga start memory
• Run cuga start demo_crm --sample-memory-data
• go to the cuga webpage and type Identify the common cities between my cuga_workspace/cities.txt and cuga_workspace/company.txt . Here you should see the errors related to CodeAgent. Wait for a minute for tips to be generated. Tips generation can be confirmed from the terminal where cuga start memory was run
• Re-run the same utterance again and it should finish in lesser number of steps

🔧 Advanced Usage

💾 Save & Reuse

Setup

• Change ./src/cuga/settings.toml: cuga_mode = "save_reuse_fast" • Run: cuga start demo

Demo Steps

• First run: get top account by revenue

• This is a new flow (first time)
• Wait for task to finish
• Approve to save the workflow
• Provide another example to help generalization of flow e.g. get top 2 accounts by revenue

• Flow now will be saved:

• May take some time
• Flow will be successfully saved

• Verify reuse: get top 4 accounts by revenue

• Should run faster using saved workflow

🔧 Adding Tools: Comprehensive Examples

CUGA supports three types of tool integrations. Each approach has its own use cases and benefits:

📋 Tool Types Overview

Tool Type	Best For	Configuration	Runtime Loading
OpenAPI	REST APIs, existing services	mcp_servers.yaml	✅ Build
MCP	Custom protocols, complex integrations	mcp_servers.yaml	✅ Build
LangChain	Python functions, rapid prototyping	Direct import	✅ Runtime

📚 Additional Resources

• Tool Registry: ./src/cuga/backend/tools_env/registry/README.md
• Comprehensive example with different tools + MCP: [./docs/examples/cuga_with_runtime_tools/README.md](Adding Tools)
• CUGA as MCP: ./docs/examples/cuga_as_mcp/README.md

Test Scenarios - E2E

The test suite covers various execution modes across different scenarios:

Scenario	Fast Mode	Balanced Mode	Accurate Mode	Save & Reuse Mode
Find VP Sales High-Value Accounts	✓	✓	✓	-
Get top account by revenue	✓	✓	✓	✓
List my accounts	✓	✓	✓	-

Additional Test Categories

Unit Tests

• Variables Manager: Core functionality, metadata handling, singleton pattern, reset operations
• Value Preview: Intelligent truncation, nested structure preservation, length-aware formatting

Integration Tests

• API Response Handling: Error cases, validation, timeout scenarios, parameter extraction
• Registry Services: OpenAPI integration, MCP server functionality, mixed service configurations
• Tool Environment: Service loading, parameter handling, function calling, isolation testing

🧪 Running Tests

Focused suites:

./src/scripts/run_tests.sh

📊 Evaluation

For information on how to evaluate, see the CUGA Evaluation Documentation

📚 Resources

• 📖 Example applications
• 📧 Contact: CUGA Team

Call for the Community

CUGA is open source because we believe trustworthy enterprise agents must be built together.
Here's how you can help:

• Share use cases → Show us how you'd use CUGA in real workflows.
• Request features → Suggest capabilities that would make it more useful.
• Report bugs → Help improve stability by filing clear, reproducible reports.

All contributions are welcome through GitHub Issues - whether it's sharing use cases, requesting features, or reporting bugs!

Roadmap

Amongst other, we're exploring the following directions:

• Policy support: procedural SOPs, domain knowledge, input/output guards, context- and tool-based constraints
• Performance improvements: dynamic reasoning strategies that adapt to task complexity

Before Submitting a PR

Please follow the contribution guide in CONTRIBUTING.md.

Contributors