MaxKB4j = Max Knowledge Brain for Java A ready-to-use, secure, model-agnostic RAG (Retrieval-Augmented Generation) + LLM workflow engine, purpose-built for enterprise-grade intelligent Q&A systems. Widely used in scenarios such as intelligent customer service, internal enterprise knowledge bases, data analysis, academic research, and education.
[ไธญๆ(็ฎไฝ)] | [English]
In today's AI application boom, are you facing these challenges?
- โ Complex Integration: Existing solutions rely on Python ecosystem, making it costly for Java teams to get started?
- โ Serious Hallucinations: Generic large models answer inaccurately and cannot integrate with internal enterprise data?
- โ Concurrency Bottlenecks: Traditional architectures struggle to support high-concurrency scenarios with high response latency?
- โ Limited Functionality: Only simple Q&A, unable to handle complex business workflows and multi-Agent collaboration?
MaxKB4j Provides You with a One-Stop Solution:
Built on Java 21 + Spring Boot 3 + Virtual Threads, perfectly integrating RAG (Retrieval-Augmented Generation) with visual workflow. Empower your applications with AI capabilities of "understanding, reasoning, and execution" without modifying existing systems.
| Feature Category | Detailed Description |
|---|---|
| โฐ Triggers | โข Scheduled Task Trigger: Supports configuring Cron expressions or visual timeline for unattended automation of agents and tools (e.g., daily automatic data report generation, scheduled competitor information crawling). โข Event Callback Trigger: Supports Webhook integration with external system events for real-time response (e.g., automatically trigger customer profiling Agent when new leads are added in CRM, trigger alert notifications when database data changes). |
| ๐ Out-of-the-Box Knowledge Base Q&A | โข Supports uploading local documents (PDF/Word/TXT/Markdown, etc.) โข Supports automatic web content crawling โข Supports custom workflow knowledge base writing โข Automatically handles: text chunking โ vectorization โ storage in vector database โ RAG pipeline construction โข Significantly reduces LLM "hallucinations", improves answer accuracy and reliability |
| โก High Concurrency & High Performance | โข Built on Java 21 + Spring Boot 3 + Virtual Threads (Project Loom), fully leveraging modern JVM's lightweight concurrency capabilities for significantly improved throughput and response speed. โข Adopts reactive programming model (Reactor) and asynchronous non-blocking I/O, effectively handling thousands of concurrent requests with lower resource usage and lower latency. โข Built-in multi-level caching mechanism to accelerate knowledge retrieval and model call chains. |
| ๐ Model-Agnostic & Flexible Integration | Supports various mainstream large language models, including: โข Local Private Models: DeepSeek-R1, Llama 3, Qwen 2, etc. (via Ollama / Xorbits Inference / LocalAI) โข Chinese Public Models: Tongyi Qianwen, Tencent HunYuan, ByteDance Doubao, Baidu Qianfan, Zhipu GLM, Kimi, DeepSeek, etc. โข International Public Models: OpenAI (GPT), Anthropic (Claude), Google (Gemini) |
| โ๏ธ Visual Workflow Orchestration | โข Built-in low-code AI workflow engine, supports conditional branching, function calling, multi-turn conversation memory โข Provides rich built-in function library (HTTP requests, database queries, time processing, regex extraction, etc.) โข Suitable for complex business scenarios: customer support ticket generation, data report interpretation, internal policy Q&A, etc. |
| ๐ค Multi-Agent Collaboration | โข Built-in Multi-Agent collaboration framework, supports multiple specialized AI Agents working in parallel or sequentially โข Each Agent can be configured with independent roles (e.g., data analyst, code reviewer, customer service specialist), dedicated knowledge bases and toolsets โข Supports dynamic task distribution and context-aware Agent routing, complex tasks are automatically decomposed, assigned, and aggregated (e.g., user question โ requirement understanding Agent โ data query Agent โ report generation Agent) โข Provides inter-Agent communication mechanism and shared memory bus, ensuring information consistency and collaboration coherence โข Suitable for advanced scenarios: cross-department process automation, end-to-end product design, joint fault diagnosis, etc. |
| ๐งฉ Seamless Integration into Existing Systems | โข Provides RESTful API and frontend embedding components (iframe / Web SDK) โข No need to modify existing systems, integrate intelligent Q&A capabilities in 5 minutes โข Provides OpenAI-compatible dialogue interface |
| ๐ค Skill Tools | โข Supports MCP protocol, enabling AI to understand code context, project structure, and dependencies โข Supports local code function programming tool calls โข Supports HTTP interface tool calls โข Supports Claude SKILLS skill calls |
| ๐๏ธ Multimodal Extensions | โข Speech Recognition (ASR), Speech Synthesis (TTS) โข Image Recognition (OCR), Image Generation (Stable Diffusion) |
| ๐ User Permission Management | โข Fine-grained permission control (application / knowledge base / tool / model) โข Audit logs, authentication and authorization (based on Sa-Token) |
