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.
⭐ Before you get started, please star us on GitHub — your support drives us forward!
No installation required — jump right in with our online demo environment to experience Nexent's capabilities instantly.
If you need to run Nexent locally or in your private infrastructure, we offer two deployment options:
| 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.
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 dockerThe 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 k8sThe 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.
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 |
If you want to go fast, go alone; if you want to go far, go together.
We have released Nexent v2.0! A comprehensive upgrade from v1.0, featuring A2A protocol support, progressive Skill disclosure, layered memory mechanism, user management with multi-tenancy, agent version management, agent marketplace, and more.
- 🗺️ Check our Feature Map to explore current and upcoming features.
- 🔍 Try the current build and leave ideas or bugs in the Issues tab.
Rome wasn't built in a day.
If our vision speaks to you, jump in via the Contribution Guide and shape Nexent with us.
Early contributors won't go unnoticed: from special badges and swag to other tangible rewards, we're committed to thanking the pioneers who help bring Nexent to life.
Most of all, we need visibility. Star ⭐ and watch the repo, share it with friends, and help more developers discover Nexent — your click brings new hands to the project and keeps the momentum growing.
Ready to dive deeper? Here are the main documentation entry points:
- Quick Start — System requirements and deployment guide
- Core Features — Comprehensive feature documentation
- User Guide — Agent development and usage
- Developer Guide — Build from source and customization
- FAQ — Common questions and troubleshooting
Nexent is licensed under the MIT License.