📋

Cuga Agent

CUGA is an open-source generalist agent for the enterprise, supporting complex task execution on web and APIs, OpenAPI/MCP integrations, composable architecture, reasoning modes, and policy-aware features.

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CUGA: Configurable Generalist Agent — Agent Harness for the Enterprise

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.

🎉 NEW: CUGA Enterprise SDK with Policy System — Build production-ready AI agents with enterprise-grade governance. Programmatically configure safety guards, workflow controls, and compliance policies via Python SDK or visual UI. Ensure consistent, secure, and compliant agent behavior across your organization.

Policy Types & Enterprise Value:

Policy Type Value Use Cases
Intent Guard Block unauthorized actions Data deletion prevention, access restrictions, compliance enforcement
Playbook Standardize workflows Onboarding, audit workflows, regulatory compliance
Tool Approval Human oversight Financial transactions, data modifications
Tool Guide Domain knowledge Compliance notes, domain context
Output Formatter Format, redirect, govern outputs report generation, response routing, output masking

📚 Documentation: SDK Guide | Policies Guide | Quick Start →

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
Policy System — Configure agent behavior with 5 policy types (Intent Guard, Playbook, Tool Approval, Tool Guide, Output Formatter) via the Python SDK or standalone UI in demo mode. Includes human-in-the-loop approval gates for safe agent behavior in enterprise contexts. See SDK Docs and Policies Guide
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

# 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_crm --read-only

# Chrome will open automatically at https://localhost:7860
# then try sending your task to CUGA: 'from contacts.txt show me which users belong to the crm system'

# 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
Groq - High-performance inference platform with fast LLM models
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: Groq Support ⚡

Setup Instructions:

Create an account at groq.com
Generate an API key from your API keys page

Add to your .env file:

# Groq Configuration
GROQ_API_KEY=your-groq-api-key-here
AGENT_SETTING_CONFIG="settings.groq.toml"

# Optional override
MODEL_NAME=llama-3.1-70b-versatile  # Override model name

Default Values:

Model: Configured in settings.groq.toml
Base URL: Groq's default endpoint

Option 6: 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.groq.toml - Groq 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.

📦 Using CUGA as a Python SDK

CUGA can be easily integrated into your Python applications as a library. The SDK provides a clean, minimal API for creating and invoking agents with custom tools.

📚 SDK Documentation: SDK Documentation

Quick Start

from cuga import CugaAgent
from langchain_core.tools import tool
import asyncio

@tool
def add_numbers(a: int, b: int) -> int:
    '''Add two numbers together'''
    return a + b

@tool
def multiply_numbers(a: int, b: int) -> int:
    '''Multiply two numbers together'''
    return a * b

# Create agent with tools
agent = CugaAgent(tools=[add_numbers, multiply_numbers])


async def main():
    # Add an Intent Guard to block specific operations
    await agent.policies.add_intent_guard(
        name="Block Delete Operations",
        description="Prevents deletion of critical data",
        keywords=["delete", "remove", "erase"],
        response="Deletion operations are not permitted for security reasons.",
        priority=100  # Higher priority = checked first
    )

    # Add a Playbook to provide step-by-step guidance for complex workflows
    await agent.policies.add_playbook(
        name="Budget Analysis Workflow",
        description="Multi-step process for analyzing financial budgets",
        natural_language_trigger=["When user asks to analyze their budget"],
        content="""# Budget Analysis Workflow

    ## Step 1: Calculate Total Expenses
    - Sum all expense categories using add_numbers
    - Document each category amount

    ## Step 2: Calculate Total Revenue
    - Sum all revenue streams using add_numbers
    - Include all income sources

    ## Step 3: Calculate Profit Margin
    - Use multiply_numbers to calculate profit (revenue - expenses)
    - Calculate margin percentage

    ## Step 4: Generate Recommendations
    - Compare against target budget
    - Identify areas for optimization
    - Provide actionable insights""",
        priority=50
    )

    result = await agent.invoke("Analyze my budget: expenses are 5000 and 3000, revenue is 12000")
    print(result.answer)  # The agent's response

if __name__ == "__main__":
    asyncio.run(main())

