Yuanjing Wanwu Agent Platform
An all-in-one enterprise-grade multi-agent development platform covering RAG, workflows, MCP, and general agents for fast AI delivery.
Evidence shows: README mentions multi-tenant isolation, fine-grained permission control, RSA encrypted passwords, JWT signing key configuration, but lacks implementation details or security audit. Deductions: no user confirmation mechanism, data flow transparency, external effects control, rollback mechanism evidence.
Evidence shows: README provides deployment steps and configuration instructions, but lacks error handling or recovery information. Deductions: no failure messages and error handling documentation.
Evidence shows: README describes multiple application scenarios and deployment methods, but lacks clear capability boundaries or trigger conditions. Deductions: no trigger precision and explicit capability boundary description.
Evidence shows: README has clear structure, provides installation instructions, license, and version upgrade guide, but lacks known limitations and detailed changelog. Deductions: no known limitations and detailed changelog.
Evidence shows: README emphasizes rich features and ease of use, but lacks performance benchmarks or cost analysis. Deductions: no specific evidence for output usability and cost-benefit.
Evidence shows: README includes feature comparisons and claims, but lacks verifiable test results or third-party validation. Deductions: no cross-source verification and separation of facts and inferences.
- No user confirmation mechanism, potentially executing high-risk operations automatically.
- Insufficient data flow transparency, users cannot track data usage.
- No rollback mechanism, operations may not be recoverable after failure.
- Dependency security not assessed, potential vulnerability risks.
What does this agent do, and when should you use it?
Yuanjing Wanwu Agent Platform is China Unicom's enterprise-grade, multi-tenant agent development platform released under the Apache-2.0 license. It integrates model management, MCP marketplace, high-precision RAG, visual workflows, general agents, and skill orchestration, and provides BaaS APIs for seamless integration with enterprise systems. The platform can be deployed via Docker or from source, and supports Xinchuang (domestic) environments such as Kunpeng CPUs, openEuler, Kylin, TiDB, and OceanBase. Its core focus is providing a full-stack FDE toolchain to lower the barrier to AI project delivery.
The platform provides: unified model hub for hundreds of models (e.g., GPT, Claude, Llama) with vLLM/TGI inference backends; 100+ pre-integrated MCP servers; RAG knowledge base supporting 12 file formats and URL crawling, with OCR/MinerU parsing and GraphRAG enhancement; a low-code visual workflow editor; functionality to build general agents (WanwuBot) and vertical skills with zero-code invocation; and REST APIs for BaaS, enabling integration with OA/CRM/ERP. Deployment options include Docker Compose, source code compilation, and the UniClaw desktop client for high-privilege operations.
- Customer service teams: Build high-accuracy business Q&A and ticket handling with RAG and agents.
- Knowledge managers: Create enterprise knowledge bases with semantic search and intelligent summarization.
- Process automation teams: Use workflow engine for AI-assisted contract review and expense approval.
- Xinchuang-compliant enterprises: Deploy on Kunpeng CPUs with domestic OS and TiDB/OceanBase databases.
- FDE engineers: Leverage the full-stack toolchain to deliver AI projects on-site with deep integrations.
What are this agent's strengths and limitations?
- Apache-2.0 license permits free extension, secondary development, and commercial use.
- Unique features like GraphRAG and Ontology Agent outperform competitors on MultiHop-RAG benchmark (F1 +14% vs Dify, +3.5% vs LightRAG).
- Docker one-click deployment and Xinchuang support lower operational barriers.
- 100+ built-in MCP servers and Skill Plaza provide out-of-the-box capabilities.
- Default credentials and keys must be changed, posing security risk if ignored.
- Newer workflow module depends on a separate wanwu-workflow project, adding complexity.
- GUI agent requires downloading a desktop client (via Baidu netdisk), which may be inconvenient.
- Steep learning curve due to extensive features; relies on documentation.
How do you install or deploy this agent?
Docker installation: 1) cp .env.example .env; 2) Edit .env setting WANWU_ARCH (amd64/arm64) and WANWU_EXTERNAL_IP; 3) Set WANWU_BFF_JWT_SIGNING_KEY; 4) cp .env.ontology.example .env.ontology; 5) Create network docker network create wanwu-net; 6) Start with docker compose --env-file .env --env-file .env.ontology --env-file .env.image.amd64 up -d (use arm64 flavor for ARM); 7) Access http://localhost:8081, default user admin, password Wanwu123456. For development, stop services with Makefile and compile bff-service.
How do you use this agent?
After login, import models via Model Management (e.g., Unicom Yuanjing LLM, OpenAI-compatible), create a knowledge base and upload documents, design workflows using the visual editor, and build agent applications. Detailed manuals are under configs/microservice/bff-service/static/manual.
How does this agent compare with similar options?
Compared with Dify.AI, Wanwu offers direct OCR import, GraphRAG, Ontology Agent, and General Agent+Skills framework, and has a more friendly license (Dify is commercially restricted). Versus FastGPT, Wanwu supports model import and more advanced capabilities. Against RagFlow, Wanwu provides GraphRAG and permissive licensing. Compared to Coze open-source version, Wanwu supports local deployment and multi-tenancy.
FAQ
Which model providers are supported?
Is GPU required for deployment?
Does it work in domestic (Xinchuang) environments?
How is data security handled?
.env, RSA encryption is used for login, and multi-tenancy isolates data, but users must change default credentials.