Find AI tools that automatically generate unit tests, integration tests, and test suites for your codebase.
AI-Driven Automated Red Team Orchestration Framework | AI驱动的自动化红队编排框架 | 101 MCP Tools | 2000+ Payloads | Full ATT&CK Coverage | MCTS Attack Planner | Knowledge Graph | Cross-platform
Regression testing for AI agents. Snapshot behavior,diff tool calls,catch regressions in CI. Works with LangGraph, CrewAI, OpenAI, Anthropic.
A multi-agent AI architecture that connects 25+ specialized agents through n8n and MCP servers. Project NOVA routes requests to domain-specific experts, enabling control of applications from knowledge bases to DAWs, home automation to development tools. Includes system prompts, Dockerfiles, and workflows for a complete AI assistant ecosystem.
Architecture-first skill lifecycle for AI agents. 5 modes: CREATE → EVAL → EDIT → REVIEW → PACKAGE. Integrates Anthropic's eval engine (grader/comparator/analyzer agents, blind A/B, benchmarks) with architecture patterns, TDD baseline, and 5-axis scoring. Not just testing - full design-to-distribution.
Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.
Convert plain English test specs into self-healing Playwright tests using AI. Browser exploration, auto-fix, load/security/API/LLM testing. Open source.
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
The most powerful Android RPA agent framework, next generation of mobile automation robots.
HexStrike AI MCP Agents is an advanced MCP server that lets AI agents (Claude, GPT, Copilot, etc.) autonomously run 150+ cybersecurity tools for automated pentesting, vulnerability discovery, bug bounty automation, and security research. Seamlessly bridge LLMs with real-world offensive security capabilities.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| AutoRedTeam-Orchestrator | ★ 187 | Python | — | 36 |
| eval-view | ★ 76 | Python | Apache-2.0 | 35 |
| project-nova | ★ 28 | Shell | — | 48 |
| skill-conductor | ★ 28 | Python | MIT | 36 |
| promptfoo | ★ 18.7k | TypeScript | MIT | 47 |
| quorvex_ai | ★ 23 | TypeScript | MIT | 31 |
| RagaAI-Catalyst | ★ 16.1k | Python | Apache-2.0 | 42 |
| lamda | ★ 7.7k | Python | MIT | 46 |
| hexstrike-ai | ★ 7.3k | Python | MIT | 48 |
| shortest | ★ 5.5k | TypeScript | MIT | 34 |
The top test generation tools include AutoRedTeam-Orchestrator, eval-view, project-nova. These are ranked by our composite score based on GitHub stars, community activity, and code quality.
Most tools listed here are open-source. 8 out of 10 have explicit open-source licenses, making them free to use and modify.
Consider your tech stack (language compatibility), project scale (stars indicate community trust), and specific features you need. Use the comparison table above to evaluate side by side.
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