Key Features

Simple API: CugaAgent(tools=[...]) → await agent.invoke(message)
Streaming: Monitor execution in real-time with agent.stream()
State Isolation: Per-user sessions with thread_id
LangGraph Integration: Access underlying graph for advanced use cases
Flexible Tools: Direct tools or custom tool providers
Policy System: Comprehensive policy framework with 5 types:
- Intent Guard: Block or modify specific user intents
- Playbook: Step-by-step guidance for complex workflows
- Tool Approval: Require human approval before executing tools
- Tool Guide: Enhance tool descriptions with additional context
- Output Formatter: Format agent responses based on triggers

📚 Documentation: SDK Guide | Policies Guide

Knowledge Base

CUGA includes a built-in knowledge base powered by LangChain and local vector stores. Docling is integrated for document ingestion: it parses and normalizes PDFs, Office files, HTML, Markdown, images, and other supported types before chunking and embedding, so the pipeline stays self-contained with no external document services.

When enabled, the agent can search, ingest, and manage documents.

Try the knowledge demo: same as the main demo but with the knowledge engine on (upload documents and query them):

cuga start demo_knowledge

Knowledge is enabled by default via settings.toml. The SDK auto-injects knowledge tools and awareness into the agent, so it knows what documents are available and how to search them.

Programmatic Access

from cuga import CugaAgent
import asyncio

agent = CugaAgent(enable_knowledge=True)

async def main():
    # Ingest a document
    await agent.knowledge.ingest("/path/to/quarterly_report.pdf")

    # The agent now automatically knows about this document
    result = await agent.invoke("What does the report say about Q4 revenue?")
    print(result.answer)  # Agent searches knowledge base and answers

    # Direct search
    results = await agent.knowledge.search("Q4 revenue figures")
    for r in results:
        print(f"{r['filename']} (page {r['page']}): {r['text'][:100]}")

    # List documents
    docs = await agent.knowledge.list_documents()

    # Clean up
    await agent.aclose()

asyncio.run(main())

Session-Scoped Knowledge

Documents can be scoped to a specific conversation thread:

thread_id = "user-session-123"

# Ingest into session scope (temporary, per-conversation)
await agent.knowledge.ingest("/path/to/file.pdf", scope="session", thread_id=thread_id)

# Search session documents
results = await agent.knowledge.search("query", scope="session", thread_id=thread_id)

# Agent scope (default) — permanent, shared across conversations
await agent.knowledge.ingest("/path/to/file.pdf", scope="agent")

Disabling Knowledge

agent = CugaAgent(tools=[my_tools], enable_knowledge=False)

Supported Document Types

PDF, DOCX, XLSX, PPTX, HTML, Markdown, images, and more (via Docling).

CugaSupervisor (Multi-Agent)

Orchestrate multiple agents with a single supervisor: delegate tasks to specialized sub-agents, mix local agents with remote A2A agents, and pass data between them.

📚 Documentation: CugaSupervisor

Try the supervisor demo: run the multi-agent demo (CRM + email sub-agents) with:

cuga start demo_supervisor

Quick Start

from cuga import CugaAgent, CugaSupervisor
from langchain_core.tools import tool
import asyncio

@tool
def get_customers(limit: int = 10) -> str:
    """Fetch top customers from CRM with name, email, and revenue. Returns a formatted string."""
    customers = [
        "Alice (alice@example.com, $250,000)",
        "Bob (bob@example.com, $180,000)",
        "Carol (carol@example.com, $120,000)",
        "Dave (dave@example.com, $95,000)",
        "Eve (eve@example.com, $88,000)",
    ]
    top = customers[: min(limit, len(customers))]
    return "Top customers by revenue: " + "; ".join(f"{i+1}. {c}" for i, c in enumerate(top))

