AI Skill Hub 强烈推荐:Nexent AI智能体平台 是一款优质的AI工具。已获得 4.6k 颗 GitHub Star,AI 综合评分 8.2 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。
零代码开源平台,自动生成生产级AI智能体。支持MCP协议、RAG工作流、多智能体编排。适合企业开发者快速构建复杂AI应用,降低开发门槛,提升交付效率。
Nexent AI智能体平台 是一款基于 Python 开发的开源工具,专注于 零代码平台、AI智能体、MCP协议 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
零代码开源平台,自动生成生产级AI智能体。支持MCP协议、RAG工作流、多智能体编排。适合企业开发者快速构建复杂AI应用,降低开发门槛,提升交付效率。
Nexent AI智能体平台 是一款基于 Python 开发的开源工具,专注于 零代码平台、AI智能体、MCP协议 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:pip 安装(推荐)
pip install nexent
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install nexent
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/ModelEngine-Group/nexent
cd nexent
pip install -e .
# 验证安装
python -c "import nexent; print('安装成功')"
# 命令行使用
nexent --help
# 基本用法
nexent input_file -o output_file
# Python 代码中调用
import nexent
# 示例
result = nexent.process("input")
print(result)
# nexent 配置文件示例(config.yml) app: name: "nexent" debug: false log_level: "INFO" # 运行时指定配置文件 nexent --config config.yml # 或通过环境变量配置 export NEXENT_API_KEY="your-key" export NEXENT_OUTPUT_DIR="./output"
Nexent is a zero-code platform for auto-generating production-grade AI agents, built on Harness Engineering principles. It provides unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes — no orchestration, no complex drag-and-drop required, using pure language to develop any agent you want.
One prompt. Endless reach.
<video controls width="100%" style="max-width: 800px;"> <source src="https://github.com/user-attachments/assets/db6b7f5a-9ee8-4327-ae6f-c5af896126b4" type="video/mp4" /> <p><a href="https://github.com/user-attachments/assets/db6b7f5a-9ee8-4327-ae6f-c5af896126b4">Watch the demo video</a></p> </video>
Nexent provides a comprehensive feature set for building powerful AI agents:
| Feature | Description |
|---|---|
| **⚙️ Multi-Model Integration** | OpenAI-compatible with any provider, full LLM/Embedding/VLM/STT/TTS coverage, supports domestic model switching |
| **🤖 Zero-Code Agent Generation** | Describe requirements in natural language, generate executable agents instantly, what you think is what you get |
| **🤝 A2A Agent Collaboration** | Agent-to-Agent protocol enables seamless multi-agent cooperation and distributed workflows |
| **🧠 Layered Memory Mechanism** | Two-tier memory (user-level + user-agent-level) for persistent context across conversations |
| **📝 Progressive Skill Disclosure** | Dynamically loads Skill into context, maximizing context window efficiency |
| **🗄️ Personal-Grade Knowledge Base** | Real-time import and intelligent retrieval for 20+ document formats, auto summaries, fine-grained access control |
| **🔧 MCP Tool Ecosystem** | Plug-and-play extension system with custom development and third-party MCP service support |
| **🌐 Internet Knowledge Integration** | Multi-source search blending real-time information with private data |
| **🔍 Knowledge-Level Traceability** | Precise citations and source verification, full transparency for every fact |
| **🎭 Multimodal Interaction** | Voice, text, images, files — comprehensive natural dialogue |
| **🔢 Agent Version Management** | Version iteration and history rollback, safe and controllable |
| **🏪 Agent Marketplace** | Official and community curated agents, one-click install and use |
| **👥 Multi-Tenancy & RBAC** | Multi-tenant isolation, role-based access control, fine-grained resource management |
Ready to dive deeper? Here are the main documentation entry points:
| Resource | Docker | Kubernetes |
|---|---|---|
| **CPU** | 4 cores (min) / 8 cores (rec.) | 4 cores (min) / 8 cores (rec.) |
| **Memory** | 8 GiB (min) / 16 GiB (rec.) | 16 GiB (min) / 64 GiB (rec.) |
| **Disk** | 40 GiB (min) / 100 GiB (rec.) | 100 GiB (min) / 200 GiB (rec.) |
| **Architecture** | x86_64 / ARM64 | x86_64 / ARM64 |
| **Software** | Docker 24+, Docker Compose v2+ | Kubernetes 1.24+, Helm 3+ |
Note: Recommended configurations ensure optimal performance in production environments.
