{"data":{"slug":"verifywise-ai-verifywise","name":"verifywise","tagline":"Complete AI governance and LLM Evals platform with support for EU AI Act, ISO 42001, NIST AI RMF and 20+ more AI frameworks and regulations. Join our Discord channel: https://discord.com/invite/d3k3E4","github_url":"https://github.com/verifywise-ai/verifywise","owner":"verifywise-ai","repo":"verifywise","owner_avatar_url":"https://avatars.githubusercontent.com/u/262239808?v=4","primary_language":"TypeScript","stars":319,"forks":107,"topics":["ai","ai-auditing","ai-compliance","ai-governance","ai-governance-model","ai-risk","audit","auditing","compliance","eu-ai-act","governance","grc","iso27001","iso42001","llm-eval","llm-evaluation","nist-ai-rmf","risk-management"],"archived":false,"github_pushed_at":"2026-07-10T05:39:08+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/verifywise-ai-verifywise","markdown_url":"https://www.graphcanon.com/tools/verifywise-ai-verifywise.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/verifywise-ai-verifywise","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=verifywise-ai-verifywise","description":"Complete AI governance and LLM Evals platform with support for EU AI Act, ISO 42001, NIST AI RMF and 20+ more AI frameworks and regulations. Join our Discord channel: https://discord.com/invite/d3k3E4uEpR","homepage_url":"https://verifywise.ai","license":"Other","open_issues":74,"watchers":3,"ai_summary":null,"readme_excerpt":"## Installation\n\nThe VerifyWise application has two components: a frontend built with React.js and a backend built with Node.js. At present, you can use `npm` (for development) or Docker/Kubernetes (production) to run VerifyWise. A PostgreSQL database is required.\n\n---\n\n### Installation using npm (for development)\n\nPrerequisites:\n\n- npm and Docker\n- Python 3.12+ (for EvalServer)\n- A running PostgreSQL, preferably as a Docker image (eg. using `docker pull postgres:latest`)\n- Available ports: 5173 (frontend), 3000 (backend), 5432 (database), 6379 (Redis), 8000 (EvalServer)\n\n#### Step 1: Clone, install dependencies, and set up databases\n\nFirst, clone the repository to your local machine and go to verifywise directory. Then, navigate to the Clients directory and install the dependencies:\n\n```\ngit clone https://github.com/bluewave-labs/verifywise.git\ncd verifywise\ncd Clients\nnpm install\ncd ../Servers\nnpm install\n```\n\nGo to the root directory and copy the contents of .env.dev to the .env file. For security, you must set a strong and unpredictable JWT_SECRET in your .env file. This secret is used to sign and verify your JWT tokens, so it must be kept private and cryptographically secure. You can generate a 256-bit base64-encoded secret using `openssl rand -base64 32`.\n\n```\ncd ..\ncp .env.dev Servers/.env\n```\n\nIn `.env` file, change FRONTEND_URL and set your super admin credentials:\n\n```\nFRONTEND_URL=http://localhost:5173\nSUPERADMIN_EMAIL=admin@verifywise.com\nSUPERADMIN_PASSWORD=ChangeMe!Str0ng\n```\n\n**Important:** Change `SUPERADMIN_PASSWORD` to a strong password (minimum 8 characters). These credentials are used to create the initial super admin account on first setup.\n\nNote: CORS is automatically configured to allow requests from the same host (localhost, 127.0.0.1) where the backend is running.\n\nRun the PostgreSQL container with the following command:\n\n```\ndocker run -d --name mypostgres -p 5432:5432 -e POSTGRES_PASSWORD={env variable password} postgres\n```\n\nRun redis with following command:\n\n```\ndocker run -d --name myredis -p 6379:6379 redis\n```\n\nAccess the PostgreSQL container and create the verifywise database:\n\n```\ndocker exec -it mypostgres psql -U postgres\nCREATE DATABASE verifywise;\n```\n\n#### Step 2: Set up EvalServer (for LLM evaluations)\n\nEvalServer is a Python-based service that handles LLM evaluations. If you want to use the evaluation features, follow these steps:\n\n```\ncd EvalServer\npython3.12 -m venv venv\nsource venv/bin/activate\npip install -r requirements.txt\n```\n\nSet up the environment file. You can copy the minimal `.env.example` file in the EvalServer directory:\n\n```\ncp .env.example .env\n```\n\n#### Step 3: Start the application\n\nNavigate to the EvalServer/src directory, activate the virtual environment (if not already activated), and start the server:\n\n```\ncd EvalServer/src\nsource ../venv/bin/activate\npython app.py\n```\n\nNavigate to the Servers directory and start the server in watch mode:\n\n```\ncd Servers\nnpm run watch\n```\n\nNavigate to the Clients directory and start the client in development mode:\n\n```\ncd Clients\nnpm run dev\n```\n\n**Note:** Make sure to replace {env variable password} with the actual password from your environment variables.\n\n**Note:** On a fresh setup, a super admin account is created automatically using the `SUPERADMIN_EMAIL` and `SUPERADMIN_PASSWORD` environment variables. Log in with these credentials, then create an organization and invite users to get started. The login page will display a banner guiding you through this process.\n\n---\n\n### Installation using Docker (production)\n\nFirst, ensure you have the following installed:\n\n- npm\n- Docker\n- Docker Compose\n\nCreate a directory in your desired folder:\n\n```\nmkdir verifywise\ncd verifywise\n```\n\nDownload the required files using wget:\n\n```\ncurl -O https://raw.githubusercontent.com/bluewave-labs/verifywise/develop/install.sh\ncurl -O https://raw.githubusercontent.com/bluewave-labs/verifywise/develop/.env.prod\n```\n\nMake sure to change the JWT_SECRET","github_created_at":"2024-08-18T23:02:10+00:00","created_at":"2026-07-11T12:00:29.097399+00:00","updated_at":"2026-07-11T12:00:52.535692+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"},{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"}],"tags":[{"slug":"auditing","name":"auditing"},{"slug":"ai","name":"ai"},{"slug":"ai-risk","name":"ai-risk"},{"slug":"audit","name":"audit"},{"slug":"ai-governance-model","name":"ai-governance-model"},{"slug":"ai-auditing","name":"ai-auditing"},{"slug":"ai-compliance","name":"ai-compliance"},{"slug":"ai-governance","name":"ai-governance"}],"trust":{"provenance":{"is_fork":false,"github_id":844279282,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T12:00:29.763Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":7,"days_since_push":1,"last_release_at":"2026-06-24T03:00:47Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":1,"high_count":0,"last_scan_at":"2026-07-11T12:00:36.394Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"mcp":{"source":"repo_scan","observed_at":"2026-07-11T12:00:35.695Z","server_manifest":false},"scan":{"source":"repo_scan","observed_at":"2026-07-11T12:00:35.695Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-07-11T12:00:35.695Z","managed_saas":false},"languages":{"value":["typescript","javascript"],"source":"github.language+package.json","observed_at":"2026-07-11T12:00:35.695Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-07-11T12:00:35.695Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-07-11T12:00:35.695Z"}}}}