@tool
def send_email(to: str, body: str) -> str:
    """Send an email. Returns confirmation."""
    return f"Email sent successfully to {to}"

async def main():
    crm_agent = CugaAgent(tools=[get_customers])
    crm_agent.description = "CRM and customer data"

    email_agent = CugaAgent(tools=[send_email])
    email_agent.description = "Sending emails and notifications"

    supervisor = CugaSupervisor(agents={
        "crm": crm_agent,
        "email": email_agent,
    })

    result = await supervisor.invoke("Get our top 5 customers by revenue, then send the top customer a thank-you email")
    print(result.answer)

asyncio.run(main())

To add a remote agent via A2A, pass an external config in agents: "analytics": {"type": "external", "description": "...", "config": {"a2a_protocol": {"endpoint": "http://localhost:9999", "transport": "http"}}}.

Supervisor features

Delegation: Supervisor hands work to sub-agents and can pass variables between them when needed.
Internal + external: Combine local CugaAgent instances with external agents via A2A, task-only or variables in metadata if enabled.
Variable passing: Use variables=["var_name"] to pass previous agent outputs or context to the next agent (for internal agents, or A2A when pass_variables_a2a is enabled in settings).
Agent cards: For A2A agents, capabilities and description are taken from the agent card and shown in the supervisor prompt.

You can also load agents from YAML with CugaSupervisor.from_yaml("path/to/config.yaml"). Enable the supervisor in settings.toml under [supervisor] when using the server.

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.

☁️ Running with E2B Cloud Sandbox

CUGA supports E2B for cloud-based code execution in secure, ephemeral sandboxes. This provides better isolation than local execution while being faster than Docker/Podman containers.

Prerequisites:

Get an E2B API key:
- Sign up at e2b.dev
- Create an API key from your dashboard

Set up the E2B template:

# Install E2B CLI
npm install -g @e2b/cli

# Login with your API key
e2b auth login

# Create a template (one-time setup)
# This creates a 'cuga-langchain' template that CUGA uses
e2b template build --name cuga-langchain

Install E2B dependencies:
```
uv sync --group e2b
```
Configure environment: Add to your .env file:
```
E2B_API_KEY=your-e2b-api-key-here
```

Exposing Registry to E2B (Required)

E2B runs in the cloud and needs to call your local API registry to execute tools. You need to expose your local registry publicly using a tunneling service like ngrok.

Option 1: Expose Registry Directly (Port 8001)

Best if you have multiple ports available:

# In a separate terminal, start ngrok tunnel to registry
ngrok http 8001

# You'll get a public URL like: https://abc123.ngrok.io
# Copy this URL

Then edit ./src/cuga/settings.toml:

[server_ports]
function_call_host = "https://abc123.ngrok.io"  # Your ngrok URL

Option 2: Expose CUGA Port with Proxy (Port 7860)

Best if you're restricted to 1 port - CUGA will proxy calls to the registry:

# In a separate terminal, start ngrok tunnel to CUGA
ngrok http 7860

# You'll get a public URL like: https://xyz789.ngrok.io
# Copy this URL

Then edit ./src/cuga/settings.toml:

[server_ports]
function_call_host = "https://xyz789.ngrok.io"  # Your ngrok URL

CUGA automatically proxies /functions/call requests to the registry when using the CUGA port.

Enable E2B in Settings

Edit ./src/cuga/settings.toml:

[advanced_features]
e2b_sandbox = true
e2b_sandbox_mode = "per-session"  # Options: "per-session" | "single" | "per-call"
e2b_sandbox_ttl = 600  # Cache TTL in seconds (10 minutes)

Sandbox Modes:

per-session (default): One sandbox per conversation thread, cached for reuse
single: Single shared sandbox across all threads (most cost-effective)
per-call: New sandbox for each execution (most isolated, highest cost)

Start CUGA with E2B:

# Make sure ngrok is running in another terminal
cuga start demo

E2B will automatically execute code in cloud sandboxes. You'll see logs indicating "CODE SENT TO E2B SANDBOX" when E2B is active.