If you need to run Nexent locally or in your private infrastructure, we offer two deployment options:
Quick and straightforward for most users. Prerequisites: Docker 24+ and Docker Compose v2+:
git clone https://github.com/ModelEngine-Group/nexent.git
cd nexent
bash deploy.sh docker
The root deploy.sh only forwards to the target deploy script; the native Docker implementation is bash deploy/docker/deploy.sh. The Docker and Kubernetes deploy scripts share the same deployment configuration model. Interactive runs show Bash TUI menus for component selection, port policy, and image source. infrastructure is required; application, data-process, and supabase are selected by default and can be disabled when you want a smaller deployment. Use b/Backspace to return to the previous TUI step and q to quit. Non-interactive runs can pass the same choices with --version, --components, --port-policy development|production, and --image-source general|mainland|local-latest. Successful deployments save non-sensitive choices to each deploy directory's deploy.options for reuse on the next run.
Docker and Kubernetes both use deploy/env/.env as the runtime configuration file. Existing deploy/env/.env is kept as-is. If it does not exist, the deploy scripts first reuse docker/.env, then fall back to deploy/env/.env.example.
Docker uninstall is handled by bash uninstall.sh docker. It can preserve or delete data volumes: run it interactively, pass --delete-volumes true|false, or use bash uninstall.sh docker delete-all to remove containers and persistent data.
Offline image packages can be built with bash deploy/offline/build_offline_package.sh --target docker --compress true. The package includes image tar files, load-images.sh, root deploy/uninstall entrypoints, deployment scripts, SQL files, manifest.yaml, and checksums.txt; deploy it with bash deploy.sh --load-images docker ... on the target host.
For detailed deployment instructions, see Docker Installation.
Ideal for enterprise scenarios requiring high availability and elastic scaling. Prerequisites: Kubernetes 1.24+ and Helm 3+:
git clone https://github.com/ModelEngine-Group/nexent.git
cd nexent
bash deploy.sh k8s
The native Kubernetes implementation is bash deploy/k8s/deploy.sh. It reads the same deploy/env/.env as Docker and renders explicit values into Helm ConfigMap and Secret overrides. Use --persistence-mode local|dynamic|existing, --storage-class/--sc, --local-path, --local-node-name, and --existing-claim-prefix to control PVC behavior. Local mode renders hostPath PVs and does not require node affinity.
Kubernetes uninstall is handled by bash uninstall.sh k8s. It removes the Helm release first, then can optionally delete the namespace and local PV data. Use --delete-namespace true|false, --delete-local-data true|false, or bash uninstall.sh k8s delete-all; pass --keep-local-data with delete-all to preserve local volume contents.
Kubernetes offline packages use the same builder with --target k8s or --target all. Run load-images.sh on every cluster node that needs the images, or push the loaded images to an internal registry before deploying with the same version and image-source options used during packaging.
For detailed deployment instructions, see Kubernetes Installation.
No installation required — jump right in with our online demo environment to experience Nexent's capabilities instantly.
aiskill88点评:Nexent以零代码+MCP为核心创新,4.6k星量表明社区认可度高。架构设计完整支持RAG和工作流,是AI应用快速原型化的优秀选择,但成熟度仍需验证。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
总体来看,Nexent AI智能体平台 是一款质量优秀的AI工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | nexent |
| 原始描述 | 开源MCP工具:Nexent is a zero-code platform for auto-generating production-grade AI agents us。⭐4.6k · Python |
| Topics | 零代码平台AI智能体MCP协议工作流编排RAG框架 |
| GitHub | https://github.com/ModelEngine-Group/nexent |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-22 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。