| ๐ฑ Ecosystem Extensions (Extensibility & Out-of-the-Box) | โข Rich Agent template library: Provides dozens of pre-built Agent templates (e.g., customer service assistant, data analyst, code mentor, meeting note taker), one-click enable, quick adaptation to business scenarios. โข Flexible plugin tool marketplace: Supports dynamic loading of functional modules through plugin mechanism, including: โ Data connectors (MySQL, PostgreSQL, MongoDB, etc.) โ Third-party service integrations (Feishu, DingTalk, WeCom) โ Web search tools (Google Search, SearchApi, SearXNg, etc.) |
- Java 21+
- PostgreSQL 12+ (with pgvector extension enabled)
- MongoDB 6.0+ (optional, for full-text search)
# Start the application
java -jar maxkb4j-start.jardocker run --name maxkb4j -d --restart always -p 8080:8080 -e SPRING_DATASOURCE_URL=jdbc:postgresql://localhost:5432/MaxKB4j -e SPRING_DATASOURCE_USERNAME=postgres -e SPRING_DATASOURCE_PASSWORD=123456 -e SPRING_DATA_MONGODB_URI=mongodb://admin:123456@localhost:27017/MaxKB4j?authSource=admin registry.cn-hangzhou.aliyuncs.com/tarzanx/maxkb4j- The first 8080 in
-p 8080:8080is the host port, the second 8080 is the container port -e SPRING_DATASOURCE_URL=jdbc:postgresql://localhost:5432/MaxKB4j -e SPRING_DATASOURCE_USERNAME=postgres -e SPRING_DATASOURCE_PASSWORD=123456are PostgreSQL database connection parameters, can be modified as needed-e SPRING_DATA_MONGODB_URI=mongodb://admin:123456@localhost:27017/MaxKB4j?authSource=adminis MongoDB connection parameter, can be modified as needed
# See docker-compose.yml example in project root directory
docker-compose up -dDeploy to Sealos
Sealos servers are located overseas, no need to handle network issues separately, supports high concurrency & dynamic scaling.
Click the button below for one-click deployment:
- URL: http://localhost:8080/admin/login
- Default username:
admin - Default password:
tarzan@123456
On first launch, the database (PostgreSQL + MongoDB) will be automatically initialized, please ensure ports are not occupied.
| Category | Technology |
|---|---|
| Backend | Java 21, Spring Boot 3, Sa-Token (Authentication) |
| AI Framework | LangChain4j |
| Vector Database | PostgreSQL 15 + pgvector |
| Full-Text Search | MongoDB 5.0+ |
| Caching | Caffeine |
| Frontend | Vue 3, Node.js v20.16.0 |
We welcome community contributions! If you have suggestions, bug reports, or new feature requests, please submit them via Issue or directly submit a Pull Request.
| Category | Description |
|---|---|
| ๐ฏ How to Contribute | Fix bugs, develop new features, improve documentation, write tests, or optimize UI/UX. |
| ๐ Process | Fork project โ Create branch โ Commit changes โ Push branch โ Open Pull Request. |
| ๐จ Standards | Follow Alibaba Java Coding Guidelines, include unit tests, and update documentation. |
๐ Open source is not easy, persistence is harder MaxKB4j is maintained by individual developers and community members. Your support will be directly used for server costs, token testing consumption, API testing, bug fixes, and new feature development!
| Tier | Amount | Core Benefits | Target Audience |
|---|---|---|---|
| โ Coffee Support | ยฅ10 | โข Add author on WeChat vxhqqhโข Join core discussion group (mention "sponsored") โข Priority notification of project updates |
Individual developers who recognize project value |
| ๐ Learning Member | ยฅ50 | โข All Coffee Support benefits โข Free access to Knowledge Planet๐ฅ โข Priority answers to questions in the Planet |
Developers who want to learn in depth |
| ๐ง Advanced Developer | ยฅ200 | โข All Learning Member benefits โข Project deployment assistance โข Priority technical support response โข Participate in new feature requirement discussions |
Developers who want to deeply understand architecture |
| ๐ข Enterprise Partner | ยฅ650 | โข All Advanced Developer benefits โข V2 complete frontend and backend source code โข Issue after-sales technical support |
Enterprise users / Advanced users |
| ๐ Strategic Partner | ยฅ1299 | โข All Enterprise Partner benefits โข Free project upgrades within one year โข Enterprise logo displayed on official website sponsor wall |
Deep cooperation partners |
| Alipay QR Code |
WeChat QR Code |
|---|---|
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Sponsorship amount is only used for continuous project development and maintenance, not for profit purposes. ๐ก Open source is not easy, thank you for every support!
Copyright ยฉ 2025โ2035 Luoyang Taishan TARZAN. All rights reserved.
Licensed under the GNU General Public License Version 3 (GPLv3) ("License"); you may not use this project file except in compliance with the License. You may obtain a copy of the License at
๐https://www.gnu.org/licenses/gpl-3.0.html
Unless required by applicable law or agreed to in writing, software distributed under the License is provided on an "AS IS" basis, without warranties or conditions of any kind, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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๐ฏ Check this out! ๐ Learn about AI large model application development in practice! ๐ฅ
โ MaxKB4j โ Easily build high-performance and stable agent workflows and RAG knowledge base solutions