Troubleshooting:

Error: "function_call_host not configured": Make sure you've set function_call_host in settings.toml with your ngrok URL
Tool execution fails: Verify ngrok is running and the URL in settings.toml matches your ngrok URL
Connection timeout: Check that your firewall allows ngrok connections

Benefits of E2B:

✅ No Docker/Podman required
✅ Faster than container-based sandboxing
✅ Cloud-native with automatic scaling
✅ Better isolation than local execution
✅ Supports per-session caching for cost optimization

Note: E2B is a paid service with a free tier. Check e2b.dev/pricing for details.

⚙️ 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: Use Evolve with CugaLite

Evolve can now be used with CugaLite to bring task-specific guidance into the prompt before execution and save completed trajectories after the run.

This flow is:

Opt-in - disabled by default
Non-blocking - Evolve failures do not fail the task
CugaLite-focused - enabled for lite mode by default
Optional integration - install cuga[evolve] if you want the upstream Evolve package available locally, or let uvx fetch it on demand

Setup Steps:

Choose how Evolve will be started. Recommended for normal CUGA usage: let the CUGA MCP registry launch Evolve for you. In the manager UI, add an MCP tool with:

Name: evolve
Connection type: Command (stdio)
Command: uvx
Args: --from altk-evolve --with setuptools<70 evolve-mcp Important: this command starts Evolve in stdio mode through the upstream Evolve package. It is intended to be launched by the CUGA registry, not run manually in a separate terminal. Alternative for standalone/manual debugging: run Evolve yourself as an SSE server: If you run Evolve from a checked-out altk-evolve repo instead of uvx, install the Postgres extras first with uv sync --extra pgvector.

Add these environment values in the MCP tool UI:

EVOLVE_BACKEND=postgres
EVOLVE_PG_HOST=localhost
EVOLVE_PG_PORT=5432
EVOLVE_PG_USER=postgres
EVOLVE_PG_PASSWORD=postgres
EVOLVE_PG_DBNAME=evolve
EVOLVE_MODEL_NAME=Azure/gpt-4o
OPENAI_API_KEY=env://OPENAI_API_KEY
OPENAI_BASE_URL=env://OPENAI_BASE_URL

Each env://... value tells CUGA to read the real secret or setting from its own process environment at runtime, so make sure PostgreSQL is reachable, pgvector is available, and the configured OpenAI/LiteLLM-compatible model is one your gateway is allowed to use.

[Optional] Edit ./src/cuga/settings.toml and enable lite mode plus Evolve:

[advanced_features]
lite_mode = true

[evolve]
enabled = true
url = "http://127.0.0.1:8201/sse"
mode = "auto"
app_name = "evolve"
lite_mode_only = true
save_on_success = true
save_on_failure = true
async_save = true
timeout = 30.0

If you use the recommended registry-managed setup above, keep mode = "auto" or set mode = "registry".

If you run Evolve manually as a standalone SSE server, keep url = "http://127.0.0.1:8201/sse" and set mode = "direct" if you want to skip registry lookup entirely.

If you use Evolve tip generation, make sure the environment for the Evolve MCP server includes the required Evolve model settings. Otherwise save_trajectory may fail later with a LiteLLM/OpenAI model access error even when the MCP connection itself works.

Start the same CRM demo with sample workspace files:

cuga start demo_crm --sample-memory-data

Run a task that routes through CugaLite, for example:

Identify the common cities between my cuga_workspace/cities.txt and cuga_workspace/company.txt

What happens during a run?

CUGA derives the task description from the current sub-task or first user message
CugaLite asks Evolve for relevant guidelines
Returned guidelines are appended to the system prompt under an Evolve Guidelines section
The task executes normally
The user / assistant trajectory is saved back to Evolve after completion

Notes

async_save = true saves trajectories in the background and avoids blocking the response
save_on_success and save_on_failure let you control which runs are recorded
mode = "auto" lets CUGA use a registry-managed Evolve MCP server when available and fall back to the direct SSE URL otherwise
mode = "registry" is best when you want Evolve to be fully managed as a normal CUGA MCP tool
mode = "direct" is best when you are manually running an SSE Evolve server outside CUGA
If Evolve is unavailable, times out, or returns no guidance, CUGA continues normally

