{"data":{"slug":"unicomai-wanwu","name":"wanwu","tagline":"China Unicom's Yuanjing Wanwu Agent Platform is an enterprise-grade, multi-tenant AI agent development platform. It helps users build applications such as intelligent agents, workflows, and rag, and a","github_url":"https://github.com/UnicomAI/wanwu","owner":"UnicomAI","repo":"wanwu","owner_avatar_url":"https://avatars.githubusercontent.com/u/167508496?v=4","primary_language":"Go","stars":2559,"forks":117,"topics":["agent","agentic-ai","agentic-framework","ai","ai-agent","ai-agent-development-framework","ai-agents-framework","development","genai","golang","llm","mcp","open-ai","rag","wanwu","workflow"],"archived":false,"github_pushed_at":"2026-07-15T01:40:48+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/unicomai-wanwu","markdown_url":"https://www.graphcanon.com/tools/unicomai-wanwu.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/unicomai-wanwu","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=unicomai-wanwu","description":"China Unicom's Yuanjing Wanwu Agent Platform is an enterprise-grade, multi-tenant AI agent development platform. It helps users build applications such as intelligent agents, workflows, and rag, and also supports model management. The platform features a developer-friendly license, and we welcome all developers to build upon the platform.","homepage_url":null,"license":"Apache-2.0","open_issues":26,"watchers":31,"ai_summary":null,"readme_excerpt":"### 🚀 3 Deployment Methods: Reach Business On‑Site\n\nAfter capability building, Wanwu provides 3 deployment paths to minimize FDE delivery difficulty:\n\n#### 📦 Method 1: Out‑of‑the‑Box Platform\n\nUse directly via visual interface; no coding required for creating agents, workflows, and Q&A. Zero‑threshold AI productivity for rapid on‑site validation and delivery.\n\n#### 🔗 Method 2: API Seamless Integration\n\nRESTful API (BaaS) for embedding into OA, CRM, ERP, etc. Fine‑grained permission control enables deep AI integration without changing user habits.\n\n#### 🖥️Method 3: Skill + UniClaw Dedicated Client\n\nFor high‑privilege scenarios (local PC control, DingTalk messages, etc.). FDEs develop Skills and execute via UniClaw to handle cross‑system high‑privilege on‑site operations.\n\nUniClaw download: https://maas.ai-yuanjing.com/app/uniclaw/uniclaw-official.html\n\n------\n\n---\n\n### 🛠️ Infrastructure & Ecosystem\n\n- 🔥 **Apache‑2.0 License**: Free extension, secondary development, and commercial use.\n- ✔ **Model Hub**: Unified access to hundreds of proprietary/open‑source models; deep OpenAI API compatibility and Yuanjing ecosystem support; multiple inference backends.\n- ✔ **Skill Plaza**: 100+ built‑in industry Skills ready to use; no adapters needed for external capabilities.\n- ✔ **Web Search**: Real‑time information, multi‑source integration, intelligent retrieval strategies.\n- ✔ **Multi‑tenant architecture**: Isolated accounts for cost control, data security, and elastic scaling.\n- **✔ Xinchuang compliance**: Certified *Xinchuang AI Software/Hardware System Inspection Certificate*. Supports Kunpeng CPUs, Euler, Kylin, CULinux, TiDB, OceanBase, etc.\n\n------\n\n---\n\n### 🚀 Quick Start\n\n- The workflow module of the Wanwu AI Agent Platform uses the following project, you can go to its warehouse to view the details.\n  - v0.1.8 and earlier: wanwu-agentscope project\n  - v0.2.0 and later: [wanwu-workflow](https://github.com/UnicomAI/wanwu-workflow/tree/dev/wanwu-backend) project\n\n- **Recommended Configuration:**\n  - CPU: 8-core or 16-core; RAM: 32GB; Storage: 200GB or more; GPU: Not required.\n\n- **Model Requirements:**\n  - When using WanwuBot (General Agent) or creating Skills with a single command, the selected model must have a context length >= 32000 when importing.\n\n- **Security Statement:**\n  - All middleware components (MySQL, Redis, MinIO, Kafka, Elasticsearch, etc.) support custom passwords configured in the `.env` file.\n  - For front-end and back-end user password transmission, RSA asymmetric encryption is used to encrypt passwords. The key pair is automatically generated on first service startup; custom key pairs can also be configured via `WANWU_BFF_LOGIN_RSA_PATH` in `.env`.\n  - It is strongly recommended to change all default passwords and keep them secure before deploying in a production environment to avoid security risks associated with default credentials.\n\n- **Docker Installation (Recommended)**\n\n1. Before the first run\n\n    1.1 Copy the environment variable file\n    ```bash\n    cp .env.example .env\n    ```\n\n    1.2 Modify the `WANWU_ARCH` and `WANWU_EXTERNAL_IP` variables in the .env file according to the system\n    ```\n    # amd64 / arm64\n    WANWU_ARCH=amd64\n    \n    # external ip port (Note: if the browser accesses Wanwu deployed on a non-localhost server, you need to change localhost to the external IP, for example, 192.168.xx.xx)\n    WANWU_EXTERNAL_IP=localhost\n    ```\n\n    1.3 Configure the `WANWU_BFF_JWT_SIGNING_KEY` variable in the .env file, a custom complex random string used for generating JWT tokens\n    ```\n    # bff\n    WANWU_BFF_JWT_SIGNING_KEY=\n    ```\n\n    1.4 Copy environment variable file (Ontology Agent)\n    ```bash\n    cp .env.ontology.example .env.ontology\n    ```\n\n    1.5 (Optional) Generate custom RSA keys before the first run (Ontology Agent)\n    > Skip this step to use the default keys baked into the images. To use independent keys in production, generate them as follows.\n\n    - 1.5.1 Generate RSA ke","github_created_at":"2025-06-06T02:45:28+00:00","created_at":"2026-07-15T10:48:36.22359+00:00","updated_at":"2026-07-15T10:48:39.881796+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"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"}],"tags":[{"slug":"agent","name":"agent"},{"slug":"agentic-ai","name":"agentic-ai"},{"slug":"agentic-framework","name":"agentic-framework"},{"slug":"ai","name":"ai"},{"slug":"ai-agent","name":"ai-agent"},{"slug":"ai-agent-development-framework","name":"ai-agent-development-framework"},{"slug":"ai-agents-framework","name":"ai-agents-framework"},{"slug":"development","name":"development"}],"trust":{"provenance":{"is_fork":false,"github_id":997136302,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-15T10:48:37.192Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":11,"days_since_push":0,"last_release_at":"2026-07-10T10:14:53Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":23,"high_count":0,"last_scan_at":"2026-07-15T10:48:37.646Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-15T10:48:36.962Z"},"languages":{"value":["go"],"source":"github.language","observed_at":"2026-07-15T10:48:36.962Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-15T10:48:36.962Z"}}}}