cuga

What it is and what it does

CUGA is a configurable agent harness designed to reduce the engineering burden of building domain-specific enterprise agents. Rather than orchestrating tools, planning logic, and safety policies from scratch, you configure CUGA with your APIs (via OpenAPI specs or MCP servers), tune reasoning modes (fast/balanced/accurate), and layer policies (Intent Guard, Playbook, Tool Approval, Tool Guide, Output Formatter) to govern behavior. It combines web automation (Playwright) and API calls in a single task, supports multi-agent supervision, and includes a knowledge engine for RAG over ingested documents.

The framework ships with a web UI for drafting and publishing agent configurations, Kubernetes deployment templates for self-hosting, and integrations with LangChain, LangFlow, and external agents. It targets enterprises that need a generalist starting point rather than a blank-slate agent framework, with built-in support for human-in-the-loop approval gates and policy enforcement. Maintenance is active, and the package carries 48 runtime dependencies spanning LLM providers (OpenAI, IBM, Groq, LiteLLM), async HTTP clients, browser automation, and observability tools.

Use it for:

• Automate multi-step workflows combining web form filling and backend API calls without custom orchestration code
• Deploy a domain-specific agent with safety policies (e.g., tool approval gates, output formatting rules) via configuration rather than reimplementation
• Build a multi-agent supervisor that routes tasks to specialized agents or external A2A services and aggregates results
• Ingest and reason over enterprise documents (PDFs, Office files, HTML) scoped to agent-level or per-session knowledge contexts
• Prototype and iterate on agent behavior in a web UI, then publish versioned configurations to production chat or Kubernetes clusters
• Integrate custom tools and MCP servers into a reasoning agent without rewriting core orchestration or planning logic

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

CUGA is an enterprise-grade generalist agent framework that orchestrates complex tasks across web interfaces and APIs using configurable reasoning modes, integrated tools (OpenAPI, MCP, LangChain), and policy-driven behavior controls.

Yes, if you need an enterprise agent harness with built-in tool orchestration, policy controls, and multi-agent supervision. The active maintenance, permissive license, low install friction, and zero known vulnerabilities support adoption. Start with a quick evaluation on your domain tasks—the benchmarks (AppWorld, WebArena) are strong but domain-specific, so verify reasoning modes and cost tradeoffs fit your workload. Experimental features (save-and-reuse, skills sandbox) are worth monitoring but not blockers for production use of core capabilities.

Install

cuga on PyPI

pip install cuga

uv add cuga

poetry add cuga

Installing cuga

Before you install

Low friction install with a pure Python wheel. Active maintenance (last commit 2026-08-14, 867 repository stars). Requires Python 3.10 or later and manages 48 runtime dependencies including LangChain, FastAPI, Playwright, and Docker—a substantial but well-established ecosystem.

License in practice

Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions, suitable for enterprise deployment and integration.

Quickstart

pip install cuga

from cuga import CugaAgent

agent = CugaAgent(tools=[...])
result = agent.run("your task here")

Requires Python 3.10+. Hybrid mode (web + API tasks) requires Playwright and Chromium: `playwright install chromium`. MCP server integration and knowledge features require additional configuration via settings.toml or environment setup.

Verify before relying

• Actual performance on your specific domain tasks (benchmarks cited are AppWorld and WebArena, not general-purpose workloads)
• Cost and latency tradeoffs across the three reasoning modes (fast/balanced/accurate) in production
• Stability and maturity of experimental features (save-and-reuse, agent skills sandbox modes)
• Kubernetes deployment complexity and operational overhead for self-hosting

Package facts

Evidence: cuga-0.3.1-py3-none-any.whl

Tags

More Artificial Intelligence packages

Further reading