🔧 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

All tests are available through ./src/scripts/run_tests.sh:

Unit Tests

Registry: OpenAPI integration, MCP server functionality, service configurations
Variables Manager: Core functionality, metadata handling, singleton pattern
Code Executors: Local sandbox and E2B lite execution

Policy Integration Tests (src/cuga/backend/cuga_graph/policy/tests/)

Intent Guard: Blocking behavior, priority resolution, multiple guard scenarios
Playbook: Guidance injection, plan refinement, workflow execution
Tool Approval: Human-in-the-loop approval flows (approve/deny)
Tool Guide: Context enhancement and metadata injection
Output Formatter: Response formatting and routing
NL Trigger Conflict Resolution: Embedding-based similarity search with LLM conflict resolution
Embedding Similarity: Vector search, policy matching, threshold validation
Keyword Operators: AND/OR logic, case sensitivity, multi-keyword matching

SDK Integration Tests (src/cuga/sdk_core/tests/)

SDK functionality: Agent invocation, streaming, tool integration
Policy management: Policy loading, matching, and execution via SDK

Stability Tests (run_stability_tests.py)

Fast Mode: Get top account by revenue, list accounts, find VP sales high-value accounts
CRM Workflows: Contacts management, email operations, tool discovery
HF Utterances: Account queries, revenue calculations, playbook execution
Execution: Supports local and Docker execution, parallel/sequential modes, cross-version testing

🧪 Running Tests

Run all tests (unit, integration, and stability):

./src/scripts/run_tests.sh

Run unit tests only:

./src/scripts/run_tests.sh unit_tests

📊 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.

Policy Type	Value	Use Cases
Intent Guard	Block unauthorized actions	Data deletion prevention, access restrictions, compliance enforcement
Playbook	Standardize workflows	Onboarding, audit workflows, regulatory compliance
Tool Approval	Human oversight	Financial transactions, data modifications
Tool Guide	Domain knowledge	Compliance notes, domain context
Output Formatter	Format, redirect, govern outputs	report generation, response routing, output masking

Cuga Agent

Installation

Reviews

Documentation

CUGA: Configurable Generalist Agent — Agent Harness for the Enterprise

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

Why CUGA?

🏆 Benchmark Performance

✨ Key Features & Capabilities

🎬 CUGA in Action

Hybrid Task Execution

Setup Steps:

Human in the Loop Task Execution

Setup Steps:

🚀 Quick Start

Supported Platforms

Configuration Priority

Option 1: OpenAI 🌐

Option 2: IBM WatsonX 🔵

Option 3: Azure OpenAI

Option 4: LiteLLM Support

Option 5: Groq Support ⚡

Option 6: OpenRouter Support

Configuration Files

📦 Using CUGA as a Python SDK

Quick Start

Key Features

Knowledge Base

Programmatic Access

Session-Scoped Knowledge

Disabling Knowledge

Supported Document Types

CugaSupervisor (Multi-Agent)

Quick Start

Supervisor features

Configurations

Prerequisites:

Exposing Registry to E2B (Required)

Option 1: Expose Registry Directly (Port 8001)

Option 2: Expose CUGA Port with Proxy (Port 7860)

Enable E2B in Settings

Sandbox Modes:

Start CUGA with E2B:

Troubleshooting:

Available Modes under ./src/cuga

Configuration

Available Task Modes

How Task Modes Work

API Mode (mode = 'api')

Web Mode (mode = 'web')

Hybrid Mode (mode = 'hybrid')

Configuration

How It Works

Configuration

Setup Steps:

What happens during a run?

Notes

🔧 Advanced Usage

Setup

Demo Steps

📋 Tool Types Overview

📚 Additional Resources

Test Scenarios - E2E

🧪 Running Tests

📊 Evaluation

📚 Resources

Call for the Community

Roadmap

Before Submitting a PR

Contributors

Security Checklist

Available Modes under `./src/cuga`

API Mode (`mode = 'api'`)

Web Mode (`mode = 'web'`)

Hybrid Mode (`mode = 'hybrid'`)